diff --git a/litellm/__init__.py b/litellm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b9cf85a55e42f19fcc06024c2abf52035bcb88ef --- /dev/null +++ b/litellm/__init__.py @@ -0,0 +1,408 @@ +### INIT VARIABLES ### +import threading, requests +from typing import Callable, List, Optional, Dict, Union, Any +from litellm.caching import Cache +import httpx + +input_callback: List[Union[str, Callable]] = [] +success_callback: List[Union[str, Callable]] = [] +failure_callback: List[Union[str, Callable]] = [] +callbacks: List[Callable] = [] +_async_success_callback: List[Callable] = [] # internal variable - async custom callbacks are routed here. +pre_call_rules: List[Callable] = [] +post_call_rules: List[Callable] = [] +set_verbose = False +email: Optional[ + str +] = None # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648 +token: Optional[ + str +] = None # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648 +telemetry = True +max_tokens = 256 # OpenAI Defaults +drop_params = False +retry = True +api_key: Optional[str] = None +openai_key: Optional[str] = None +azure_key: Optional[str] = None +anthropic_key: Optional[str] = None +replicate_key: Optional[str] = None +cohere_key: Optional[str] = None +maritalk_key: Optional[str] = None +ai21_key: Optional[str] = None +openrouter_key: Optional[str] = None +huggingface_key: Optional[str] = None +vertex_project: Optional[str] = None +vertex_location: Optional[str] = None +togetherai_api_key: Optional[str] = None +baseten_key: Optional[str] = None +aleph_alpha_key: Optional[str] = None +nlp_cloud_key: Optional[str] = None +use_client: bool = False +logging: bool = True +caching: bool = False # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648 +caching_with_models: bool = False # # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648 +cache: Optional[Cache] = None # cache object <- use this - https://docs.litellm.ai/docs/caching +model_alias_map: Dict[str, str] = {} +max_budget: float = 0.0 # set the max budget across all providers +_current_cost = 0 # private variable, used if max budget is set +error_logs: Dict = {} +add_function_to_prompt: bool = False # if function calling not supported by api, append function call details to system prompt +client_session: Optional[httpx.Client] = None +aclient_session: Optional[httpx.AsyncClient] = None +model_fallbacks: Optional[List] = None # Deprecated for 'litellm.fallbacks' +model_cost_map_url: str = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json" +suppress_debug_info = False +#### RELIABILITY #### +request_timeout: Optional[float] = 6000 +num_retries: Optional[int] = None +fallbacks: Optional[List] = None +context_window_fallbacks: Optional[List] = None +allowed_fails: int = 0 +####### SECRET MANAGERS ##################### +secret_manager_client: Optional[Any] = None # list of instantiated key management clients - e.g. azure kv, infisical, etc. +############################################# + +def get_model_cost_map(url: str): + try: + with requests.get(url, timeout=5) as response: # set a 5 second timeout for the get request + response.raise_for_status() # Raise an exception if the request is unsuccessful + content = response.json() + return content + except Exception as e: + import importlib.resources + import json + with importlib.resources.open_text("litellm", "model_prices_and_context_window_backup.json") as f: + content = json.load(f) + return content +model_cost = get_model_cost_map(url=model_cost_map_url) +custom_prompt_dict:Dict[str, dict] = {} +####### THREAD-SPECIFIC DATA ################### +class MyLocal(threading.local): + def __init__(self): + self.user = "Hello World" + + +_thread_context = MyLocal() + + +def identify(event_details): + # Store user in thread local data + if "user" in event_details: + _thread_context.user = event_details["user"] + + +####### ADDITIONAL PARAMS ################### configurable params if you use proxy models like Helicone, map spend to org id, etc. +api_base = None +headers = None +api_version = None +organization = None +config_path = None +####### COMPLETION MODELS ################### +open_ai_chat_completion_models: List = [] +open_ai_text_completion_models: List = [] +cohere_models: List = [] +anthropic_models: List = [] +openrouter_models: List = [] +vertex_chat_models: List = [] +vertex_code_chat_models: List = [] +vertex_text_models: List = [] +vertex_code_text_models: List = [] +ai21_models: List = [] +nlp_cloud_models: List = [] +aleph_alpha_models: List = [] +bedrock_models: List = [] +deepinfra_models: List = [] +perplexity_models: List = [] +for key, value in model_cost.items(): + if value.get('litellm_provider') == 'openai': + open_ai_chat_completion_models.append(key) + elif value.get('litellm_provider') == 'text-completion-openai': + open_ai_text_completion_models.append(key) + elif value.get('litellm_provider') == 'cohere': + cohere_models.append(key) + elif value.get('litellm_provider') == 'anthropic': + anthropic_models.append(key) + elif value.get('litellm_provider') == 'openrouter': + split_string = key.split('/', 1) + openrouter_models.append(split_string[1]) + elif value.get('litellm_provider') == 'vertex_ai-text-models': + vertex_text_models.append(key) + elif value.get('litellm_provider') == 'vertex_ai-code-text-models': + vertex_code_text_models.append(key) + elif value.get('litellm_provider') == 'vertex_ai-chat-models': + vertex_chat_models.append(key) + elif value.get('litellm_provider') == 'vertex_ai-code-chat-models': + vertex_code_chat_models.append(key) + elif value.get('litellm_provider') == 'ai21': + ai21_models.append(key) + elif value.get('litellm_provider') == 'nlp_cloud': + nlp_cloud_models.append(key) + elif value.get('litellm_provider') == 'aleph_alpha': + aleph_alpha_models.append(key) + elif value.get('litellm_provider') == 'bedrock': + bedrock_models.append(key) + elif value.get('litellm_provider') == 'deepinfra': + deepinfra_models.append(key) + elif value.get('litellm_provider') == 'perplexity': + perplexity_models.append(key) + +# known openai compatible endpoints - we'll eventually move this list to the model_prices_and_context_window.json dictionary +openai_compatible_endpoints: List = [ + "api.perplexity.ai", + "api.endpoints.anyscale.com/v1", + "api.deepinfra.com/v1/openai" +] + + +# well supported replicate llms +replicate_models: List = [ + # llama replicate supported LLMs + "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", + "a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52", + "meta/codellama-13b:1c914d844307b0588599b8393480a3ba917b660c7e9dfae681542b5325f228db", + # Vicuna + "replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b", + "joehoover/instructblip-vicuna13b:c4c54e3c8c97cd50c2d2fec9be3b6065563ccf7d43787fb99f84151b867178fe", + # Flan T-5 + "daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f" + # Others + "replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5", + "replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad", +] + +huggingface_models: List = [ + "meta-llama/Llama-2-7b-hf", + "meta-llama/Llama-2-7b-chat-hf", + "meta-llama/Llama-2-13b-hf", + "meta-llama/Llama-2-13b-chat-hf", + "meta-llama/Llama-2-70b-hf", + "meta-llama/Llama-2-70b-chat-hf", + "meta-llama/Llama-2-7b", + "meta-llama/Llama-2-7b-chat", + "meta-llama/Llama-2-13b", + "meta-llama/Llama-2-13b-chat", + "meta-llama/Llama-2-70b", + "meta-llama/Llama-2-70b-chat", +] # these have been tested on extensively. But by default all text2text-generation and text-generation models are supported by liteLLM. - https://docs.litellm.ai/docs/providers + +together_ai_models: List = [ + # llama llms - chat + "togethercomputer/llama-2-70b-chat", + + # llama llms - language / instruct + "togethercomputer/llama-2-70b", + "togethercomputer/LLaMA-2-7B-32K", + "togethercomputer/Llama-2-7B-32K-Instruct", + "togethercomputer/llama-2-7b", + + # falcon llms + "togethercomputer/falcon-40b-instruct", + "togethercomputer/falcon-7b-instruct", + + # alpaca + "togethercomputer/alpaca-7b", + + # chat llms + "HuggingFaceH4/starchat-alpha", + + # code llms + "togethercomputer/CodeLlama-34b", + "togethercomputer/CodeLlama-34b-Instruct", + "togethercomputer/CodeLlama-34b-Python", + "defog/sqlcoder", + "NumbersStation/nsql-llama-2-7B", + "WizardLM/WizardCoder-15B-V1.0", + "WizardLM/WizardCoder-Python-34B-V1.0", + + # language llms + "NousResearch/Nous-Hermes-Llama2-13b", + "Austism/chronos-hermes-13b", + "upstage/SOLAR-0-70b-16bit", + "WizardLM/WizardLM-70B-V1.0", + +] # supports all together ai models, just pass in the model id e.g. completion(model="together_computer/replit_code_3b",...) + + +baseten_models: List = ["qvv0xeq", "q841o8w", "31dxrj3"] # FALCON 7B # WizardLM # Mosaic ML + +petals_models = [ + "petals-team/StableBeluga2", +] + +ollama_models = [ + "llama2" +] + +maritalk_models = [ + "maritalk" +] + +model_list = ( + open_ai_chat_completion_models + + open_ai_text_completion_models + + cohere_models + + anthropic_models + + replicate_models + + openrouter_models + + huggingface_models + + vertex_chat_models + + vertex_text_models + + ai21_models + + together_ai_models + + baseten_models + + aleph_alpha_models + + nlp_cloud_models + + ollama_models + + bedrock_models + + deepinfra_models + + perplexity_models + + maritalk_models +) + +provider_list: List = [ + "openai", + "custom_openai", + "cohere", + "anthropic", + "replicate", + "huggingface", + "together_ai", + "openrouter", + "vertex_ai", + "palm", + "ai21", + "baseten", + "azure", + "sagemaker", + "bedrock", + "vllm", + "nlp_cloud", + "petals", + "oobabooga", + "ollama", + "deepinfra", + "perplexity", + "anyscale", + "maritalk", + "custom", # custom apis +] + +models_by_provider: dict = { + "openai": open_ai_chat_completion_models + open_ai_text_completion_models, + "cohere": cohere_models, + "anthropic": anthropic_models, + "replicate": replicate_models, + "huggingface": huggingface_models, + "together_ai": together_ai_models, + "baseten": baseten_models, + "openrouter": openrouter_models, + "vertex_ai": vertex_chat_models + vertex_text_models, + "ai21": ai21_models, + "bedrock": bedrock_models, + "petals": petals_models, + "ollama": ollama_models, + "deepinfra": deepinfra_models, + "perplexity": perplexity_models, + "maritalk": maritalk_models +} + +# mapping for those models which have larger equivalents +longer_context_model_fallback_dict: dict = { + # openai chat completion models + "gpt-3.5-turbo": "gpt-3.5-turbo-16k", + "gpt-3.5-turbo-0301": "gpt-3.5-turbo-16k-0301", + "gpt-3.5-turbo-0613": "gpt-3.5-turbo-16k-0613", + "gpt-4": "gpt-4-32k", + "gpt-4-0314": "gpt-4-32k-0314", + "gpt-4-0613": "gpt-4-32k-0613", + # anthropic + "claude-instant-1": "claude-2", + "claude-instant-1.2": "claude-2", + # vertexai + "chat-bison": "chat-bison-32k", + "chat-bison@001": "chat-bison-32k", + "codechat-bison": "codechat-bison-32k", + "codechat-bison@001": "codechat-bison-32k", + # openrouter + "openrouter/openai/gpt-3.5-turbo": "openrouter/openai/gpt-3.5-turbo-16k", + "openrouter/anthropic/claude-instant-v1": "openrouter/anthropic/claude-2", +} + +####### EMBEDDING MODELS ################### +open_ai_embedding_models: List = ["text-embedding-ada-002"] +cohere_embedding_models: List = [ + "embed-english-v3.0", + "embed-english-light-v3.0", + "embed-multilingual-v3.0", + "embed-english-v2.0", + "embed-english-light-v2.0", + "embed-multilingual-v2.0", +] +bedrock_embedding_models: List = ["amazon.titan-embed-text-v1"] + +all_embedding_models = open_ai_embedding_models + cohere_embedding_models + bedrock_embedding_models + +from .timeout import timeout +from .utils import ( + client, + exception_type, + get_optional_params, + modify_integration, + token_counter, + cost_per_token, + completion_cost, + get_litellm_params, + Logging, + acreate, + get_model_list, + get_max_tokens, + get_model_info, + register_prompt_template, + validate_environment, + check_valid_key, + get_llm_provider, + completion_with_config, + register_model, + encode, + decode, + _calculate_retry_after, + _should_retry, + get_secret +) +from .llms.huggingface_restapi import HuggingfaceConfig +from .llms.anthropic import AnthropicConfig +from .llms.replicate import ReplicateConfig +from .llms.cohere import CohereConfig +from .llms.ai21 import AI21Config +from .llms.together_ai import TogetherAIConfig +from .llms.palm import PalmConfig +from .llms.nlp_cloud import NLPCloudConfig +from .llms.aleph_alpha import AlephAlphaConfig +from .llms.petals import PetalsConfig +from .llms.vertex_ai import VertexAIConfig +from .llms.sagemaker import SagemakerConfig +from .llms.ollama import OllamaConfig +from .llms.maritalk import MaritTalkConfig +from .llms.bedrock import AmazonTitanConfig, AmazonAI21Config, AmazonAnthropicConfig, AmazonCohereConfig, AmazonLlamaConfig +from .llms.openai import OpenAIConfig, OpenAITextCompletionConfig +from .llms.azure import AzureOpenAIConfig +from .main import * # type: ignore +from .integrations import * +from .exceptions import ( + AuthenticationError, + InvalidRequestError, + BadRequestError, + RateLimitError, + ServiceUnavailableError, + OpenAIError, + ContextWindowExceededError, + BudgetExceededError, + APIError, + Timeout, + APIConnectionError, + APIResponseValidationError +) +from .budget_manager import BudgetManager +from .proxy.proxy_cli import run_server +from .router import Router \ No newline at end of file diff --git a/litellm/_version.py b/litellm/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..7b38bae2c2188ae2850421feb6e6a63ef70431e3 --- /dev/null +++ b/litellm/_version.py @@ -0,0 +1,6 @@ +import importlib_metadata + +try: + version = importlib_metadata.version("litellm") +except: + pass diff --git a/litellm/budget_manager.py b/litellm/budget_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..6a9d1e5207adda5c46ad382a2fdd16e8132dc7ae --- /dev/null +++ b/litellm/budget_manager.py @@ -0,0 +1,155 @@ +import os, json, time +import litellm +from litellm.utils import ModelResponse +import requests, threading +from typing import Optional, Union, Literal + +class BudgetManager: + def __init__(self, project_name: str, client_type: str = "local", api_base: Optional[str] = None): + self.client_type = client_type + self.project_name = project_name + self.api_base = api_base or "https://api.litellm.ai" + ## load the data or init the initial dictionaries + self.load_data() + + def print_verbose(self, print_statement): + if litellm.set_verbose: + import logging + logging.info(print_statement) + + def load_data(self): + if self.client_type == "local": + # Check if user dict file exists + if os.path.isfile("user_cost.json"): + # Load the user dict + with open("user_cost.json", 'r') as json_file: + self.user_dict = json.load(json_file) + else: + self.print_verbose("User Dictionary not found!") + self.user_dict = {} + self.print_verbose(f"user dict from local: {self.user_dict}") + elif self.client_type == "hosted": + # Load the user_dict from hosted db + url = self.api_base + "/get_budget" + headers = {'Content-Type': 'application/json'} + data = { + 'project_name' : self.project_name + } + response = requests.post(url, headers=headers, json=data) + response = response.json() + if response["status"] == "error": + self.user_dict = {} # assume this means the user dict hasn't been stored yet + else: + self.user_dict = response["data"] + + def create_budget(self, total_budget: float, user: str, duration: Optional[Literal["daily", "weekly", "monthly", "yearly"]] = None, created_at: float = time.time()): + self.user_dict[user] = {"total_budget": total_budget} + if duration is None: + return self.user_dict[user] + + if duration == 'daily': + duration_in_days = 1 + elif duration == 'weekly': + duration_in_days = 7 + elif duration == 'monthly': + duration_in_days = 28 + elif duration == 'yearly': + duration_in_days = 365 + else: + raise ValueError("""duration needs to be one of ["daily", "weekly", "monthly", "yearly"]""") + self.user_dict[user] = {"total_budget": total_budget, "duration": duration_in_days, "created_at": created_at, "last_updated_at": created_at} + self._save_data_thread() # [Non-Blocking] Update persistent storage without blocking execution + return self.user_dict[user] + + def projected_cost(self, model: str, messages: list, user: str): + text = "".join(message["content"] for message in messages) + prompt_tokens = litellm.token_counter(model=model, text=text) + prompt_cost, _ = litellm.cost_per_token(model=model, prompt_tokens=prompt_tokens, completion_tokens=0) + current_cost = self.user_dict[user].get("current_cost", 0) + projected_cost = prompt_cost + current_cost + return projected_cost + + def get_total_budget(self, user: str): + return self.user_dict[user]["total_budget"] + + def update_cost(self, user: str, completion_obj: Optional[ModelResponse] = None, model: Optional[str] = None, input_text: Optional[str] = None, output_text: Optional[str] = None): + if model and input_text and output_text: + prompt_tokens = litellm.token_counter(model=model, messages=[{"role": "user", "content": input_text}]) + completion_tokens = litellm.token_counter(model=model, messages=[{"role": "user", "content": output_text}]) + prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar = litellm.cost_per_token(model=model, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens) + cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar + elif completion_obj: + cost = litellm.completion_cost(completion_response=completion_obj) + model = completion_obj['model'] # if this throws an error try, model = completion_obj['model'] + else: + raise ValueError("Either a chat completion object or the text response needs to be passed in. Learn more - https://docs.litellm.ai/docs/budget_manager") + + self.user_dict[user]["current_cost"] = cost + self.user_dict[user].get("current_cost", 0) + if "model_cost" in self.user_dict[user]: + self.user_dict[user]["model_cost"][model] = cost + self.user_dict[user]["model_cost"].get(model, 0) + else: + self.user_dict[user]["model_cost"] = {model: cost} + + self._save_data_thread() # [Non-Blocking] Update persistent storage without blocking execution + return {"user": self.user_dict[user]} + + + def get_current_cost(self, user): + return self.user_dict[user].get("current_cost", 0) + + def get_model_cost(self, user): + return self.user_dict[user].get("model_cost", 0) + + def is_valid_user(self, user: str) -> bool: + return user in self.user_dict + + def get_users(self): + return list(self.user_dict.keys()) + + def reset_cost(self, user): + self.user_dict[user]["current_cost"] = 0 + self.user_dict[user]["model_cost"] = {} + return {"user": self.user_dict[user]} + + def reset_on_duration(self, user: str): + # Get current and creation time + last_updated_at = self.user_dict[user]["last_updated_at"] + current_time = time.time() + + # Convert duration from days to seconds + duration_in_seconds = self.user_dict[user]["duration"] * 24 * 60 * 60 + + # Check if duration has elapsed + if current_time - last_updated_at >= duration_in_seconds: + # Reset cost if duration has elapsed and update the creation time + self.reset_cost(user) + self.user_dict[user]["last_updated_at"] = current_time + self._save_data_thread() # Save the data + + def update_budget_all_users(self): + for user in self.get_users(): + if "duration" in self.user_dict[user]: + self.reset_on_duration(user) + + def _save_data_thread(self): + thread = threading.Thread(target=self.save_data) # [Non-Blocking]: saves data without blocking execution + thread.start() + + def save_data(self): + if self.client_type == "local": + import json + + # save the user dict + with open("user_cost.json", 'w') as json_file: + json.dump(self.user_dict, json_file, indent=4) # Indent for pretty formatting + return {"status": "success"} + elif self.client_type == "hosted": + url = self.api_base + "/set_budget" + headers = {'Content-Type': 'application/json'} + data = { + 'project_name' : self.project_name, + "user_dict": self.user_dict + } + response = requests.post(url, headers=headers, json=data) + response = response.json() + return response \ No newline at end of file diff --git a/litellm/caching.py b/litellm/caching.py new file mode 100644 index 0000000000000000000000000000000000000000..d9b94b9586ed3bc88440c67b28c7c9ca6f74ceea --- /dev/null +++ b/litellm/caching.py @@ -0,0 +1,275 @@ +# +-----------------------------------------------+ +# | | +# | Give Feedback / Get Help | +# | https://github.com/BerriAI/litellm/issues/new | +# | | +# +-----------------------------------------------+ +# +# Thank you users! We ❤️ you! - Krrish & Ishaan + +import litellm +import time, logging +import json, traceback, ast +from typing import Optional + +def get_prompt(*args, **kwargs): + # make this safe checks, it should not throw any exceptions + if len(args) > 1: + messages = args[1] + prompt = " ".join(message["content"] for message in messages) + return prompt + if "messages" in kwargs: + messages = kwargs["messages"] + prompt = " ".join(message["content"] for message in messages) + return prompt + return None + +def print_verbose(print_statement): + if litellm.set_verbose: + print(print_statement) # noqa + +class BaseCache: + def set_cache(self, key, value, **kwargs): + raise NotImplementedError + + def get_cache(self, key, **kwargs): + raise NotImplementedError + + +class InMemoryCache(BaseCache): + def __init__(self): + # if users don't provider one, use the default litellm cache + self.cache_dict = {} + self.ttl_dict = {} + + def set_cache(self, key, value, **kwargs): + self.cache_dict[key] = value + if "ttl" in kwargs: + self.ttl_dict[key] = time.time() + kwargs["ttl"] + + def get_cache(self, key, **kwargs): + if key in self.cache_dict: + if key in self.ttl_dict: + if time.time() > self.ttl_dict[key]: + self.cache_dict.pop(key, None) + return None + original_cached_response = self.cache_dict[key] + try: + cached_response = json.loads(original_cached_response) + except: + cached_response = original_cached_response + if isinstance(cached_response, dict): + cached_response['cache'] = True # set cache-hit flag to True + return cached_response + return None + + def flush_cache(self): + self.cache_dict.clear() + self.ttl_dict.clear() + + +class RedisCache(BaseCache): + def __init__(self, host, port, password, **kwargs): + import redis + # if users don't provider one, use the default litellm cache + self.redis_client = redis.Redis(host=host, port=port, password=password, **kwargs) + + def set_cache(self, key, value, **kwargs): + ttl = kwargs.get("ttl", None) + try: + self.redis_client.set(name=key, value=str(value), ex=ttl) + except Exception as e: + # NON blocking - notify users Redis is throwing an exception + logging.debug("LiteLLM Caching: set() - Got exception from REDIS : ", e) + + def get_cache(self, key, **kwargs): + try: + # TODO convert this to a ModelResponse object + cached_response = self.redis_client.get(key) + if cached_response != None: + # cached_response is in `b{} convert it to ModelResponse + cached_response = cached_response.decode("utf-8") # Convert bytes to string + try: + cached_response = json.loads(cached_response) # Convert string to dictionary + except: + cached_response = ast.literal_eval(cached_response) + if isinstance(cached_response, dict): + cached_response['cache'] = True # set cache-hit flag to True + return cached_response + except Exception as e: + # NON blocking - notify users Redis is throwing an exception + traceback.print_exc() + logging.debug("LiteLLM Caching: get() - Got exception from REDIS: ", e) + + def flush_cache(self): + self.redis_client.flushall() + +class DualCache(BaseCache): + """ + This updates both Redis and an in-memory cache simultaneously. + When data is updated or inserted, it is written to both the in-memory cache + Redis. + This ensures that even if Redis hasn't been updated yet, the in-memory cache reflects the most recent data. + """ + def __init__(self, in_memory_cache: Optional[InMemoryCache] =None, redis_cache: Optional[RedisCache] =None) -> None: + super().__init__() + # If in_memory_cache is not provided, use the default InMemoryCache + self.in_memory_cache = in_memory_cache or InMemoryCache() + # If redis_cache is not provided, use the default RedisCache + self.redis_cache = redis_cache + + def set_cache(self, key, value, **kwargs): + # Update both Redis and in-memory cache + try: + print_verbose(f"set cache: key: {key}; value: {value}") + if self.in_memory_cache is not None: + self.in_memory_cache.set_cache(key, value, **kwargs) + + if self.redis_cache is not None: + self.redis_cache.set_cache(key, value, **kwargs) + except Exception as e: + print_verbose(e) + + def get_cache(self, key, **kwargs): + # Try to fetch from in-memory cache first + try: + print_verbose(f"get cache: cache key: {key}") + result = None + if self.in_memory_cache is not None: + in_memory_result = self.in_memory_cache.get_cache(key, **kwargs) + + if in_memory_result is not None: + result = in_memory_result + + if self.redis_cache is not None: + # If not found in in-memory cache, try fetching from Redis + redis_result = self.redis_cache.get_cache(key, **kwargs) + + if redis_result is not None: + # Update in-memory cache with the value from Redis + self.in_memory_cache.set_cache(key, redis_result, **kwargs) + + result = redis_result + + print_verbose(f"get cache: cache result: {result}") + return result + except Exception as e: + traceback.print_exc() + + def flush_cache(self): + if self.in_memory_cache is not None: + self.in_memory_cache.flush_cache() + if self.redis_cache is not None: + self.redis_cache.flush_cache() + +#### LiteLLM.Completion Cache #### +class Cache: + def __init__( + self, + type="local", + host=None, + port=None, + password=None, + **kwargs + ): + """ + Initializes the cache based on the given type. + + Args: + type (str, optional): The type of cache to initialize. Defaults to "local". + host (str, optional): The host address for the Redis cache. Required if type is "redis". + port (int, optional): The port number for the Redis cache. Required if type is "redis". + password (str, optional): The password for the Redis cache. Required if type is "redis". + **kwargs: Additional keyword arguments for redis.Redis() cache + + Raises: + ValueError: If an invalid cache type is provided. + + Returns: + None + """ + if type == "redis": + self.cache = RedisCache(host, port, password, **kwargs) + if type == "local": + self.cache = InMemoryCache() + if "cache" not in litellm.input_callback: + litellm.input_callback.append("cache") + if "cache" not in litellm.success_callback: + litellm.success_callback.append("cache") + + def get_cache_key(self, *args, **kwargs): + """ + Get the cache key for the given arguments. + + Args: + *args: args to litellm.completion() or embedding() + **kwargs: kwargs to litellm.completion() or embedding() + + Returns: + str: The cache key generated from the arguments, or None if no cache key could be generated. + """ + cache_key ="" + for param in kwargs: + # ignore litellm params here + if param in set(["model", "messages", "temperature", "top_p", "n", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "response_format", "seed", "tools", "tool_choice"]): + # check if param == model and model_group is passed in, then override model with model_group + if param == "model" and kwargs.get("metadata", None) is not None and kwargs["metadata"].get("model_group", None) is not None: + param_value = kwargs["metadata"].get("model_group", None) # for litellm.Router use model_group for caching over `model` + else: + param_value = kwargs[param] + cache_key+= f"{str(param)}: {str(param_value)}" + return cache_key + + def generate_streaming_content(self, content): + chunk_size = 5 # Adjust the chunk size as needed + for i in range(0, len(content), chunk_size): + yield {'choices': [{'delta': {'role': 'assistant', 'content': content[i:i + chunk_size]}}]} + time.sleep(0.02) + + def get_cache(self, *args, **kwargs): + """ + Retrieves the cached result for the given arguments. + + Args: + *args: args to litellm.completion() or embedding() + **kwargs: kwargs to litellm.completion() or embedding() + + Returns: + The cached result if it exists, otherwise None. + """ + try: # never block execution + if "cache_key" in kwargs: + cache_key = kwargs["cache_key"] + else: + cache_key = self.get_cache_key(*args, **kwargs) + if cache_key is not None: + cached_result = self.cache.get_cache(cache_key) + if cached_result != None and 'stream' in kwargs and kwargs['stream'] == True: + # if streaming is true and we got a cache hit, return a generator + return self.generate_streaming_content(cached_result["choices"][0]['message']['content']) + return cached_result + except Exception as e: + logging.debug(f"An exception occurred: {traceback.format_exc()}") + return None + + def add_cache(self, result, *args, **kwargs): + """ + Adds a result to the cache. + + Args: + *args: args to litellm.completion() or embedding() + **kwargs: kwargs to litellm.completion() or embedding() + + Returns: + None + """ + try: + if "cache_key" in kwargs: + cache_key = kwargs["cache_key"] + else: + cache_key = self.get_cache_key(*args, **kwargs) + if cache_key is not None: + if isinstance(result, litellm.ModelResponse): + result = result.model_dump_json() + self.cache.set_cache(cache_key, result, **kwargs) + except Exception as e: + pass diff --git a/litellm/cost.json b/litellm/cost.json new file mode 100644 index 0000000000000000000000000000000000000000..360f981e455d09ef175b351215fe5500fc96309b --- /dev/null +++ b/litellm/cost.json @@ -0,0 +1,5 @@ +{ + "gpt-3.5-turbo-0613": 0.00015000000000000001, + "claude-2": 0.00016454, + "gpt-4-0613": 0.015408 +} \ No newline at end of file diff --git a/litellm/deprecated_litellm_server/.env.template b/litellm/deprecated_litellm_server/.env.template new file mode 100644 index 0000000000000000000000000000000000000000..a1c32a4549570b176a5e56a6aaa25da9b0b5b1c8 --- /dev/null +++ b/litellm/deprecated_litellm_server/.env.template @@ -0,0 +1,43 @@ +# # set AUTH STRATEGY FOR LLM APIs - Defaults to using Environment Variables +# AUTH_STRATEGY = "ENV" # ENV or DYNAMIC, ENV always reads from environment variables, DYNAMIC reads request headers to set LLM api keys + +# OPENAI_API_KEY = "" + +# HUGGINGFACE_API_KEY="" + +# TOGETHERAI_API_KEY="" + +# REPLICATE_API_KEY="" + +# ## bedrock / sagemaker +# AWS_ACCESS_KEY_ID = "" +# AWS_SECRET_ACCESS_KEY = "" + +# AZURE_API_KEY = "" +# AZURE_API_BASE = "" +# AZURE_API_VERSION = "" + +# ANTHROPIC_API_KEY = "" + +# COHERE_API_KEY = "" + +# ## CONFIG FILE ## +# # CONFIG_FILE_PATH = "" # uncomment to point to config file + +# ## LOGGING ## + +# SET_VERBOSE = "False" # set to 'True' to see detailed input/output logs + +# ### LANGFUSE +# LANGFUSE_PUBLIC_KEY = "" +# LANGFUSE_SECRET_KEY = "" +# # Optional, defaults to https://cloud.langfuse.com +# LANGFUSE_HOST = "" # optional + + +# ## CACHING ## + +# ### REDIS +# REDIS_HOST = "" +# REDIS_PORT = "" +# REDIS_PASSWORD = "" diff --git a/litellm/deprecated_litellm_server/Dockerfile b/litellm/deprecated_litellm_server/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..9b3b314c4b7aca217d00d26253c914c546dc0cda --- /dev/null +++ b/litellm/deprecated_litellm_server/Dockerfile @@ -0,0 +1,10 @@ +# FROM python:3.10 + +# ENV LITELLM_CONFIG_PATH="/litellm.secrets.toml" +# COPY . /app +# WORKDIR /app +# RUN pip install -r requirements.txt + +# EXPOSE $PORT + +# CMD exec uvicorn main:app --host 0.0.0.0 --port $PORT --workers 10 \ No newline at end of file diff --git a/litellm/deprecated_litellm_server/README.md b/litellm/deprecated_litellm_server/README.md new file mode 100644 index 0000000000000000000000000000000000000000..142bad18503c6206eac9399d43b5cfceef3fcbe1 --- /dev/null +++ b/litellm/deprecated_litellm_server/README.md @@ -0,0 +1,3 @@ +# litellm-server [experimental] + +Deprecated. See litellm/proxy \ No newline at end of file diff --git a/litellm/deprecated_litellm_server/__init__.py b/litellm/deprecated_litellm_server/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..019bc5a11775b741ce4f565f7c384dfa5746fd06 --- /dev/null +++ b/litellm/deprecated_litellm_server/__init__.py @@ -0,0 +1,2 @@ +# from .main import * +# from .server_utils import * \ No newline at end of file diff --git a/litellm/deprecated_litellm_server/main.py b/litellm/deprecated_litellm_server/main.py new file mode 100644 index 0000000000000000000000000000000000000000..11f011db3c1df49f79877ac18975dd2f36502179 --- /dev/null +++ b/litellm/deprecated_litellm_server/main.py @@ -0,0 +1,193 @@ +# import os, traceback +# from fastapi import FastAPI, Request, HTTPException +# from fastapi.routing import APIRouter +# from fastapi.responses import StreamingResponse, FileResponse +# from fastapi.middleware.cors import CORSMiddleware +# import json, sys +# from typing import Optional +# sys.path.insert( +# 0, os.path.abspath("../") +# ) # Adds the parent directory to the system path - for litellm local dev +# import litellm + +# try: +# from litellm.deprecated_litellm_server.server_utils import set_callbacks, load_router_config, print_verbose +# except ImportError: +# from litellm.deprecated_litellm_server.server_utils import set_callbacks, load_router_config, print_verbose +# import dotenv +# dotenv.load_dotenv() # load env variables + +# app = FastAPI(docs_url="/", title="LiteLLM API") +# router = APIRouter() +# origins = ["*"] + +# app.add_middleware( +# CORSMiddleware, +# allow_origins=origins, +# allow_credentials=True, +# allow_methods=["*"], +# allow_headers=["*"], +# ) +# #### GLOBAL VARIABLES #### +# llm_router: Optional[litellm.Router] = None +# llm_model_list: Optional[list] = None +# server_settings: Optional[dict] = None + +# set_callbacks() # sets litellm callbacks for logging if they exist in the environment + +# if "CONFIG_FILE_PATH" in os.environ: +# llm_router, llm_model_list, server_settings = load_router_config(router=llm_router, config_file_path=os.getenv("CONFIG_FILE_PATH")) +# else: +# llm_router, llm_model_list, server_settings = load_router_config(router=llm_router) +# #### API ENDPOINTS #### +# @router.get("/v1/models") +# @router.get("/models") # if project requires model list +# def model_list(): +# all_models = litellm.utils.get_valid_models() +# if llm_model_list: +# all_models += llm_model_list +# return dict( +# data=[ +# { +# "id": model, +# "object": "model", +# "created": 1677610602, +# "owned_by": "openai", +# } +# for model in all_models +# ], +# object="list", +# ) +# # for streaming +# def data_generator(response): + +# for chunk in response: + +# yield f"data: {json.dumps(chunk)}\n\n" + +# @router.post("/v1/completions") +# @router.post("/completions") +# async def completion(request: Request): +# data = await request.json() +# response = litellm.completion( +# **data +# ) +# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses +# return StreamingResponse(data_generator(response), media_type='text/event-stream') +# return response + +# @router.post("/v1/embeddings") +# @router.post("/embeddings") +# async def embedding(request: Request): +# try: +# data = await request.json() +# # default to always using the "ENV" variables, only if AUTH_STRATEGY==DYNAMIC then reads headers +# if os.getenv("AUTH_STRATEGY", None) == "DYNAMIC" and "authorization" in request.headers: # if users pass LLM api keys as part of header +# api_key = request.headers.get("authorization") +# api_key = api_key.replace("Bearer", "").strip() # type: ignore +# if len(api_key.strip()) > 0: +# api_key = api_key +# data["api_key"] = api_key +# response = litellm.embedding( +# **data +# ) +# return response +# except Exception as e: +# error_traceback = traceback.format_exc() +# error_msg = f"{str(e)}\n\n{error_traceback}" +# return {"error": error_msg} + +# @router.post("/v1/chat/completions") +# @router.post("/chat/completions") +# @router.post("/openai/deployments/{model:path}/chat/completions") # azure compatible endpoint +# async def chat_completion(request: Request, model: Optional[str] = None): +# global llm_model_list, server_settings +# try: +# data = await request.json() +# server_model = server_settings.get("completion_model", None) if server_settings else None +# data["model"] = server_model or model or data["model"] +# ## CHECK KEYS ## +# # default to always using the "ENV" variables, only if AUTH_STRATEGY==DYNAMIC then reads headers +# # env_validation = litellm.validate_environment(model=data["model"]) +# # if (env_validation['keys_in_environment'] is False or os.getenv("AUTH_STRATEGY", None) == "DYNAMIC") and ("authorization" in request.headers or "api-key" in request.headers): # if users pass LLM api keys as part of header +# # if "authorization" in request.headers: +# # api_key = request.headers.get("authorization") +# # elif "api-key" in request.headers: +# # api_key = request.headers.get("api-key") +# # print(f"api_key in headers: {api_key}") +# # if " " in api_key: +# # api_key = api_key.split(" ")[1] +# # print(f"api_key split: {api_key}") +# # if len(api_key) > 0: +# # api_key = api_key +# # data["api_key"] = api_key +# # print(f"api_key in data: {api_key}") +# ## CHECK CONFIG ## +# if llm_model_list and data["model"] in [m["model_name"] for m in llm_model_list]: +# for m in llm_model_list: +# if data["model"] == m["model_name"]: +# for key, value in m["litellm_params"].items(): +# data[key] = value +# break +# response = litellm.completion( +# **data +# ) +# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses +# return StreamingResponse(data_generator(response), media_type='text/event-stream') +# return response +# except Exception as e: +# error_traceback = traceback.format_exc() + +# error_msg = f"{str(e)}\n\n{error_traceback}" +# # return {"error": error_msg} +# raise HTTPException(status_code=500, detail=error_msg) + +# @router.post("/router/completions") +# async def router_completion(request: Request): +# global llm_router +# try: +# data = await request.json() +# if "model_list" in data: +# llm_router = litellm.Router(model_list=data.pop("model_list")) +# if llm_router is None: +# raise Exception("Save model list via config.yaml. Eg.: ` docker build -t myapp --build-arg CONFIG_FILE=myconfig.yaml .` or pass it in as model_list=[..] as part of the request body") + +# # openai.ChatCompletion.create replacement +# response = await llm_router.acompletion(model="gpt-3.5-turbo", +# messages=[{"role": "user", "content": "Hey, how's it going?"}]) + +# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses +# return StreamingResponse(data_generator(response), media_type='text/event-stream') +# return response +# except Exception as e: +# error_traceback = traceback.format_exc() +# error_msg = f"{str(e)}\n\n{error_traceback}" +# return {"error": error_msg} + +# @router.post("/router/embedding") +# async def router_embedding(request: Request): +# global llm_router +# try: +# data = await request.json() +# if "model_list" in data: +# llm_router = litellm.Router(model_list=data.pop("model_list")) +# if llm_router is None: +# raise Exception("Save model list via config.yaml. Eg.: ` docker build -t myapp --build-arg CONFIG_FILE=myconfig.yaml .` or pass it in as model_list=[..] as part of the request body") + +# response = await llm_router.aembedding(model="gpt-3.5-turbo", # type: ignore +# messages=[{"role": "user", "content": "Hey, how's it going?"}]) + +# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses +# return StreamingResponse(data_generator(response), media_type='text/event-stream') +# return response +# except Exception as e: +# error_traceback = traceback.format_exc() +# error_msg = f"{str(e)}\n\n{error_traceback}" +# return {"error": error_msg} + +# @router.get("/") +# async def home(request: Request): +# return "LiteLLM: RUNNING" + + +# app.include_router(router) \ No newline at end of file diff --git a/litellm/deprecated_litellm_server/requirements.txt b/litellm/deprecated_litellm_server/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..09f6dba5729f776d686405572476d48f1c408845 --- /dev/null +++ b/litellm/deprecated_litellm_server/requirements.txt @@ -0,0 +1,7 @@ +# openai +# fastapi +# uvicorn +# boto3 +# litellm +# python-dotenv +# redis \ No newline at end of file diff --git a/litellm/deprecated_litellm_server/server_utils.py b/litellm/deprecated_litellm_server/server_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..209acc8b9a45b78e188c18422bf08fb48300214a --- /dev/null +++ b/litellm/deprecated_litellm_server/server_utils.py @@ -0,0 +1,86 @@ +# import os, litellm +# import pkg_resources +# import dotenv +# dotenv.load_dotenv() # load env variables + +# def print_verbose(print_statement): +# pass + +# def get_package_version(package_name): +# try: +# package = pkg_resources.get_distribution(package_name) +# return package.version +# except pkg_resources.DistributionNotFound: +# return None + +# # Usage example +# package_name = "litellm" +# version = get_package_version(package_name) +# if version: +# print_verbose(f"The version of {package_name} is {version}") +# else: +# print_verbose(f"{package_name} is not installed") +# import yaml +# import dotenv +# from typing import Optional +# dotenv.load_dotenv() # load env variables + +# def set_callbacks(): +# ## LOGGING +# if len(os.getenv("SET_VERBOSE", "")) > 0: +# if os.getenv("SET_VERBOSE") == "True": +# litellm.set_verbose = True +# print_verbose("\033[92mLiteLLM: Switched on verbose logging\033[0m") +# else: +# litellm.set_verbose = False + +# ### LANGFUSE +# if (len(os.getenv("LANGFUSE_PUBLIC_KEY", "")) > 0 and len(os.getenv("LANGFUSE_SECRET_KEY", ""))) > 0 or len(os.getenv("LANGFUSE_HOST", "")) > 0: +# litellm.success_callback = ["langfuse"] +# print_verbose("\033[92mLiteLLM: Switched on Langfuse feature\033[0m") + +# ## CACHING +# ### REDIS +# # if len(os.getenv("REDIS_HOST", "")) > 0 and len(os.getenv("REDIS_PORT", "")) > 0 and len(os.getenv("REDIS_PASSWORD", "")) > 0: +# # print(f"redis host: {os.getenv('REDIS_HOST')}; redis port: {os.getenv('REDIS_PORT')}; password: {os.getenv('REDIS_PASSWORD')}") +# # from litellm.caching import Cache +# # litellm.cache = Cache(type="redis", host=os.getenv("REDIS_HOST"), port=os.getenv("REDIS_PORT"), password=os.getenv("REDIS_PASSWORD")) +# # print("\033[92mLiteLLM: Switched on Redis caching\033[0m") + + + +# def load_router_config(router: Optional[litellm.Router], config_file_path: Optional[str]='/app/config.yaml'): +# config = {} +# server_settings = {} +# try: +# if os.path.exists(config_file_path): # type: ignore +# with open(config_file_path, 'r') as file: # type: ignore +# config = yaml.safe_load(file) +# else: +# pass +# except: +# pass + +# ## SERVER SETTINGS (e.g. default completion model = 'ollama/mistral') +# server_settings = config.get("server_settings", None) +# if server_settings: +# server_settings = server_settings + +# ## LITELLM MODULE SETTINGS (e.g. litellm.drop_params=True,..) +# litellm_settings = config.get('litellm_settings', None) +# if litellm_settings: +# for key, value in litellm_settings.items(): +# setattr(litellm, key, value) + +# ## MODEL LIST +# model_list = config.get('model_list', None) +# if model_list: +# router = litellm.Router(model_list=model_list) + +# ## ENVIRONMENT VARIABLES +# environment_variables = config.get('environment_variables', None) +# if environment_variables: +# for key, value in environment_variables.items(): +# os.environ[key] = value + +# return router, model_list, server_settings diff --git a/litellm/exceptions.py b/litellm/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..ec0fe00495e4be7c7a1a305c593be5b3303fa25b --- /dev/null +++ b/litellm/exceptions.py @@ -0,0 +1,166 @@ +# +-----------------------------------------------+ +# | | +# | Give Feedback / Get Help | +# | https://github.com/BerriAI/litellm/issues/new | +# | | +# +-----------------------------------------------+ +# +# Thank you users! We ❤️ you! - Krrish & Ishaan + +## LiteLLM versions of the OpenAI Exception Types + +from openai import ( + AuthenticationError, + BadRequestError, + RateLimitError, + APIStatusError, + OpenAIError, + APIError, + APITimeoutError, + APIConnectionError, + APIResponseValidationError +) +import httpx + +class AuthenticationError(AuthenticationError): # type: ignore + def __init__(self, message, llm_provider, model, response: httpx.Response): + self.status_code = 401 + self.message = message + self.llm_provider = llm_provider + self.model = model + super().__init__( + self.message, + response=response, + body=None + ) # Call the base class constructor with the parameters it needs + +class BadRequestError(BadRequestError): # type: ignore + def __init__(self, message, model, llm_provider, response: httpx.Response): + self.status_code = 400 + self.message = message + self.model = model + self.llm_provider = llm_provider + super().__init__( + self.message, + response=response, + body=None + ) # Call the base class constructor with the parameters it needs + +class Timeout(APITimeoutError): # type: ignore + def __init__(self, message, model, llm_provider): + self.status_code = 408 + self.message = message + self.model = model + self.llm_provider = llm_provider + request = httpx.Request(method="POST", url="https://api.openai.com/v1") + super().__init__( + request=request + ) # Call the base class constructor with the parameters it needs + +class RateLimitError(RateLimitError): # type: ignore + def __init__(self, message, llm_provider, model, response: httpx.Response): + self.status_code = 429 + self.message = message + self.llm_provider = llm_provider + self.modle = model + super().__init__( + self.message, + response=response, + body=None + ) # Call the base class constructor with the parameters it needs + +# sub class of rate limit error - meant to give more granularity for error handling context window exceeded errors +class ContextWindowExceededError(BadRequestError): # type: ignore + def __init__(self, message, model, llm_provider, response: httpx.Response): + self.status_code = 400 + self.message = message + self.model = model + self.llm_provider = llm_provider + super().__init__( + message=self.message, + model=self.model, # type: ignore + llm_provider=self.llm_provider, # type: ignore + response=response + ) # Call the base class constructor with the parameters it needs + +class ServiceUnavailableError(APIStatusError): # type: ignore + def __init__(self, message, llm_provider, model, response: httpx.Response): + self.status_code = 503 + self.message = message + self.llm_provider = llm_provider + self.model = model + super().__init__( + self.message, + response=response, + body=None + ) # Call the base class constructor with the parameters it needs + + +# raise this when the API returns an invalid response object - https://github.com/openai/openai-python/blob/1be14ee34a0f8e42d3f9aa5451aa4cb161f1781f/openai/api_requestor.py#L401 +class APIError(APIError): # type: ignore + def __init__(self, status_code, message, llm_provider, model, request: httpx.Request): + self.status_code = status_code + self.message = message + self.llm_provider = llm_provider + self.model = model + super().__init__( + self.message, + request=request, # type: ignore + body=None + ) + +# raised if an invalid request (not get, delete, put, post) is made +class APIConnectionError(APIConnectionError): # type: ignore + def __init__(self, message, llm_provider, model, request: httpx.Request): + self.message = message + self.llm_provider = llm_provider + self.model = model + self.status_code = 500 + super().__init__( + message=self.message, + request=request + ) + +# raised if an invalid request (not get, delete, put, post) is made +class APIResponseValidationError(APIResponseValidationError): # type: ignore + def __init__(self, message, llm_provider, model): + self.message = message + self.llm_provider = llm_provider + self.model = model + request = httpx.Request(method="POST", url="https://api.openai.com/v1") + response = httpx.Response(status_code=500, request=request) + super().__init__( + response=response, + body=None, + message=message + ) + +class OpenAIError(OpenAIError): # type: ignore + def __init__(self, original_exception): + self.status_code = original_exception.http_status + super().__init__( + http_body=original_exception.http_body, + http_status=original_exception.http_status, + json_body=original_exception.json_body, + headers=original_exception.headers, + code=original_exception.code, + ) + self.llm_provider = "openai" + +class BudgetExceededError(Exception): + def __init__(self, current_cost, max_budget): + self.current_cost = current_cost + self.max_budget = max_budget + message = f"Budget has been exceeded! Current cost: {current_cost}, Max budget: {max_budget}" + super().__init__(message) + +## DEPRECATED ## +class InvalidRequestError(BadRequestError): # type: ignore + def __init__(self, message, model, llm_provider): + self.status_code = 400 + self.message = message + self.model = model + self.llm_provider = llm_provider + super().__init__( + self.message, f"{self.model}" + ) # Call the base class constructor with the parameters it needs diff --git a/litellm/integrations/__init__.py b/litellm/integrations/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b6e690fd59145ce8900fd9ab8d8a996ee7d33834 --- /dev/null +++ b/litellm/integrations/__init__.py @@ -0,0 +1 @@ +from . import * diff --git a/litellm/integrations/aispend.py b/litellm/integrations/aispend.py new file mode 100644 index 0000000000000000000000000000000000000000..2015d45ddd6e22fb695b4d1183189db7d8a05e16 --- /dev/null +++ b/litellm/integrations/aispend.py @@ -0,0 +1,177 @@ +#### What this does #### +# On success + failure, log events to aispend.io +import dotenv, os +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback +import datetime + +model_cost = { + "gpt-3.5-turbo": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, + "gpt-35-turbo": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, # azure model name + "gpt-3.5-turbo-0613": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, + "gpt-3.5-turbo-0301": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, + "gpt-3.5-turbo-16k": { + "max_tokens": 16000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000004, + }, + "gpt-35-turbo-16k": { + "max_tokens": 16000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000004, + }, # azure model name + "gpt-3.5-turbo-16k-0613": { + "max_tokens": 16000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000004, + }, + "gpt-4": { + "max_tokens": 8000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.00006, + }, + "gpt-4-0613": { + "max_tokens": 8000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.00006, + }, + "gpt-4-32k": { + "max_tokens": 8000, + "input_cost_per_token": 0.00006, + "output_cost_per_token": 0.00012, + }, + "claude-instant-1": { + "max_tokens": 100000, + "input_cost_per_token": 0.00000163, + "output_cost_per_token": 0.00000551, + }, + "claude-2": { + "max_tokens": 100000, + "input_cost_per_token": 0.00001102, + "output_cost_per_token": 0.00003268, + }, + "text-bison-001": { + "max_tokens": 8192, + "input_cost_per_token": 0.000004, + "output_cost_per_token": 0.000004, + }, + "chat-bison-001": { + "max_tokens": 4096, + "input_cost_per_token": 0.000002, + "output_cost_per_token": 0.000002, + }, + "command-nightly": { + "max_tokens": 4096, + "input_cost_per_token": 0.000015, + "output_cost_per_token": 0.000015, + }, +} + + +class AISpendLogger: + # Class variables or attributes + def __init__(self): + # Instance variables + self.account_id = os.getenv("AISPEND_ACCOUNT_ID") + self.api_key = os.getenv("AISPEND_API_KEY") + + def price_calculator(self, model, response_obj, start_time, end_time): + # try and find if the model is in the model_cost map + # else default to the average of the costs + prompt_tokens_cost_usd_dollar = 0 + completion_tokens_cost_usd_dollar = 0 + if model in model_cost: + prompt_tokens_cost_usd_dollar = ( + model_cost[model]["input_cost_per_token"] + * response_obj["usage"]["prompt_tokens"] + ) + completion_tokens_cost_usd_dollar = ( + model_cost[model]["output_cost_per_token"] + * response_obj["usage"]["completion_tokens"] + ) + elif "replicate" in model: + # replicate models are charged based on time + # llama 2 runs on an nvidia a100 which costs $0.0032 per second - https://replicate.com/replicate/llama-2-70b-chat + model_run_time = end_time - start_time # assuming time in seconds + cost_usd_dollar = model_run_time * 0.0032 + prompt_tokens_cost_usd_dollar = cost_usd_dollar / 2 + completion_tokens_cost_usd_dollar = cost_usd_dollar / 2 + else: + # calculate average input cost + input_cost_sum = 0 + output_cost_sum = 0 + for model in model_cost: + input_cost_sum += model_cost[model]["input_cost_per_token"] + output_cost_sum += model_cost[model]["output_cost_per_token"] + avg_input_cost = input_cost_sum / len(model_cost.keys()) + avg_output_cost = output_cost_sum / len(model_cost.keys()) + prompt_tokens_cost_usd_dollar = ( + model_cost[model]["input_cost_per_token"] + * response_obj["usage"]["prompt_tokens"] + ) + completion_tokens_cost_usd_dollar = ( + model_cost[model]["output_cost_per_token"] + * response_obj["usage"]["completion_tokens"] + ) + return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar + + def log_event(self, model, response_obj, start_time, end_time, print_verbose): + # Method definition + try: + print_verbose( + f"AISpend Logging - Enters logging function for model {model}" + ) + + url = f"https://aispend.io/api/v1/accounts/{self.account_id}/data" + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json", + } + + response_timestamp = datetime.datetime.fromtimestamp( + int(response_obj["created"]) + ).strftime("%Y-%m-%d") + + ( + prompt_tokens_cost_usd_dollar, + completion_tokens_cost_usd_dollar, + ) = self.price_calculator(model, response_obj, start_time, end_time) + prompt_tokens_cost_usd_cent = prompt_tokens_cost_usd_dollar * 100 + completion_tokens_cost_usd_cent = completion_tokens_cost_usd_dollar * 100 + data = [ + { + "requests": 1, + "requests_context": 1, + "context_tokens": response_obj["usage"]["prompt_tokens"], + "requests_generated": 1, + "generated_tokens": response_obj["usage"]["completion_tokens"], + "recorded_date": response_timestamp, + "model_id": response_obj["model"], + "generated_tokens_cost_usd_cent": prompt_tokens_cost_usd_cent, + "context_tokens_cost_usd_cent": completion_tokens_cost_usd_cent, + } + ] + + print_verbose(f"AISpend Logging - final data object: {data}") + except: + # traceback.print_exc() + print_verbose(f"AISpend Logging Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/berrispend.py b/litellm/integrations/berrispend.py new file mode 100644 index 0000000000000000000000000000000000000000..7d91ffca7f1bd17239c03332d4136537f778a2f3 --- /dev/null +++ b/litellm/integrations/berrispend.py @@ -0,0 +1,184 @@ +#### What this does #### +# On success + failure, log events to aispend.io +import dotenv, os +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback +import datetime + +model_cost = { + "gpt-3.5-turbo": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, + "gpt-35-turbo": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, # azure model name + "gpt-3.5-turbo-0613": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, + "gpt-3.5-turbo-0301": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, + "gpt-3.5-turbo-16k": { + "max_tokens": 16000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000004, + }, + "gpt-35-turbo-16k": { + "max_tokens": 16000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000004, + }, # azure model name + "gpt-3.5-turbo-16k-0613": { + "max_tokens": 16000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000004, + }, + "gpt-4": { + "max_tokens": 8000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.00006, + }, + "gpt-4-0613": { + "max_tokens": 8000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.00006, + }, + "gpt-4-32k": { + "max_tokens": 8000, + "input_cost_per_token": 0.00006, + "output_cost_per_token": 0.00012, + }, + "claude-instant-1": { + "max_tokens": 100000, + "input_cost_per_token": 0.00000163, + "output_cost_per_token": 0.00000551, + }, + "claude-2": { + "max_tokens": 100000, + "input_cost_per_token": 0.00001102, + "output_cost_per_token": 0.00003268, + }, + "text-bison-001": { + "max_tokens": 8192, + "input_cost_per_token": 0.000004, + "output_cost_per_token": 0.000004, + }, + "chat-bison-001": { + "max_tokens": 4096, + "input_cost_per_token": 0.000002, + "output_cost_per_token": 0.000002, + }, + "command-nightly": { + "max_tokens": 4096, + "input_cost_per_token": 0.000015, + "output_cost_per_token": 0.000015, + }, +} + + +class BerriSpendLogger: + # Class variables or attributes + def __init__(self): + # Instance variables + self.account_id = os.getenv("BERRISPEND_ACCOUNT_ID") + + def price_calculator(self, model, response_obj, start_time, end_time): + # try and find if the model is in the model_cost map + # else default to the average of the costs + prompt_tokens_cost_usd_dollar = 0 + completion_tokens_cost_usd_dollar = 0 + if model in model_cost: + prompt_tokens_cost_usd_dollar = ( + model_cost[model]["input_cost_per_token"] + * response_obj["usage"]["prompt_tokens"] + ) + completion_tokens_cost_usd_dollar = ( + model_cost[model]["output_cost_per_token"] + * response_obj["usage"]["completion_tokens"] + ) + elif "replicate" in model: + # replicate models are charged based on time + # llama 2 runs on an nvidia a100 which costs $0.0032 per second - https://replicate.com/replicate/llama-2-70b-chat + model_run_time = end_time - start_time # assuming time in seconds + cost_usd_dollar = model_run_time * 0.0032 + prompt_tokens_cost_usd_dollar = cost_usd_dollar / 2 + completion_tokens_cost_usd_dollar = cost_usd_dollar / 2 + else: + # calculate average input cost + input_cost_sum = 0 + output_cost_sum = 0 + for model in model_cost: + input_cost_sum += model_cost[model]["input_cost_per_token"] + output_cost_sum += model_cost[model]["output_cost_per_token"] + avg_input_cost = input_cost_sum / len(model_cost.keys()) + avg_output_cost = output_cost_sum / len(model_cost.keys()) + prompt_tokens_cost_usd_dollar = ( + model_cost[model]["input_cost_per_token"] + * response_obj["usage"]["prompt_tokens"] + ) + completion_tokens_cost_usd_dollar = ( + model_cost[model]["output_cost_per_token"] + * response_obj["usage"]["completion_tokens"] + ) + return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar + + def log_event( + self, model, messages, response_obj, start_time, end_time, print_verbose + ): + # Method definition + try: + print_verbose( + f"BerriSpend Logging - Enters logging function for model {model}" + ) + + url = f"https://berrispend.berri.ai/spend" + headers = {"Content-Type": "application/json"} + + ( + prompt_tokens_cost_usd_dollar, + completion_tokens_cost_usd_dollar, + ) = self.price_calculator(model, response_obj, start_time, end_time) + total_cost = ( + prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar + ) + + response_time = (end_time - start_time).total_seconds() + if "response" in response_obj: + data = [ + { + "response_time": response_time, + "model_id": response_obj["model"], + "total_cost": total_cost, + "messages": messages, + "response": response_obj["choices"][0]["message"]["content"], + "account_id": self.account_id, + } + ] + elif "error" in response_obj: + data = [ + { + "response_time": response_time, + "model_id": response_obj["model"], + "total_cost": total_cost, + "messages": messages, + "error": response_obj["error"], + "account_id": self.account_id, + } + ] + + print_verbose(f"BerriSpend Logging - final data object: {data}") + response = requests.post(url, headers=headers, json=data) + except: + # traceback.print_exc() + print_verbose(f"BerriSpend Logging Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..e502439a95fd1f241b75b80a45cefcc1f47664db --- /dev/null +++ b/litellm/integrations/custom_logger.py @@ -0,0 +1,83 @@ +#### What this does #### +# On success, logs events to Promptlayer +import dotenv, os +import requests +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback + + +class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callback#callback-class + # Class variables or attributes + def __init__(self): + pass + + def log_pre_api_call(self, model, messages, kwargs): + pass + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + pass + + def log_stream_event(self, kwargs, response_obj, start_time, end_time): + pass + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + pass + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + pass + + + #### SINGLE-USE #### - https://docs.litellm.ai/docs/observability/custom_callback#using-your-custom-callback-function + + def log_input_event(self, model, messages, kwargs, print_verbose, callback_func): + try: + kwargs["model"] = model + kwargs["messages"] = messages + kwargs["log_event_type"] = "pre_api_call" + callback_func( + kwargs, + ) + print_verbose( + f"Custom Logger - model call details: {kwargs}" + ) + except: + traceback.print_exc() + print_verbose(f"Custom Logger Error - {traceback.format_exc()}") + + def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose, callback_func): + # Method definition + try: + kwargs["log_event_type"] = "post_api_call" + callback_func( + kwargs, # kwargs to func + response_obj, + start_time, + end_time, + ) + print_verbose( + f"Custom Logger - final response object: {response_obj}" + ) + except: + # traceback.print_exc() + print_verbose(f"Custom Logger Error - {traceback.format_exc()}") + pass + + async def async_log_event(self, kwargs, response_obj, start_time, end_time, print_verbose, callback_func): + # Method definition + try: + kwargs["log_event_type"] = "post_api_call" + await callback_func( + kwargs, # kwargs to func + response_obj, + start_time, + end_time, + ) + print_verbose( + f"Custom Logger - final response object: {response_obj}" + ) + except: + # traceback.print_exc() + print_verbose(f"Custom Logger Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/helicone.py b/litellm/integrations/helicone.py new file mode 100644 index 0000000000000000000000000000000000000000..f9dff85dbf3808f9f0411d26457965130cb3253f --- /dev/null +++ b/litellm/integrations/helicone.py @@ -0,0 +1,114 @@ +#### What this does #### +# On success, logs events to Helicone +import dotenv, os +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback + + +class HeliconeLogger: + # Class variables or attributes + helicone_model_list = ["gpt", "claude"] + + def __init__(self): + # Instance variables + self.provider_url = "https://api.openai.com/v1" + self.key = os.getenv("HELICONE_API_KEY") + + def claude_mapping(self, model, messages, response_obj): + from anthropic import HUMAN_PROMPT, AI_PROMPT + + prompt = f"{HUMAN_PROMPT}" + for message in messages: + if "role" in message: + if message["role"] == "user": + prompt += f"{HUMAN_PROMPT}{message['content']}" + else: + prompt += f"{AI_PROMPT}{message['content']}" + else: + prompt += f"{HUMAN_PROMPT}{message['content']}" + prompt += f"{AI_PROMPT}" + claude_provider_request = {"model": model, "prompt": prompt} + + claude_response_obj = { + "completion": response_obj["choices"][0]["message"]["content"], + "model": model, + "stop_reason": "stop_sequence", + } + + return claude_provider_request, claude_response_obj + + def log_success( + self, model, messages, response_obj, start_time, end_time, print_verbose + ): + # Method definition + try: + print_verbose( + f"Helicone Logging - Enters logging function for model {model}" + ) + model = ( + model + if any( + accepted_model in model + for accepted_model in self.helicone_model_list + ) + else "gpt-3.5-turbo" + ) + provider_request = {"model": model, "messages": messages} + + if "claude" in model: + provider_request, response_obj = self.claude_mapping( + model=model, messages=messages, response_obj=response_obj + ) + + providerResponse = { + "json": response_obj, + "headers": {"openai-version": "2020-10-01"}, + "status": 200, + } + + # Code to be executed + url = "https://api.hconeai.com/oai/v1/log" + headers = { + "Authorization": f"Bearer {self.key}", + "Content-Type": "application/json", + } + start_time_seconds = int(start_time.timestamp()) + start_time_milliseconds = int( + (start_time.timestamp() - start_time_seconds) * 1000 + ) + end_time_seconds = int(end_time.timestamp()) + end_time_milliseconds = int( + (end_time.timestamp() - end_time_seconds) * 1000 + ) + data = { + "providerRequest": { + "url": self.provider_url, + "json": provider_request, + "meta": {"Helicone-Auth": f"Bearer {self.key}"}, + }, + "providerResponse": providerResponse, + "timing": { + "startTime": { + "seconds": start_time_seconds, + "milliseconds": start_time_milliseconds, + }, + "endTime": { + "seconds": end_time_seconds, + "milliseconds": end_time_milliseconds, + }, + }, # {"seconds": .., "milliseconds": ..} + } + response = requests.post(url, headers=headers, json=data) + if response.status_code == 200: + print_verbose("Helicone Logging - Success!") + else: + print_verbose( + f"Helicone Logging - Error Request was not successful. Status Code: {response.status_code}" + ) + print_verbose(f"Helicone Logging - Error {response.text}") + except: + # traceback.print_exc() + print_verbose(f"Helicone Logging Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/langfuse.py b/litellm/integrations/langfuse.py new file mode 100644 index 0000000000000000000000000000000000000000..ef90e6865e9d0b35164614ff45d0f3e51a7e0adf --- /dev/null +++ b/litellm/integrations/langfuse.py @@ -0,0 +1,75 @@ +#### What this does #### +# On success, logs events to Langfuse +import dotenv, os +import requests +import requests +from datetime import datetime + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback + +class LangFuseLogger: + # Class variables or attributes + def __init__(self): + try: + from langfuse import Langfuse + except Exception as e: + raise Exception(f"\033[91mLangfuse not installed, try running 'pip install langfuse' to fix this error: {e}\033[0m") + # Instance variables + self.secret_key = os.getenv("LANGFUSE_SECRET_KEY") + self.public_key = os.getenv("LANGFUSE_PUBLIC_KEY") + self.langfuse_host = os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com") + self.langfuse_release = os.getenv("LANGFUSE_RELEASE") + self.langfuse_debug = os.getenv("LANGFUSE_DEBUG") + self.Langfuse = Langfuse( + public_key=self.public_key, + secret_key=self.secret_key, + host=self.langfuse_host, + release=self.langfuse_release, + debug=self.langfuse_debug + ) + + def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + # Method definition + from langfuse.model import InitialGeneration, Usage + try: + print_verbose( + f"Langfuse Logging - Enters logging function for model {kwargs}" + ) + litellm_params = kwargs.get("litellm_params", {}) + metadata = litellm_params.get("metadata", {}) + prompt = [kwargs.get('messages')] + optional_params = kwargs.get("optional_params", {}) + + # langfuse only accepts str, int, bool, float for logging + for param, value in optional_params.items(): + if not isinstance(value, (str, int, bool, float)): + try: + optional_params[param] = str(value) + except: + # if casting value to str fails don't block logging + pass + + # end of processing langfuse ######################## + self.Langfuse.generation(InitialGeneration( + name=metadata.get("generation_name", "litellm-completion"), + startTime=start_time, + endTime=end_time, + model=kwargs['model'], + modelParameters=optional_params, + prompt=prompt, + completion=response_obj['choices'][0]['message'], + usage=Usage( + prompt_tokens=response_obj['usage']['prompt_tokens'], + completion_tokens=response_obj['usage']['completion_tokens'] + ), + metadata=metadata + )) + self.Langfuse.flush() + print_verbose( + f"Langfuse Layer Logging - final response object: {response_obj}" + ) + except: + # traceback.print_exc() + print_verbose(f"Langfuse Layer Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py new file mode 100644 index 0000000000000000000000000000000000000000..43e9de4d34a53555faaf8f41dd3e7b0f8a203cf2 --- /dev/null +++ b/litellm/integrations/langsmith.py @@ -0,0 +1,76 @@ +#### What this does #### +# On success, logs events to Langsmith +import dotenv, os +import requests +import requests +from datetime import datetime + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback + +class LangsmithLogger: + # Class variables or attributes + def __init__(self): + self.langsmith_api_key = os.getenv("LANGSMITH_API_KEY") + + + def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + # Method definition + # inspired by Langsmith http api here: https://github.com/langchain-ai/langsmith-cookbook/blob/main/tracing-examples/rest/rest.ipynb + metadata = {} + if "litellm_params" in kwargs: + metadata = kwargs["litellm_params"].get("metadata", {}) + # set project name and run_name for langsmith logging + # users can pass project_name and run name to litellm.completion() + # Example: litellm.completion(model, messages, metadata={"project_name": "my-litellm-project", "run_name": "my-langsmith-run"}) + # if not set litellm will use default project_name = litellm-completion, run_name = LLMRun + project_name = metadata.get("project_name", "litellm-completion") + run_name = metadata.get("run_name", "LLMRun") + print_verbose(f"Langsmith Logging - project_name: {project_name}, run_name {run_name}") + try: + print_verbose( + f"Langsmith Logging - Enters logging function for model {kwargs}" + ) + import requests + import datetime + from datetime import timezone + try: + start_time = kwargs["start_time"].astimezone(timezone.utc).isoformat() + end_time = kwargs["end_time"].astimezone(timezone.utc).isoformat() + except: + start_time = datetime.datetime.utcnow().isoformat() + end_time = datetime.datetime.utcnow().isoformat() + + # filter out kwargs to not include any dicts, langsmith throws an erros when trying to log kwargs + new_kwargs = {} + for key in kwargs: + value = kwargs[key] + if key == "start_time" or key =="end_time": + pass + elif type(value) != dict: + new_kwargs[key] = value + + requests.post( + "https://api.smith.langchain.com/runs", + json={ + "name": run_name, + "run_type": "llm", # this should always be llm, since litellm always logs llm calls. Langsmith allow us to log "chain" + "inputs": { + **new_kwargs + }, + "outputs": response_obj, + "session_name": project_name, + "start_time": start_time, + "end_time": end_time, + }, + headers={ + "x-api-key": self.langsmith_api_key + } + ) + print_verbose( + f"Langsmith Layer Logging - final response object: {response_obj}" + ) + except: + # traceback.print_exc() + print_verbose(f"Langsmith Layer Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/litedebugger.py b/litellm/integrations/litedebugger.py new file mode 100644 index 0000000000000000000000000000000000000000..d69602d5cae8c8d3fe60dd5f074e9ac6b40da693 --- /dev/null +++ b/litellm/integrations/litedebugger.py @@ -0,0 +1,231 @@ +import requests, traceback, json, os +import types + +class LiteDebugger: + user_email = None + dashboard_url = None + + def __init__(self, email=None): + self.api_url = "https://api.litellm.ai/debugger" + self.validate_environment(email) + pass + + def validate_environment(self, email): + try: + self.user_email = (email or os.getenv("LITELLM_TOKEN") or os.getenv("LITELLM_EMAIL")) + if self.user_email == None: # if users are trying to use_client=True but token not set + raise ValueError("litellm.use_client = True but no token or email passed. Please set it in litellm.token") + self.dashboard_url = "https://admin.litellm.ai/" + self.user_email + try: + print( + f"\033[92mHere's your LiteLLM Dashboard 👉 \033[94m\033[4m{self.dashboard_url}\033[0m" + ) + except: + print(f"Here's your LiteLLM Dashboard 👉 {self.dashboard_url}") + if self.user_email == None: + raise ValueError( + "[Non-Blocking Error] LiteLLMDebugger: Missing LITELLM_TOKEN. Set it in your environment. Eg.: os.environ['LITELLM_TOKEN']= " + ) + except Exception as e: + raise ValueError( + "[Non-Blocking Error] LiteLLMDebugger: Missing LITELLM_TOKEN. Set it in your environment. Eg.: os.environ['LITELLM_TOKEN']= " + ) + + def input_log_event( + self, + model, + messages, + end_user, + litellm_call_id, + call_type, + print_verbose, + litellm_params, + optional_params, + ): + print_verbose(f"LiteDebugger: Pre-API Call Logging for call id {litellm_call_id}") + try: + print_verbose( + f"LiteLLMDebugger: Logging - Enters input logging function for model {model}" + ) + + def remove_key_value(dictionary, key): + new_dict = dictionary.copy() # Create a copy of the original dictionary + new_dict.pop(key) # Remove the specified key-value pair from the copy + return new_dict + + updated_litellm_params = remove_key_value(litellm_params, "logger_fn") + + if call_type == "embedding": + for message in messages: # assuming the input is a list as required by the embedding function + litellm_data_obj = { + "model": model, + "messages": [{"role": "user", "content": message}], + "end_user": end_user, + "status": "initiated", + "litellm_call_id": litellm_call_id, + "user_email": self.user_email, + "litellm_params": updated_litellm_params, + "optional_params": optional_params, + } + print_verbose( + f"LiteLLMDebugger: Logging - logged data obj {litellm_data_obj}" + ) + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + print_verbose(f"LiteDebugger: embedding api response - {response.text}") + elif call_type == "completion": + litellm_data_obj = { + "model": model, + "messages": messages if isinstance(messages, list) else [{"role": "user", "content": messages}], + "end_user": end_user, + "status": "initiated", + "litellm_call_id": litellm_call_id, + "user_email": self.user_email, + "litellm_params": updated_litellm_params, + "optional_params": optional_params, + } + print_verbose( + f"LiteLLMDebugger: Logging - logged data obj {litellm_data_obj}" + ) + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + print_verbose(f"LiteDebugger: completion api response - {response.text}") + except: + print_verbose( + f"[Non-Blocking Error] LiteDebugger: Logging Error - {traceback.format_exc()}" + ) + pass + + def post_call_log_event(self, original_response, litellm_call_id, print_verbose, call_type, stream): + print_verbose(f"LiteDebugger: Post-API Call Logging for call id {litellm_call_id}") + try: + if call_type == "embedding": + litellm_data_obj = { + "status": "received", + "additional_details": {"original_response": str(original_response["data"][0]["embedding"][:5])}, # don't store the entire vector + "litellm_call_id": litellm_call_id, + "user_email": self.user_email, + } + elif call_type == "completion" and not stream: + litellm_data_obj = { + "status": "received", + "additional_details": {"original_response": original_response}, + "litellm_call_id": litellm_call_id, + "user_email": self.user_email, + } + elif call_type == "completion" and stream: + litellm_data_obj = { + "status": "received", + "additional_details": {"original_response": "Streamed response" if isinstance(original_response, types.GeneratorType) else original_response}, + "litellm_call_id": litellm_call_id, + "user_email": self.user_email, + } + print_verbose(f"litedebugger post-call data object - {litellm_data_obj}") + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + print_verbose(f"LiteDebugger: api response - {response.text}") + except: + print_verbose( + f"[Non-Blocking Error] LiteDebugger: Logging Error - {traceback.format_exc()}" + ) + + def log_event( + self, + end_user, + response_obj, + start_time, + end_time, + litellm_call_id, + print_verbose, + call_type, + stream = False + ): + print_verbose(f"LiteDebugger: Success/Failure Call Logging for call id {litellm_call_id}") + try: + print_verbose( + f"LiteLLMDebugger: Success/Failure Logging - Enters handler logging function for function {call_type} and stream set to {stream} with response object {response_obj}" + ) + total_cost = 0 # [TODO] implement cost tracking + response_time = (end_time - start_time).total_seconds() + if call_type == "completion" and stream == False: + litellm_data_obj = { + "response_time": response_time, + "total_cost": total_cost, + "response": response_obj["choices"][0]["message"]["content"], + "litellm_call_id": litellm_call_id, + "status": "success", + } + print_verbose( + f"LiteDebugger: Logging - final data object: {litellm_data_obj}" + ) + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + elif call_type == "embedding": + litellm_data_obj = { + "response_time": response_time, + "total_cost": total_cost, + "response": str(response_obj["data"][0]["embedding"][:5]), + "litellm_call_id": litellm_call_id, + "status": "success", + } + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + elif call_type == "completion" and stream == True: + if len(response_obj["content"]) > 0: # don't log the empty strings + litellm_data_obj = { + "response_time": response_time, + "total_cost": total_cost, + "response": response_obj["content"], + "litellm_call_id": litellm_call_id, + "status": "success", + } + print_verbose( + f"LiteDebugger: Logging - final data object: {litellm_data_obj}" + ) + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + elif "error" in response_obj: + if "Unable to map your input to a model." in response_obj["error"]: + total_cost = 0 + litellm_data_obj = { + "response_time": response_time, + "model": response_obj["model"], + "total_cost": total_cost, + "error": response_obj["error"], + "end_user": end_user, + "litellm_call_id": litellm_call_id, + "status": "failure", + "user_email": self.user_email, + } + print_verbose( + f"LiteDebugger: Logging - final data object: {litellm_data_obj}" + ) + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + print_verbose(f"LiteDebugger: api response - {response.text}") + except: + print_verbose( + f"[Non-Blocking Error] LiteDebugger: Logging Error - {traceback.format_exc()}" + ) + pass diff --git a/litellm/integrations/llmonitor.py b/litellm/integrations/llmonitor.py new file mode 100644 index 0000000000000000000000000000000000000000..ff4c3990f83f2f5ffe0ce8573ca5c19cdafea185 --- /dev/null +++ b/litellm/integrations/llmonitor.py @@ -0,0 +1,127 @@ +#### What this does #### +# On success + failure, log events to aispend.io +import datetime +import traceback +import dotenv +import os +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv + + +# convert to {completion: xx, tokens: xx} +def parse_usage(usage): + return { + "completion": usage["completion_tokens"] if "completion_tokens" in usage else 0, + "prompt": usage["prompt_tokens"] if "prompt_tokens" in usage else 0, + } + + +def parse_messages(input): + if input is None: + return None + + def clean_message(message): + # if is strin, return as is + if isinstance(message, str): + return message + + if "message" in message: + return clean_message(message["message"]) + text = message["content"] + if text == None: + text = message.get("function_call", None) + + return { + "role": message["role"], + "text": text, + } + + if isinstance(input, list): + if len(input) == 1: + return clean_message(input[0]) + else: + return [clean_message(msg) for msg in input] + else: + return clean_message(input) + + +class LLMonitorLogger: + # Class variables or attributes + def __init__(self): + # Instance variables + self.api_url = os.getenv("LLMONITOR_API_URL") or "https://app.llmonitor.com" + self.app_id = os.getenv("LLMONITOR_APP_ID") + + def log_event( + self, + type, + event, + run_id, + model, + print_verbose, + input=None, + user_id=None, + response_obj=None, + start_time=datetime.datetime.now(), + end_time=datetime.datetime.now(), + error=None, + ): + # Method definition + try: + print_verbose(f"LLMonitor Logging - Logging request for model {model}") + + if response_obj: + usage = ( + parse_usage(response_obj["usage"]) + if "usage" in response_obj + else None + ) + output = response_obj["choices"] if "choices" in response_obj else None + else: + usage = None + output = None + + if error: + error_obj = {"stack": error} + + else: + error_obj = None + + data = [ + { + "type": type, + "name": model, + "runId": run_id, + "app": self.app_id, + "event": "start", + "timestamp": start_time.isoformat(), + "userId": user_id, + "input": parse_messages(input), + }, + { + "type": type, + "runId": run_id, + "app": self.app_id, + "event": event, + "error": error_obj, + "timestamp": end_time.isoformat(), + "userId": user_id, + "output": parse_messages(output), + "tokensUsage": usage, + }, + ] + + print_verbose(f"LLMonitor Logging - final data object: {data}") + + response = requests.post( + self.api_url + "/api/report", + headers={"Content-Type": "application/json"}, + json={"events": data}, + ) + + print_verbose(f"LLMonitor Logging - response: {response}") + except: + # traceback.print_exc() + print_verbose(f"LLMonitor Logging Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/prompt_layer.py b/litellm/integrations/prompt_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..4167ea60fe4ce43111945a34d1d392f84a49e65d --- /dev/null +++ b/litellm/integrations/prompt_layer.py @@ -0,0 +1,72 @@ +#### What this does #### +# On success, logs events to Promptlayer +import dotenv, os +import requests +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback + + +class PromptLayerLogger: + # Class variables or attributes + def __init__(self): + # Instance variables + self.key = os.getenv("PROMPTLAYER_API_KEY") + + def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + # Method definition + try: + new_kwargs = {} + new_kwargs['model'] = kwargs['model'] + new_kwargs['messages'] = kwargs['messages'] + + # add kwargs["optional_params"] to new_kwargs + for optional_param in kwargs["optional_params"]: + new_kwargs[optional_param] = kwargs["optional_params"][optional_param] + + + print_verbose( + f"Prompt Layer Logging - Enters logging function for model kwargs: {new_kwargs}\n, response: {response_obj}" + ) + + + request_response = requests.post( + "https://api.promptlayer.com/rest/track-request", + json={ + "function_name": "openai.ChatCompletion.create", + "kwargs": new_kwargs, + "tags": ["hello", "world"], + "request_response": dict(response_obj), + "request_start_time": int(start_time.timestamp()), + "request_end_time": int(end_time.timestamp()), + "api_key": self.key, + # Optional params for PromptLayer + # "prompt_id": "", + # "prompt_input_variables": "", + # "prompt_version":1, + }, + ) + print_verbose( + f"Prompt Layer Logging: success - final response object: {request_response.text}" + ) + response_json = request_response.json() + if "success" not in request_response.json(): + raise Exception("Promptlayer did not successfully log the response!") + + if "request_id" in response_json: + print(kwargs["litellm_params"]["metadata"]) + if kwargs["litellm_params"]["metadata"] is not None: + response = requests.post( + "https://api.promptlayer.com/rest/track-metadata", + json={ + "request_id": response_json["request_id"], + "api_key": self.key, + "metadata": kwargs["litellm_params"]["metadata"] + }, + ) + print_verbose(f"Prompt Layer Logging: success - metadata post response object: {response.text}") + + except: + print_verbose(f"error: Prompt Layer Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/supabase.py b/litellm/integrations/supabase.py new file mode 100644 index 0000000000000000000000000000000000000000..bca48a451b7fb63bb9bf4affeed81d6b6705c0bd --- /dev/null +++ b/litellm/integrations/supabase.py @@ -0,0 +1,116 @@ +#### What this does #### +# On success + failure, log events to Supabase + +import dotenv, os +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback +import datetime, subprocess, sys +import litellm + +class Supabase: + # Class variables or attributes + supabase_table_name = "request_logs" + + def __init__(self): + # Instance variables + self.supabase_url = os.getenv("SUPABASE_URL") + self.supabase_key = os.getenv("SUPABASE_KEY") + try: + import supabase + except ImportError: + subprocess.check_call([sys.executable, "-m", "pip", "install", "supabase"]) + import supabase + self.supabase_client = supabase.create_client( + self.supabase_url, self.supabase_key + ) + + def input_log_event( + self, model, messages, end_user, litellm_call_id, print_verbose + ): + try: + print_verbose( + f"Supabase Logging - Enters input logging function for model {model}" + ) + supabase_data_obj = { + "model": model, + "messages": messages, + "end_user": end_user, + "status": "initiated", + "litellm_call_id": litellm_call_id, + } + data, count = ( + self.supabase_client.table(self.supabase_table_name) + .insert(supabase_data_obj) + .execute() + ) + print_verbose(f"data: {data}") + except: + print_verbose(f"Supabase Logging Error - {traceback.format_exc()}") + pass + + def log_event( + self, + model, + messages, + end_user, + response_obj, + start_time, + end_time, + litellm_call_id, + print_verbose, + ): + try: + print_verbose( + f"Supabase Logging - Enters logging function for model {model}, response_obj: {response_obj}" + ) + + total_cost = litellm.completion_cost(completion_response=response_obj) + + response_time = (end_time - start_time).total_seconds() + if "choices" in response_obj: + supabase_data_obj = { + "response_time": response_time, + "model": response_obj["model"], + "total_cost": total_cost, + "messages": messages, + "response": response_obj["choices"][0]["message"]["content"], + "end_user": end_user, + "litellm_call_id": litellm_call_id, + "status": "success", + } + print_verbose( + f"Supabase Logging - final data object: {supabase_data_obj}" + ) + data, count = ( + self.supabase_client.table(self.supabase_table_name) + .upsert(supabase_data_obj, on_conflict="litellm_call_id") + .execute() + ) + elif "error" in response_obj: + if "Unable to map your input to a model." in response_obj["error"]: + total_cost = 0 + supabase_data_obj = { + "response_time": response_time, + "model": response_obj["model"], + "total_cost": total_cost, + "messages": messages, + "error": response_obj["error"], + "end_user": end_user, + "litellm_call_id": litellm_call_id, + "status": "failure", + } + print_verbose( + f"Supabase Logging - final data object: {supabase_data_obj}" + ) + data, count = ( + self.supabase_client.table(self.supabase_table_name) + .upsert(supabase_data_obj, on_conflict="litellm_call_id") + .execute() + ) + + except: + # traceback.print_exc() + print_verbose(f"Supabase Logging Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/traceloop.py b/litellm/integrations/traceloop.py new file mode 100644 index 0000000000000000000000000000000000000000..be53de0e90129f39f485b66a4fbccee4daa3e7e8 --- /dev/null +++ b/litellm/integrations/traceloop.py @@ -0,0 +1,78 @@ +class TraceloopLogger: + def __init__(self): + from traceloop.sdk.tracing.tracing import TracerWrapper + + self.tracer_wrapper = TracerWrapper() + + def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + from opentelemetry.trace import SpanKind + from opentelemetry.semconv.ai import SpanAttributes + + try: + tracer = self.tracer_wrapper.get_tracer() + + model = kwargs.get("model") + + # LiteLLM uses the standard OpenAI library, so it's already handled by Traceloop SDK + if "gpt" in model: + return + + with tracer.start_as_current_span( + "litellm.completion", + kind=SpanKind.CLIENT, + ) as span: + if span.is_recording(): + span.set_attribute( + SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model") + ) + span.set_attribute( + SpanAttributes.LLM_REQUEST_MAX_TOKENS, kwargs.get("max_tokens") + ) + span.set_attribute( + SpanAttributes.LLM_TEMPERATURE, kwargs.get("temperature") + ) + + for idx, prompt in enumerate(kwargs.get("messages")): + span.set_attribute( + f"{SpanAttributes.LLM_PROMPTS}.{idx}.role", + prompt.get("role"), + ) + span.set_attribute( + f"{SpanAttributes.LLM_PROMPTS}.{idx}.content", + prompt.get("content"), + ) + + span.set_attribute( + SpanAttributes.LLM_RESPONSE_MODEL, response_obj.get("model") + ) + usage = response_obj.get("usage") + if usage: + span.set_attribute( + SpanAttributes.LLM_USAGE_TOTAL_TOKENS, + usage.get("total_tokens"), + ) + span.set_attribute( + SpanAttributes.LLM_USAGE_COMPLETION_TOKENS, + usage.get("completion_tokens"), + ) + span.set_attribute( + SpanAttributes.LLM_USAGE_PROMPT_TOKENS, + usage.get("prompt_tokens"), + ) + + for idx, choice in enumerate(response_obj.get("choices")): + span.set_attribute( + f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.finish_reason", + choice.get("finish_reason"), + ) + span.set_attribute( + f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.role", + choice.get("message").get("role"), + ) + span.set_attribute( + f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.content", + choice.get("message").get("content"), + ) + + except Exception as e: + print_verbose(f"Traceloop Layer Error - {e}") diff --git a/litellm/integrations/weights_biases.py b/litellm/integrations/weights_biases.py new file mode 100644 index 0000000000000000000000000000000000000000..c571eca3ad2b127d833617fbff79e4b3e5d82cd2 --- /dev/null +++ b/litellm/integrations/weights_biases.py @@ -0,0 +1,219 @@ +imported_openAIResponse=True +try: + import io + import logging + import sys + from typing import Any, Dict, List, Optional, TypeVar + + from wandb.sdk.data_types import trace_tree + + if sys.version_info >= (3, 8): + from typing import Literal, Protocol + else: + from typing_extensions import Literal, Protocol + + + logger = logging.getLogger(__name__) + + + K = TypeVar("K", bound=str) + V = TypeVar("V") + + + class OpenAIResponse(Protocol[K, V]): # type: ignore + # contains a (known) object attribute + object: Literal["chat.completion", "edit", "text_completion"] + + def __getitem__(self, key: K) -> V: + ... # pragma: no cover + + def get(self, key: K, default: Optional[V] = None) -> Optional[V]: + ... # pragma: no cover + + + class OpenAIRequestResponseResolver: + def __call__( + self, + request: Dict[str, Any], + response: OpenAIResponse, + time_elapsed: float, + ) -> Optional[trace_tree.WBTraceTree]: + try: + if response["object"] == "edit": + return self._resolve_edit(request, response, time_elapsed) + elif response["object"] == "text_completion": + return self._resolve_completion(request, response, time_elapsed) + elif response["object"] == "chat.completion": + return self._resolve_chat_completion(request, response, time_elapsed) + else: + logger.info(f"Unknown OpenAI response object: {response['object']}") + except Exception as e: + logger.warning(f"Failed to resolve request/response: {e}") + return None + + @staticmethod + def results_to_trace_tree( + request: Dict[str, Any], + response: OpenAIResponse, + results: List[trace_tree.Result], + time_elapsed: float, + ) -> trace_tree.WBTraceTree: + """Converts the request, response, and results into a trace tree. + + params: + request: The request dictionary + response: The response object + results: A list of results object + time_elapsed: The time elapsed in seconds + returns: + A wandb trace tree object. + """ + start_time_ms = int(round(response["created"] * 1000)) + end_time_ms = start_time_ms + int(round(time_elapsed * 1000)) + span = trace_tree.Span( + name=f"{response.get('model', 'openai')}_{response['object']}_{response.get('created')}", + attributes=dict(response), # type: ignore + start_time_ms=start_time_ms, + end_time_ms=end_time_ms, + span_kind=trace_tree.SpanKind.LLM, + results=results, + ) + model_obj = {"request": request, "response": response, "_kind": "openai"} + return trace_tree.WBTraceTree(root_span=span, model_dict=model_obj) + + def _resolve_edit( + self, + request: Dict[str, Any], + response: OpenAIResponse, + time_elapsed: float, + ) -> trace_tree.WBTraceTree: + """Resolves the request and response objects for `openai.Edit`.""" + request_str = ( + f"\n\n**Instruction**: {request['instruction']}\n\n" + f"**Input**: {request['input']}\n" + ) + choices = [ + f"\n\n**Edited**: {choice['text']}\n" for choice in response["choices"] + ] + + return self._request_response_result_to_trace( + request=request, + response=response, + request_str=request_str, + choices=choices, + time_elapsed=time_elapsed, + ) + + def _resolve_completion( + self, + request: Dict[str, Any], + response: OpenAIResponse, + time_elapsed: float, + ) -> trace_tree.WBTraceTree: + """Resolves the request and response objects for `openai.Completion`.""" + request_str = f"\n\n**Prompt**: {request['prompt']}\n" + choices = [ + f"\n\n**Completion**: {choice['text']}\n" for choice in response["choices"] + ] + + return self._request_response_result_to_trace( + request=request, + response=response, + request_str=request_str, + choices=choices, + time_elapsed=time_elapsed, + ) + + def _resolve_chat_completion( + self, + request: Dict[str, Any], + response: OpenAIResponse, + time_elapsed: float, + ) -> trace_tree.WBTraceTree: + """Resolves the request and response objects for `openai.Completion`.""" + prompt = io.StringIO() + for message in request["messages"]: + prompt.write(f"\n\n**{message['role']}**: {message['content']}\n") + request_str = prompt.getvalue() + + choices = [ + f"\n\n**{choice['message']['role']}**: {choice['message']['content']}\n" + for choice in response["choices"] + ] + + return self._request_response_result_to_trace( + request=request, + response=response, + request_str=request_str, + choices=choices, + time_elapsed=time_elapsed, + ) + + def _request_response_result_to_trace( + self, + request: Dict[str, Any], + response: OpenAIResponse, + request_str: str, + choices: List[str], + time_elapsed: float, + ) -> trace_tree.WBTraceTree: + """Resolves the request and response objects for `openai.Completion`.""" + results = [ + trace_tree.Result( + inputs={"request": request_str}, + outputs={"response": choice}, + ) + for choice in choices + ] + trace = self.results_to_trace_tree(request, response, results, time_elapsed) + return trace +except: + imported_openAIResponse=False + + + +#### What this does #### +# On success, logs events to Langfuse +import dotenv, os +import requests +import requests +from datetime import datetime + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback + +class WeightsBiasesLogger: + # Class variables or attributes + def __init__(self): + try: + import wandb + except: + raise Exception("\033[91m wandb not installed, try running 'pip install wandb' to fix this error\033[0m") + if imported_openAIResponse==False: + raise Exception("\033[91m wandb not installed, try running 'pip install wandb' to fix this error\033[0m") + self.resolver = OpenAIRequestResponseResolver() + + def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + # Method definition + import wandb + + try: + print_verbose( + f"W&B Logging - Enters logging function for model {kwargs}" + ) + run = wandb.init() + print_verbose(response_obj) + + trace = self.resolver(kwargs, response_obj, (end_time-start_time).total_seconds()) + + if trace is not None: + run.log({"trace": trace}) + + run.finish() + print_verbose( + f"W&B Logging Logging - final response object: {response_obj}" + ) + except: + # traceback.print_exc() + print_verbose(f"W&B Logging Layer Error - {traceback.format_exc()}") + pass diff --git a/litellm/llms/__init__.py b/litellm/llms/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b6e690fd59145ce8900fd9ab8d8a996ee7d33834 --- /dev/null +++ b/litellm/llms/__init__.py @@ -0,0 +1 @@ +from . import * diff --git a/litellm/llms/ai21.py b/litellm/llms/ai21.py new file mode 100644 index 0000000000000000000000000000000000000000..e2099775138449d7db8a0a0d7b725771ea2fe8f0 --- /dev/null +++ b/litellm/llms/ai21.py @@ -0,0 +1,194 @@ +import os, types, traceback +import json +from enum import Enum +import requests +import time, httpx +from typing import Callable, Optional +from litellm.utils import ModelResponse, Choices, Message +import litellm + +class AI21Error(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="https://api.ai21.com/studio/v1/") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class AI21Config(): + """ + Reference: https://docs.ai21.com/reference/j2-complete-ref + + The class `AI21Config` provides configuration for the AI21's API interface. Below are the parameters: + + - `numResults` (int32): Number of completions to sample and return. Optional, default is 1. If the temperature is greater than 0 (non-greedy decoding), a value greater than 1 can be meaningful. + + - `maxTokens` (int32): The maximum number of tokens to generate per result. Optional, default is 16. If no `stopSequences` are given, generation stops after producing `maxTokens`. + + - `minTokens` (int32): The minimum number of tokens to generate per result. Optional, default is 0. If `stopSequences` are given, they are ignored until `minTokens` are generated. + + - `temperature` (float): Modifies the distribution from which tokens are sampled. Optional, default is 0.7. A value of 0 essentially disables sampling and results in greedy decoding. + + - `topP` (float): Used for sampling tokens from the corresponding top percentile of probability mass. Optional, default is 1. For instance, a value of 0.9 considers only tokens comprising the top 90% probability mass. + + - `stopSequences` (array of strings): Stops decoding if any of the input strings is generated. Optional. + + - `topKReturn` (int32): Range between 0 to 10, including both. Optional, default is 0. Specifies the top-K alternative tokens to return. A non-zero value includes the string representations and log-probabilities for each of the top-K alternatives at each position. + + - `frequencyPenalty` (object): Placeholder for frequency penalty object. + + - `presencePenalty` (object): Placeholder for presence penalty object. + + - `countPenalty` (object): Placeholder for count penalty object. + """ + numResults: Optional[int]=None + maxTokens: Optional[int]=None + minTokens: Optional[int]=None + temperature: Optional[float]=None + topP: Optional[float]=None + stopSequences: Optional[list]=None + topKReturn: Optional[int]=None + frequencePenalty: Optional[dict]=None + presencePenalty: Optional[dict]=None + countPenalty: Optional[dict]=None + + def __init__(self, + numResults: Optional[int]=None, + maxTokens: Optional[int]=None, + minTokens: Optional[int]=None, + temperature: Optional[float]=None, + topP: Optional[float]=None, + stopSequences: Optional[list]=None, + topKReturn: Optional[int]=None, + frequencePenalty: Optional[dict]=None, + presencePenalty: Optional[dict]=None, + countPenalty: Optional[dict]=None) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + + + +def validate_environment(api_key): + if api_key is None: + raise ValueError( + "Missing AI21 API Key - A call is being made to ai21 but no key is set either in the environment variables or via params" + ) + headers = { + "accept": "application/json", + "content-type": "application/json", + "Authorization": "Bearer " + api_key, + } + return headers + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + model = model + prompt = "" + for message in messages: + if "role" in message: + if message["role"] == "user": + prompt += ( + f"{message['content']}" + ) + else: + prompt += ( + f"{message['content']}" + ) + else: + prompt += f"{message['content']}" + + ## Load Config + config = litellm.AI21Config.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > ai21_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + data = { + "prompt": prompt, + # "instruction": prompt, # some baseten models require the prompt to be passed in via the 'instruction' kwarg + **optional_params, + } + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={"complete_input_dict": data}, + ) + ## COMPLETION CALL + response = requests.post( + api_base + model + "/complete", headers=headers, data=json.dumps(data) + ) + if response.status_code != 200: + raise AI21Error( + status_code=response.status_code, + message=response.text + ) + if "stream" in optional_params and optional_params["stream"] == True: + return response.iter_lines() + else: + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + ## RESPONSE OBJECT + completion_response = response.json() + try: + choices_list = [] + for idx, item in enumerate(completion_response["completions"]): + if len(item["data"]["text"]) > 0: + message_obj = Message(content=item["data"]["text"]) + else: + message_obj = Message(content=None) + choice_obj = Choices(finish_reason=item["finishReason"]["reason"], index=idx+1, message=message_obj) + choices_list.append(choice_obj) + model_response["choices"] = choices_list + except Exception as e: + raise AI21Error(message=traceback.format_exc(), status_code=response.status_code) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = len( + encoding.encode(prompt) + ) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + model_response["usage"] = { + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + "total_tokens": prompt_tokens + completion_tokens, + } + return model_response + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/aleph_alpha.py b/litellm/llms/aleph_alpha.py new file mode 100644 index 0000000000000000000000000000000000000000..9bceec51bdb4438fc734f28ffc90f67b8f59d304 --- /dev/null +++ b/litellm/llms/aleph_alpha.py @@ -0,0 +1,278 @@ +import os, types +import json +from enum import Enum +import requests +import time +from typing import Callable, Optional +import litellm +from litellm.utils import ModelResponse, Choices, Message, Usage +import httpx + +class AlephAlphaError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="https://api.aleph-alpha.com/complete") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class AlephAlphaConfig(): + """ + Reference: https://docs.aleph-alpha.com/api/complete/ + + The `AlephAlphaConfig` class represents the configuration for the Aleph Alpha API. Here are the properties: + + - `maximum_tokens` (integer, required): The maximum number of tokens to be generated by the completion. The sum of input tokens and maximum tokens may not exceed 2048. + + - `minimum_tokens` (integer, optional; default value: 0): Generate at least this number of tokens before an end-of-text token is generated. + + - `echo` (boolean, optional; default value: false): Whether to echo the prompt in the completion. + + - `temperature` (number, nullable; default value: 0): Adjusts how creatively the model generates outputs. Use combinations of temperature, top_k, and top_p sensibly. + + - `top_k` (integer, nullable; default value: 0): Introduces randomness into token generation by considering the top k most likely options. + + - `top_p` (number, nullable; default value: 0): Adds randomness by considering the smallest set of tokens whose cumulative probability exceeds top_p. + + - `presence_penalty`, `frequency_penalty`, `sequence_penalty` (number, nullable; default value: 0): Various penalties that can reduce repetition. + + - `sequence_penalty_min_length` (integer; default value: 2): Minimum number of tokens to be considered as a sequence. + + - `repetition_penalties_include_prompt`, `repetition_penalties_include_completion`, `use_multiplicative_presence_penalty`,`use_multiplicative_frequency_penalty`,`use_multiplicative_sequence_penalty` (boolean, nullable; default value: false): Various settings that adjust how the repetition penalties are applied. + + - `penalty_bias` (string, nullable): Text used in addition to the penalized tokens for repetition penalties. + + - `penalty_exceptions` (string[], nullable): Strings that may be generated without penalty. + + - `penalty_exceptions_include_stop_sequences` (boolean, nullable; default value: true): Include all stop_sequences in penalty_exceptions. + + - `best_of` (integer, nullable; default value: 1): The number of completions will be generated on the server side. + + - `n` (integer, nullable; default value: 1): The number of completions to return. + + - `logit_bias` (object, nullable): Adjust the logit scores before sampling. + + - `log_probs` (integer, nullable): Number of top log probabilities for each token generated. + + - `stop_sequences` (string[], nullable): List of strings that will stop generation if they're generated. + + - `tokens` (boolean, nullable; default value: false): Flag indicating whether individual tokens of the completion should be returned or not. + + - `raw_completion` (boolean; default value: false): if True, the raw completion of the model will be returned. + + - `disable_optimizations` (boolean, nullable; default value: false): Disables any applied optimizations to both your prompt and completion. + + - `completion_bias_inclusion`, `completion_bias_exclusion` (string[], default value: []): Set of strings to bias the generation of tokens. + + - `completion_bias_inclusion_first_token_only`, `completion_bias_exclusion_first_token_only` (boolean; default value: false): Consider only the first token for the completion_bias_inclusion/exclusion. + + - `contextual_control_threshold` (number, nullable): Control over how similar tokens are controlled. + + - `control_log_additive` (boolean; default value: true): Method of applying control to attention scores. + """ + maximum_tokens: Optional[int]=litellm.max_tokens # aleph alpha requires max tokens + minimum_tokens: Optional[int]=None + echo: Optional[bool]=None + temperature: Optional[int]=None + top_k: Optional[int]=None + top_p: Optional[int]=None + presence_penalty: Optional[int]=None + frequency_penalty: Optional[int]=None + sequence_penalty: Optional[int]=None + sequence_penalty_min_length: Optional[int]=None + repetition_penalties_include_prompt: Optional[bool]=None + repetition_penalties_include_completion: Optional[bool]=None + use_multiplicative_presence_penalty: Optional[bool]=None + use_multiplicative_frequency_penalty: Optional[bool]=None + use_multiplicative_sequence_penalty: Optional[bool]=None + penalty_bias: Optional[str]=None + penalty_exceptions_include_stop_sequences: Optional[bool]=None + best_of: Optional[int]=None + n: Optional[int]=None + logit_bias: Optional[dict]=None + log_probs: Optional[int]=None + stop_sequences: Optional[list]=None + tokens: Optional[bool]=None + raw_completion: Optional[bool]=None + disable_optimizations: Optional[bool]=None + completion_bias_inclusion: Optional[list]=None + completion_bias_exclusion: Optional[list]=None + completion_bias_inclusion_first_token_only: Optional[bool]=None + completion_bias_exclusion_first_token_only: Optional[bool]=None + contextual_control_threshold: Optional[int]=None + control_log_additive: Optional[bool]=None + + + def __init__(self, + maximum_tokens: Optional[int]=None, + minimum_tokens: Optional[int]=None, + echo: Optional[bool]=None, + temperature: Optional[int]=None, + top_k: Optional[int]=None, + top_p: Optional[int]=None, + presence_penalty: Optional[int]=None, + frequency_penalty: Optional[int]=None, + sequence_penalty: Optional[int]=None, + sequence_penalty_min_length: Optional[int]=None, + repetition_penalties_include_prompt: Optional[bool]=None, + repetition_penalties_include_completion: Optional[bool]=None, + use_multiplicative_presence_penalty: Optional[bool]=None, + use_multiplicative_frequency_penalty: Optional[bool]=None, + use_multiplicative_sequence_penalty: Optional[bool]=None, + penalty_bias: Optional[str]=None, + penalty_exceptions_include_stop_sequences: Optional[bool]=None, + best_of: Optional[int]=None, + n: Optional[int]=None, + logit_bias: Optional[dict]=None, + log_probs: Optional[int]=None, + stop_sequences: Optional[list]=None, + tokens: Optional[bool]=None, + raw_completion: Optional[bool]=None, + disable_optimizations: Optional[bool]=None, + completion_bias_inclusion: Optional[list]=None, + completion_bias_exclusion: Optional[list]=None, + completion_bias_inclusion_first_token_only: Optional[bool]=None, + completion_bias_exclusion_first_token_only: Optional[bool]=None, + contextual_control_threshold: Optional[int]=None, + control_log_additive: Optional[bool]=None) -> None: + + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + + +def validate_environment(api_key): + headers = { + "accept": "application/json", + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + return headers + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, + default_max_tokens_to_sample=None, +): + headers = validate_environment(api_key) + + ## Load Config + config = litellm.AlephAlphaConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > aleph_alpha_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + completion_url = api_base + model = model + prompt = "" + if "control" in model: # follow the ###Instruction / ###Response format + for idx, message in enumerate(messages): + if "role" in message: + if idx == 0: # set first message as instruction (required), let later user messages be input + prompt += f"###Instruction: {message['content']}" + else: + if message["role"] == "system": + prompt += ( + f"###Instruction: {message['content']}" + ) + elif message["role"] == "user": + prompt += ( + f"###Input: {message['content']}" + ) + else: + prompt += ( + f"###Response: {message['content']}" + ) + else: + prompt += f"{message['content']}" + else: + prompt = " ".join(message["content"] for message in messages) + data = { + "model": model, + "prompt": prompt, + **optional_params, + } + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={"complete_input_dict": data}, + ) + ## COMPLETION CALL + response = requests.post( + completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False + ) + if "stream" in optional_params and optional_params["stream"] == True: + return response.iter_lines() + else: + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + completion_response = response.json() + if "error" in completion_response: + raise AlephAlphaError( + message=completion_response["error"], + status_code=response.status_code, + ) + else: + try: + choices_list = [] + for idx, item in enumerate(completion_response["completions"]): + if len(item["completion"]) > 0: + message_obj = Message(content=item["completion"]) + else: + message_obj = Message(content=None) + choice_obj = Choices(finish_reason=item["finish_reason"], index=idx+1, message=message_obj) + choices_list.append(choice_obj) + model_response["choices"] = choices_list + except: + raise AlephAlphaError(message=json.dumps(completion_response), status_code=response.status_code) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = len( + encoding.encode(prompt) + ) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"]["content"]) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/anthropic.py b/litellm/llms/anthropic.py new file mode 100644 index 0000000000000000000000000000000000000000..6b1d50ff8bd7db4796910c6255b6fafd828518e6 --- /dev/null +++ b/litellm/llms/anthropic.py @@ -0,0 +1,187 @@ +import os, types +import json +from enum import Enum +import requests +import time +from typing import Callable, Optional +from litellm.utils import ModelResponse, Usage +import litellm +from .prompt_templates.factory import prompt_factory, custom_prompt +import httpx + +class AnthropicConstants(Enum): + HUMAN_PROMPT = "\n\nHuman: " + AI_PROMPT = "\n\nAssistant: " + +class AnthropicError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="https://api.anthropic.com/v1/complete") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class AnthropicConfig(): + """ + Reference: https://docs.anthropic.com/claude/reference/complete_post + + to pass metadata to anthropic, it's {"user_id": "any-relevant-information"} + """ + max_tokens_to_sample: Optional[int]=litellm.max_tokens # anthropic requires a default + stop_sequences: Optional[list]=None + temperature: Optional[int]=None + top_p: Optional[int]=None + top_k: Optional[int]=None + metadata: Optional[dict]=None + + def __init__(self, + max_tokens_to_sample: Optional[int]=256, # anthropic requires a default + stop_sequences: Optional[list]=None, + temperature: Optional[int]=None, + top_p: Optional[int]=None, + top_k: Optional[int]=None, + metadata: Optional[dict]=None) -> None: + + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + + +# makes headers for API call +def validate_environment(api_key): + if api_key is None: + raise ValueError( + "Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params" + ) + headers = { + "accept": "application/json", + "anthropic-version": "2023-06-01", + "content-type": "application/json", + "x-api-key": api_key, + } + return headers + +def completion( + model: str, + messages: list, + api_base: str, + custom_prompt_dict: dict, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=messages + ) + else: + prompt = prompt_factory(model=model, messages=messages, custom_llm_provider="anthropic") + + ## Load Config + config = litellm.AnthropicConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + data = { + "model": model, + "prompt": prompt, + **optional_params, + } + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={"complete_input_dict": data, "api_base": api_base}, + ) + + ## COMPLETION CALL + if "stream" in optional_params and optional_params["stream"] == True: + response = requests.post( + api_base, + headers=headers, + data=json.dumps(data), + stream=optional_params["stream"], + ) + + if response.status_code != 200: + raise AnthropicError(status_code=response.status_code, message=response.text) + + return response.iter_lines() + else: + response = requests.post( + api_base, headers=headers, data=json.dumps(data) + ) + if response.status_code != 200: + raise AnthropicError(status_code=response.status_code, message=response.text) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + try: + completion_response = response.json() + except: + raise AnthropicError( + message=response.text, status_code=response.status_code + ) + if "error" in completion_response: + raise AnthropicError( + message=str(completion_response["error"]), + status_code=response.status_code, + ) + else: + if len(completion_response["completion"]) > 0: + model_response["choices"][0]["message"]["content"] = completion_response[ + "completion" + ] + model_response.choices[0].finish_reason = completion_response["stop_reason"] + + ## CALCULATING USAGE + prompt_tokens = len( + encoding.encode(prompt) + ) ##[TODO] use the anthropic tokenizer here + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) ##[TODO] use the anthropic tokenizer here + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/azure.py b/litellm/llms/azure.py new file mode 100644 index 0000000000000000000000000000000000000000..f760e9fcef0b99c9bd0023a53cbcdb19d77bdc14 --- /dev/null +++ b/litellm/llms/azure.py @@ -0,0 +1,414 @@ +from typing import Optional, Union, Any +import types, requests +from .base import BaseLLM +from litellm.utils import ModelResponse, Choices, Message, CustomStreamWrapper, convert_to_model_response_object +from typing import Callable, Optional +from litellm import OpenAIConfig +import litellm, json +import httpx +from openai import AzureOpenAI, AsyncAzureOpenAI + +class AzureOpenAIError(Exception): + def __init__(self, status_code, message, request: Optional[httpx.Request]=None, response: Optional[httpx.Response]=None): + self.status_code = status_code + self.message = message + if request: + self.request = request + else: + self.request = httpx.Request(method="POST", url="https://api.openai.com/v1") + if response: + self.response = response + else: + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class AzureOpenAIConfig(OpenAIConfig): + """ + Reference: https://platform.openai.com/docs/api-reference/chat/create + + The class `AzureOpenAIConfig` provides configuration for the OpenAI's Chat API interface, for use with Azure. It inherits from `OpenAIConfig`. Below are the parameters:: + + - `frequency_penalty` (number or null): Defaults to 0. Allows a value between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, thereby minimizing repetition. + + - `function_call` (string or object): This optional parameter controls how the model calls functions. + + - `functions` (array): An optional parameter. It is a list of functions for which the model may generate JSON inputs. + + - `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion. + + - `max_tokens` (integer or null): This optional parameter helps to set the maximum number of tokens to generate in the chat completion. + + - `n` (integer or null): This optional parameter helps to set how many chat completion choices to generate for each input message. + + - `presence_penalty` (number or null): Defaults to 0. It penalizes new tokens based on if they appear in the text so far, hence increasing the model's likelihood to talk about new topics. + + - `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens. + + - `temperature` (number or null): Defines the sampling temperature to use, varying between 0 and 2. + + - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling. + """ + + def __init__(self, + frequency_penalty: Optional[int] = None, + function_call: Optional[Union[str, dict]]= None, + functions: Optional[list]= None, + logit_bias: Optional[dict]= None, + max_tokens: Optional[int]= None, + n: Optional[int]= None, + presence_penalty: Optional[int]= None, + stop: Optional[Union[str,list]]=None, + temperature: Optional[int]= None, + top_p: Optional[int]= None) -> None: + super().__init__(frequency_penalty, + function_call, + functions, + logit_bias, + max_tokens, + n, + presence_penalty, + stop, + temperature, + top_p) + +class AzureChatCompletion(BaseLLM): + + def __init__(self) -> None: + super().__init__() + + def validate_environment(self, api_key, azure_ad_token): + headers = { + "content-type": "application/json", + } + if api_key is not None: + headers["api-key"] = api_key + elif azure_ad_token is not None: + headers["Authorization"] = f"Bearer {azure_ad_token}" + return headers + + def completion(self, + model: str, + messages: list, + model_response: ModelResponse, + api_key: str, + api_base: str, + api_version: str, + api_type: str, + azure_ad_token: str, + print_verbose: Callable, + timeout, + logging_obj, + optional_params, + litellm_params, + logger_fn, + acompletion: bool = False, + headers: Optional[dict]=None, + client = None, + ): + super().completion() + exception_mapping_worked = False + try: + + if model is None or messages is None: + raise AzureOpenAIError(status_code=422, message=f"Missing model or messages") + + max_retries = optional_params.pop("max_retries", 2) + + ### CHECK IF CLOUDFLARE AI GATEWAY ### + ### if so - set the model as part of the base url + if "gateway.ai.cloudflare.com" in api_base: + ## build base url - assume api base includes resource name + if client is None: + if not api_base.endswith("/"): + api_base += "/" + api_base += f"{model}" + + azure_client_params = { + "api_version": api_version, + "base_url": f"{api_base}", + "http_client": litellm.client_session, + "max_retries": max_retries, + "timeout": timeout + } + if api_key is not None: + azure_client_params["api_key"] = api_key + elif azure_ad_token is not None: + azure_client_params["azure_ad_token"] = azure_ad_token + + if acompletion is True: + client = AsyncAzureOpenAI(**azure_client_params) + else: + client = AzureOpenAI(**azure_client_params) + + data = { + "model": None, + "messages": messages, + **optional_params + } + else: + data = { + "model": model, # type: ignore + "messages": messages, + **optional_params + } + ## LOGGING + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={ + "headers": { + "api_key": api_key, + "azure_ad_token": azure_ad_token + }, + "api_version": api_version, + "api_base": api_base, + "complete_input_dict": data, + }, + ) + + if acompletion is True: + if optional_params.get("stream", False): + return self.async_streaming(logging_obj=logging_obj, api_base=api_base, data=data, model=model, api_key=api_key, api_version=api_version, azure_ad_token=azure_ad_token, timeout=timeout, client=client) + else: + return self.acompletion(api_base=api_base, data=data, model_response=model_response, api_key=api_key, api_version=api_version, model=model, azure_ad_token=azure_ad_token, timeout=timeout, client=client) + elif "stream" in optional_params and optional_params["stream"] == True: + return self.streaming(logging_obj=logging_obj, api_base=api_base, data=data, model=model, api_key=api_key, api_version=api_version, azure_ad_token=azure_ad_token, timeout=timeout, client=client) + else: + if not isinstance(max_retries, int): + raise AzureOpenAIError(status_code=422, message="max retries must be an int") + # init AzureOpenAI Client + azure_client_params = { + "api_version": api_version, + "azure_endpoint": api_base, + "azure_deployment": model, + "http_client": litellm.client_session, + "max_retries": max_retries, + "timeout": timeout + } + if api_key is not None: + azure_client_params["api_key"] = api_key + elif azure_ad_token is not None: + azure_client_params["azure_ad_token"] = azure_ad_token + if client is None: + azure_client = AzureOpenAI(**azure_client_params) + else: + azure_client = client + response = azure_client.chat.completions.create(**data) # type: ignore + response.model = "azure/" + str(response.model) + return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response) + except AzureOpenAIError as e: + exception_mapping_worked = True + raise e + except Exception as e: + raise e + + async def acompletion(self, + api_key: str, + api_version: str, + model: str, + api_base: str, + data: dict, + timeout: Any, + model_response: ModelResponse, + azure_ad_token: Optional[str]=None, + client = None, # this is the AsyncAzureOpenAI + ): + response = None + try: + max_retries = data.pop("max_retries", 2) + if not isinstance(max_retries, int): + raise AzureOpenAIError(status_code=422, message="max retries must be an int") + # init AzureOpenAI Client + azure_client_params = { + "api_version": api_version, + "azure_endpoint": api_base, + "azure_deployment": model, + "http_client": litellm.client_session, + "max_retries": max_retries, + "timeout": timeout + } + if api_key is not None: + azure_client_params["api_key"] = api_key + elif azure_ad_token is not None: + azure_client_params["azure_ad_token"] = azure_ad_token + if client is None: + azure_client = AsyncAzureOpenAI(**azure_client_params) + else: + azure_client = client + response = await azure_client.chat.completions.create(**data) + return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response) + except AzureOpenAIError as e: + exception_mapping_worked = True + raise e + except Exception as e: + raise e + + def streaming(self, + logging_obj, + api_base: str, + api_key: str, + api_version: str, + data: dict, + model: str, + timeout: Any, + azure_ad_token: Optional[str]=None, + client=None, + ): + max_retries = data.pop("max_retries", 2) + if not isinstance(max_retries, int): + raise AzureOpenAIError(status_code=422, message="max retries must be an int") + # init AzureOpenAI Client + azure_client_params = { + "api_version": api_version, + "azure_endpoint": api_base, + "azure_deployment": model, + "http_client": litellm.client_session, + "max_retries": max_retries, + "timeout": timeout + } + if api_key is not None: + azure_client_params["api_key"] = api_key + elif azure_ad_token is not None: + azure_client_params["azure_ad_token"] = azure_ad_token + if client is None: + azure_client = AzureOpenAI(**azure_client_params) + else: + azure_client = client + response = azure_client.chat.completions.create(**data) + streamwrapper = CustomStreamWrapper(completion_stream=response, model=model, custom_llm_provider="azure",logging_obj=logging_obj) + return streamwrapper + + async def async_streaming(self, + logging_obj, + api_base: str, + api_key: str, + api_version: str, + data: dict, + model: str, + timeout: Any, + azure_ad_token: Optional[str]=None, + client = None, + ): + # init AzureOpenAI Client + azure_client_params = { + "api_version": api_version, + "azure_endpoint": api_base, + "azure_deployment": model, + "http_client": litellm.client_session, + "max_retries": data.pop("max_retries", 2), + "timeout": timeout + } + if api_key is not None: + azure_client_params["api_key"] = api_key + elif azure_ad_token is not None: + azure_client_params["azure_ad_token"] = azure_ad_token + if client is None: + azure_client = AsyncAzureOpenAI(**azure_client_params) + else: + azure_client = client + response = await azure_client.chat.completions.create(**data) + streamwrapper = CustomStreamWrapper(completion_stream=response, model=model, custom_llm_provider="azure",logging_obj=logging_obj) + async for transformed_chunk in streamwrapper: + yield transformed_chunk + + async def aembedding( + self, + data: dict, + model_response: ModelResponse, + azure_client_params: dict, + client=None, + ): + response = None + try: + if client is None: + openai_aclient = AsyncAzureOpenAI(**azure_client_params) + else: + openai_aclient = client + response = await openai_aclient.embeddings.create(**data) + return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response, response_type="embedding") + except Exception as e: + raise e + + def embedding(self, + model: str, + input: list, + api_key: str, + api_base: str, + api_version: str, + timeout: float, + logging_obj=None, + model_response=None, + optional_params=None, + azure_ad_token: Optional[str]=None, + client = None, + aembedding=None, + ): + super().embedding() + exception_mapping_worked = False + if self._client_session is None: + self._client_session = self.create_client_session() + try: + data = { + "model": model, + "input": input, + **optional_params + } + max_retries = data.pop("max_retries", 2) + if not isinstance(max_retries, int): + raise AzureOpenAIError(status_code=422, message="max retries must be an int") + + # init AzureOpenAI Client + azure_client_params = { + "api_version": api_version, + "azure_endpoint": api_base, + "azure_deployment": model, + "http_client": litellm.client_session, + "max_retries": max_retries, + "timeout": timeout + } + if api_key is not None: + azure_client_params["api_key"] = api_key + elif azure_ad_token is not None: + azure_client_params["azure_ad_token"] = azure_ad_token + if aembedding == True: + response = self.aembedding(data=data, model_response=model_response, azure_client_params=azure_client_params) + return response + if client is None: + azure_client = AzureOpenAI(**azure_client_params) # type: ignore + else: + azure_client = client + ## LOGGING + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "headers": { + "api_key": api_key, + "azure_ad_token": azure_ad_token + } + }, + ) + ## COMPLETION CALL + response = azure_client.embeddings.create(**data) # type: ignore + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data, "api_base": api_base}, + original_response=response, + ) + + + return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response, response_type="embedding") # type: ignore + except AzureOpenAIError as e: + exception_mapping_worked = True + raise e + except Exception as e: + if exception_mapping_worked: + raise e + else: + import traceback + raise AzureOpenAIError(status_code=500, message=traceback.format_exc()) \ No newline at end of file diff --git a/litellm/llms/base.py b/litellm/llms/base.py new file mode 100644 index 0000000000000000000000000000000000000000..a4c056e1afe5c1e601b6c92b7c1f9f3aefd5b00d --- /dev/null +++ b/litellm/llms/base.py @@ -0,0 +1,47 @@ +## This is a template base class to be used for adding new LLM providers via API calls +import litellm +import httpx, certifi, ssl +from typing import Optional + +class BaseLLM: + _client_session: Optional[httpx.Client] = None + def create_client_session(self): + if litellm.client_session: + _client_session = litellm.client_session + else: + _client_session = httpx.Client() + + return _client_session + + def create_aclient_session(self): + if litellm.aclient_session: + _aclient_session = litellm.aclient_session + else: + _aclient_session = httpx.AsyncClient() + + return _aclient_session + + def __exit__(self): + if hasattr(self, '_client_session'): + self._client_session.close() + + async def __aexit__(self, exc_type, exc_val, exc_tb): + if hasattr(self, '_aclient_session'): + await self._aclient_session.aclose() + + def validate_environment(self): # set up the environment required to run the model + pass + + def completion( + self, + *args, + **kwargs + ): # logic for parsing in - calling - parsing out model completion calls + pass + + def embedding( + self, + *args, + **kwargs + ): # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/baseten.py b/litellm/llms/baseten.py new file mode 100644 index 0000000000000000000000000000000000000000..b1e904c7a16bd372f083566de12ed50fe2811871 --- /dev/null +++ b/litellm/llms/baseten.py @@ -0,0 +1,149 @@ +import os +import json +from enum import Enum +import requests +import time +from typing import Callable +from litellm.utils import ModelResponse, Usage + +class BasetenError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +def validate_environment(api_key): + headers = { + "accept": "application/json", + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Api-Key {api_key}" + return headers + +def completion( + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + completion_url_fragment_1 = "https://app.baseten.co/models/" + completion_url_fragment_2 = "/predict" + model = model + prompt = "" + for message in messages: + if "role" in message: + if message["role"] == "user": + prompt += f"{message['content']}" + else: + prompt += f"{message['content']}" + else: + prompt += f"{message['content']}" + data = { + "inputs": prompt, + "prompt": prompt, + "parameters": optional_params, + "stream": True if "stream" in optional_params and optional_params["stream"] == True else False + } + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={"complete_input_dict": data}, + ) + ## COMPLETION CALL + response = requests.post( + completion_url_fragment_1 + model + completion_url_fragment_2, + headers=headers, + data=json.dumps(data), + stream=True if "stream" in optional_params and optional_params["stream"] == True else False + ) + if 'text/event-stream' in response.headers['Content-Type'] or ("stream" in optional_params and optional_params["stream"] == True): + return response.iter_lines() + else: + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + completion_response = response.json() + if "error" in completion_response: + raise BasetenError( + message=completion_response["error"], + status_code=response.status_code, + ) + else: + if "model_output" in completion_response: + if ( + isinstance(completion_response["model_output"], dict) + and "data" in completion_response["model_output"] + and isinstance( + completion_response["model_output"]["data"], list + ) + ): + model_response["choices"][0]["message"][ + "content" + ] = completion_response["model_output"]["data"][0] + elif isinstance(completion_response["model_output"], str): + model_response["choices"][0]["message"][ + "content" + ] = completion_response["model_output"] + elif "completion" in completion_response and isinstance( + completion_response["completion"], str + ): + model_response["choices"][0]["message"][ + "content" + ] = completion_response["completion"] + elif isinstance(completion_response, list) and len(completion_response) > 0: + if "generated_text" not in completion_response: + raise BasetenError( + message=f"Unable to parse response. Original response: {response.text}", + status_code=response.status_code + ) + model_response["choices"][0]["message"]["content"] = completion_response[0]["generated_text"] + ## GETTING LOGPROBS + if "details" in completion_response[0] and "tokens" in completion_response[0]["details"]: + model_response.choices[0].finish_reason = completion_response[0]["details"]["finish_reason"] + sum_logprob = 0 + for token in completion_response[0]["details"]["tokens"]: + sum_logprob += token["logprob"] + model_response["choices"][0]["message"]._logprobs = sum_logprob + else: + raise BasetenError( + message=f"Unable to parse response. Original response: {response.text}", + status_code=response.status_code + ) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"]["content"]) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/bedrock.py b/litellm/llms/bedrock.py new file mode 100644 index 0000000000000000000000000000000000000000..30aa3e6cef0842168cb82b63bcd8a7b17a550d33 --- /dev/null +++ b/litellm/llms/bedrock.py @@ -0,0 +1,627 @@ +import json, copy, types +import os +from enum import Enum +import time +from typing import Callable, Optional +import litellm +from litellm.utils import ModelResponse, get_secret, Usage +from .prompt_templates.factory import prompt_factory, custom_prompt +import httpx + +class BedrockError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="https://us-west-2.console.aws.amazon.com/bedrock") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class AmazonTitanConfig(): + """ + Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-text-express-v1 + + Supported Params for the Amazon Titan models: + + - `maxTokenCount` (integer) max tokens, + - `stopSequences` (string[]) list of stop sequence strings + - `temperature` (float) temperature for model, + - `topP` (int) top p for model + """ + maxTokenCount: Optional[int]=None + stopSequences: Optional[list]=None + temperature: Optional[float]=None + topP: Optional[int]=None + + def __init__(self, + maxTokenCount: Optional[int]=None, + stopSequences: Optional[list]=None, + temperature: Optional[float]=None, + topP: Optional[int]=None) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + +class AmazonAnthropicConfig(): + """ + Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=claude + + Supported Params for the Amazon / Anthropic models: + + - `max_tokens_to_sample` (integer) max tokens, + - `temperature` (float) model temperature, + - `top_k` (integer) top k, + - `top_p` (integer) top p, + - `stop_sequences` (string[]) list of stop sequences - e.g. ["\\n\\nHuman:"], + - `anthropic_version` (string) version of anthropic for bedrock - e.g. "bedrock-2023-05-31" + """ + max_tokens_to_sample: Optional[int]=litellm.max_tokens + stop_sequences: Optional[list]=None + temperature: Optional[float]=None + top_k: Optional[int]=None + top_p: Optional[int]=None + anthropic_version: Optional[str]=None + + def __init__(self, + max_tokens_to_sample: Optional[int]=None, + stop_sequences: Optional[list]=None, + temperature: Optional[float]=None, + top_k: Optional[int]=None, + top_p: Optional[int]=None, + anthropic_version: Optional[str]=None) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + +class AmazonCohereConfig(): + """ + Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=command + + Supported Params for the Amazon / Cohere models: + + - `max_tokens` (integer) max tokens, + - `temperature` (float) model temperature, + - `return_likelihood` (string) n/a + """ + max_tokens: Optional[int]=None + temperature: Optional[float]=None + return_likelihood: Optional[str]=None + + def __init__(self, + max_tokens: Optional[int]=None, + temperature: Optional[float]=None, + return_likelihood: Optional[str]=None) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + +class AmazonAI21Config(): + """ + Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=j2-ultra + + Supported Params for the Amazon / AI21 models: + + - `maxTokens` (int32): The maximum number of tokens to generate per result. Optional, default is 16. If no `stopSequences` are given, generation stops after producing `maxTokens`. + + - `temperature` (float): Modifies the distribution from which tokens are sampled. Optional, default is 0.7. A value of 0 essentially disables sampling and results in greedy decoding. + + - `topP` (float): Used for sampling tokens from the corresponding top percentile of probability mass. Optional, default is 1. For instance, a value of 0.9 considers only tokens comprising the top 90% probability mass. + + - `stopSequences` (array of strings): Stops decoding if any of the input strings is generated. Optional. + + - `frequencyPenalty` (object): Placeholder for frequency penalty object. + + - `presencePenalty` (object): Placeholder for presence penalty object. + + - `countPenalty` (object): Placeholder for count penalty object. + """ + maxTokens: Optional[int]=None + temperature: Optional[float]=None + topP: Optional[float]=None + stopSequences: Optional[list]=None + frequencePenalty: Optional[dict]=None + presencePenalty: Optional[dict]=None + countPenalty: Optional[dict]=None + + def __init__(self, + maxTokens: Optional[int]=None, + temperature: Optional[float]=None, + topP: Optional[float]=None, + stopSequences: Optional[list]=None, + frequencePenalty: Optional[dict]=None, + presencePenalty: Optional[dict]=None, + countPenalty: Optional[dict]=None) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + +class AnthropicConstants(Enum): + HUMAN_PROMPT = "\n\nHuman: " + AI_PROMPT = "\n\nAssistant: " + +class AmazonLlamaConfig(): + """ + Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=meta.llama2-13b-chat-v1 + + Supported Params for the Amazon / Meta Llama models: + + - `max_gen_len` (integer) max tokens, + - `temperature` (float) temperature for model, + - `top_p` (float) top p for model + """ + max_gen_len: Optional[int]=None + temperature: Optional[float]=None + topP: Optional[float]=None + + def __init__(self, + maxTokenCount: Optional[int]=None, + temperature: Optional[float]=None, + topP: Optional[int]=None) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + + +def init_bedrock_client( + region_name = None, + aws_access_key_id = None, + aws_secret_access_key = None, + aws_region_name=None, + aws_bedrock_runtime_endpoint=None, + ): + + # check for custom AWS_REGION_NAME and use it if not passed to init_bedrock_client + litellm_aws_region_name = get_secret("AWS_REGION_NAME") + standard_aws_region_name = get_secret("AWS_REGION") + if region_name: + pass + elif aws_region_name: + region_name = aws_region_name + elif litellm_aws_region_name: + region_name = litellm_aws_region_name + elif standard_aws_region_name: + region_name = standard_aws_region_name + else: + raise BedrockError(message="AWS region not set: set AWS_REGION_NAME or AWS_REGION env variable or in .env file", status_code=401) + + # check for custom AWS_BEDROCK_RUNTIME_ENDPOINT and use it if not passed to init_bedrock_client + env_aws_bedrock_runtime_endpoint = get_secret("AWS_BEDROCK_RUNTIME_ENDPOINT") + if aws_bedrock_runtime_endpoint: + endpoint_url = aws_bedrock_runtime_endpoint + elif env_aws_bedrock_runtime_endpoint: + endpoint_url = env_aws_bedrock_runtime_endpoint + else: + endpoint_url = f'https://bedrock-runtime.{region_name}.amazonaws.com' + + import boto3 + if aws_access_key_id != None: + # uses auth params passed to completion + # aws_access_key_id is not None, assume user is trying to auth using litellm.completion + + client = boto3.client( + service_name="bedrock-runtime", + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + region_name=region_name, + endpoint_url=endpoint_url, + ) + else: + # aws_access_key_id is None, assume user is trying to auth using env variables + # boto3 automatically reads env variables + + client = boto3.client( + service_name="bedrock-runtime", + region_name=region_name, + endpoint_url=endpoint_url, + ) + + return client + + +def convert_messages_to_prompt(model, messages, provider, custom_prompt_dict): + # handle anthropic prompts using anthropic constants + if provider == "anthropic": + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=messages + ) + else: + prompt = prompt_factory(model=model, messages=messages, custom_llm_provider="anthropic") + else: + prompt = "" + for message in messages: + if "role" in message: + if message["role"] == "user": + prompt += ( + f"{message['content']}" + ) + else: + prompt += ( + f"{message['content']}" + ) + else: + prompt += f"{message['content']}" + return prompt + + +""" +BEDROCK AUTH Keys/Vars +os.environ['AWS_ACCESS_KEY_ID'] = "" +os.environ['AWS_SECRET_ACCESS_KEY'] = "" +""" + + +# set os.environ['AWS_REGION_NAME'] = + +def completion( + model: str, + messages: list, + custom_prompt_dict: dict, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + exception_mapping_worked = False + try: + # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them + aws_secret_access_key = optional_params.pop("aws_secret_access_key", None) + aws_access_key_id = optional_params.pop("aws_access_key_id", None) + aws_region_name = optional_params.pop("aws_region_name", None) + + # use passed in BedrockRuntime.Client if provided, otherwise create a new one + client = optional_params.pop( + "aws_bedrock_client", + # only pass variables that are not None + init_bedrock_client( + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + aws_region_name=aws_region_name, + ), + ) + + model = model + provider = model.split(".")[0] + prompt = convert_messages_to_prompt(model, messages, provider, custom_prompt_dict) + inference_params = copy.deepcopy(optional_params) + stream = inference_params.pop("stream", False) + if provider == "anthropic": + ## LOAD CONFIG + config = litellm.AmazonAnthropicConfig.get_config() + for k, v in config.items(): + if k not in inference_params: # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + data = json.dumps({ + "prompt": prompt, + **inference_params + }) + elif provider == "ai21": + ## LOAD CONFIG + config = litellm.AmazonAI21Config.get_config() + for k, v in config.items(): + if k not in inference_params: # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + + data = json.dumps({ + "prompt": prompt, + **inference_params + }) + elif provider == "cohere": + ## LOAD CONFIG + config = litellm.AmazonCohereConfig.get_config() + for k, v in config.items(): + if k not in inference_params: # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + if optional_params.get("stream", False) == True: + inference_params["stream"] = True # cohere requires stream = True in inference params + data = json.dumps({ + "prompt": prompt, + **inference_params + }) + elif provider == "meta": + ## LOAD CONFIG + config = litellm.AmazonLlamaConfig.get_config() + for k, v in config.items(): + if k not in inference_params: # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + data = json.dumps({ + "prompt": prompt, + **inference_params + }) + elif provider == "amazon": # amazon titan + ## LOAD CONFIG + config = litellm.AmazonTitanConfig.get_config() + for k, v in config.items(): + if k not in inference_params: # completion(top_k=3) > amazon_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + + data = json.dumps({ + "inputText": prompt, + "textGenerationConfig": inference_params, + }) + + ## COMPLETION CALL + accept = 'application/json' + contentType = 'application/json' + if stream == True: + if provider == "ai21": + ## LOGGING + request_str = f""" + response = client.invoke_model( + body={data}, + modelId={model}, + accept=accept, + contentType=contentType + ) + """ + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={"complete_input_dict": data, "request_str": request_str}, + ) + + response = client.invoke_model( + body=data, + modelId=model, + accept=accept, + contentType=contentType + ) + + response = response.get('body').read() + return response + else: + ## LOGGING + request_str = f""" + response = client.invoke_model_with_response_stream( + body={data}, + modelId={model}, + accept=accept, + contentType=contentType + ) + """ + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={"complete_input_dict": data, "request_str": request_str}, + ) + + response = client.invoke_model_with_response_stream( + body=data, + modelId=model, + accept=accept, + contentType=contentType + ) + response = response.get('body') + return response + try: + ## LOGGING + request_str = f""" + response = client.invoke_model( + body={data}, + modelId={model}, + accept=accept, + contentType=contentType + ) + """ + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={"complete_input_dict": data, "request_str": request_str}, + ) + response = client.invoke_model( + body=data, + modelId=model, + accept=accept, + contentType=contentType + ) + except Exception as e: + raise BedrockError(status_code=500, message=str(e)) + + response_body = json.loads(response.get('body').read()) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=response_body, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response}") + ## RESPONSE OBJECT + outputText = "default" + if provider == "ai21": + outputText = response_body.get('completions')[0].get('data').get('text') + elif provider == "anthropic": + outputText = response_body['completion'] + model_response["finish_reason"] = response_body["stop_reason"] + elif provider == "cohere": + outputText = response_body["generations"][0]["text"] + elif provider == "meta": + outputText = response_body["generation"] + else: # amazon titan + outputText = response_body.get('results')[0].get('outputText') + + response_metadata = response.get("ResponseMetadata", {}) + if response_metadata.get("HTTPStatusCode", 500) >= 400: + raise BedrockError( + message=outputText, + status_code=response_metadata.get("HTTPStatusCode", 500), + ) + else: + try: + if len(outputText) > 0: + model_response["choices"][0]["message"]["content"] = outputText + except: + raise BedrockError(message=json.dumps(outputText), status_code=response_metadata.get("HTTPStatusCode", 500)) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = len( + encoding.encode(prompt) + ) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens = prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + except BedrockError as e: + exception_mapping_worked = True + raise e + except Exception as e: + if exception_mapping_worked: + raise e + else: + import traceback + raise BedrockError(status_code=500, message=traceback.format_exc()) + +def _embedding_func_single( + model: str, + input: str, + optional_params=None, + encoding=None, +): + # logic for parsing in - calling - parsing out model embedding calls + # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them + aws_secret_access_key = optional_params.pop("aws_secret_access_key", None) + aws_access_key_id = optional_params.pop("aws_access_key_id", None) + aws_region_name = optional_params.pop("aws_region_name", None) + + # use passed in BedrockRuntime.Client if provided, otherwise create a new one + client = optional_params.pop( + "aws_bedrock_client", + # only pass variables that are not None + init_bedrock_client( + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + aws_region_name=aws_region_name, + ), + ) + + input = input.replace(os.linesep, " ") + body = json.dumps({"inputText": input}) + try: + response = client.invoke_model( + body=body, + modelId=model, + accept="application/json", + contentType="application/json", + ) + response_body = json.loads(response.get("body").read()) + return response_body.get("embedding") + except Exception as e: + raise BedrockError(message=f"Embedding Error with model {model}: {e}", status_code=500) + +def embedding( + model: str, + input: list, + api_key: Optional[str] = None, + logging_obj=None, + model_response=None, + optional_params=None, + encoding=None, +): + + ## LOGGING + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": {"model": model, + "texts": input}}, + ) + + ## Embedding Call + embeddings = [_embedding_func_single(model, i, optional_params) for i in input] + + + ## Populate OpenAI compliant dictionary + embedding_response = [] + for idx, embedding in enumerate(embeddings): + embedding_response.append( + { + "object": "embedding", + "index": idx, + "embedding": embedding, + } + ) + model_response["object"] = "list" + model_response["data"] = embedding_response + model_response["model"] = model + input_tokens = 0 + + input_str = "".join(input) + + input_tokens+=len(encoding.encode(input_str)) + + usage = Usage( + prompt_tokens=input_tokens, + completion_tokens=0, + total_tokens=input_tokens + 0 + ) + model_response.usage = usage + + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": {"model": model, + "texts": input}}, + original_response=embeddings, + ) + + return model_response diff --git a/litellm/llms/cohere.py b/litellm/llms/cohere.py new file mode 100644 index 0000000000000000000000000000000000000000..8581731b5aca26cc31bee2bc229c2761f27de553 --- /dev/null +++ b/litellm/llms/cohere.py @@ -0,0 +1,273 @@ +import os, types +import json +from enum import Enum +import requests +import time, traceback +from typing import Callable, Optional +from litellm.utils import ModelResponse, Choices, Message, Usage +import litellm +import httpx + +class CohereError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="https://api.cohere.ai/v1/generate") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class CohereConfig(): + """ + Reference: https://docs.cohere.com/reference/generate + + The class `CohereConfig` provides configuration for the Cohere's API interface. Below are the parameters: + + - `num_generations` (integer): Maximum number of generations returned. Default is 1, with a minimum value of 1 and a maximum value of 5. + + - `max_tokens` (integer): Maximum number of tokens the model will generate as part of the response. Default value is 20. + + - `truncate` (string): Specifies how the API handles inputs longer than maximum token length. Options include NONE, START, END. Default is END. + + - `temperature` (number): A non-negative float controlling the randomness in generation. Lower temperatures result in less random generations. Default is 0.75. + + - `preset` (string): Identifier of a custom preset, a combination of parameters such as prompt, temperature etc. + + - `end_sequences` (array of strings): The generated text gets cut at the beginning of the earliest occurrence of an end sequence, which will be excluded from the text. + + - `stop_sequences` (array of strings): The generated text gets cut at the end of the earliest occurrence of a stop sequence, which will be included in the text. + + - `k` (integer): Limits generation at each step to top `k` most likely tokens. Default is 0. + + - `p` (number): Limits generation at each step to most likely tokens with total probability mass of `p`. Default is 0. + + - `frequency_penalty` (number): Reduces repetitiveness of generated tokens. Higher values apply stronger penalties to previously occurred tokens. + + - `presence_penalty` (number): Reduces repetitiveness of generated tokens. Similar to frequency_penalty, but this penalty applies equally to all tokens that have already appeared. + + - `return_likelihoods` (string): Specifies how and if token likelihoods are returned with the response. Options include GENERATION, ALL and NONE. + + - `logit_bias` (object): Used to prevent the model from generating unwanted tokens or to incentivize it to include desired tokens. e.g. {"hello_world": 1233} + """ + num_generations: Optional[int]=None + max_tokens: Optional[int]=None + truncate: Optional[str]=None + temperature: Optional[int]=None + preset: Optional[str]=None + end_sequences: Optional[list]=None + stop_sequences: Optional[list]=None + k: Optional[int]=None + p: Optional[int]=None + frequency_penalty: Optional[int]=None + presence_penalty: Optional[int]=None + return_likelihoods: Optional[str]=None + logit_bias: Optional[dict]=None + + def __init__(self, + num_generations: Optional[int]=None, + max_tokens: Optional[int]=None, + truncate: Optional[str]=None, + temperature: Optional[int]=None, + preset: Optional[str]=None, + end_sequences: Optional[list]=None, + stop_sequences: Optional[list]=None, + k: Optional[int]=None, + p: Optional[int]=None, + frequency_penalty: Optional[int]=None, + presence_penalty: Optional[int]=None, + return_likelihoods: Optional[str]=None, + logit_bias: Optional[dict]=None) -> None: + + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + +def validate_environment(api_key): + headers = { + "accept": "application/json", + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + return headers + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + completion_url = api_base + model = model + prompt = " ".join(message["content"] for message in messages) + + ## Load Config + config=litellm.CohereConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + data = { + "model": model, + "prompt": prompt, + **optional_params, + } + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={"complete_input_dict": data, "headers": headers, "api_base": completion_url}, + ) + ## COMPLETION CALL + response = requests.post( + completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False + ) + ## error handling for cohere calls + if response.status_code!=200: + raise CohereError(message=response.text, status_code=response.status_code) + + if "stream" in optional_params and optional_params["stream"] == True: + return response.iter_lines() + else: + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + completion_response = response.json() + if "error" in completion_response: + raise CohereError( + message=completion_response["error"], + status_code=response.status_code, + ) + else: + try: + choices_list = [] + for idx, item in enumerate(completion_response["generations"]): + if len(item["text"]) > 0: + message_obj = Message(content=item["text"]) + else: + message_obj = Message(content=None) + choice_obj = Choices(finish_reason=item["finish_reason"], index=idx+1, message=message_obj) + choices_list.append(choice_obj) + model_response["choices"] = choices_list + except Exception as e: + raise CohereError(message=response.text, status_code=response.status_code) + + ## CALCULATING USAGE + prompt_tokens = len( + encoding.encode(prompt) + ) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + +def embedding( + model: str, + input: list, + api_key: Optional[str] = None, + logging_obj=None, + model_response=None, + encoding=None, + optional_params=None, +): + headers = validate_environment(api_key) + embed_url = "https://api.cohere.ai/v1/embed" + model = model + data = { + "model": model, + "texts": input, + **optional_params + } + + if "3" in model and "input_type" not in data: + # cohere v3 embedding models require input_type, if no input_type is provided, default to "search_document" + data["input_type"] = "search_document" + + ## LOGGING + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + ) + ## COMPLETION CALL + response = requests.post( + embed_url, headers=headers, data=json.dumps(data) + ) + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=response, + ) + """ + response + { + 'object': "list", + 'data': [ + + ] + 'model', + 'usage' + } + """ + if response.status_code!=200: + raise CohereError(message=response.text, status_code=response.status_code) + embeddings = response.json()['embeddings'] + output_data = [] + for idx, embedding in enumerate(embeddings): + output_data.append( + { + "object": "embedding", + "index": idx, + "embedding": embedding + } + ) + model_response["object"] = "list" + model_response["data"] = output_data + model_response["model"] = model + input_tokens = 0 + for text in input: + input_tokens+=len(encoding.encode(text)) + + model_response["usage"] = { + "prompt_tokens": input_tokens, + "total_tokens": input_tokens, + } + return model_response + + + \ No newline at end of file diff --git a/litellm/llms/huggingface_llms_metadata/hf_conversational_models.txt b/litellm/llms/huggingface_llms_metadata/hf_conversational_models.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa978352b2337ab58b744287921c60ec8bde66cf --- /dev/null +++ b/litellm/llms/huggingface_llms_metadata/hf_conversational_models.txt @@ -0,0 +1,2523 @@ +0xDEADBEA7/DialoGPT-small-rick +1Basco/DialoGPT-small-jake +2early4coffee/DialoGPT-medium-deadpool +2early4coffee/DialoGPT-small-deadpool +2gud/DialogGPT-small-Koopsbot +ABBHISHEK/DialoGPT-small-harrypotter +AIDynamics/DialoGPT-medium-MentorDealerGuy +AJ/DialoGPT-small-ricksanchez +AJ/rick-discord-bot +AJ/rick-sanchez-bot +AJ-Dude/DialoGPT-small-harrypotter +AK270802/DialoGPT-small-harrypotter +ATGdev/DialoGPT-small-harrypotter +AVeryRealHuman/DialoGPT-small-TonyStark +AbhinavSaiTheGreat/DialoGPT-small-harrypotter +AccurateIsaiah/DialoGPT-small-jefftastic +AccurateIsaiah/DialoGPT-small-mozark +AccurateIsaiah/DialoGPT-small-mozarkv2 +AccurateIsaiah/DialoGPT-small-sinclair +AdharshJolly/HarryPotterBot-Model +AdrianGzz/DialoGPT-small-harrypotter +Aero/Tsubomi-Haruno +AetherIT/DialoGPT-small-Hal +AiPorter/DialoGPT-small-Back_to_the_future +Aibox/DialoGPT-small-rick +Akjder/DialoGPT-small-harrypotter +AllwynJ/HarryBoy +AnthonyNelson/DialoGPT-small-ricksanchez +Apisate/DialoGPT-small-jordan +ArJakusz/DialoGPT-small-stark +Aran/DialoGPT-medium-harrypotter +Aran/DialoGPT-small-harrypotter +Arcktosh/DialoGPT-small-rick +AriakimTaiyo/DialoGPT-cultured-Kumiko +AriakimTaiyo/DialoGPT-medium-Kumiko +AriakimTaiyo/DialoGPT-revised-Kumiko +AriakimTaiyo/DialoGPT-small-Kumiko +AriakimTaiyo/DialoGPT-small-Rikka +ArtemisZealot/DialoGTP-small-Qkarin +Aruden/DialoGPT-medium-harrypotterall +Aspect11/DialoGPT-Medium-LiSBot 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+siddanshchawla/Llama-2-7b-chat-finetune_inference +aman-mehra/gpt2-medium-finetune-squad-ep-0.2-lr-2e-06-wd-0.0001-glb_sd-1-data_sd-7-fx_head +FPHam/Jackson_The_Formalizer_13b_GPTQ +TheBlake/Llama-2-7b +gwlms/t5-efficient-base-dewiki-v1-germeval14 +notaphoenix/argument-transfer-conservative_l0.2 +notaphoenix/argument-transfer-conservative_l0.5 +cmu-mlsp/guanaco-7B-HF-gpt3.5-atk2-first_last +cmu-mlsp/vicuna-7b-v1.5-gpt3.5-atk2-first_last +vjeronymo2/monot5-3b-msmarco-10k-half +ryanyard/llama-2-7b-miniguanaco +luozhuanggary/vicuna-7b-v1.5-sft-math-merged +Globaly/globaly-1-llama2-13b-OpenOrca-v0.2 +cmu-mlsp/guanaco-13B-HF-gpt3.5-first_last-global +research-dump/t5-base_hoax_timestamp_classifier_v1 +wtang06/mpt-125m-c4 +PocketDoc/Dans-MysteryModel-13b-exl2-6.0bpw +Asap7772/sft-review-model-20230927-215151 +Asap7772/sft-review-model-20230927-220132 +Asap7772/sft-review-model-20230927-220131 +Mintrz/Loobe-3 +badokorach/flan-t5-small-qa-9 +nick-1234/llama-2-7b-finetuned-for-news_comments_generation +lajosd/llama-2-7b-miniguanaco +aman-mehra/gpt2-medium-finetune-squad-ep-2.0-lr-3e-06-wd-0.0001-glb_sd-1-data_sd-500-fx_head diff --git a/litellm/llms/huggingface_restapi.py b/litellm/llms/huggingface_restapi.py new file mode 100644 index 0000000000000000000000000000000000000000..25aa1c574c8b34b0ef230c0baa4c457ba75ef2ff --- /dev/null +++ b/litellm/llms/huggingface_restapi.py @@ -0,0 +1,604 @@ +## Uses the huggingface text generation inference API +import os, copy, types +import json +from enum import Enum +import httpx, requests +from .base import BaseLLM +import time +import litellm +from typing import Callable, Dict, List, Any +from litellm.utils import ModelResponse, Choices, Message, CustomStreamWrapper, Usage +from typing import Optional +from .prompt_templates.factory import prompt_factory, custom_prompt + +class HuggingfaceError(Exception): + def __init__(self, status_code, message, request: Optional[httpx.Request]=None, response: Optional[httpx.Response]=None): + self.status_code = status_code + self.message = message + if request is not None: + self.request = request + else: + self.request = httpx.Request(method="POST", url="/static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co%2Fmodels") + if response is not None: + self.response = response + else: + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class HuggingfaceConfig(): + """ + Reference: https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/compat_generate + """ + best_of: Optional[int] = None + decoder_input_details: Optional[bool] = None + details: Optional[bool] = True # enables returning logprobs + best of + max_new_tokens: Optional[int] = None + repetition_penalty: Optional[float] = None + return_full_text: Optional[bool] = False # by default don't return the input as part of the output + seed: Optional[int] = None + temperature: Optional[float] = None + top_k: Optional[int] = None + top_n_tokens: Optional[int] = None + top_p: Optional[int] = None + truncate: Optional[int] = None + typical_p: Optional[float] = None + watermark: Optional[bool] = None + + def __init__(self, + best_of: Optional[int] = None, + decoder_input_details: Optional[bool] = None, + details: Optional[bool] = None, + max_new_tokens: Optional[int] = None, + repetition_penalty: Optional[float] = None, + return_full_text: Optional[bool] = None, + seed: Optional[int] = None, + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_n_tokens: Optional[int] = None, + top_p: Optional[int] = None, + truncate: Optional[int] = None, + typical_p: Optional[float] = None, + watermark: Optional[bool] = None + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + +def output_parser(generated_text: str): + """ + Parse the output text to remove any special characters. In our current approach we just check for ChatML tokens. + + Initial issue that prompted this - https://github.com/BerriAI/litellm/issues/763 + """ + chat_template_tokens = ["<|assistant|>", "<|system|>", "<|user|>", "", ""] + for token in chat_template_tokens: + if generated_text.strip().startswith(token): + generated_text = generated_text.replace(token, "", 1) + if generated_text.endswith(token): + generated_text = generated_text[::-1].replace(token[::-1], "", 1)[::-1] + return generated_text + +tgi_models_cache = None +conv_models_cache = None +def read_tgi_conv_models(): + try: + global tgi_models_cache, conv_models_cache + # Check if the cache is already populated + # so we don't keep on reading txt file if there are 1k requests + if (tgi_models_cache is not None) and (conv_models_cache is not None): + return tgi_models_cache, conv_models_cache + # If not, read the file and populate the cache + tgi_models = set() + script_directory = os.path.dirname(os.path.abspath(__file__)) + # Construct the file path relative to the script's directory + file_path = os.path.join(script_directory, "huggingface_llms_metadata", "hf_text_generation_models.txt") + + with open(file_path, 'r') as file: + for line in file: + tgi_models.add(line.strip()) + + # Cache the set for future use + tgi_models_cache = tgi_models + + # If not, read the file and populate the cache + file_path = os.path.join(script_directory, "huggingface_llms_metadata", "hf_conversational_models.txt") + conv_models = set() + with open(file_path, 'r') as file: + for line in file: + conv_models.add(line.strip()) + # Cache the set for future use + conv_models_cache = conv_models + return tgi_models, conv_models + except: + return set(), set() + + +def get_hf_task_for_model(model): + # read text file, cast it to set + # read the file called "huggingface_llms_metadata/hf_text_generation_models.txt" + tgi_models, conversational_models = read_tgi_conv_models() + if model in tgi_models: + return "text-generation-inference" + elif model in conversational_models: + return "conversational" + elif "roneneldan/TinyStories" in model: + return None + else: + return "text-generation-inference" # default to tgi + +class Huggingface(BaseLLM): + _client_session: Optional[httpx.Client] = None + _aclient_session: Optional[httpx.AsyncClient] = None + + def __init__(self) -> None: + super().__init__() + + def validate_environment(self, api_key, headers): + default_headers = { + "content-type": "application/json", + } + if api_key and headers is None: + default_headers["Authorization"] = f"Bearer {api_key}" # Huggingface Inference Endpoint default is to accept bearer tokens + headers = default_headers + elif headers: + headers=headers + else: + headers = default_headers + return headers + + def convert_to_model_response_object(self, + completion_response, + model_response, + task, + optional_params, + encoding, + input_text, + model): + if task == "conversational": + if len(completion_response["generated_text"]) > 0: # type: ignore + model_response["choices"][0]["message"][ + "content" + ] = completion_response["generated_text"] # type: ignore + elif task == "text-generation-inference": + if len(completion_response[0]["generated_text"]) > 0: + model_response["choices"][0]["message"][ + "content" + ] = output_parser(completion_response[0]["generated_text"]) + ## GETTING LOGPROBS + FINISH REASON + if "details" in completion_response[0] and "tokens" in completion_response[0]["details"]: + model_response.choices[0].finish_reason = completion_response[0]["details"]["finish_reason"] + sum_logprob = 0 + for token in completion_response[0]["details"]["tokens"]: + if token["logprob"] != None: + sum_logprob += token["logprob"] + model_response["choices"][0]["message"]._logprob = sum_logprob + if "best_of" in optional_params and optional_params["best_of"] > 1: + if "details" in completion_response[0] and "best_of_sequences" in completion_response[0]["details"]: + choices_list = [] + for idx, item in enumerate(completion_response[0]["details"]["best_of_sequences"]): + sum_logprob = 0 + for token in item["tokens"]: + if token["logprob"] != None: + sum_logprob += token["logprob"] + if len(item["generated_text"]) > 0: + message_obj = Message(content=output_parser(item["generated_text"]), logprobs=sum_logprob) + else: + message_obj = Message(content=None) + choice_obj = Choices(finish_reason=item["finish_reason"], index=idx+1, message=message_obj) + choices_list.append(choice_obj) + model_response["choices"].extend(choices_list) + else: + if len(completion_response[0]["generated_text"]) > 0: + model_response["choices"][0]["message"][ + "content" + ] = output_parser(completion_response[0]["generated_text"]) + ## CALCULATING USAGE + prompt_tokens = 0 + try: + prompt_tokens = len( + encoding.encode(input_text) + ) ##[TODO] use the llama2 tokenizer here + except: + # this should remain non blocking we should not block a response returning if calculating usage fails + pass + output_text = model_response["choices"][0]["message"].get("content", "") + if output_text is not None and len(output_text) > 0: + completion_tokens = 0 + try: + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) ##[TODO] use the llama2 tokenizer here + except: + # this should remain non blocking we should not block a response returning if calculating usage fails + pass + else: + completion_tokens = 0 + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + model_response._hidden_params["original_response"] = completion_response + return model_response + + def completion(self, + model: str, + messages: list, + api_base: Optional[str], + headers: Optional[dict], + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + custom_prompt_dict={}, + acompletion: bool = False, + optional_params=None, + litellm_params=None, + logger_fn=None, + ): + super().completion() + exception_mapping_worked = False + try: + headers = self.validate_environment(api_key, headers) + task = get_hf_task_for_model(model) + print_verbose(f"{model}, {task}") + completion_url = "" + input_text = "" + if "https" in model: + completion_url = model + elif api_base: + completion_url = api_base + elif "HF_API_BASE" in os.environ: + completion_url = os.getenv("HF_API_BASE", "") + elif "HUGGINGFACE_API_BASE" in os.environ: + completion_url = os.getenv("HUGGINGFACE_API_BASE", "") + else: + completion_url = f"/static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co%2Fmodels%2F%7Bmodel%7D" + + ## Load Config + config=litellm.HuggingfaceConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > huggingfaceConfig(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + ### MAP INPUT PARAMS + if task == "conversational": + inference_params = copy.deepcopy(optional_params) + inference_params.pop("details") + inference_params.pop("return_full_text") + past_user_inputs = [] + generated_responses = [] + text = "" + for message in messages: + if message["role"] == "user": + if text != "": + past_user_inputs.append(text) + text = message["content"] + elif message["role"] == "assistant" or message["role"] == "system": + generated_responses.append(message["content"]) + data = { + "inputs": { + "text": text, + "past_user_inputs": past_user_inputs, + "generated_responses": generated_responses + }, + "parameters": inference_params + } + input_text = "".join(message["content"] for message in messages) + elif task == "text-generation-inference": + # always send "details" and "return_full_text" as params + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details.get("roles", None), + initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""), + final_prompt_value=model_prompt_details.get("final_prompt_value", ""), + messages=messages + ) + else: + prompt = prompt_factory(model=model, messages=messages) + data = { + "inputs": prompt, + "parameters": optional_params, + "stream": True if "stream" in optional_params and optional_params["stream"] == True else False, + } + input_text = prompt + else: + # Non TGI and Conversational llms + # We need this branch, it removes 'details' and 'return_full_text' from params + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details.get("roles", {}), + initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""), + final_prompt_value=model_prompt_details.get("final_prompt_value", ""), + bos_token=model_prompt_details.get("bos_token", ""), + eos_token=model_prompt_details.get("eos_token", ""), + messages=messages, + ) + else: + prompt = prompt_factory(model=model, messages=messages) + inference_params = copy.deepcopy(optional_params) + inference_params.pop("details") + inference_params.pop("return_full_text") + data = { + "inputs": prompt, + "parameters": inference_params, + "stream": True if "stream" in optional_params and optional_params["stream"] == True else False, + } + input_text = prompt + ## LOGGING + logging_obj.pre_call( + input=input_text, + api_key=api_key, + additional_args={"complete_input_dict": data, "task": task, "headers": headers, "api_base": completion_url, "acompletion": acompletion}, + ) + ## COMPLETION CALL + if acompletion is True: + ### ASYNC STREAMING + if optional_params.get("stream", False): + return self.async_streaming(logging_obj=logging_obj, api_base=completion_url, data=data, headers=headers, model_response=model_response, model=model) # type: ignore + else: + ### ASYNC COMPLETION + return self.acompletion(api_base=completion_url, data=data, headers=headers, model_response=model_response, task=task, encoding=encoding, input_text=input_text, model=model, optional_params=optional_params) # type: ignore + ### SYNC STREAMING + if "stream" in optional_params and optional_params["stream"] == True: + response = requests.post( + completion_url, + headers=headers, + data=json.dumps(data), + stream=optional_params["stream"] + ) + return response.iter_lines() + ### SYNC COMPLETION + else: + response = requests.post( + completion_url, + headers=headers, + data=json.dumps(data) + ) + + ## Some servers might return streaming responses even though stream was not set to true. (e.g. Baseten) + is_streamed = False + if response.__dict__['headers'].get("Content-Type", "") == "text/event-stream": + is_streamed = True + + # iterate over the complete streamed response, and return the final answer + if is_streamed: + streamed_response = CustomStreamWrapper(completion_stream=response.iter_lines(), model=model, custom_llm_provider="huggingface", logging_obj=logging_obj) + content = "" + for chunk in streamed_response: + content += chunk["choices"][0]["delta"]["content"] + completion_response: List[Dict[str, Any]] = [{"generated_text": content}] + ## LOGGING + logging_obj.post_call( + input=input_text, + api_key=api_key, + original_response=completion_response, + additional_args={"complete_input_dict": data, "task": task}, + ) + else: + ## LOGGING + logging_obj.post_call( + input=input_text, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data, "task": task}, + ) + ## RESPONSE OBJECT + try: + completion_response = response.json() + if isinstance(completion_response, dict): + completion_response = [completion_response] + except: + import traceback + raise HuggingfaceError( + message=f"Original Response received: {response.text}; Stacktrace: {traceback.format_exc()}", status_code=response.status_code + ) + print_verbose(f"response: {completion_response}") + if isinstance(completion_response, dict) and "error" in completion_response: + print_verbose(f"completion error: {completion_response['error']}") + print_verbose(f"response.status_code: {response.status_code}") + raise HuggingfaceError( + message=completion_response["error"], + status_code=response.status_code, + ) + return self.convert_to_model_response_object( + completion_response=completion_response, + model_response=model_response, + task=task, + optional_params=optional_params, + encoding=encoding, + input_text=input_text, + model=model + ) + except HuggingfaceError as e: + exception_mapping_worked = True + raise e + except Exception as e: + if exception_mapping_worked: + raise e + else: + import traceback + raise HuggingfaceError(status_code=500, message=traceback.format_exc()) + + async def acompletion(self, + api_base: str, + data: dict, + headers: dict, + model_response: ModelResponse, + task: str, + encoding: Any, + input_text: str, + model: str, + optional_params: dict): + response = None + try: + async with httpx.AsyncClient() as client: + response = await client.post(url=api_base, json=data, headers=headers, timeout=None) + response_json = response.json() + if response.status_code != 200: + raise HuggingfaceError(status_code=response.status_code, message=response.text, request=response.request, response=response) + + ## RESPONSE OBJECT + return self.convert_to_model_response_object(completion_response=response_json, + model_response=model_response, + task=task, + encoding=encoding, + input_text=input_text, + model=model, + optional_params=optional_params) + except Exception as e: + if isinstance(e,httpx.TimeoutException): + raise HuggingfaceError(status_code=500, message="Request Timeout Error") + elif response is not None and hasattr(response, "text"): + raise HuggingfaceError(status_code=500, message=f"{str(e)}\n\nOriginal Response: {response.text}") + else: + raise HuggingfaceError(status_code=500, message=f"{str(e)}") + + async def async_streaming(self, + logging_obj, + api_base: str, + data: dict, + headers: dict, + model_response: ModelResponse, + model: str): + async with httpx.AsyncClient() as client: + response = client.stream( + "POST", + url=f"{api_base}", + json=data, + headers=headers + ) + async with response as r: + if r.status_code != 200: + raise HuggingfaceError(status_code=r.status_code, message="An error occurred while streaming") + + streamwrapper = CustomStreamWrapper(completion_stream=r.aiter_lines(), model=model, custom_llm_provider="huggingface",logging_obj=logging_obj) + async for transformed_chunk in streamwrapper: + yield transformed_chunk + + def embedding(self, + model: str, + input: list, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + logging_obj=None, + model_response=None, + encoding=None, + ): + super().embedding() + headers = self.validate_environment(api_key, headers=None) + # print_verbose(f"{model}, {task}") + embed_url = "" + if "https" in model: + embed_url = model + elif api_base: + embed_url = api_base + elif "HF_API_BASE" in os.environ: + embed_url = os.getenv("HF_API_BASE", "") + elif "HUGGINGFACE_API_BASE" in os.environ: + embed_url = os.getenv("HUGGINGFACE_API_BASE", "") + else: + embed_url = f"/static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co%2Fmodels%2F%7Bmodel%7D" + + if "sentence-transformers" in model: + if len(input) == 0: + raise HuggingfaceError(status_code=400, message="sentence transformers requires 2+ sentences") + data = { + "inputs": { + "source_sentence": input[0], + "sentences": [ "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] + } + } + else: + data = { + "inputs": input # type: ignore + } + + ## LOGGING + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + ) + ## COMPLETION CALL + response = requests.post( + embed_url, headers=headers, data=json.dumps(data) + ) + + + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=response, + ) + + + embeddings = response.json() + + if "error" in embeddings: + raise HuggingfaceError(status_code=500, message=embeddings['error']) + + output_data = [] + if "similarities" in embeddings: + for idx, embedding in embeddings["similarities"]: + output_data.append( + { + "object": "embedding", + "index": idx, + "embedding": embedding # flatten list returned from hf + } + ) + else: + for idx, embedding in enumerate(embeddings): + if isinstance(embedding, float): + output_data.append( + { + "object": "embedding", + "index": idx, + "embedding": embedding # flatten list returned from hf + } + ) + else: + output_data.append( + { + "object": "embedding", + "index": idx, + "embedding": embedding[0][0] # flatten list returned from hf + } + ) + model_response["object"] = "list" + model_response["data"] = output_data + model_response["model"] = model + input_tokens = 0 + for text in input: + input_tokens+=len(encoding.encode(text)) + + model_response["usage"] = { + "prompt_tokens": input_tokens, + "total_tokens": input_tokens, + } + return model_response + + + diff --git a/litellm/llms/maritalk.py b/litellm/llms/maritalk.py new file mode 100644 index 0000000000000000000000000000000000000000..68a3a4e32d177405e87d0b6d8b36b7eb6e4643ba --- /dev/null +++ b/litellm/llms/maritalk.py @@ -0,0 +1,164 @@ +import os, types +import json +from enum import Enum +import requests +import time, traceback +from typing import Callable, Optional, List +from litellm.utils import ModelResponse, Choices, Message, Usage +import litellm + +class MaritalkError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class MaritTalkConfig(): + """ + The class `MaritTalkConfig` provides configuration for the MaritTalk's API interface. Here are the parameters: + + - `max_tokens` (integer): Maximum number of tokens the model will generate as part of the response. Default is 1. + + - `model` (string): The model used for conversation. Default is 'maritalk'. + + - `do_sample` (boolean): If set to True, the API will generate a response using sampling. Default is True. + + - `temperature` (number): A non-negative float controlling the randomness in generation. Lower temperatures result in less random generations. Default is 0.7. + + - `top_p` (number): Selection threshold for token inclusion based on cumulative probability. Default is 0.95. + + - `repetition_penalty` (number): Penalty for repetition in the generated conversation. Default is 1. + + - `stopping_tokens` (list of string): List of tokens where the conversation can be stopped/stopped. + """ + max_tokens: Optional[int] = None + model: Optional[str] = None + do_sample: Optional[bool] = None + temperature: Optional[float] = None + top_p: Optional[float] = None + repetition_penalty: Optional[float] = None + stopping_tokens: Optional[List[str]] = None + + def __init__(self, + max_tokens: Optional[int]=None, + model: Optional[str] = None, + do_sample: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + repetition_penalty: Optional[float] = None, + stopping_tokens: Optional[List[str]] = None) -> None: + + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + +def validate_environment(api_key): + headers = { + "accept": "application/json", + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Key {api_key}" + return headers + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + completion_url = api_base + model = model + + ## Load Config + config=litellm.MaritTalkConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > maritalk_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + data = { + "messages": messages, + **optional_params, + } + + ## LOGGING + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={"complete_input_dict": data}, + ) + ## COMPLETION CALL + response = requests.post( + completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False + ) + if "stream" in optional_params and optional_params["stream"] == True: + return response.iter_lines() + else: + ## LOGGING + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + completion_response = response.json() + if "error" in completion_response: + raise MaritalkError( + message=completion_response["error"], + status_code=response.status_code, + ) + else: + try: + if len(completion_response["answer"]) > 0: + model_response["choices"][0]["message"]["content"] = completion_response["answer"] + except Exception as e: + raise MaritalkError(message=response.text, status_code=response.status_code) + + ## CALCULATING USAGE + prompt = "".join(m["content"] for m in messages) + prompt_tokens = len( + encoding.encode(prompt) + ) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + +def embedding( + model: str, + input: list, + api_key: Optional[str] = None, + logging_obj=None, + model_response=None, + encoding=None, +): + pass \ No newline at end of file diff --git a/litellm/llms/nlp_cloud.py b/litellm/llms/nlp_cloud.py new file mode 100644 index 0000000000000000000000000000000000000000..8d09b85ea9b63de77e6572efbc48ebd12355a7e3 --- /dev/null +++ b/litellm/llms/nlp_cloud.py @@ -0,0 +1,212 @@ +import os, types +import json +from enum import Enum +import requests +import time +from typing import Callable, Optional +import litellm +from litellm.utils import ModelResponse, Usage + +class NLPCloudError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class NLPCloudConfig(): + """ + Reference: https://docs.nlpcloud.com/#generation + + - `max_length` (int): Optional. The maximum number of tokens that the generated text should contain. + + - `length_no_input` (boolean): Optional. Whether `min_length` and `max_length` should not include the length of the input text. + + - `end_sequence` (string): Optional. A specific token that should be the end of the generated sequence. + + - `remove_end_sequence` (boolean): Optional. Whether to remove the `end_sequence` string from the result. + + - `remove_input` (boolean): Optional. Whether to remove the input text from the result. + + - `bad_words` (list of strings): Optional. List of tokens that are not allowed to be generated. + + - `temperature` (float): Optional. Temperature sampling. It modulates the next token probabilities. + + - `top_p` (float): Optional. Top P sampling. Below 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + + - `top_k` (int): Optional. Top K sampling. The number of highest probability vocabulary tokens to keep for top k filtering. + + - `repetition_penalty` (float): Optional. Prevents the same word from being repeated too many times. + + - `num_beams` (int): Optional. Number of beams for beam search. + + - `num_return_sequences` (int): Optional. The number of independently computed returned sequences. + """ + max_length: Optional[int]=None + length_no_input: Optional[bool]=None + end_sequence: Optional[str]=None + remove_end_sequence: Optional[bool]=None + remove_input: Optional[bool]=None + bad_words: Optional[list]=None + temperature: Optional[float]=None + top_p: Optional[float]=None + top_k: Optional[int]=None + repetition_penalty: Optional[float]=None + num_beams: Optional[int]=None + num_return_sequences: Optional[int]=None + + + def __init__(self, + max_length: Optional[int]=None, + length_no_input: Optional[bool]=None, + end_sequence: Optional[str]=None, + remove_end_sequence: Optional[bool]=None, + remove_input: Optional[bool]=None, + bad_words: Optional[list]=None, + temperature: Optional[float]=None, + top_p: Optional[float]=None, + top_k: Optional[int]=None, + repetition_penalty: Optional[float]=None, + num_beams: Optional[int]=None, + num_return_sequences: Optional[int]=None) -> None: + + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + + +def validate_environment(api_key): + headers = { + "accept": "application/json", + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Token {api_key}" + return headers + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, + default_max_tokens_to_sample=None, +): + headers = validate_environment(api_key) + + ## Load Config + config = litellm.NLPCloudConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + completion_url_fragment_1 = api_base + completion_url_fragment_2 = "/generation" + model = model + text = " ".join(message["content"] for message in messages) + + data = { + "text": text, + **optional_params, + } + + completion_url = completion_url_fragment_1 + model + completion_url_fragment_2 + + ## LOGGING + logging_obj.pre_call( + input=text, + api_key=api_key, + additional_args={"complete_input_dict": data, "headers": headers, "api_base": completion_url}, + ) + ## COMPLETION CALL + response = requests.post( + completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False + ) + if "stream" in optional_params and optional_params["stream"] == True: + return clean_and_iterate_chunks(response) + else: + ## LOGGING + logging_obj.post_call( + input=text, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + try: + completion_response = response.json() + except: + raise NLPCloudError(message=response.text, status_code=response.status_code) + if "error" in completion_response: + raise NLPCloudError( + message=completion_response["error"], + status_code=response.status_code, + ) + else: + try: + if len(completion_response["generated_text"]) > 0: + model_response["choices"][0]["message"]["content"] = completion_response["generated_text"] + except: + raise NLPCloudError(message=json.dumps(completion_response), status_code=response.status_code) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = completion_response["nb_input_tokens"] + completion_tokens = completion_response["nb_generated_tokens"] + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + + +# def clean_and_iterate_chunks(response): +# def process_chunk(chunk): +# print(f"received chunk: {chunk}") +# cleaned_chunk = chunk.decode("utf-8") +# # Perform further processing based on your needs +# return cleaned_chunk + +# for line in response.iter_lines(): +# if line: +# yield process_chunk(line) +def clean_and_iterate_chunks(response): + buffer = b'' + + for chunk in response.iter_content(chunk_size=1024): + if not chunk: + break + + buffer += chunk + while b'\x00' in buffer: + buffer = buffer.replace(b'\x00', b'') + yield buffer.decode('utf-8') + buffer = b'' + + # No more data expected, yield any remaining data in the buffer + if buffer: + yield buffer.decode('utf-8') + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/ollama.py b/litellm/llms/ollama.py new file mode 100644 index 0000000000000000000000000000000000000000..a24e47c074c7eeb3b8ef49558d7cda2db386c79a --- /dev/null +++ b/litellm/llms/ollama.py @@ -0,0 +1,231 @@ +import requests, types +import json +import traceback +from typing import Optional +import litellm +import httpx + +try: + from async_generator import async_generator, yield_ # optional dependency + async_generator_imported = True +except ImportError: + async_generator_imported = False # this should not throw an error, it will impact the 'import litellm' statement + +class OllamaError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="http://localhost:11434") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class OllamaConfig(): + """ + Reference: https://github.com/jmorganca/ollama/blob/main/docs/api.md#parameters + + The class `OllamaConfig` provides the configuration for the Ollama's API interface. Below are the parameters: + + - `mirostat` (int): Enable Mirostat sampling for controlling perplexity. Default is 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0. Example usage: mirostat 0 + + - `mirostat_eta` (float): Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. Default: 0.1. Example usage: mirostat_eta 0.1 + + - `mirostat_tau` (float): Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. Default: 5.0. Example usage: mirostat_tau 5.0 + + - `num_ctx` (int): Sets the size of the context window used to generate the next token. Default: 2048. Example usage: num_ctx 4096 + + - `num_gqa` (int): The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b. Example usage: num_gqa 1 + + - `num_gpu` (int): The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable. Example usage: num_gpu 0 + + - `num_thread` (int): Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Example usage: num_thread 8 + + - `repeat_last_n` (int): Sets how far back for the model to look back to prevent repetition. Default: 64, 0 = disabled, -1 = num_ctx. Example usage: repeat_last_n 64 + + - `repeat_penalty` (float): Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. Default: 1.1. Example usage: repeat_penalty 1.1 + + - `temperature` (float): The temperature of the model. Increasing the temperature will make the model answer more creatively. Default: 0.8. Example usage: temperature 0.7 + + - `stop` (string[]): Sets the stop sequences to use. Example usage: stop "AI assistant:" + + - `tfs_z` (float): Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. Default: 1. Example usage: tfs_z 1 + + - `num_predict` (int): Maximum number of tokens to predict when generating text. Default: 128, -1 = infinite generation, -2 = fill context. Example usage: num_predict 42 + + - `top_k` (int): Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. Default: 40. Example usage: top_k 40 + + - `top_p` (float): Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. Default: 0.9. Example usage: top_p 0.9 + + - `system` (string): system prompt for model (overrides what is defined in the Modelfile) + + - `template` (string): the full prompt or prompt template (overrides what is defined in the Modelfile) + """ + mirostat: Optional[int]=None + mirostat_eta: Optional[float]=None + mirostat_tau: Optional[float]=None + num_ctx: Optional[int]=None + num_gqa: Optional[int]=None + num_thread: Optional[int]=None + repeat_last_n: Optional[int]=None + repeat_penalty: Optional[float]=None + temperature: Optional[float]=None + stop: Optional[list]=None # stop is a list based on this - https://github.com/jmorganca/ollama/pull/442 + tfs_z: Optional[float]=None + num_predict: Optional[int]=None + top_k: Optional[int]=None + top_p: Optional[float]=None + system: Optional[str]=None + template: Optional[str]=None + + def __init__(self, + mirostat: Optional[int]=None, + mirostat_eta: Optional[float]=None, + mirostat_tau: Optional[float]=None, + num_ctx: Optional[int]=None, + num_gqa: Optional[int]=None, + num_thread: Optional[int]=None, + repeat_last_n: Optional[int]=None, + repeat_penalty: Optional[float]=None, + temperature: Optional[float]=None, + stop: Optional[list]=None, + tfs_z: Optional[float]=None, + num_predict: Optional[int]=None, + top_k: Optional[int]=None, + top_p: Optional[float]=None, + system: Optional[str]=None, + template: Optional[str]=None) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + + +# ollama implementation +def get_ollama_response_stream( + api_base="http://localhost:11434", + model="llama2", + prompt="Why is the sky blue?", + optional_params=None, + logging_obj=None, + ): + if api_base.endswith("/api/generate"): + url = api_base + else: + url = f"{api_base}/api/generate" + + ## Load Config + config=litellm.OllamaConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + data = { + "model": model, + "prompt": prompt, + **optional_params + } + ## LOGGING + logging_obj.pre_call( + input=None, + api_key=None, + additional_args={"api_base": url, "complete_input_dict": data}, + ) + session = requests.Session() + + with session.post(url, json=data, stream=True) as resp: + if resp.status_code != 200: + raise OllamaError(status_code=resp.status_code, message=resp.text) + for line in resp.iter_lines(): + if line: + try: + json_chunk = line.decode("utf-8") + chunks = json_chunk.split("\n") + for chunk in chunks: + if chunk.strip() != "": + j = json.loads(chunk) + if "error" in j: + completion_obj = { + "role": "assistant", + "content": "", + "error": j + } + yield completion_obj + if "response" in j: + completion_obj = { + "role": "assistant", + "content": "", + } + completion_obj["content"] = j["response"] + yield completion_obj + except Exception as e: + traceback.print_exc() + session.close() + +if async_generator_imported: + # ollama implementation + @async_generator + async def async_get_ollama_response_stream( + api_base="http://localhost:11434", + model="llama2", + prompt="Why is the sky blue?", + optional_params=None, + logging_obj=None, + ): + url = f"{api_base}/api/generate" + + ## Load Config + config=litellm.OllamaConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + data = { + "model": model, + "prompt": prompt, + **optional_params + } + ## LOGGING + logging_obj.pre_call( + input=None, + api_key=None, + additional_args={"api_base": url, "complete_input_dict": data}, + ) + session = requests.Session() + + with session.post(url, json=data, stream=True) as resp: + if resp.status_code != 200: + raise OllamaError(status_code=resp.status_code, message=resp.text) + for line in resp.iter_lines(): + if line: + try: + json_chunk = line.decode("utf-8") + chunks = json_chunk.split("\n") + for chunk in chunks: + if chunk.strip() != "": + j = json.loads(chunk) + if "error" in j: + completion_obj = { + "role": "assistant", + "content": "", + "error": j + } + await yield_({"choices": [{"delta": completion_obj}]}) + if "response" in j: + completion_obj = { + "role": "assistant", + "content": "", + } + completion_obj["content"] = j["response"] + await yield_({"choices": [{"delta": completion_obj}]}) + except Exception as e: + import logging + logging.debug(f"Error decoding JSON: {e}") + session.close() \ No newline at end of file diff --git a/litellm/llms/oobabooga.py b/litellm/llms/oobabooga.py new file mode 100644 index 0000000000000000000000000000000000000000..db0403c965a4bc26a8a5078583ace68e4252680b --- /dev/null +++ b/litellm/llms/oobabooga.py @@ -0,0 +1,124 @@ +import os +import json +from enum import Enum +import requests +import time +from typing import Callable, Optional +from litellm.utils import ModelResponse, Usage +from .prompt_templates.factory import prompt_factory, custom_prompt + +class OobaboogaError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +def validate_environment(api_key): + headers = { + "accept": "application/json", + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Token {api_key}" + return headers + +def completion( + model: str, + messages: list, + api_base: Optional[str], + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + custom_prompt_dict={}, + optional_params=None, + litellm_params=None, + logger_fn=None, + default_max_tokens_to_sample=None, +): + headers = validate_environment(api_key) + if "https" in model: + completion_url = model + elif api_base: + completion_url = api_base + else: + raise OobaboogaError(status_code=404, message="API Base not set. Set one via completion(..,api_base='your-api-url')") + model = model + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=messages + ) + else: + prompt = prompt_factory(model=model, messages=messages) + + completion_url = completion_url + "/api/v1/generate" + data = { + "prompt": prompt, + **optional_params, + } + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={"complete_input_dict": data}, + ) + ## COMPLETION CALL + response = requests.post( + completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False + ) + if "stream" in optional_params and optional_params["stream"] == True: + return response.iter_lines() + else: + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + try: + completion_response = response.json() + except: + raise OobaboogaError(message=response.text, status_code=response.status_code) + if "error" in completion_response: + raise OobaboogaError( + message=completion_response["error"], + status_code=response.status_code, + ) + else: + try: + model_response["choices"][0]["message"]["content"] = completion_response['results'][0]['text'] + except: + raise OobaboogaError(message=json.dumps(completion_response), status_code=response.status_code) + + ## CALCULATING USAGE + prompt_tokens = len( + encoding.encode(prompt) + ) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"]["content"]) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/openai.py b/litellm/llms/openai.py new file mode 100644 index 0000000000000000000000000000000000000000..29934d13041634f5a3741f7e808b6208c65a3db1 --- /dev/null +++ b/litellm/llms/openai.py @@ -0,0 +1,590 @@ +from typing import Optional, Union, Any +import types, time, json +import httpx +from .base import BaseLLM +from litellm.utils import ModelResponse, Choices, Message, CustomStreamWrapper, convert_to_model_response_object, Usage +from typing import Callable, Optional +import aiohttp, requests +import litellm +from .prompt_templates.factory import prompt_factory, custom_prompt +from openai import OpenAI, AsyncOpenAI + +class OpenAIError(Exception): + def __init__(self, status_code, message, request: Optional[httpx.Request]=None, response: Optional[httpx.Response]=None): + self.status_code = status_code + self.message = message + if request: + self.request = request + else: + self.request = httpx.Request(method="POST", url="https://api.openai.com/v1") + if response: + self.response = response + else: + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class OpenAIConfig(): + """ + Reference: https://platform.openai.com/docs/api-reference/chat/create + + The class `OpenAIConfig` provides configuration for the OpenAI's Chat API interface. Below are the parameters: + + - `frequency_penalty` (number or null): Defaults to 0. Allows a value between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, thereby minimizing repetition. + + - `function_call` (string or object): This optional parameter controls how the model calls functions. + + - `functions` (array): An optional parameter. It is a list of functions for which the model may generate JSON inputs. + + - `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion. + + - `max_tokens` (integer or null): This optional parameter helps to set the maximum number of tokens to generate in the chat completion. + + - `n` (integer or null): This optional parameter helps to set how many chat completion choices to generate for each input message. + + - `presence_penalty` (number or null): Defaults to 0. It penalizes new tokens based on if they appear in the text so far, hence increasing the model's likelihood to talk about new topics. + + - `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens. + + - `temperature` (number or null): Defines the sampling temperature to use, varying between 0 and 2. + + - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling. + """ + frequency_penalty: Optional[int]=None + function_call: Optional[Union[str, dict]]=None + functions: Optional[list]=None + logit_bias: Optional[dict]=None + max_tokens: Optional[int]=None + n: Optional[int]=None + presence_penalty: Optional[int]=None + stop: Optional[Union[str, list]]=None + temperature: Optional[int]=None + top_p: Optional[int]=None + + def __init__(self, + frequency_penalty: Optional[int]=None, + function_call: Optional[Union[str, dict]]=None, + functions: Optional[list]=None, + logit_bias: Optional[dict]=None, + max_tokens: Optional[int]=None, + n: Optional[int]=None, + presence_penalty: Optional[int]=None, + stop: Optional[Union[str, list]]=None, + temperature: Optional[int]=None, + top_p: Optional[int]=None,) -> None: + + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + +class OpenAITextCompletionConfig(): + """ + Reference: https://platform.openai.com/docs/api-reference/completions/create + + The class `OpenAITextCompletionConfig` provides configuration for the OpenAI's text completion API interface. Below are the parameters: + + - `best_of` (integer or null): This optional parameter generates server-side completions and returns the one with the highest log probability per token. + + - `echo` (boolean or null): This optional parameter will echo back the prompt in addition to the completion. + + - `frequency_penalty` (number or null): Defaults to 0. It is a numbers from -2.0 to 2.0, where positive values decrease the model's likelihood to repeat the same line. + + - `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion. + + - `logprobs` (integer or null): This optional parameter includes the log probabilities on the most likely tokens as well as the chosen tokens. + + - `max_tokens` (integer or null): This optional parameter sets the maximum number of tokens to generate in the completion. + + - `n` (integer or null): This optional parameter sets how many completions to generate for each prompt. + + - `presence_penalty` (number or null): Defaults to 0 and can be between -2.0 and 2.0. Positive values increase the model's likelihood to talk about new topics. + + - `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens. + + - `suffix` (string or null): Defines the suffix that comes after a completion of inserted text. + + - `temperature` (number or null): This optional parameter defines the sampling temperature to use. + + - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling. + """ + best_of: Optional[int]=None + echo: Optional[bool]=None + frequency_penalty: Optional[int]=None + logit_bias: Optional[dict]=None + logprobs: Optional[int]=None + max_tokens: Optional[int]=None + n: Optional[int]=None + presence_penalty: Optional[int]=None + stop: Optional[Union[str, list]]=None + suffix: Optional[str]=None + temperature: Optional[float]=None + top_p: Optional[float]=None + + def __init__(self, + best_of: Optional[int]=None, + echo: Optional[bool]=None, + frequency_penalty: Optional[int]=None, + logit_bias: Optional[dict]=None, + logprobs: Optional[int]=None, + max_tokens: Optional[int]=None, + n: Optional[int]=None, + presence_penalty: Optional[int]=None, + stop: Optional[Union[str, list]]=None, + suffix: Optional[str]=None, + temperature: Optional[float]=None, + top_p: Optional[float]=None) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + +class OpenAIChatCompletion(BaseLLM): + + def __init__(self) -> None: + super().__init__() + + def completion(self, + model_response: ModelResponse, + timeout: float, + model: Optional[str]=None, + messages: Optional[list]=None, + print_verbose: Optional[Callable]=None, + api_key: Optional[str]=None, + api_base: Optional[str]=None, + acompletion: bool = False, + logging_obj=None, + optional_params=None, + litellm_params=None, + logger_fn=None, + headers: Optional[dict]=None, + custom_prompt_dict: dict={}, + client=None + ): + super().completion() + exception_mapping_worked = False + try: + if headers: + optional_params["extra_headers"] = headers + if model is None or messages is None: + raise OpenAIError(status_code=422, message=f"Missing model or messages") + + if not isinstance(timeout, float): + raise OpenAIError(status_code=422, message=f"Timeout needs to be a float") + + for _ in range(2): # if call fails due to alternating messages, retry with reformatted message + data = { + "model": model, + "messages": messages, + **optional_params + } + + ## LOGGING + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={"headers": headers, "api_base": api_base, "acompletion": acompletion, "complete_input_dict": data}, + ) + + try: + max_retries = data.pop("max_retries", 2) + if acompletion is True: + if optional_params.get("stream", False): + return self.async_streaming(logging_obj=logging_obj, data=data, model=model, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) + else: + return self.acompletion(data=data, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) + elif optional_params.get("stream", False): + return self.streaming(logging_obj=logging_obj, data=data, model=model, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) + else: + if not isinstance(max_retries, int): + raise OpenAIError(status_code=422, message="max retries must be an int") + if client is None: + openai_client = OpenAI(api_key=api_key, base_url=api_base, http_client=litellm.client_session, timeout=timeout, max_retries=max_retries) + else: + openai_client = client + response = openai_client.chat.completions.create(**data) # type: ignore + logging_obj.post_call( + input=None, + api_key=api_key, + original_response=response, + additional_args={"complete_input_dict": data}, + ) + return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response) + except Exception as e: + if "Conversation roles must alternate user/assistant" in str(e) or "user and assistant roles should be alternating" in str(e): + # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, add a blank 'user' or 'assistant' message to ensure compatibility + new_messages = [] + for i in range(len(messages)-1): + new_messages.append(messages[i]) + if messages[i]["role"] == messages[i+1]["role"]: + if messages[i]["role"] == "user": + new_messages.append({"role": "assistant", "content": ""}) + else: + new_messages.append({"role": "user", "content": ""}) + new_messages.append(messages[-1]) + messages = new_messages + elif "Last message must have role `user`" in str(e): + new_messages = messages + new_messages.append({"role": "user", "content": ""}) + messages = new_messages + else: + raise e + except OpenAIError as e: + exception_mapping_worked = True + raise e + except Exception as e: + raise e + + async def acompletion(self, + data: dict, + model_response: ModelResponse, + timeout: float, + api_key: Optional[str]=None, + api_base: Optional[str]=None, + client=None, + max_retries=None, + ): + response = None + try: + if client is None: + openai_aclient = AsyncOpenAI(api_key=api_key, base_url=api_base, http_client=litellm.aclient_session, timeout=timeout, max_retries=max_retries) + else: + openai_aclient = client + response = await openai_aclient.chat.completions.create(**data) + return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response) + except Exception as e: + if response and hasattr(response, "text"): + raise OpenAIError(status_code=500, message=f"{str(e)}\n\nOriginal Response: {response.text}") + else: + if type(e).__name__ == "ReadTimeout": + raise OpenAIError(status_code=408, message=f"{type(e).__name__}") + else: + raise OpenAIError(status_code=500, message=f"{str(e)}") + + def streaming(self, + logging_obj, + timeout: float, + data: dict, + model: str, + api_key: Optional[str]=None, + api_base: Optional[str]=None, + client = None, + max_retries=None + ): + if client is None: + openai_client = OpenAI(api_key=api_key, base_url=api_base, http_client=litellm.client_session, timeout=timeout, max_retries=max_retries) + else: + openai_client = client + response = openai_client.chat.completions.create(**data) + streamwrapper = CustomStreamWrapper(completion_stream=response, model=model, custom_llm_provider="openai",logging_obj=logging_obj) + return streamwrapper + + async def async_streaming(self, + logging_obj, + timeout: float, + data: dict, + model: str, + api_key: Optional[str]=None, + api_base: Optional[str]=None, + client=None, + max_retries=None, + ): + response = None + try: + if client is None: + openai_aclient = AsyncOpenAI(api_key=api_key, base_url=api_base, http_client=litellm.aclient_session, timeout=timeout, max_retries=max_retries) + else: + openai_aclient = client + response = await openai_aclient.chat.completions.create(**data) + streamwrapper = CustomStreamWrapper(completion_stream=response, model=model, custom_llm_provider="openai",logging_obj=logging_obj) + async for transformed_chunk in streamwrapper: + yield transformed_chunk + except Exception as e: # need to exception handle here. async exceptions don't get caught in sync functions. + if response is not None and hasattr(response, "text"): + raise OpenAIError(status_code=500, message=f"{str(e)}\n\nOriginal Response: {response.text}") + else: + if type(e).__name__ == "ReadTimeout": + raise OpenAIError(status_code=408, message=f"{type(e).__name__}") + else: + raise OpenAIError(status_code=500, message=f"{str(e)}") + async def aembedding( + self, + data: dict, + model_response: ModelResponse, + timeout: float, + api_key: Optional[str]=None, + api_base: Optional[str]=None, + client=None, + max_retries=None, + ): + response = None + try: + if client is None: + openai_aclient = AsyncOpenAI(api_key=api_key, base_url=api_base, http_client=litellm.aclient_session, timeout=timeout, max_retries=max_retries) + else: + openai_aclient = client + response = await openai_aclient.embeddings.create(**data) # type: ignore + return response + except Exception as e: + raise e + def embedding(self, + model: str, + input: list, + timeout: float, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + model_response: Optional[litellm.utils.EmbeddingResponse] = None, + logging_obj=None, + optional_params=None, + client=None, + aembedding=None, + ): + super().embedding() + exception_mapping_worked = False + try: + model = model + data = { + "model": model, + "input": input, + **optional_params + } + max_retries = data.pop("max_retries", 2) + if not isinstance(max_retries, int): + raise OpenAIError(status_code=422, message="max retries must be an int") + if aembedding == True: + response = self.aembedding(data=data, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) # type: ignore + return response + if client is None: + openai_client = OpenAI(api_key=api_key, base_url=api_base, http_client=litellm.client_session, timeout=timeout, max_retries=max_retries) + else: + openai_client = client + ## LOGGING + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data, "api_base": api_base}, + ) + + ## COMPLETION CALL + response = openai_client.embeddings.create(**data) # type: ignore + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=response, + ) + + return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response, response_type="embedding") # type: ignore + except OpenAIError as e: + exception_mapping_worked = True + raise e + except Exception as e: + if exception_mapping_worked: + raise e + else: + import traceback + raise OpenAIError(status_code=500, message=traceback.format_exc()) + + +class OpenAITextCompletion(BaseLLM): + _client_session: httpx.Client + + def __init__(self) -> None: + super().__init__() + self._client_session = self.create_client_session() + + def validate_environment(self, api_key): + headers = { + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + return headers + + def convert_to_model_response_object(self, response_object: Optional[dict]=None, model_response_object: Optional[ModelResponse]=None): + try: + ## RESPONSE OBJECT + if response_object is None or model_response_object is None: + raise ValueError("Error in response object format") + choice_list=[] + for idx, choice in enumerate(response_object["choices"]): + message = Message(content=choice["text"], role="assistant") + choice = Choices(finish_reason=choice["finish_reason"], index=idx, message=message) + choice_list.append(choice) + model_response_object.choices = choice_list + + if "usage" in response_object: + model_response_object.usage = response_object["usage"] + + if "id" in response_object: + model_response_object.id = response_object["id"] + + if "model" in response_object: + model_response_object.model = response_object["model"] + + model_response_object._hidden_params["original_response"] = response_object # track original response, if users make a litellm.text_completion() request, we can return the original response + return model_response_object + except Exception as e: + raise e + + def completion(self, + model_response: ModelResponse, + api_key: str, + model: str, + messages: list, + print_verbose: Optional[Callable]=None, + api_base: Optional[str]=None, + logging_obj=None, + acompletion: bool = False, + optional_params=None, + litellm_params=None, + logger_fn=None, + headers: Optional[dict]=None): + super().completion() + exception_mapping_worked = False + try: + if headers is None: + headers = self.validate_environment(api_key=api_key) + if model is None or messages is None: + raise OpenAIError(status_code=422, message=f"Missing model or messages") + + api_base = f"{api_base}/completions" + + if len(messages)>0 and "content" in messages[0] and type(messages[0]["content"]) == list: + prompt = messages[0]["content"] + else: + prompt = " ".join([message["content"] for message in messages]) # type: ignore + + data = { + "model": model, + "prompt": prompt, + **optional_params + } + + ## LOGGING + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={"headers": headers, "api_base": api_base, "complete_input_dict": data}, + ) + if acompletion == True: + if optional_params.get("stream", False): + return self.async_streaming(logging_obj=logging_obj, api_base=api_base, data=data, headers=headers, model_response=model_response, model=model) + else: + return self.acompletion(api_base=api_base, data=data, headers=headers, model_response=model_response, prompt=prompt, api_key=api_key, logging_obj=logging_obj, model=model) # type: ignore + elif optional_params.get("stream", False): + return self.streaming(logging_obj=logging_obj, api_base=api_base, data=data, headers=headers, model_response=model_response, model=model) + else: + response = httpx.post( + url=f"{api_base}", + json=data, + headers=headers, + ) + if response.status_code != 200: + raise OpenAIError(status_code=response.status_code, message=response.text) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response, + additional_args={ + "headers": headers, + "api_base": api_base, + }, + ) + + ## RESPONSE OBJECT + return self.convert_to_model_response_object(response_object=response.json(), model_response_object=model_response) + except Exception as e: + raise e + + async def acompletion(self, + logging_obj, + api_base: str, + data: dict, + headers: dict, + model_response: ModelResponse, + prompt: str, + api_key: str, + model: str): + async with httpx.AsyncClient() as client: + response = await client.post(api_base, json=data, headers=headers, timeout=litellm.request_timeout) + response_json = response.json() + if response.status_code != 200: + raise OpenAIError(status_code=response.status_code, message=response.text) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response, + additional_args={ + "headers": headers, + "api_base": api_base, + }, + ) + + ## RESPONSE OBJECT + return self.convert_to_model_response_object(response_object=response_json, model_response_object=model_response) + + def streaming(self, + logging_obj, + api_base: str, + data: dict, + headers: dict, + model_response: ModelResponse, + model: str + ): + with httpx.stream( + url=f"{api_base}", + json=data, + headers=headers, + method="POST", + timeout=litellm.request_timeout + ) as response: + if response.status_code != 200: + raise OpenAIError(status_code=response.status_code, message=response.text) + + streamwrapper = CustomStreamWrapper(completion_stream=response.iter_lines(), model=model, custom_llm_provider="text-completion-openai",logging_obj=logging_obj) + for transformed_chunk in streamwrapper: + yield transformed_chunk + + async def async_streaming(self, + logging_obj, + api_base: str, + data: dict, + headers: dict, + model_response: ModelResponse, + model: str): + client = httpx.AsyncClient() + async with client.stream( + url=f"{api_base}", + json=data, + headers=headers, + method="POST", + timeout=litellm.request_timeout + ) as response: + if response.status_code != 200: + raise OpenAIError(status_code=response.status_code, message=response.text) + + streamwrapper = CustomStreamWrapper(completion_stream=response.aiter_lines(), model=model, custom_llm_provider="text-completion-openai",logging_obj=logging_obj) + async for transformed_chunk in streamwrapper: + yield transformed_chunk \ No newline at end of file diff --git a/litellm/llms/palm.py b/litellm/llms/palm.py new file mode 100644 index 0000000000000000000000000000000000000000..010e6720c4485a1173da666e4e8eda5decba045a --- /dev/null +++ b/litellm/llms/palm.py @@ -0,0 +1,177 @@ +import os, types, traceback, copy +import json +from enum import Enum +import time +from typing import Callable, Optional +from litellm.utils import ModelResponse, get_secret, Choices, Message, Usage +import litellm +import sys, httpx + +class PalmError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="https://developers.generativeai.google/api/python/google/generativeai/chat") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class PalmConfig(): + """ + Reference: https://developers.generativeai.google/api/python/google/generativeai/chat + + The class `PalmConfig` provides configuration for the Palm's API interface. Here are the parameters: + + - `context` (string): Text that should be provided to the model first, to ground the response. This could be a prompt to guide the model's responses. + + - `examples` (list): Examples of what the model should generate. They are treated identically to conversation messages except that they take precedence over the history in messages if the total input size exceeds the model's input_token_limit. + + - `temperature` (float): Controls the randomness of the output. Must be positive. Higher values produce a more random and varied response. A temperature of zero will be deterministic. + + - `candidate_count` (int): Maximum number of generated response messages to return. This value must be between [1, 8], inclusive. Only unique candidates are returned. + + - `top_k` (int): The API uses combined nucleus and top-k sampling. `top_k` sets the maximum number of tokens to sample from on each step. + + - `top_p` (float): The API uses combined nucleus and top-k sampling. `top_p` configures the nucleus sampling. It sets the maximum cumulative probability of tokens to sample from. + + - `max_output_tokens` (int): Sets the maximum number of tokens to be returned in the output + """ + context: Optional[str]=None + examples: Optional[list]=None + temperature: Optional[float]=None + candidate_count: Optional[int]=None + top_k: Optional[int]=None + top_p: Optional[float]=None + max_output_tokens: Optional[int]=None + + def __init__(self, + context: Optional[str]=None, + examples: Optional[list]=None, + temperature: Optional[float]=None, + candidate_count: Optional[int]=None, + top_k: Optional[int]=None, + top_p: Optional[float]=None, + max_output_tokens: Optional[int]=None) -> None: + + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + + + +def completion( + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + api_key, + encoding, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + try: + import google.generativeai as palm + except: + raise Exception("Importing google.generativeai failed, please run 'pip install -q google-generativeai") + palm.configure(api_key=api_key) + + model = model + + ## Load Config + inference_params = copy.deepcopy(optional_params) + inference_params.pop("stream", None) # palm does not support streaming, so we handle this by fake streaming in main.py + config = litellm.PalmConfig.get_config() + for k, v in config.items(): + if k not in inference_params: # completion(top_k=3) > palm_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + + prompt = "" + for message in messages: + if "role" in message: + if message["role"] == "user": + prompt += ( + f"{message['content']}" + ) + else: + prompt += ( + f"{message['content']}" + ) + else: + prompt += f"{message['content']}" + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={"complete_input_dict": {"inference_params": inference_params}}, + ) + ## COMPLETION CALL + try: + response = palm.generate_text(prompt=prompt, **inference_params) + except Exception as e: + raise PalmError( + message=str(e), + status_code=500, + ) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=response, + additional_args={"complete_input_dict": {}}, + ) + print_verbose(f"raw model_response: {response}") + ## RESPONSE OBJECT + completion_response = response + try: + choices_list = [] + for idx, item in enumerate(completion_response.candidates): + if len(item["output"]) > 0: + message_obj = Message(content=item["output"]) + else: + message_obj = Message(content=None) + choice_obj = Choices(index=idx+1, message=message_obj) + choices_list.append(choice_obj) + model_response["choices"] = choices_list + except Exception as e: + traceback.print_exc() + raise PalmError(message=traceback.format_exc(), status_code=response.status_code) + + try: + completion_response = model_response["choices"][0]["message"].get("content") + except: + raise PalmError(status_code=400, message=f"No response received. Original response - {response}") + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = len( + encoding.encode(prompt) + ) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = "palm/" + model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/petals.py b/litellm/llms/petals.py new file mode 100644 index 0000000000000000000000000000000000000000..f9ce3ad0cef301edac66a01344913637f414743a --- /dev/null +++ b/litellm/llms/petals.py @@ -0,0 +1,189 @@ +import os, types +import json +from enum import Enum +import requests +import time +from typing import Callable, Optional +import litellm +from litellm.utils import ModelResponse, Usage +from .prompt_templates.factory import prompt_factory, custom_prompt + +class PetalsError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class PetalsConfig(): + """ + Reference: https://github.com/petals-infra/chat.petals.dev#post-apiv1generate + The `PetalsConfig` class encapsulates the configuration for the Petals API. The properties of this class are described below: + + - `max_length` (integer): This represents the maximum length of the generated text (including the prefix) in tokens. + + - `max_new_tokens` (integer): This represents the maximum number of newly generated tokens (excluding the prefix). + + The generation parameters are compatible with `.generate()` from Hugging Face's Transformers library: + + - `do_sample` (boolean, optional): If set to 0 (default), the API runs greedy generation. If set to 1, the API performs sampling using the parameters below: + + - `temperature` (float, optional): This value sets the temperature for sampling. + + - `top_k` (integer, optional): This value sets the limit for top-k sampling. + + - `top_p` (float, optional): This value sets the limit for top-p (nucleus) sampling. + + - `repetition_penalty` (float, optional): This helps apply the repetition penalty during text generation, as discussed in this paper. + """ + max_length: Optional[int]=None + max_new_tokens: Optional[int]=litellm.max_tokens # petals requires max tokens to be set + do_sample: Optional[bool]=None + temperature: Optional[float]=None + top_k: Optional[int]=None + top_p: Optional[float]=None + repetition_penalty: Optional[float]=None + + def __init__(self, + max_length: Optional[int]=None, + max_new_tokens: Optional[int]=litellm.max_tokens, # petals requires max tokens to be set + do_sample: Optional[bool]=None, + temperature: Optional[float]=None, + top_k: Optional[int]=None, + top_p: Optional[float]=None, + repetition_penalty: Optional[float]=None) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + +def completion( + model: str, + messages: list, + api_base: Optional[str], + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + optional_params=None, + stream=False, + litellm_params=None, + logger_fn=None, +): + ## Load Config + config = litellm.PetalsConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > petals_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + if model in litellm.custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = litellm.custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=messages + ) + else: + prompt = prompt_factory(model=model, messages=messages) + + if api_base: + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={"complete_input_dict": optional_params, "api_base": api_base}, + ) + data = { + "model": model, + "inputs": prompt, + **optional_params + } + + ## COMPLETION CALL + response = requests.post(api_base, data=data) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=response.text, + additional_args={"complete_input_dict": optional_params}, + ) + + ## RESPONSE OBJECT + try: + output_text = response.json()["outputs"] + except Exception as e: + PetalsError(status_code=response.status_code, message=str(e)) + + else: + try: + import torch + from transformers import AutoTokenizer + from petals import AutoDistributedModelForCausalLM # type: ignore + except: + raise Exception( + "Importing torch, transformers, petals failed\nTry pip installing petals \npip install git+https://github.com/bigscience-workshop/petals" + ) + + model = model + + tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False, add_bos_token=False) + model_obj = AutoDistributedModelForCausalLM.from_pretrained(model) + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={"complete_input_dict": optional_params}, + ) + + ## COMPLETION CALL + inputs = tokenizer(prompt, return_tensors="pt")["input_ids"] + + # optional params: max_new_tokens=1,temperature=0.9, top_p=0.6 + outputs = model_obj.generate(inputs, **optional_params) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=outputs, + additional_args={"complete_input_dict": optional_params}, + ) + ## RESPONSE OBJECT + output_text = tokenizer.decode(outputs[0]) + + if len(output_text) > 0: + model_response["choices"][0]["message"]["content"] = output_text + + prompt_tokens = len( + encoding.encode(prompt) + ) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py new file mode 100644 index 0000000000000000000000000000000000000000..2aac70d6e35d6383f3c36587cd23544236f9d1f7 --- /dev/null +++ b/litellm/llms/prompt_templates/factory.py @@ -0,0 +1,360 @@ +from enum import Enum +import requests, traceback +import json +from jinja2 import Template, exceptions, Environment, meta +from typing import Optional + +def default_pt(messages): + return " ".join(message["content"] for message in messages) + +# alpaca prompt template - for models like mythomax, etc. +def alpaca_pt(messages): + prompt = custom_prompt( + role_dict={ + "system": { + "pre_message": "### Instruction:\n", + "post_message": "\n\n", + }, + "user": { + "pre_message": "### Instruction:\n", + "post_message": "\n\n", + }, + "assistant": { + "pre_message": "### Response:\n", + "post_message": "\n\n" + } + }, + bos_token="", + eos_token="", + messages=messages + ) + return prompt + +# Llama2 prompt template +def llama_2_chat_pt(messages): + prompt = custom_prompt( + role_dict={ + "system": { + "pre_message": "[INST] <>\n", + "post_message": "\n<>\n [/INST]\n" + }, + "user": { # follow this format https://github.com/facebookresearch/llama/blob/77062717054710e352a99add63d160274ce670c6/llama/generation.py#L348 + "pre_message": "[INST] ", + "post_message": " [/INST]\n" + }, + "assistant": { + "post_message": "\n" # follows this - https://replicate.com/blog/how-to-prompt-llama + } + }, + messages=messages, + bos_token="", + eos_token="" + ) + return prompt + +def ollama_pt(model, messages): # https://github.com/jmorganca/ollama/blob/af4cf55884ac54b9e637cd71dadfe9b7a5685877/docs/modelfile.md#template + + if "instruct" in model: + prompt = custom_prompt( + role_dict={ + "system": { + "pre_message": "### System:\n", + "post_message": "\n" + }, + "user": { + "pre_message": "### User:\n", + "post_message": "\n", + }, + "assistant": { + "pre_message": "### Response:\n", + "post_message": "\n", + } + }, + final_prompt_value="### Response:", + messages=messages + ) + else: + prompt = "".join(m["content"] for m in messages) + return prompt + +def mistral_instruct_pt(messages): + prompt = custom_prompt( + initial_prompt_value="", + role_dict={ + "system": { + "pre_message": "[INST]", + "post_message": "[/INST]" + }, + "user": { + "pre_message": "[INST]", + "post_message": "[/INST]" + }, + "assistant": { + "pre_message": "[INST]", + "post_message": "[/INST]" + } + }, + final_prompt_value="", + messages=messages + ) + return prompt + +# Falcon prompt template - from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py#L110 +def falcon_instruct_pt(messages): + prompt = "" + for message in messages: + if message["role"] == "system": + prompt += message["content"] + else: + prompt += message['role']+":"+ message["content"].replace("\r\n", "\n").replace("\n\n", "\n") + prompt += "\n\n" + + return prompt + +def falcon_chat_pt(messages): + prompt = "" + for message in messages: + if message["role"] == "system": + prompt += "System: " + message["content"] + elif message["role"] == "assistant": + prompt += "Falcon: " + message["content"] + elif message["role"] == "user": + prompt += "User: " + message["content"] + + return prompt + +# MPT prompt template - from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py#L110 +def mpt_chat_pt(messages): + prompt = "" + for message in messages: + if message["role"] == "system": + prompt += "<|im_start|>system" + message["content"] + "<|im_end|>" + "\n" + elif message["role"] == "assistant": + prompt += "<|im_start|>assistant" + message["content"] + "<|im_end|>" + "\n" + elif message["role"] == "user": + prompt += "<|im_start|>user" + message["content"] + "<|im_end|>" + "\n" + return prompt + +# WizardCoder prompt template - https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0#prompt-format +def wizardcoder_pt(messages): + prompt = "" + for message in messages: + if message["role"] == "system": + prompt += message["content"] + "\n\n" + elif message["role"] == "user": # map to 'Instruction' + prompt += "### Instruction:\n" + message["content"] + "\n\n" + elif message["role"] == "assistant": # map to 'Response' + prompt += "### Response:\n" + message["content"] + "\n\n" + return prompt + +# Phind-CodeLlama prompt template - https://huggingface.co/Phind/Phind-CodeLlama-34B-v2#how-to-prompt-the-model +def phind_codellama_pt(messages): + prompt = "" + for message in messages: + if message["role"] == "system": + prompt += "### System Prompt\n" + message["content"] + "\n\n" + elif message["role"] == "user": + prompt += "### User Message\n" + message["content"] + "\n\n" + elif message["role"] == "assistant": + prompt += "### Assistant\n" + message["content"] + "\n\n" + return prompt + +def hf_chat_template(model: str, messages: list): + ## get the tokenizer config from huggingface + def _get_tokenizer_config(hf_model_name): + url = f"https://huggingface.co/{hf_model_name}/raw/main/tokenizer_config.json" + # Make a GET request to fetch the JSON data + response = requests.get(url) + if response.status_code == 200: + # Parse the JSON data + tokenizer_config = json.loads(response.content) + return {"status": "success", "tokenizer": tokenizer_config} + else: + return {"status": "failure"} + tokenizer_config = _get_tokenizer_config(model) + if tokenizer_config["status"] == "failure" or "chat_template" not in tokenizer_config["tokenizer"]: + raise Exception("No chat template found") + ## read the bos token, eos token and chat template from the json + tokenizer_config = tokenizer_config["tokenizer"] + bos_token = tokenizer_config["bos_token"] + eos_token = tokenizer_config["eos_token"] + chat_template = tokenizer_config["chat_template"] + + def raise_exception(message): + raise Exception(f"Error message - {message}") + + # Create a template object from the template text + env = Environment() + env.globals['raise_exception'] = raise_exception + template = env.from_string(chat_template) + + def _is_system_in_template(): + try: + # Try rendering the template with a system message + response = template.render(messages=[{"role": "system", "content": "test"}], eos_token= "", bos_token= "") + return True + + # This will be raised if Jinja attempts to render the system message and it can't + except: + return False + + try: + # Render the template with the provided values + if _is_system_in_template(): + rendered_text = template.render(bos_token=bos_token, eos_token=eos_token, messages=messages) + else: + # treat a system message as a user message, if system not in template + try: + reformatted_messages = [] + for message in messages: + if message["role"] == "system": + reformatted_messages.append({"role": "user", "content": message["content"]}) + else: + reformatted_messages.append(message) + rendered_text = template.render(bos_token=bos_token, eos_token=eos_token, messages=reformatted_messages) + except Exception as e: + if "Conversation roles must alternate user/assistant" in str(e): + # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, add a blank 'user' or 'assistant' message to ensure compatibility + new_messages = [] + for i in range(len(reformatted_messages)-1): + new_messages.append(reformatted_messages[i]) + if reformatted_messages[i]["role"] == reformatted_messages[i+1]["role"]: + if reformatted_messages[i]["role"] == "user": + new_messages.append({"role": "assistant", "content": ""}) + else: + new_messages.append({"role": "user", "content": ""}) + new_messages.append(reformatted_messages[-1]) + rendered_text = template.render(bos_token=bos_token, eos_token=eos_token, messages=new_messages) + return rendered_text + except: + raise Exception("Error rendering template") + +# Anthropic template +def claude_2_1_pt(messages: list): # format - https://docs.anthropic.com/claude/docs/how-to-use-system-prompts + class AnthropicConstants(Enum): + HUMAN_PROMPT = "\n\nHuman: " + AI_PROMPT = "\n\nAssistant: " + + prompt = "" + for idx, message in enumerate(messages): # needs to start with `\n\nHuman: ` and end with `\n\nAssistant: ` + if message["role"] == "user": + prompt += ( + f"{AnthropicConstants.HUMAN_PROMPT.value}{message['content']}" + ) + elif message["role"] == "system": + prompt += ( + f"{message['content']}" + ) + else: + prompt += ( + f"{AnthropicConstants.AI_PROMPT.value}{message['content']}" + ) + if idx == 0 and message["role"] == "assistant": # ensure the prompt always starts with `\n\nHuman: ` + prompt = f"{AnthropicConstants.HUMAN_PROMPT.value}" + prompt + prompt += f"{AnthropicConstants.AI_PROMPT.value}" + return prompt + +def anthropic_pt(messages: list): # format - https://docs.anthropic.com/claude/reference/complete_post + class AnthropicConstants(Enum): + HUMAN_PROMPT = "\n\nHuman: " + AI_PROMPT = "\n\nAssistant: " + + prompt = "" + for idx, message in enumerate(messages): # needs to start with `\n\nHuman: ` and end with `\n\nAssistant: ` + if message["role"] == "user": + prompt += ( + f"{AnthropicConstants.HUMAN_PROMPT.value}{message['content']}" + ) + elif message["role"] == "system": + prompt += ( + f"{AnthropicConstants.HUMAN_PROMPT.value}{message['content']}" + ) + else: + prompt += ( + f"{AnthropicConstants.AI_PROMPT.value}{message['content']}" + ) + if idx == 0 and message["role"] == "assistant": # ensure the prompt always starts with `\n\nHuman: ` + prompt = f"{AnthropicConstants.HUMAN_PROMPT.value}" + prompt + prompt += f"{AnthropicConstants.AI_PROMPT.value}" + return prompt + +# Function call template +def function_call_prompt(messages: list, functions: list): + function_prompt = "The following functions are available to you:" + for function in functions: + function_prompt += f"""\n{function}\n""" + + function_added_to_prompt = False + for message in messages: + if "system" in message["role"]: + message['content'] += f"""{function_prompt}""" + function_added_to_prompt = True + + if function_added_to_prompt == False: + messages.append({'role': 'system', 'content': f"""{function_prompt}"""}) + + return messages + + +# Custom prompt template +def custom_prompt(role_dict: dict, messages: list, initial_prompt_value: str="", final_prompt_value: str="", bos_token: str="", eos_token: str=""): + prompt = bos_token + initial_prompt_value + bos_open = True + ## a bos token is at the start of a system / human message + ## an eos token is at the end of the assistant response to the message + for message in messages: + role = message["role"] + + if role in ["system", "human"] and not bos_open: + prompt += bos_token + bos_open = True + + pre_message_str = role_dict[role]["pre_message"] if role in role_dict and "pre_message" in role_dict[role] else "" + post_message_str = role_dict[role]["post_message"] if role in role_dict and "post_message" in role_dict[role] else "" + prompt += pre_message_str + message["content"] + post_message_str + + if role == "assistant": + prompt += eos_token + bos_open = False + + prompt += final_prompt_value + return prompt + +def prompt_factory(model: str, messages: list, custom_llm_provider: Optional[str]=None): + original_model_name = model + model = model.lower() + if custom_llm_provider == "ollama": + return ollama_pt(model=model, messages=messages) + elif custom_llm_provider == "anthropic": + if "claude-2.1" in model: + return claude_2_1_pt(messages=messages) + else: + return anthropic_pt(messages=messages) + + try: + if "meta-llama/llama-2" in model and "chat" in model: + return llama_2_chat_pt(messages=messages) + elif "tiiuae/falcon" in model: # Note: for the instruct models, it's best to use a User: .., Assistant:.. approach in your prompt template. + if model == "tiiuae/falcon-180B-chat": + return falcon_chat_pt(messages=messages) + elif "instruct" in model: + return falcon_instruct_pt(messages=messages) + elif "mosaicml/mpt" in model: + if "chat" in model: + return mpt_chat_pt(messages=messages) + elif "codellama/codellama" in model: + if "instruct" in model: + return llama_2_chat_pt(messages=messages) # https://huggingface.co/blog/codellama#conversational-instructions + elif "wizardlm/wizardcoder" in model: + return wizardcoder_pt(messages=messages) + elif "phind/phind-codellama" in model: + return phind_codellama_pt(messages=messages) + elif "togethercomputer/llama-2" in model and ("instruct" in model or "chat" in model): + return llama_2_chat_pt(messages=messages) + elif model in ["gryphe/mythomax-l2-13b", "gryphe/mythomix-l2-13b", "gryphe/mythologic-l2-13b"]: + return alpaca_pt(messages=messages) + else: + return hf_chat_template(original_model_name, messages) + except: + return default_pt(messages=messages) # default that covers Bloom, T-5, any non-chat tuned model (e.g. base Llama2) + \ No newline at end of file diff --git a/litellm/llms/replicate.py b/litellm/llms/replicate.py new file mode 100644 index 0000000000000000000000000000000000000000..a4ecbdc1c0bfb722e42a2a190236820a2b3e38c8 --- /dev/null +++ b/litellm/llms/replicate.py @@ -0,0 +1,302 @@ +import os, types +import json +import requests +import time +from typing import Callable, Optional +from litellm.utils import ModelResponse, Usage +import litellm +import httpx +from .prompt_templates.factory import prompt_factory, custom_prompt + +class ReplicateError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="https://api.replicate.com/v1/deployments") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class ReplicateConfig(): + """ + Reference: https://replicate.com/meta/llama-2-70b-chat/api + - `prompt` (string): The prompt to send to the model. + + - `system_prompt` (string): The system prompt to send to the model. This is prepended to the prompt and helps guide system behavior. Default value: `You are a helpful assistant`. + + - `max_new_tokens` (integer): Maximum number of tokens to generate. Typically, a word is made up of 2-3 tokens. Default value: `128`. + + - `min_new_tokens` (integer): Minimum number of tokens to generate. To disable, set to `-1`. A word is usually 2-3 tokens. Default value: `-1`. + + - `temperature` (number): Adjusts the randomness of outputs. Values greater than 1 increase randomness, 0 is deterministic, and 0.75 is a reasonable starting value. Default value: `0.75`. + + - `top_p` (number): During text decoding, it samples from the top `p` percentage of most likely tokens. Reduce this to ignore less probable tokens. Default value: `0.9`. + + - `top_k` (integer): During text decoding, samples from the top `k` most likely tokens. Reduce this to ignore less probable tokens. Default value: `50`. + + - `stop_sequences` (string): A comma-separated list of sequences to stop generation at. For example, inputting ',' will cease generation at the first occurrence of either 'end' or ''. + + - `seed` (integer): This is the seed for the random generator. Leave it blank to randomize the seed. + + - `debug` (boolean): If set to `True`, it provides debugging output in logs. + + Please note that Replicate's mapping of these parameters can be inconsistent across different models, indicating that not all of these parameters may be available for use with all models. + """ + system_prompt: Optional[str]=None + max_new_tokens: Optional[int]=None + min_new_tokens: Optional[int]=None + temperature: Optional[int]=None + top_p: Optional[int]=None + top_k: Optional[int]=None + stop_sequences: Optional[str]=None + seed: Optional[int]=None + debug: Optional[bool]=None + + def __init__(self, + system_prompt: Optional[str]=None, + max_new_tokens: Optional[int]=None, + min_new_tokens: Optional[int]=None, + temperature: Optional[int]=None, + top_p: Optional[int]=None, + top_k: Optional[int]=None, + stop_sequences: Optional[str]=None, + seed: Optional[int]=None, + debug: Optional[bool]=None) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + + + +# Function to start a prediction and get the prediction URL +def start_prediction(version_id, input_data, api_token, api_base, logging_obj, print_verbose): + base_url = api_base + if "deployments" in version_id: + print_verbose("\nLiteLLM: Request to custom replicate deployment") + version_id = version_id.replace("deployments/", "") + base_url = f"https://api.replicate.com/v1/deployments/{version_id}" + print_verbose(f"Deployment base URL: {base_url}\n") + + headers = { + "Authorization": f"Token {api_token}", + "Content-Type": "application/json" + } + + initial_prediction_data = { + "version": version_id, + "input": input_data, + } + + ## LOGGING + logging_obj.pre_call( + input=input_data["prompt"], + api_key="", + additional_args={"complete_input_dict": initial_prediction_data, "headers": headers, "api_base": base_url}, + ) + + response = requests.post(f"{base_url}/predictions", json=initial_prediction_data, headers=headers) + if response.status_code == 201: + response_data = response.json() + return response_data.get("urls", {}).get("get") + else: + raise ReplicateError(response.status_code, f"Failed to start prediction {response.text}") + +# Function to handle prediction response (non-streaming) +def handle_prediction_response(prediction_url, api_token, print_verbose): + output_string = "" + headers = { + "Authorization": f"Token {api_token}", + "Content-Type": "application/json" + } + + status = "" + logs = "" + while True and (status not in ["succeeded", "failed", "canceled"]): + print_verbose(f"replicate: polling endpoint: {prediction_url}") + time.sleep(0.5) + response = requests.get(prediction_url, headers=headers) + if response.status_code == 200: + response_data = response.json() + if "output" in response_data: + output_string = "".join(response_data['output']) + print_verbose(f"Non-streamed output:{output_string}") + status = response_data.get('status', None) + logs = response_data.get("logs", "") + if status == "failed": + replicate_error = response_data.get("error", "") + raise ReplicateError(status_code=400, message=f"Error: {replicate_error}, \nReplicate logs:{logs}") + else: + # this can fail temporarily but it does not mean the replicate request failed, replicate request fails when status=="failed" + print_verbose("Replicate: Failed to fetch prediction status and output.") + return output_string, logs + +# Function to handle prediction response (streaming) +def handle_prediction_response_streaming(prediction_url, api_token, print_verbose): + previous_output = "" + output_string = "" + + headers = { + "Authorization": f"Token {api_token}", + "Content-Type": "application/json" + } + status = "" + while True and (status not in ["succeeded", "failed", "canceled"]): + time.sleep(0.5) # prevent being rate limited by replicate + print_verbose(f"replicate: polling endpoint: {prediction_url}") + response = requests.get(prediction_url, headers=headers) + if response.status_code == 200: + response_data = response.json() + status = response_data['status'] + if "output" in response_data: + output_string = "".join(response_data['output']) + new_output = output_string[len(previous_output):] + print_verbose(f"New chunk: {new_output}") + yield {"output": new_output, "status": status} + previous_output = output_string + status = response_data['status'] + if status == "failed": + replicate_error = response_data.get("error", "") + raise ReplicateError(status_code=400, message=f"Error: {replicate_error}") + else: + # this can fail temporarily but it does not mean the replicate request failed, replicate request fails when status=="failed" + print_verbose(f"Replicate: Failed to fetch prediction status and output.{response.status_code}{response.text}") + + +# Function to extract version ID from model string +def model_to_version_id(model): + if ":" in model: + split_model = model.split(":") + return split_model[1] + return model + +# Main function for prediction completion +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + logging_obj, + api_key, + encoding, + custom_prompt_dict={}, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + # Start a prediction and get the prediction URL + version_id = model_to_version_id(model) + ## Load Config + config = litellm.ReplicateConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > replicate_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + system_prompt = None + if optional_params is not None and "supports_system_prompt" in optional_params: + supports_sys_prompt = optional_params.pop("supports_system_prompt") + else: + supports_sys_prompt = False + + if supports_sys_prompt: + for i in range(len(messages)): + if messages[i]["role"] == "system": + first_sys_message = messages.pop(i) + system_prompt = first_sys_message["content"] + break + + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details.get("roles", {}), + initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""), + final_prompt_value=model_prompt_details.get("final_prompt_value", ""), + bos_token=model_prompt_details.get("bos_token", ""), + eos_token=model_prompt_details.get("eos_token", ""), + messages=messages, + ) + else: + prompt = prompt_factory(model=model, messages=messages) + + # If system prompt is supported, and a system prompt is provided, use it + if system_prompt is not None: + input_data = { + "prompt": prompt, + "system_prompt": system_prompt + } + # Otherwise, use the prompt as is + else: + input_data = { + "prompt": prompt, + **optional_params + } + + + ## COMPLETION CALL + ## Replicate Compeltion calls have 2 steps + ## Step1: Start Prediction: gets a prediction url + ## Step2: Poll prediction url for response + ## Step2: is handled with and without streaming + model_response["created"] = int(time.time()) # for pricing this must remain right before calling api + prediction_url = start_prediction(version_id, input_data, api_key, api_base, logging_obj=logging_obj, print_verbose=print_verbose) + print_verbose(prediction_url) + + # Handle the prediction response (streaming or non-streaming) + if "stream" in optional_params and optional_params["stream"] == True: + print_verbose("streaming request") + return handle_prediction_response_streaming(prediction_url, api_key, print_verbose) + else: + result, logs = handle_prediction_response(prediction_url, api_key, print_verbose) + model_response["ended"] = time.time() # for pricing this must remain right after calling api + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=result, + additional_args={"complete_input_dict": input_data,"logs": logs, "api_base": prediction_url, }, + ) + + print_verbose(f"raw model_response: {result}") + + if len(result) == 0: # edge case, where result from replicate is empty + result = " " + + ## Building RESPONSE OBJECT + if len(result) > 1: + model_response["choices"][0]["message"]["content"] = result + + # Calculate usage + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len(encoding.encode(model_response["choices"][0]["message"].get("content", ""))) + model_response["model"] = "replicate/" + model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + + +# # Example usage: +# response = completion( +# api_key="", +# messages=[{"content": "good morning"}], +# model="replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", +# model_response=ModelResponse(), +# print_verbose=print, +# logging_obj=print, # stub logging_obj +# optional_params={"stream": False} +# ) + +# print(response) diff --git a/litellm/llms/sagemaker.py b/litellm/llms/sagemaker.py new file mode 100644 index 0000000000000000000000000000000000000000..51be1f53d372f0be938ca4c1d30bf138d7ec7ea6 --- /dev/null +++ b/litellm/llms/sagemaker.py @@ -0,0 +1,190 @@ +import os, types +from enum import Enum +import json +import requests +import time +from typing import Callable, Optional +import litellm +from litellm.utils import ModelResponse, get_secret, Usage +import sys +from copy import deepcopy +import httpx + +class SagemakerError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="https://us-west-2.console.aws.amazon.com/sagemaker") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class SagemakerConfig(): + """ + Reference: https://d-uuwbxj1u4cnu.studio.us-west-2.sagemaker.aws/jupyter/default/lab/workspaces/auto-q/tree/DemoNotebooks/meta-textgeneration-llama-2-7b-SDK_1.ipynb + """ + max_new_tokens: Optional[int]=None + top_p: Optional[float]=None + temperature: Optional[float]=None + return_full_text: Optional[bool]=None + + def __init__(self, + max_new_tokens: Optional[int]=None, + top_p: Optional[float]=None, + temperature: Optional[float]=None, + return_full_text: Optional[bool]=None) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + +""" +SAGEMAKER AUTH Keys/Vars +os.environ['AWS_ACCESS_KEY_ID'] = "" +os.environ['AWS_SECRET_ACCESS_KEY'] = "" +""" + +# set os.environ['AWS_REGION_NAME'] = + +def completion( + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + import boto3 + + # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them + aws_secret_access_key = optional_params.pop("aws_secret_access_key", None) + aws_access_key_id = optional_params.pop("aws_access_key_id", None) + aws_region_name = optional_params.pop("aws_region_name", None) + + if aws_access_key_id != None: + # uses auth params passed to completion + # aws_access_key_id is not None, assume user is trying to auth using litellm.completion + client = boto3.client( + service_name="sagemaker-runtime", + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + region_name=aws_region_name, + ) + else: + # aws_access_key_id is None, assume user is trying to auth using env variables + # boto3 automaticaly reads env variables + + # we need to read region name from env + # I assume majority of users use .env for auth + region_name = ( + get_secret("AWS_REGION_NAME") or + "us-west-2" # default to us-west-2 if user not specified + ) + client = boto3.client( + service_name="sagemaker-runtime", + region_name=region_name, + ) + + # pop streaming if it's in the optional params as 'stream' raises an error with sagemaker + inference_params = deepcopy(optional_params) + inference_params.pop("stream", None) + + ## Load Config + config = litellm.SagemakerConfig.get_config() + for k, v in config.items(): + if k not in inference_params: # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + + model = model + prompt = "" + for message in messages: + if "role" in message: + if message["role"] == "user": + prompt += ( + f"{message['content']}" + ) + else: + prompt += ( + f"{message['content']}" + ) + else: + prompt += f"{message['content']}" + + data = json.dumps({ + "inputs": prompt, + "parameters": inference_params + }).encode('utf-8') + + ## LOGGING + request_str = f""" + response = client.invoke_endpoint( + EndpointName={model}, + ContentType="application/json", + Body={data}, + CustomAttributes="accept_eula=true", + ) + """ # type: ignore + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={"complete_input_dict": data, "request_str": request_str}, + ) + ## COMPLETION CALL + response = client.invoke_endpoint( + EndpointName=model, + ContentType="application/json", + Body=data, + CustomAttributes="accept_eula=true", + ) + response = response["Body"].read().decode("utf8") + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=response, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response}") + ## RESPONSE OBJECT + completion_response = json.loads(response) + try: + completion_response_choices = completion_response[0] + if "generation" in completion_response_choices: + model_response["choices"][0]["message"]["content"] = completion_response_choices["generation"] + elif "generated_text" in completion_response_choices: + model_response["choices"][0]["message"]["content"] = completion_response_choices["generated_text"] + except: + raise SagemakerError(message=f"LiteLLM Error: Unable to parse sagemaker RAW RESPONSE {json.dumps(completion_response)}", status_code=500) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = len( + encoding.encode(prompt) + ) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/together_ai.py b/litellm/llms/together_ai.py new file mode 100644 index 0000000000000000000000000000000000000000..8e4970a7b6d48ec0f9465aaa61c6f5918b73632e --- /dev/null +++ b/litellm/llms/together_ai.py @@ -0,0 +1,198 @@ +import os, types +import json +from enum import Enum +import requests +import time +from typing import Callable, Optional +import litellm +import httpx +from litellm.utils import ModelResponse, Usage +from .prompt_templates.factory import prompt_factory, custom_prompt + +class TogetherAIError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="https://api.together.xyz/inference") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class TogetherAIConfig(): + """ + Reference: https://docs.together.ai/reference/inference + + The class `TogetherAIConfig` provides configuration for the TogetherAI's API interface. Here are the parameters: + + - `max_tokens` (int32, required): The maximum number of tokens to generate. + + - `stop` (string, optional): A string sequence that will truncate (stop) the inference text output. For example, "\n\n" will stop generation as soon as the model generates two newlines. + + - `temperature` (float, optional): A decimal number that determines the degree of randomness in the response. A value of 1 will always yield the same output. A temperature less than 1 favors more correctness and is appropriate for question answering or summarization. A value greater than 1 introduces more randomness in the output. + + - `top_p` (float, optional): The `top_p` (nucleus) parameter is used to dynamically adjust the number of choices for each predicted token based on the cumulative probabilities. It specifies a probability threshold, below which all less likely tokens are filtered out. This technique helps to maintain diversity and generate more fluent and natural-sounding text. + + - `top_k` (int32, optional): The `top_k` parameter is used to limit the number of choices for the next predicted word or token. It specifies the maximum number of tokens to consider at each step, based on their probability of occurrence. This technique helps to speed up the generation process and can improve the quality of the generated text by focusing on the most likely options. + + - `repetition_penalty` (float, optional): A number that controls the diversity of generated text by reducing the likelihood of repeated sequences. Higher values decrease repetition. + + - `logprobs` (int32, optional): This parameter is not described in the prompt. + """ + max_tokens: Optional[int]=None + stop: Optional[str]=None + temperature:Optional[int]=None + top_p: Optional[float]=None + top_k: Optional[int]=None + repetition_penalty: Optional[float]=None + logprobs: Optional[int]=None + + def __init__(self, + max_tokens: Optional[int]=None, + stop: Optional[str]=None, + temperature:Optional[int]=None, + top_p: Optional[float]=None, + top_k: Optional[int]=None, + repetition_penalty: Optional[float]=None, + logprobs: Optional[int]=None) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + + +def validate_environment(api_key): + if api_key is None: + raise ValueError( + "Missing TogetherAI API Key - A call is being made to together_ai but no key is set either in the environment variables or via params" + ) + headers = { + "accept": "application/json", + "content-type": "application/json", + "Authorization": "Bearer " + api_key, + } + return headers + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + custom_prompt_dict={}, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + + ## Load Config + config = litellm.TogetherAIConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + print_verbose(f"CUSTOM PROMPT DICT: {custom_prompt_dict}; model: {model}") + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details.get("roles", {}), + initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""), + final_prompt_value=model_prompt_details.get("final_prompt_value", ""), + bos_token=model_prompt_details.get("bos_token", ""), + eos_token=model_prompt_details.get("eos_token", ""), + messages=messages, + ) + else: + prompt = prompt_factory(model=model, messages=messages) + + data = { + "model": model, + "prompt": prompt, + "request_type": "language-model-inference", + **optional_params, + } + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={"complete_input_dict": data, "headers": headers, "api_base": api_base}, + ) + ## COMPLETION CALL + if ( + "stream_tokens" in optional_params + and optional_params["stream_tokens"] == True + ): + response = requests.post( + api_base, + headers=headers, + data=json.dumps(data), + stream=optional_params["stream_tokens"], + ) + return response.iter_lines() + else: + response = requests.post( + api_base, + headers=headers, + data=json.dumps(data) + ) + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + if response.status_code != 200: + raise TogetherAIError( + status_code=response.status_code, message=response.text + ) + completion_response = response.json() + + if "error" in completion_response: + raise TogetherAIError( + message=json.dumps(completion_response), + status_code=response.status_code, + ) + elif "error" in completion_response["output"]: + raise TogetherAIError( + message=json.dumps(completion_response["output"]), status_code=response.status_code + ) + + if len(completion_response["output"]["choices"][0]["text"]) > 0: + model_response["choices"][0]["message"]["content"] = completion_response["output"]["choices"][0]["text"] + + ## CALCULATING USAGE + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + if "finish_reason" in completion_response["output"]["choices"][0]: + model_response.choices[0].finish_reason = completion_response["output"]["choices"][0]["finish_reason"] + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/tokenizers/anthropic_tokenizer.json b/litellm/llms/tokenizers/anthropic_tokenizer.json new file mode 100644 index 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plot","Ġpe u","Ġback s","Ġreg exp","Test All","Ġcontin uity","temp s","sl a","US A","land marks","number ed","Ġunt er","Ġgr up","88 9","ĠPar a","Ġ17 2","ograph er","Ġcr ashed","Ġface book","DR AM","Ġhalf way","Ġsepar ating","Rep ublic","ĠKey word",")+ '.","ĠAcc uracy","embed ded","RA IN","Ġcit ing","Ġjournal ism","Inst ances","ĠCommission er","WE VER","Pop ulation","?? ??","Ñħ од","Ġtruck s","Ġconsum ing","Ġ#@ -","Small IntegerField","ани е","Ġbreat he","bott leneck","4 16","I ts","S ending","h q","l ia","o zy","Ġs queeze","Ġm asters","Ġd ug","Ġh acer","Ġ\" ''","ri ad","op acity","Ġ2 80","ĠB in","ĠH ob","out side","ĠE S","Ġres istant","ĠO NE","]) **","ĠJ a","pre amble","ĠV or","ann ers","Ġcomm its","mb l","enc il","ier a","ĠRe gression","ĠUn comment","ĠEx ternal","Ġdat um","Ġchar ity","TT TT","Def ines","ĠMan uel","ĠCor inth","Ġfore head","Ġcard io","Cre ator","Ġir regular","=', ')","PAR AMETER","ĠBack ground","ðŁĶ µ","Depend ency","0025 905","iox ide","Ġdiscre p","ðŁĮ ķ","# 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'","ait i","To Use","')) ))","CO ST","ui sed","е Ñĩ","Time shift","Ġest ud","Char set","ĠDe vi","call iope","Ġax arr","))) /","Ġgame Display","ĠSh o","Ġpat ented","ĠSe al","del s","emp ted","Ġ16 777215","Ġincre ments","Ġbr as","IM ES","pen et","ÑĢ ани","åı ¤","ped ro","ze j","dev ic","Ġlaw ful","Ġdate fmt","Ġsw irling","gy m","cer ning",".... .....","ĠComm iss","Ġenc uent","cell ent","Ġdest in","ĠRes ize","Ġ13 95","Ad ic","Ġhard y","Ġhard core","ĠNot ably","Ġgovern ors","Comp ressed","Ġdesign ate","den ied","':' ',","Ġlayer ed","Ġda jax","uk es","87 22","Ġnormal izer","equal ities","Reg gie","Att acks","comple ter","LI BS","Ġign ition","Sc opes","NO OP","Ġsil houette","ida api","ĠDE FIN","cert ification","Ġfac ade","ouch ers","clean MergeVectors","Ġterm os","Ġfunc name","Ġsecret aries","vey ard","åĩ ı","Default Value","Default Deleter","SET S","produ kt","pdf s","filters flipped","MT cut","CP T","ĠModel Checkpoint","ĠSE Q","Rel ations","ĠMax Pool","ĠPal m","Ġple asures","Sim Hits","Ġut an","PF HT","Ġheavy weight","Ġcos a","PAR SE","Ġlif ts","het amine","bel ieve","ãĤĴ åıĸå¾Ĺ","EA ST","hu ang","ĠBig Query","Seq No","Func iones","Directory Item","Parse Mode","Mar ie","Ġliqu ids","Ġinstrument ation","ĠAre as","virtual ization","uten berg","ĠLand ing","Ġbrand ing","Ġreprodu cible","ĠIll umina","scroll command","Ġ-------------------------------- --------------","004 33","ĠCamb odia","Ro asted","ĠCast illo","LINK FLAGS","Ġinvent ions","ĠRom illy","âĻ ª","Ġstroke Width","Ans w","Install ation","Ġhonor able","Period s","Ġmx net","ĠDummy Request","ighth aven","Ġ}} None: + + locals_ = locals() + for key, value in locals_.items(): + if key != 'self' and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return {k: v for k, v in cls.__dict__.items() + if not k.startswith('__') + and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) + and v is not None} + +def completion( + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + vertex_project=None, + vertex_location=None, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + try: + import vertexai + except: + raise VertexAIError(status_code=400,message="vertexai import failed please run `pip install google-cloud-aiplatform`") + try: + from vertexai.preview.language_models import ChatModel, CodeChatModel, InputOutputTextPair + from vertexai.language_models import TextGenerationModel, CodeGenerationModel + + vertexai.init( + project=vertex_project, location=vertex_location + ) + + ## Load Config + config = litellm.VertexAIConfig.get_config() + for k, v in config.items(): + if k not in optional_params: + optional_params[k] = v + + # vertexai does not use an API key, it looks for credentials.json in the environment + + prompt = " ".join([message["content"] for message in messages]) + + mode = "" + if model in litellm.vertex_chat_models: + chat_model = ChatModel.from_pretrained(model) + mode = "chat" + elif model in litellm.vertex_text_models: + text_model = TextGenerationModel.from_pretrained(model) + mode = "text" + elif model in litellm.vertex_code_text_models: + text_model = CodeGenerationModel.from_pretrained(model) + mode = "text" + else: # vertex_code_chat_models + chat_model = CodeChatModel.from_pretrained(model) + mode = "chat" + + if mode == "chat": + chat = chat_model.start_chat() + + ## LOGGING + logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params}) + + if "stream" in optional_params and optional_params["stream"] == True: + # NOTE: VertexAI does not accept stream=True as a param and raises an error, + # we handle this by removing 'stream' from optional params and sending the request + # after we get the response we add optional_params["stream"] = True, since main.py needs to know it's a streaming response to then transform it for the OpenAI format + optional_params.pop("stream", None) # vertex ai raises an error when passing stream in optional params + model_response = chat.send_message_streaming(prompt, **optional_params) + optional_params["stream"] = True + return model_response + + completion_response = chat.send_message(prompt, **optional_params).text + elif mode == "text": + ## LOGGING + logging_obj.pre_call(input=prompt, api_key=None) + + if "stream" in optional_params and optional_params["stream"] == True: + optional_params.pop("stream", None) # See note above on handling streaming for vertex ai + model_response = text_model.predict_streaming(prompt, **optional_params) + optional_params["stream"] = True + return model_response + + completion_response = text_model.predict(prompt, **optional_params).text + + ## LOGGING + logging_obj.post_call( + input=prompt, api_key=None, original_response=completion_response + ) + + ## RESPONSE OBJECT + if len(str(completion_response)) > 0: + model_response["choices"][0]["message"][ + "content" + ] = str(completion_response) + model_response["choices"][0]["message"]["content"] = str(completion_response) + model_response["created"] = int(time.time()) + model_response["model"] = model + ## CALCULATING USAGE + prompt_tokens = len( + encoding.encode(prompt) + ) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + except Exception as e: + raise VertexAIError(status_code=500, message=str(e)) + + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/vllm.py b/litellm/llms/vllm.py new file mode 100644 index 0000000000000000000000000000000000000000..428dc959dd0a11476a845e50d6f2c7b2e6187209 --- /dev/null +++ b/litellm/llms/vllm.py @@ -0,0 +1,189 @@ +import os +import json +from enum import Enum +import requests +import time, httpx +from typing import Callable, Any +from litellm.utils import ModelResponse, Usage +from .prompt_templates.factory import prompt_factory, custom_prompt +llm = None +class VLLMError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="http://0.0.0.0:8000") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +# check if vllm is installed +def validate_environment(model: str): + global llm + try: + from vllm import LLM, SamplingParams # type: ignore + if llm is None: + llm = LLM(model=model) + return llm, SamplingParams + except Exception as e: + raise VLLMError(status_code=0, message=str(e)) + +def completion( + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + custom_prompt_dict={}, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + global llm + try: + llm, SamplingParams = validate_environment(model=model) + except Exception as e: + raise VLLMError(status_code=0, message=str(e)) + sampling_params = SamplingParams(**optional_params) + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=messages + ) + else: + prompt = prompt_factory(model=model, messages=messages) + + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={"complete_input_dict": sampling_params}, + ) + + if llm: + outputs = llm.generate(prompt, sampling_params) + else: + raise VLLMError(status_code=0, message="Need to pass in a model name to initialize vllm") + + + ## COMPLETION CALL + if "stream" in optional_params and optional_params["stream"] == True: + return iter(outputs) + else: + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=outputs, + additional_args={"complete_input_dict": sampling_params}, + ) + print_verbose(f"raw model_response: {outputs}") + ## RESPONSE OBJECT + model_response["choices"][0]["message"]["content"] = outputs[0].outputs[0].text + + ## CALCULATING USAGE + prompt_tokens = len(outputs[0].prompt_token_ids) + completion_tokens = len(outputs[0].outputs[0].token_ids) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + +def batch_completions( + model: str, + messages: list, + optional_params=None, + custom_prompt_dict={} +): + """ + Example usage: + import litellm + import os + from litellm import batch_completion + + + responses = batch_completion( + model="vllm/facebook/opt-125m", + messages = [ + [ + { + "role": "user", + "content": "good morning? " + } + ], + [ + { + "role": "user", + "content": "what's the time? " + } + ] + ] + ) + """ + try: + llm, SamplingParams = validate_environment(model=model) + except Exception as e: + error_str = str(e) + if "data parallel group is already initialized" in error_str: + pass + else: + raise VLLMError(status_code=0, message=error_str) + sampling_params = SamplingParams(**optional_params) + prompts = [] + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + for message in messages: + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=message + ) + prompts.append(prompt) + else: + for message in messages: + prompt = prompt_factory(model=model, messages=message) + prompts.append(prompt) + + if llm: + outputs = llm.generate(prompts, sampling_params) + else: + raise VLLMError(status_code=0, message="Need to pass in a model name to initialize vllm") + + final_outputs = [] + for output in outputs: + model_response = ModelResponse() + ## RESPONSE OBJECT + model_response["choices"][0]["message"]["content"] = output.outputs[0].text + + ## CALCULATING USAGE + prompt_tokens = len(output.prompt_token_ids) + completion_tokens = len(output.outputs[0].token_ids) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + final_outputs.append(model_response) + return final_outputs + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/main.py b/litellm/main.py new file mode 100644 index 0000000000000000000000000000000000000000..5c421a351e3beb350deb7dba04e8ce2ce26a8b8b --- /dev/null +++ b/litellm/main.py @@ -0,0 +1,2276 @@ +# +-----------------------------------------------+ +# | | +# | Give Feedback / Get Help | +# | https://github.com/BerriAI/litellm/issues/new | +# | | +# +-----------------------------------------------+ +# +# Thank you ! We ❤️ you! - Krrish & Ishaan + +import os, openai, sys, json, inspect, uuid, datetime, threading +from typing import Any +from functools import partial +import dotenv, traceback, random, asyncio, time, contextvars +from copy import deepcopy +import httpx +import litellm +from litellm import ( # type: ignore + client, + exception_type, + get_optional_params, + get_litellm_params, + Logging, +) +from litellm.utils import ( + get_secret, + CustomStreamWrapper, + read_config_args, + completion_with_fallbacks, + get_llm_provider, + get_api_key, + mock_completion_streaming_obj, + convert_to_model_response_object, + token_counter, + Usage +) +from .llms import ( + anthropic, + together_ai, + ai21, + sagemaker, + bedrock, + huggingface_restapi, + replicate, + aleph_alpha, + nlp_cloud, + baseten, + vllm, + ollama, + cohere, + petals, + oobabooga, + palm, + vertex_ai, + maritalk) +from .llms.openai import OpenAIChatCompletion, OpenAITextCompletion +from .llms.azure import AzureChatCompletion +from .llms.huggingface_restapi import Huggingface +from .llms.prompt_templates.factory import prompt_factory, custom_prompt, function_call_prompt +import tiktoken +from concurrent.futures import ThreadPoolExecutor +from typing import Callable, List, Optional, Dict, Union, Mapping + +encoding = tiktoken.get_encoding("cl100k_base") +from litellm.utils import ( + get_secret, + CustomStreamWrapper, + TextCompletionStreamWrapper, + ModelResponse, + TextCompletionResponse, + TextChoices, + EmbeddingResponse, + read_config_args, + Choices, + Message +) + +####### ENVIRONMENT VARIABLES ################### +dotenv.load_dotenv() # Loading env variables using dotenv +openai_chat_completions = OpenAIChatCompletion() +openai_text_completions = OpenAITextCompletion() +azure_chat_completions = AzureChatCompletion() +huggingface = Huggingface() +####### COMPLETION ENDPOINTS ################ + +class LiteLLM: + + def __init__(self, *, + api_key=None, + organization: Optional[str] = None, + base_url: Optional[str]= None, + timeout: Optional[float] = 600, + max_retries: Optional[int] = litellm.num_retries, + default_headers: Optional[Mapping[str, str]] = None,): + self.params = locals() + self.chat = Chat(self.params) + +class Chat(): + + def __init__(self, params): + self.params = params + self.completions = Completions(self.params) + +class Completions(): + + def __init__(self, params): + self.params = params + + def create(self, model, messages, **kwargs): + for k, v in kwargs.items(): + self.params[k] = v + response = completion(model=model, messages=messages, **self.params) + return response + +@client +async def acompletion(*args, **kwargs): + """ + Asynchronously executes a litellm.completion() call for any of litellm supported llms (example gpt-4, gpt-3.5-turbo, claude-2, command-nightly) + + Parameters: + model (str): The name of the language model to use for text completion. see all supported LLMs: https://docs.litellm.ai/docs/providers/ + messages (List): A list of message objects representing the conversation context (default is an empty list). + + OPTIONAL PARAMS + functions (List, optional): A list of functions to apply to the conversation messages (default is an empty list). + function_call (str, optional): The name of the function to call within the conversation (default is an empty string). + temperature (float, optional): The temperature parameter for controlling the randomness of the output (default is 1.0). + top_p (float, optional): The top-p parameter for nucleus sampling (default is 1.0). + n (int, optional): The number of completions to generate (default is 1). + stream (bool, optional): If True, return a streaming response (default is False). + stop(string/list, optional): - Up to 4 sequences where the LLM API will stop generating further tokens. + max_tokens (integer, optional): The maximum number of tokens in the generated completion (default is infinity). + presence_penalty (float, optional): It is used to penalize new tokens based on their existence in the text so far. + frequency_penalty: It is used to penalize new tokens based on their frequency in the text so far. + logit_bias (dict, optional): Used to modify the probability of specific tokens appearing in the completion. + user (str, optional): A unique identifier representing your end-user. This can help the LLM provider to monitor and detect abuse. + metadata (dict, optional): Pass in additional metadata to tag your completion calls - eg. prompt version, details, etc. + api_base (str, optional): Base URL for the API (default is None). + api_version (str, optional): API version (default is None). + api_key (str, optional): API key (default is None). + model_list (list, optional): List of api base, version, keys + + LITELLM Specific Params + mock_response (str, optional): If provided, return a mock completion response for testing or debugging purposes (default is None). + force_timeout (int, optional): The maximum execution time in seconds for the completion request (default is 600). + custom_llm_provider (str, optional): Used for Non-OpenAI LLMs, Example usage for bedrock, set model="amazon.titan-tg1-large" and custom_llm_provider="bedrock" + Returns: + ModelResponse: A response object containing the generated completion and associated metadata. + + Notes: + - This function is an asynchronous version of the `completion` function. + - The `completion` function is called using `run_in_executor` to execute synchronously in the event loop. + - If `stream` is True, the function returns an async generator that yields completion lines. + """ + loop = asyncio.get_event_loop() + model = args[0] if len(args) > 0 else kwargs["model"] + ### PASS ARGS TO COMPLETION ### + kwargs["acompletion"] = True + custom_llm_provider = None + try: + # Use a partial function to pass your keyword arguments + func = partial(completion, *args, **kwargs) + + # Add the context to the function + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + + _, custom_llm_provider, _, _ = get_llm_provider(model=model, api_base=kwargs.get("api_base", None)) + + if (custom_llm_provider == "openai" + or custom_llm_provider == "azure" + or custom_llm_provider == "custom_openai" + or custom_llm_provider == "anyscale" + or custom_llm_provider == "openrouter" + or custom_llm_provider == "deepinfra" + or custom_llm_provider == "perplexity" + or custom_llm_provider == "text-completion-openai" + or custom_llm_provider == "huggingface"): # currently implemented aiohttp calls for just azure and openai, soon all. + if kwargs.get("stream", False): + response = completion(*args, **kwargs) + else: + # Await normally + init_response = await loop.run_in_executor(None, func_with_context) + if isinstance(init_response, dict) or isinstance(init_response, ModelResponse): ## CACHING SCENARIO + response = init_response + elif asyncio.iscoroutine(init_response): + response = await init_response + else: + # Call the synchronous function using run_in_executor + response = await loop.run_in_executor(None, func_with_context) + if kwargs.get("stream", False): # return an async generator + return _async_streaming(response=response, model=model, custom_llm_provider=custom_llm_provider, args=args) + else: + return response + except Exception as e: + custom_llm_provider = custom_llm_provider or "openai" + raise exception_type( + model=model, custom_llm_provider=custom_llm_provider, original_exception=e, completion_kwargs=args, + ) + +async def _async_streaming(response, model, custom_llm_provider, args): + try: + async for line in response: + yield line + except Exception as e: + raise exception_type( + model=model, custom_llm_provider=custom_llm_provider, original_exception=e, completion_kwargs=args, + ) + +def mock_completion(model: str, messages: List, stream: Optional[bool] = False, mock_response: str = "This is a mock request", **kwargs): + """ + Generate a mock completion response for testing or debugging purposes. + + This is a helper function that simulates the response structure of the OpenAI completion API. + + Parameters: + model (str): The name of the language model for which the mock response is generated. + messages (List): A list of message objects representing the conversation context. + stream (bool, optional): If True, returns a mock streaming response (default is False). + mock_response (str, optional): The content of the mock response (default is "This is a mock request"). + **kwargs: Additional keyword arguments that can be used but are not required. + + Returns: + litellm.ModelResponse: A ModelResponse simulating a completion response with the specified model, messages, and mock response. + + Raises: + Exception: If an error occurs during the generation of the mock completion response. + + Note: + - This function is intended for testing or debugging purposes to generate mock completion responses. + - If 'stream' is True, it returns a response that mimics the behavior of a streaming completion. + """ + try: + model_response = ModelResponse(stream=stream) + if stream is True: + # don't try to access stream object, + response = mock_completion_streaming_obj(model_response, mock_response=mock_response, model=model) + return response + + model_response["choices"][0]["message"]["content"] = mock_response + model_response["created"] = int(time.time()) + model_response["model"] = model + return model_response + + except: + traceback.print_exc() + raise Exception("Mock completion response failed") + +@client +def completion( + model: str, + # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create + messages: List = [], + functions: List = [], + function_call: str = "", # optional params + timeout: Optional[Union[float, int]] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + n: Optional[int] = None, + stream: Optional[bool] = None, + stop=None, + max_tokens: Optional[float] = None, + presence_penalty: Optional[float] = None, + frequency_penalty: Optional[float]=None, + logit_bias: Optional[dict] = None, + user: Optional[str] = None, + # openai v1.0+ new params + response_format: Optional[dict] = None, + seed: Optional[int] = None, + tools: Optional[List] = None, + tool_choice: Optional[str] = None, + deployment_id = None, + # set api_base, api_version, api_key + base_url: Optional[str] = None, + api_version: Optional[str] = None, + api_key: Optional[str] = None, + model_list: Optional[list] = None, # pass in a list of api_base,keys, etc. + + # Optional liteLLM function params + **kwargs, +) -> ModelResponse: + """ + Perform a completion() using any of litellm supported llms (example gpt-4, gpt-3.5-turbo, claude-2, command-nightly) + Parameters: + model (str): The name of the language model to use for text completion. see all supported LLMs: https://docs.litellm.ai/docs/providers/ + messages (List): A list of message objects representing the conversation context (default is an empty list). + + OPTIONAL PARAMS + functions (List, optional): A list of functions to apply to the conversation messages (default is an empty list). + function_call (str, optional): The name of the function to call within the conversation (default is an empty string). + temperature (float, optional): The temperature parameter for controlling the randomness of the output (default is 1.0). + top_p (float, optional): The top-p parameter for nucleus sampling (default is 1.0). + n (int, optional): The number of completions to generate (default is 1). + stream (bool, optional): If True, return a streaming response (default is False). + stop(string/list, optional): - Up to 4 sequences where the LLM API will stop generating further tokens. + max_tokens (integer, optional): The maximum number of tokens in the generated completion (default is infinity). + presence_penalty (float, optional): It is used to penalize new tokens based on their existence in the text so far. + frequency_penalty: It is used to penalize new tokens based on their frequency in the text so far. + logit_bias (dict, optional): Used to modify the probability of specific tokens appearing in the completion. + user (str, optional): A unique identifier representing your end-user. This can help the LLM provider to monitor and detect abuse. + metadata (dict, optional): Pass in additional metadata to tag your completion calls - eg. prompt version, details, etc. + api_base (str, optional): Base URL for the API (default is None). + api_version (str, optional): API version (default is None). + api_key (str, optional): API key (default is None). + model_list (list, optional): List of api base, version, keys + + LITELLM Specific Params + mock_response (str, optional): If provided, return a mock completion response for testing or debugging purposes (default is None). + custom_llm_provider (str, optional): Used for Non-OpenAI LLMs, Example usage for bedrock, set model="amazon.titan-tg1-large" and custom_llm_provider="bedrock" + max_retries (int, optional): The number of retries to attempt (default is 0). + Returns: + ModelResponse: A response object containing the generated completion and associated metadata. + + Note: + - This function is used to perform completions() using the specified language model. + - It supports various optional parameters for customizing the completion behavior. + - If 'mock_response' is provided, a mock completion response is returned for testing or debugging. + """ + ######### unpacking kwargs ##################### + args = locals() + api_base = kwargs.get('api_base', None) + return_async = kwargs.get('return_async', False) + mock_response = kwargs.get('mock_response', None) + force_timeout= kwargs.get('force_timeout', 600) ## deprecated + logger_fn = kwargs.get('logger_fn', None) + verbose = kwargs.get('verbose', False) + custom_llm_provider = kwargs.get('custom_llm_provider', None) + litellm_logging_obj = kwargs.get('litellm_logging_obj', None) + id = kwargs.get('id', None) + metadata = kwargs.get('metadata', None) + fallbacks = kwargs.get('fallbacks', None) + headers = kwargs.get("headers", None) + num_retries = kwargs.get("num_retries", None) ## deprecated + max_retries = kwargs.get("max_retries", None) + context_window_fallback_dict = kwargs.get("context_window_fallback_dict", None) + ### CUSTOM MODEL COST ### + input_cost_per_token = kwargs.get("input_cost_per_token", None) + output_cost_per_token = kwargs.get("output_cost_per_token", None) + ### CUSTOM PROMPT TEMPLATE ### + initial_prompt_value = kwargs.get("initial_prompt_value", None) + roles = kwargs.get("roles", None) + final_prompt_value = kwargs.get("final_prompt_value", None) + bos_token = kwargs.get("bos_token", None) + eos_token = kwargs.get("eos_token", None) + acompletion = kwargs.get("acompletion", False) + client = kwargs.get("client", None) + ######## end of unpacking kwargs ########### + openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout", "api_base", "api_version", "api_key", "deployment_id", "organization", "base_url", "default_headers", "timeout", "response_format", "seed", "tools", "tool_choice", "max_retries"] + litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list", "num_retries", "context_window_fallback_dict", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token"] + default_params = openai_params + litellm_params + non_default_params = {k: v for k,v in kwargs.items() if k not in default_params} # model-specific params - pass them straight to the model/provider + if mock_response: + return mock_completion(model, messages, stream=stream, mock_response=mock_response) + if timeout is None: + timeout = kwargs.get("request_timeout", None) or 600 # set timeout for 10 minutes by default + timeout = float(timeout) + try: + if base_url is not None: + api_base = base_url + if max_retries is not None: # openai allows openai.OpenAI(max_retries=3) + num_retries = max_retries + logging = litellm_logging_obj + fallbacks = ( + fallbacks + or litellm.model_fallbacks + ) + if fallbacks is not None: + return completion_with_fallbacks(**args) + if model_list is not None: + deployments = [m["litellm_params"] for m in model_list if m["model_name"] == model] + return batch_completion_models(deployments=deployments, **args) + if litellm.model_alias_map and model in litellm.model_alias_map: + model = litellm.model_alias_map[ + model + ] # update the model to the actual value if an alias has been passed in + model_response = ModelResponse() + if kwargs.get('azure', False) == True: # don't remove flag check, to remain backwards compatible for repos like Codium + custom_llm_provider="azure" + if deployment_id != None: # azure llms + model=deployment_id + custom_llm_provider="azure" + model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base, api_key=api_key) + + ### REGISTER CUSTOM MODEL PRICING -- IF GIVEN ### + if input_cost_per_token is not None and output_cost_per_token is not None: + litellm.register_model({ + model: { + "input_cost_per_token": input_cost_per_token, + "output_cost_per_token": output_cost_per_token, + "litellm_provider": custom_llm_provider + } + }) + ### BUILD CUSTOM PROMPT TEMPLATE -- IF GIVEN ### + custom_prompt_dict = {} # type: ignore + if initial_prompt_value or roles or final_prompt_value or bos_token or eos_token: + custom_prompt_dict = {model: {}} + if initial_prompt_value: + custom_prompt_dict[model]["initial_prompt_value"] = initial_prompt_value + if roles: + custom_prompt_dict[model]["roles"] = roles + if final_prompt_value: + custom_prompt_dict[model]["final_prompt_value"] = final_prompt_value + if bos_token: + custom_prompt_dict[model]["bos_token"] = bos_token + if eos_token: + custom_prompt_dict[model]["eos_token"] = eos_token + model_api_key = get_api_key(llm_provider=custom_llm_provider, dynamic_api_key=api_key) # get the api key from the environment if required for the model + if model_api_key and "sk-litellm" in model_api_key: + api_base = "https://proxy.litellm.ai" + custom_llm_provider = "openai" + api_key = model_api_key + + if dynamic_api_key is not None: + api_key = dynamic_api_key + # check if user passed in any of the OpenAI optional params + optional_params = get_optional_params( + functions=functions, + function_call=function_call, + temperature=temperature, + top_p=top_p, + n=n, + stream=stream, + stop=stop, + max_tokens=max_tokens, + presence_penalty=presence_penalty, + frequency_penalty=frequency_penalty, + logit_bias=logit_bias, + user=user, + # params to identify the model + model=model, + custom_llm_provider=custom_llm_provider, + response_format=response_format, + seed=seed, + tools=tools, + tool_choice=tool_choice, + max_retries=max_retries, + **non_default_params + ) + + if litellm.add_function_to_prompt and optional_params.get("functions_unsupported_model", None): # if user opts to add it to prompt, when API doesn't support function calling + functions_unsupported_model = optional_params.pop("functions_unsupported_model") + messages = function_call_prompt(messages=messages, functions=functions_unsupported_model) + + # For logging - save the values of the litellm-specific params passed in + litellm_params = get_litellm_params( + return_async=return_async, + api_key=api_key, + force_timeout=force_timeout, + logger_fn=logger_fn, + verbose=verbose, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + litellm_call_id=kwargs.get('litellm_call_id', None), + model_alias_map=litellm.model_alias_map, + completion_call_id=id, + metadata=metadata + ) + logging.update_environment_variables(model=model, user=user, optional_params=optional_params, litellm_params=litellm_params) + if custom_llm_provider == "azure": + # azure configs + api_type = get_secret("AZURE_API_TYPE") or "azure" + + api_base = ( + api_base + or litellm.api_base + or get_secret("AZURE_API_BASE") + ) + + api_version = ( + api_version or + litellm.api_version or + get_secret("AZURE_API_VERSION") + ) + + api_key = ( + api_key or + litellm.api_key or + litellm.azure_key or + get_secret("AZURE_OPENAI_API_KEY") or + get_secret("AZURE_API_KEY") + ) + + azure_ad_token = ( + optional_params.pop("azure_ad_token", None) or + get_secret("AZURE_AD_TOKEN") + ) + + headers = ( + headers or + litellm.headers + ) + + ## LOAD CONFIG - if set + config=litellm.AzureOpenAIConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > azure_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + ## COMPLETION CALL + response = azure_chat_completions.completion( + model=model, + messages=messages, + headers=headers, + api_key=api_key, + api_base=api_base, + api_version=api_version, + api_type=api_type, + azure_ad_token=azure_ad_token, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + logging_obj=logging, + acompletion=acompletion, + timeout=timeout, + client=client # pass AsyncAzureOpenAI, AzureOpenAI client + ) + + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + additional_args={ + "headers": headers, + "api_version": api_version, + "api_base": api_base, + }, + ) + elif ( + model in litellm.open_ai_chat_completion_models + or custom_llm_provider == "custom_openai" + or custom_llm_provider == "deepinfra" + or custom_llm_provider == "perplexity" + or custom_llm_provider == "anyscale" + or custom_llm_provider == "openai" + or "ft:gpt-3.5-turbo" in model # finetune gpt-3.5-turbo + ): # allow user to make an openai call with a custom base + # note: if a user sets a custom base - we should ensure this works + # allow for the setting of dynamic and stateful api-bases + api_base = ( + api_base # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api base from there + or litellm.api_base + or get_secret("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + openai.organization = ( + litellm.organization + or get_secret("OPENAI_ORGANIZATION") + or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 + ) + # set API KEY + api_key = ( + api_key or # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there + litellm.api_key or + litellm.openai_key or + get_secret("OPENAI_API_KEY") + ) + + headers = ( + headers or + litellm.headers + ) + + ## LOAD CONFIG - if set + config=litellm.OpenAIConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + ## COMPLETION CALL + try: + response = openai_chat_completions.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + timeout=timeout, + custom_prompt_dict=custom_prompt_dict, + client=client # pass AsyncOpenAI, OpenAI client + ) + except Exception as e: + ## LOGGING - log the original exception returned + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args={"headers": headers}, + ) + raise e + + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + additional_args={"headers": headers}, + ) + elif ( + custom_llm_provider == "text-completion-openai" + or "ft:babbage-002" in model + or "ft:davinci-002" in model # support for finetuned completion models + ): + # print("calling custom openai provider") + openai.api_type = "openai" + + api_base = ( + api_base + or litellm.api_base + or get_secret("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + + openai.api_version = None + # set API KEY + + api_key = ( + api_key or + litellm.api_key or + litellm.openai_key or + get_secret("OPENAI_API_KEY") + ) + + headers = ( + headers or + litellm.headers + ) + + ## LOAD CONFIG - if set + config=litellm.OpenAITextCompletionConfig.get_config() + for k, v in config.items(): + if k not in optional_params: # completion(top_k=3) > openai_text_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + if litellm.organization: + openai.organization = litellm.organization + + if len(messages)>0 and "content" in messages[0] and type(messages[0]["content"]) == list: + # text-davinci-003 can accept a string or array, if it's an array, assume the array is set in messages[0]['content'] + # https://platform.openai.com/docs/api-reference/completions/create + prompt = messages[0]["content"] + else: + prompt = " ".join([message["content"] for message in messages]) # type: ignore + ## LOGGING + logging.pre_call( + input=prompt, + api_key=api_key, + additional_args={ + "openai_organization": litellm.organization, + "headers": headers, + "api_base": api_base, + "api_type": openai.api_type, + }, + ) + ## COMPLETION CALL + model_response = openai_text_completions.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn + ) + + # if "stream" in optional_params and optional_params["stream"] == True: + # response = CustomStreamWrapper(model_response, model, custom_llm_provider="text-completion-openai", logging_obj=logging) + # return response + response = model_response + elif ( + "replicate" in model or + custom_llm_provider == "replicate" or + model in litellm.replicate_models + ): + # Setting the relevant API KEY for replicate, replicate defaults to using os.environ.get("REPLICATE_API_TOKEN") + replicate_key = None + replicate_key = ( + api_key + or litellm.replicate_key + or litellm.api_key + or get_secret("REPLICATE_API_KEY") + or get_secret("REPLICATE_API_TOKEN") + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("REPLICATE_API_BASE") + or "https://api.replicate.com/v1" + ) + + custom_prompt_dict = ( + custom_prompt_dict + or litellm.custom_prompt_dict + ) + + model_response = replicate.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, # for calculating input/output tokens + api_key=replicate_key, + logging_obj=logging, + custom_prompt_dict=custom_prompt_dict + ) + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper(model_response, model, logging_obj=logging, custom_llm_provider="replicate") + return response + response = model_response + + elif custom_llm_provider=="anthropic": + api_key = ( + api_key + or litellm.anthropic_key + or litellm.api_key + or os.environ.get("ANTHROPIC_API_KEY") + ) + api_base = ( + api_base + or litellm.api_base + or get_secret("ANTHROPIC_API_BASE") + or "https://api.anthropic.com/v1/complete" + ) + custom_prompt_dict = ( + custom_prompt_dict + or litellm.custom_prompt_dict + ) + model_response = anthropic.completion( + model=model, + messages=messages, + api_base=api_base, + custom_prompt_dict=litellm.custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, # for calculating input/output tokens + api_key=api_key, + logging_obj=logging, + ) + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper(model_response, model, custom_llm_provider="anthropic", logging_obj=logging) + return response + response = model_response + elif custom_llm_provider == "nlp_cloud": + nlp_cloud_key = ( + api_key or litellm.nlp_cloud_key or get_secret("NLP_CLOUD_API_KEY") or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("NLP_CLOUD_API_BASE") + or "https://api.nlpcloud.io/v1/gpu/" + ) + + model_response = nlp_cloud.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=nlp_cloud_key, + logging_obj=logging + ) + + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper(model_response, model, custom_llm_provider="nlp_cloud", logging_obj=logging) + return response + response = model_response + elif custom_llm_provider == "aleph_alpha": + aleph_alpha_key = ( + api_key or litellm.aleph_alpha_key or get_secret("ALEPH_ALPHA_API_KEY") or get_secret("ALEPHALPHA_API_KEY") or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("ALEPH_ALPHA_API_BASE") + or "https://api.aleph-alpha.com/complete" + ) + + model_response = aleph_alpha.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + default_max_tokens_to_sample=litellm.max_tokens, + api_key=aleph_alpha_key, + logging_obj=logging # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + ) + + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper(model_response, model, custom_llm_provider="aleph_alpha", logging_obj=logging) + return response + response = model_response + elif custom_llm_provider == "cohere": + cohere_key = ( + api_key + or litellm.cohere_key + or get_secret("COHERE_API_KEY") + or get_secret("CO_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("COHERE_API_BASE") + or "https://api.cohere.ai/v1/generate" + ) + + model_response = cohere.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=cohere_key, + logging_obj=logging # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + ) + + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper(model_response, model, custom_llm_provider="cohere", logging_obj=logging) + return response + response = model_response + elif custom_llm_provider == "maritalk": + maritalk_key = ( + api_key + or litellm.maritalk_key + or get_secret("MARITALK_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("MARITALK_API_BASE") + or "https://chat.maritaca.ai/api/chat/inference" + ) + + model_response = maritalk.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=maritalk_key, + logging_obj=logging + ) + + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper(model_response, model, custom_llm_provider="maritalk", logging_obj=logging) + return response + response = model_response + elif ( + custom_llm_provider == "huggingface" + ): + custom_llm_provider = "huggingface" + huggingface_key = ( + api_key + or litellm.huggingface_key + or os.environ.get("HF_TOKEN") + or os.environ.get("HUGGINGFACE_API_KEY") + or litellm.api_key + ) + hf_headers = ( + headers + or litellm.headers + ) + + custom_prompt_dict = ( + custom_prompt_dict + or litellm.custom_prompt_dict + ) + model_response = huggingface.completion( + model=model, + messages=messages, + api_base=api_base, # type: ignore + headers=hf_headers, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=huggingface_key, + acompletion=acompletion, + logging_obj=logging, + custom_prompt_dict=custom_prompt_dict + ) + if "stream" in optional_params and optional_params["stream"] == True and acompletion is False: + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, model, custom_llm_provider="huggingface", logging_obj=logging + ) + return response + response = model_response + elif custom_llm_provider == "oobabooga": + custom_llm_provider = "oobabooga" + model_response = oobabooga.completion( + model=model, + messages=messages, + model_response=model_response, + api_base=api_base, # type: ignore + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + api_key=None, + logger_fn=logger_fn, + encoding=encoding, + logging_obj=logging + ) + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, model, custom_llm_provider="oobabooga", logging_obj=logging + ) + return response + response = model_response + elif custom_llm_provider == "openrouter": + api_base = ( + api_base + or litellm.api_base + or "https://openrouter.ai/api/v1" + ) + + api_key = ( + api_key or + litellm.api_key or + litellm.openrouter_key or + get_secret("OPENROUTER_API_KEY") or + get_secret("OR_API_KEY") + ) + + openrouter_site_url = ( + get_secret("OR_SITE_URL") + or "https://litellm.ai" + ) + + openrouter_app_name = ( + get_secret("OR_APP_NAME") + or "liteLLM" + ) + + headers = ( + headers or + litellm.headers or + { + "HTTP-Referer": openrouter_site_url, + "X-Title": openrouter_app_name, + } + ) + + data = { + "model": model, + "messages": messages, + **optional_params + } + ## LOGGING + logging.pre_call(input=messages, api_key=openai.api_key, additional_args={"complete_input_dict": data, "headers": headers}) + ## COMPLETION CALL + + ## COMPLETION CALL + response = openai_chat_completions.completion( + model=model, + messages=messages, + headers=headers, + api_key=api_key, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + logging_obj=logging, + acompletion=acompletion, + timeout=timeout + ) + ## LOGGING + logging.post_call( + input=messages, api_key=openai.api_key, original_response=response + ) + elif custom_llm_provider == "together_ai" or ("togethercomputer" in model) or (model in litellm.together_ai_models): + custom_llm_provider = "together_ai" + together_ai_key = ( + api_key + or litellm.togetherai_api_key + or get_secret("TOGETHER_AI_TOKEN") + or get_secret("TOGETHERAI_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("TOGETHERAI_API_BASE") + or "https://api.together.xyz/inference" + ) + + custom_prompt_dict = ( + custom_prompt_dict + or litellm.custom_prompt_dict + ) + + model_response = together_ai.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=together_ai_key, + logging_obj=logging, + custom_prompt_dict=custom_prompt_dict + ) + if "stream_tokens" in optional_params and optional_params["stream_tokens"] == True: + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, model, custom_llm_provider="together_ai", logging_obj=logging + ) + return response + response = model_response + elif custom_llm_provider == "palm": + palm_api_key = ( + api_key + or get_secret("PALM_API_KEY") + or litellm.api_key + ) + + # palm does not support streaming as yet :( + model_response = palm.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=palm_api_key, + logging_obj=logging + ) + # fake palm streaming + if "stream" in optional_params and optional_params["stream"] == True: + # fake streaming for palm + resp_string = model_response["choices"][0]["message"]["content"] + response = CustomStreamWrapper( + resp_string, model, custom_llm_provider="palm", logging_obj=logging + ) + return response + response = model_response + elif model in litellm.vertex_chat_models or model in litellm.vertex_code_chat_models or model in litellm.vertex_text_models or model in litellm.vertex_code_text_models: + vertex_ai_project = (litellm.vertex_project + or get_secret("VERTEXAI_PROJECT")) + vertex_ai_location = (litellm.vertex_location + or get_secret("VERTEXAI_LOCATION")) + + model_response = vertex_ai.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + vertex_location=vertex_ai_location, + vertex_project=vertex_ai_project, + logging_obj=logging + ) + + if "stream" in optional_params and optional_params["stream"] == True: + response = CustomStreamWrapper( + model_response, model, custom_llm_provider="vertex_ai", logging_obj=logging + ) + return response + response = model_response + elif custom_llm_provider == "ai21": + custom_llm_provider = "ai21" + ai21_key = ( + api_key + or litellm.ai21_key + or os.environ.get("AI21_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("AI21_API_BASE") + or "https://api.ai21.com/studio/v1/" + ) + + model_response = ai21.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=ai21_key, + logging_obj=logging + ) + + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, model, custom_llm_provider="ai21", logging_obj=logging + ) + return response + + ## RESPONSE OBJECT + response = model_response + elif custom_llm_provider == "sagemaker": + # boto3 reads keys from .env + model_response = sagemaker.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + logging_obj=logging + ) + if "stream" in optional_params and optional_params["stream"]==True: ## [BETA] + # sagemaker does not support streaming as of now so we're faking streaming: + # /static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fstreaming-output-text-when-deploying-on-sagemaker%2F39611 + # "SageMaker is currently not supporting streaming responses." + + # fake streaming for sagemaker + resp_string = model_response["choices"][0]["message"]["content"] + response = CustomStreamWrapper( + resp_string, model, custom_llm_provider="sagemaker", logging_obj=logging + ) + return response + + ## RESPONSE OBJECT + response = model_response + elif custom_llm_provider == "bedrock": + # boto3 reads keys from .env + custom_prompt_dict = ( + custom_prompt_dict + or litellm.custom_prompt_dict + ) + model_response = bedrock.completion( + model=model, + messages=messages, + custom_prompt_dict=litellm.custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + logging_obj=logging, + ) + + + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + if "ai21" in model: + response = CustomStreamWrapper( + model_response, model, custom_llm_provider="bedrock", logging_obj=logging + ) + else: + response = CustomStreamWrapper( + iter(model_response), model, custom_llm_provider="bedrock", logging_obj=logging + ) + return response + + ## RESPONSE OBJECT + response = model_response + elif custom_llm_provider == "vllm": + model_response = vllm.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + logging_obj=logging + ) + + if "stream" in optional_params and optional_params["stream"] == True: ## [BETA] + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, model, custom_llm_provider="vllm", logging_obj=logging + ) + return response + + ## RESPONSE OBJECT + response = model_response + elif custom_llm_provider == "ollama": + api_base = ( + litellm.api_base or + api_base or + get_secret("OLLAMA_API_BASE") or + "http://localhost:11434" + + ) + custom_prompt_dict = ( + custom_prompt_dict + or litellm.custom_prompt_dict + ) + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=messages + ) + else: + prompt = prompt_factory(model=model, messages=messages, custom_llm_provider=custom_llm_provider) + ## LOGGING + if kwargs.get('acompletion', False) == True: + if optional_params.get("stream", False) == True: + # assume all ollama responses are streamed + async_generator = ollama.async_get_ollama_response_stream(api_base, model, prompt, optional_params, logging_obj=logging) + return async_generator + + generator = ollama.get_ollama_response_stream(api_base, model, prompt, optional_params, logging_obj=logging) + if optional_params.get("stream", False) == True: + # assume all ollama responses are streamed + response = CustomStreamWrapper( + generator, model, custom_llm_provider="ollama", logging_obj=logging + ) + return response + else: + response_string = "" + for chunk in generator: + response_string+=chunk['content'] + + ## RESPONSE OBJECT + model_response["choices"][0]["finish_reason"] = "stop" + model_response["choices"][0]["message"]["content"] = response_string + model_response["created"] = int(time.time()) + model_response["model"] = "ollama/" + model + prompt_tokens = len(encoding.encode(prompt)) # type: ignore + completion_tokens = len(encoding.encode(response_string)) + model_response["usage"] = Usage(prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=prompt_tokens + completion_tokens) + response = model_response + elif ( + custom_llm_provider == "baseten" + or litellm.api_base == "https://app.baseten.co" + ): + custom_llm_provider = "baseten" + baseten_key = ( + api_key or litellm.baseten_key or os.environ.get("BASETEN_API_KEY") or litellm.api_key + ) + + model_response = baseten.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=baseten_key, + logging_obj=logging + ) + if inspect.isgenerator(model_response) or ("stream" in optional_params and optional_params["stream"] == True): + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, model, custom_llm_provider="baseten", logging_obj=logging + ) + return response + response = model_response + elif ( + custom_llm_provider == "petals" + or model in litellm.petals_models + ): + api_base = ( + api_base or + litellm.api_base + ) + + custom_llm_provider = "petals" + stream = optional_params.pop("stream", False) + model_response = petals.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + logging_obj=logging + ) + if stream==True: ## [BETA] + # Fake streaming for petals + resp_string = model_response["choices"][0]["message"]["content"] + response = CustomStreamWrapper( + resp_string, model, custom_llm_provider="petals", logging_obj=logging + ) + return response + response = model_response + elif ( + custom_llm_provider == "custom" + ): + import requests + + url = ( + litellm.api_base or + api_base or + "" + ) + if url == None or url == "": + raise ValueError("api_base not set. Set api_base or litellm.api_base for custom endpoints") + + """ + assume input to custom LLM api bases follow this format: + resp = requests.post( + api_base, + json={ + 'model': 'meta-llama/Llama-2-13b-hf', # model name + 'params': { + 'prompt': ["The capital of France is P"], + 'max_tokens': 32, + 'temperature': 0.7, + 'top_p': 1.0, + 'top_k': 40, + } + } + ) + + """ + prompt = " ".join([message["content"] for message in messages]) # type: ignore + resp = requests.post(url, json={ + 'model': model, + 'params': { + 'prompt': [prompt], + 'max_tokens': max_tokens, + 'temperature': temperature, + 'top_p': top_p, + 'top_k': kwargs.get('top_k', 40), + } + }) + response_json = resp.json() + """ + assume all responses from custom api_bases of this format: + { + 'data': [ + { + 'prompt': 'The capital of France is P', + 'output': ['The capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France'], + 'params': {'temperature': 0.7, 'top_k': 40, 'top_p': 1}}], + 'message': 'ok' + } + ] + } + """ + string_response = response_json['data'][0]['output'][0] + ## RESPONSE OBJECT + model_response["choices"][0]["message"]["content"] = string_response + model_response["created"] = int(time.time()) + model_response["model"] = model + response = model_response + else: + raise ValueError( + f"Unable to map your input to a model. Check your input - {args}" + ) + return response + except Exception as e: + ## Map to OpenAI Exception + raise exception_type( + model=model, custom_llm_provider=custom_llm_provider, original_exception=e, completion_kwargs=args, + ) + + +def completion_with_retries(*args, **kwargs): + """ + Executes a litellm.completion() with 3 retries + """ + try: + import tenacity + except Exception as e: + raise Exception(f"tenacity import failed please run `pip install tenacity`. Error{e}") + + num_retries = kwargs.pop("num_retries", 3) + retry_strategy = kwargs.pop("retry_strategy", "constant_retry") + original_function = kwargs.pop("original_function", completion) + if retry_strategy == "constant_retry": + retryer = tenacity.Retrying(stop=tenacity.stop_after_attempt(num_retries), reraise=True) + elif retry_strategy == "exponential_backoff_retry": + retryer = tenacity.Retrying(wait=tenacity.wait_exponential(multiplier=1, max=10), stop=tenacity.stop_after_attempt(num_retries), reraise=True) + return retryer(original_function, *args, **kwargs) + +async def acompletion_with_retries(*args, **kwargs): + """ + Executes a litellm.completion() with 3 retries + """ + try: + import tenacity + except Exception as e: + raise Exception(f"tenacity import failed please run `pip install tenacity`. Error{e}") + + num_retries = kwargs.pop("num_retries", 3) + retry_strategy = kwargs.pop("retry_strategy", "constant_retry") + original_function = kwargs.pop("original_function", completion) + if retry_strategy == "constant_retry": + retryer = tenacity.Retrying(stop=tenacity.stop_after_attempt(num_retries), reraise=True) + elif retry_strategy == "exponential_backoff_retry": + retryer = tenacity.Retrying(wait=tenacity.wait_exponential(multiplier=1, max=10), stop=tenacity.stop_after_attempt(num_retries), reraise=True) + return await retryer(original_function, *args, **kwargs) + + + +def batch_completion( + model: str, + # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create + messages: List = [], + functions: List = [], + function_call: str = "", # optional params + temperature: Optional[float] = None, + top_p: Optional[float] = None, + n: Optional[int] = None, + stream: Optional[bool] = None, + stop=None, + max_tokens: Optional[float] = None, + presence_penalty: Optional[float] = None, + frequency_penalty: Optional[float]=None, + logit_bias: Optional[dict] = None, + user: Optional[str] = None, + deployment_id = None, + request_timeout: Optional[int] = None, + # Optional liteLLM function params + **kwargs): + """ + Batch litellm.completion function for a given model. + + Args: + model (str): The model to use for generating completions. + messages (List, optional): List of messages to use as input for generating completions. Defaults to []. + functions (List, optional): List of functions to use as input for generating completions. Defaults to []. + function_call (str, optional): The function call to use as input for generating completions. Defaults to "". + temperature (float, optional): The temperature parameter for generating completions. Defaults to None. + top_p (float, optional): The top-p parameter for generating completions. Defaults to None. + n (int, optional): The number of completions to generate. Defaults to None. + stream (bool, optional): Whether to stream completions or not. Defaults to None. + stop (optional): The stop parameter for generating completions. Defaults to None. + max_tokens (float, optional): The maximum number of tokens to generate. Defaults to None. + presence_penalty (float, optional): The presence penalty for generating completions. Defaults to None. + frequency_penalty (float, optional): The frequency penalty for generating completions. Defaults to None. + logit_bias (dict, optional): The logit bias for generating completions. Defaults to {}. + user (str, optional): The user string for generating completions. Defaults to "". + deployment_id (optional): The deployment ID for generating completions. Defaults to None. + request_timeout (int, optional): The request timeout for generating completions. Defaults to None. + + Returns: + list: A list of completion results. + """ + args = locals() + batch_messages = messages + completions = [] + model = model + custom_llm_provider = None + if model.split("/", 1)[0] in litellm.provider_list: + custom_llm_provider = model.split("/", 1)[0] + model = model.split("/", 1)[1] + if custom_llm_provider == "vllm": + optional_params = get_optional_params( + functions=functions, + function_call=function_call, + temperature=temperature, + top_p=top_p, + n=n, + stream=stream, + stop=stop, + max_tokens=max_tokens, + presence_penalty=presence_penalty, + frequency_penalty=frequency_penalty, + logit_bias=logit_bias, + user=user, + # params to identify the model + model=model, + custom_llm_provider=custom_llm_provider + ) + results = vllm.batch_completions(model=model, messages=batch_messages, custom_prompt_dict=litellm.custom_prompt_dict, optional_params=optional_params) + # all non VLLM models for batch completion models + else: + def chunks(lst, n): + """Yield successive n-sized chunks from lst.""" + for i in range(0, len(lst), n): + yield lst[i:i + n] + with ThreadPoolExecutor(max_workers=100) as executor: + for sub_batch in chunks(batch_messages, 100): + for message_list in sub_batch: + kwargs_modified = args.copy() + kwargs_modified["messages"] = message_list + original_kwargs = {} + if "kwargs" in kwargs_modified: + original_kwargs = kwargs_modified.pop("kwargs") + future = executor.submit(completion, **kwargs_modified, **original_kwargs) + completions.append(future) + + # Retrieve the results from the futures + results = [future.result() for future in completions] + return results + +# send one request to multiple models +# return as soon as one of the llms responds +def batch_completion_models(*args, **kwargs): + """ + Send a request to multiple language models concurrently and return the response + as soon as one of the models responds. + + Args: + *args: Variable-length positional arguments passed to the completion function. + **kwargs: Additional keyword arguments: + - models (str or list of str): The language models to send requests to. + - Other keyword arguments to be passed to the completion function. + + Returns: + str or None: The response from one of the language models, or None if no response is received. + + Note: + This function utilizes a ThreadPoolExecutor to parallelize requests to multiple models. + It sends requests concurrently and returns the response from the first model that responds. + """ + import concurrent + if "model" in kwargs: + kwargs.pop("model") + if "models" in kwargs: + models = kwargs["models"] + kwargs.pop("models") + futures = {} + with concurrent.futures.ThreadPoolExecutor(max_workers=len(models)) as executor: + for model in models: + futures[model] = executor.submit(completion, *args, model=model, **kwargs) + + for model, future in sorted(futures.items(), key=lambda x: models.index(x[0])): + if future.result() is not None: + return future.result() + elif "deployments" in kwargs: + deployments = kwargs["deployments"] + kwargs.pop("deployments") + kwargs.pop("model_list") + nested_kwargs = kwargs.pop("kwargs", {}) + futures = {} + with concurrent.futures.ThreadPoolExecutor(max_workers=len(deployments)) as executor: + for deployment in deployments: + for key in kwargs.keys(): + if key not in deployment: # don't override deployment values e.g. model name, api base, etc. + deployment[key] = kwargs[key] + kwargs = {**deployment, **nested_kwargs} + futures[deployment["model"]] = executor.submit(completion, **kwargs) + + while futures: + # wait for the first returned future + print_verbose("\n\n waiting for next result\n\n") + done, _ = concurrent.futures.wait(futures.values(), return_when=concurrent.futures.FIRST_COMPLETED) + print_verbose(f"done list\n{done}") + for future in done: + try: + result = future.result() + return result + except Exception as e: + # if model 1 fails, continue with response from model 2, model3 + print_verbose(f"\n\ngot an exception, ignoring, removing from futures") + print_verbose(futures) + new_futures = {} + for key, value in futures.items(): + if future == value: + print_verbose(f"removing key{key}") + continue + else: + new_futures[key] = value + futures = new_futures + print_verbose(f"new futures{futures}") + continue + + + print_verbose("\n\ndone looping through futures\n\n") + print_verbose(futures) + + return None # If no response is received from any model + +def batch_completion_models_all_responses(*args, **kwargs): + """ + Send a request to multiple language models concurrently and return a list of responses + from all models that respond. + + Args: + *args: Variable-length positional arguments passed to the completion function. + **kwargs: Additional keyword arguments: + - models (str or list of str): The language models to send requests to. + - Other keyword arguments to be passed to the completion function. + + Returns: + list: A list of responses from the language models that responded. + + Note: + This function utilizes a ThreadPoolExecutor to parallelize requests to multiple models. + It sends requests concurrently and collects responses from all models that respond. + """ + import concurrent.futures + + # ANSI escape codes for colored output + GREEN = "\033[92m" + RED = "\033[91m" + RESET = "\033[0m" + + if "model" in kwargs: + kwargs.pop("model") + if "models" in kwargs: + models = kwargs["models"] + kwargs.pop("models") + + responses = [] + + with concurrent.futures.ThreadPoolExecutor(max_workers=len(models)) as executor: + for idx, model in enumerate(models): + future = executor.submit(completion, *args, model=model, **kwargs) + if future.result() is not None: + responses.append(future.result()) + + return responses + +### EMBEDDING ENDPOINTS #################### + +async def aembedding(*args, **kwargs): + """ + Asynchronously calls the `embedding` function with the given arguments and keyword arguments. + + Parameters: + - `args` (tuple): Positional arguments to be passed to the `embedding` function. + - `kwargs` (dict): Keyword arguments to be passed to the `embedding` function. + + Returns: + - `response` (Any): The response returned by the `embedding` function. + """ + loop = asyncio.get_event_loop() + model = args[0] if len(args) > 0 else kwargs["model"] + ### PASS ARGS TO Embedding ### + kwargs["aembedding"] = True + custom_llm_provider = None + try: + # Use a partial function to pass your keyword arguments + func = partial(embedding, *args, **kwargs) + + # Add the context to the function + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + + _, custom_llm_provider, _, _ = get_llm_provider(model=model, api_base=kwargs.get("api_base", None)) + + if (custom_llm_provider == "openai" + or custom_llm_provider == "azure" + or custom_llm_provider == "custom_openai" + or custom_llm_provider == "anyscale" + or custom_llm_provider == "openrouter" + or custom_llm_provider == "deepinfra" + or custom_llm_provider == "perplexity" + or custom_llm_provider == "huggingface"): # currently implemented aiohttp calls for just azure and openai, soon all. + # Await normally + init_response = await loop.run_in_executor(None, func_with_context) + if isinstance(init_response, dict) or isinstance(init_response, ModelResponse): ## CACHING SCENARIO + response = init_response + elif asyncio.iscoroutine(init_response): + response = await init_response + else: + # Call the synchronous function using run_in_executor + response = await loop.run_in_executor(None, func_with_context) + return response + except Exception as e: + custom_llm_provider = custom_llm_provider or "openai" + raise exception_type( + model=model, custom_llm_provider=custom_llm_provider, original_exception=e, completion_kwargs=args, + ) + +@client +def embedding( + model, + input=[], + # Optional params + timeout=600, # default to 10 minutes + # set api_base, api_version, api_key + api_base: Optional[str] = None, + api_version: Optional[str] = None, + api_key: Optional[str] = None, + api_type: Optional[str] = None, + caching: bool=False, + user: Optional[str]=None, + custom_llm_provider=None, + litellm_call_id=None, + litellm_logging_obj=None, + logger_fn=None, + **kwargs +): + """ + Embedding function that calls an API to generate embeddings for the given input. + + Parameters: + - model: The embedding model to use. + - input: The input for which embeddings are to be generated. + - timeout: The timeout value for the API call, default 10 mins + - litellm_call_id: The call ID for litellm logging. + - litellm_logging_obj: The litellm logging object. + - logger_fn: The logger function. + - api_base: Optional. The base URL for the API. + - api_version: Optional. The version of the API. + - api_key: Optional. The API key to use. + - api_type: Optional. The type of the API. + - caching: A boolean indicating whether to enable caching. + - custom_llm_provider: The custom llm provider. + + Returns: + - response: The response received from the API call. + + Raises: + - exception_type: If an exception occurs during the API call. + """ + azure = kwargs.get("azure", None) + client = kwargs.pop("client", None) + rpm = kwargs.pop("rpm", None) + tpm = kwargs.pop("tpm", None) + aembedding = kwargs.pop("aembedding", None) + + optional_params = {} + for param in kwargs: + if param != "metadata": # filter out metadata from optional_params + optional_params[param] = kwargs[param] + model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base, api_key=api_key) + try: + response = None + logging = litellm_logging_obj + logging.update_environment_variables(model=model, user="", optional_params={}, litellm_params={"timeout": timeout, "azure": azure, "litellm_call_id": litellm_call_id, "logger_fn": logger_fn}) + if azure == True or custom_llm_provider == "azure": + # azure configs + api_type = get_secret("AZURE_API_TYPE") or "azure" + + api_base = ( + api_base + or litellm.api_base + or get_secret("AZURE_API_BASE") + ) + + api_version = ( + api_version or + litellm.api_version or + get_secret("AZURE_API_VERSION") + ) + + azure_ad_token = ( + kwargs.pop("azure_ad_token", None) or + get_secret("AZURE_AD_TOKEN") + ) + + api_key = ( + api_key or + litellm.api_key or + litellm.azure_key or + get_secret("AZURE_API_KEY") + ) + ## EMBEDDING CALL + response = azure_chat_completions.embedding( + model=model, + input=input, + api_base=api_base, + api_key=api_key, + api_version=api_version, + azure_ad_token=azure_ad_token, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding + ) + elif model in litellm.open_ai_embedding_models or custom_llm_provider == "openai": + api_base = ( + api_base + or litellm.api_base + or get_secret("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + openai.organization = ( + litellm.organization + or get_secret("OPENAI_ORGANIZATION") + or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 + ) + # set API KEY + api_key = ( + api_key or + litellm.api_key or + litellm.openai_key or + get_secret("OPENAI_API_KEY") + ) + api_type = "openai" + api_version = None + + + ## EMBEDDING CALL + response = openai_chat_completions.embedding( + model=model, + input=input, + api_base=api_base, + api_key=api_key, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, + ) + elif model in litellm.cohere_embedding_models: + cohere_key = ( + api_key + or litellm.cohere_key + or get_secret("COHERE_API_KEY") + or get_secret("CO_API_KEY") + or litellm.api_key + ) + response = cohere.embedding( + model=model, + input=input, + optional_params=optional_params, + encoding=encoding, + api_key=cohere_key, + logging_obj=logging, + model_response= EmbeddingResponse() + + ) + elif custom_llm_provider == "huggingface": + api_key = ( + api_key + or litellm.huggingface_key + or get_secret("HUGGINGFACE_API_KEY") + or litellm.api_key + ) + response = huggingface.embedding( + model=model, + input=input, + encoding=encoding, + api_key=api_key, + api_base=api_base, + logging_obj=logging, + model_response= EmbeddingResponse() + ) + elif custom_llm_provider == "bedrock": + response = bedrock.embedding( + model=model, + input=input, + encoding=encoding, + logging_obj=logging, + optional_params=kwargs, + model_response= EmbeddingResponse() + ) + else: + args = locals() + raise ValueError(f"No valid embedding model args passed in - {args}") + return response + except Exception as e: + ## LOGGING + logging.post_call( + input=input, + api_key=openai.api_key, + original_response=str(e), + ) + ## Map to OpenAI Exception + raise exception_type( + model=model, + original_exception=e, + custom_llm_provider="azure" if azure == True else None, + ) + + +###### Text Completion ################ +def text_completion( + prompt: Union[str, List[Union[str, List[Union[str, List[int]]]]]], # Required: The prompt(s) to generate completions for. + model: Optional[str]=None, # Optional: either `model` or `engine` can be set + best_of: Optional[int] = None, # Optional: Generates best_of completions server-side. + echo: Optional[bool] = None, # Optional: Echo back the prompt in addition to the completion. + frequency_penalty: Optional[float] = None, # Optional: Penalize new tokens based on their existing frequency. + logit_bias: Optional[Dict[int, int]] = None, # Optional: Modify the likelihood of specified tokens. + logprobs: Optional[int] = None, # Optional: Include the log probabilities on the most likely tokens. + max_tokens: Optional[int] = None, # Optional: The maximum number of tokens to generate in the completion. + n: Optional[int] = None, # Optional: How many completions to generate for each prompt. + presence_penalty: Optional[float] = None, # Optional: Penalize new tokens based on whether they appear in the text so far. + stop: Optional[Union[str, List[str]]] = None, # Optional: Sequences where the API will stop generating further tokens. + stream: Optional[bool] = None, # Optional: Whether to stream back partial progress. + suffix: Optional[str] = None, # Optional: The suffix that comes after a completion of inserted text. + temperature: Optional[float] = None, # Optional: Sampling temperature to use. + top_p: Optional[float] = None, # Optional: Nucleus sampling parameter. + user: Optional[str] = None, # Optional: A unique identifier representing your end-user. + + # set api_base, api_version, api_key + api_base: Optional[str] = None, + api_version: Optional[str] = None, + api_key: Optional[str] = None, + model_list: Optional[list] = None, # pass in a list of api_base,keys, etc. + + # Optional liteLLM function params + custom_llm_provider: Optional[str] = None, + *args, + **kwargs +): + global print_verbose + import copy + """ + Generate text completions using the OpenAI API. + + Args: + model (str): ID of the model to use. + prompt (Union[str, List[Union[str, List[Union[str, List[int]]]]]): The prompt(s) to generate completions for. + best_of (Optional[int], optional): Generates best_of completions server-side. Defaults to 1. + echo (Optional[bool], optional): Echo back the prompt in addition to the completion. Defaults to False. + frequency_penalty (Optional[float], optional): Penalize new tokens based on their existing frequency. Defaults to 0. + logit_bias (Optional[Dict[int, int]], optional): Modify the likelihood of specified tokens. Defaults to None. + logprobs (Optional[int], optional): Include the log probabilities on the most likely tokens. Defaults to None. + max_tokens (Optional[int], optional): The maximum number of tokens to generate in the completion. Defaults to 16. + n (Optional[int], optional): How many completions to generate for each prompt. Defaults to 1. + presence_penalty (Optional[float], optional): Penalize new tokens based on whether they appear in the text so far. Defaults to 0. + stop (Optional[Union[str, List[str]]], optional): Sequences where the API will stop generating further tokens. Defaults to None. + stream (Optional[bool], optional): Whether to stream back partial progress. Defaults to False. + suffix (Optional[str], optional): The suffix that comes after a completion of inserted text. Defaults to None. + temperature (Optional[float], optional): Sampling temperature to use. Defaults to 1. + top_p (Optional[float], optional): Nucleus sampling parameter. Defaults to 1. + user (Optional[str], optional): A unique identifier representing your end-user. + Returns: + TextCompletionResponse: A response object containing the generated completion and associated metadata. + + Example: + Your example of how to use this function goes here. + """ + if "engine" in kwargs: + if model==None: + # only use engine when model not passed + model = kwargs["engine"] + kwargs.pop("engine") + + text_completion_response = TextCompletionResponse() + + optional_params: Dict[str, Any] = {} + # default values for all optional params are none, litellm only passes them to the llm when they are set to non None values + if best_of is not None: + optional_params["best_of"] = best_of + if echo is not None: + optional_params["echo"] = echo + if frequency_penalty is not None: + optional_params["frequency_penalty"] = frequency_penalty + if logit_bias is not None: + optional_params["logit_bias"] = logit_bias + if logprobs is not None: + optional_params["logprobs"] = logprobs + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + if n is not None: + optional_params["n"] = n + if presence_penalty is not None: + optional_params["presence_penalty"] = presence_penalty + if stop is not None: + optional_params["stop"] = stop + if stream is not None: + optional_params["stream"] = stream + if suffix is not None: + optional_params["suffix"] = suffix + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if user is not None: + optional_params["user"] = user + if api_base is not None: + optional_params["api_base"] = api_base + if api_version is not None: + optional_params["api_version"] = api_version + if api_key is not None: + optional_params["api_key"] = api_key + if custom_llm_provider is not None: + optional_params["custom_llm_provider"] = custom_llm_provider + + # get custom_llm_provider + _, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore + + if custom_llm_provider == "huggingface": + # if echo == True, for TGI llms we need to set top_n_tokens to 3 + if echo == True: + # for tgi llms + if "top_n_tokens" not in kwargs: + kwargs["top_n_tokens"] = 3 + + # processing prompt - users can pass raw tokens to OpenAI Completion() + if type(prompt) == list: + import concurrent.futures + tokenizer = tiktoken.encoding_for_model("text-davinci-003") + ## if it's a 2d list - each element in the list is a text_completion() request + if len(prompt) > 0 and type(prompt[0]) == list: + responses = [None for x in prompt] # init responses + def process_prompt(i, individual_prompt): + decoded_prompt = tokenizer.decode(individual_prompt) + all_params = {**kwargs, **optional_params} + response = text_completion( + model=model, + prompt=decoded_prompt, + num_retries=3,# ensure this does not fail for the batch + *args, + **all_params, + ) + #print(response) + text_completion_response["id"] = response.get("id", None) + text_completion_response["object"] = "text_completion" + text_completion_response["created"] = response.get("created", None) + text_completion_response["model"] = response.get("model", None) + return response["choices"][0] + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = [executor.submit(process_prompt, i, individual_prompt) for i, individual_prompt in enumerate(prompt)] + for i, future in enumerate(concurrent.futures.as_completed(futures)): + responses[i] = future.result() + text_completion_response.choices = responses + + return text_completion_response + # else: + # check if non default values passed in for best_of, echo, logprobs, suffix + # these are the params supported by Completion() but not ChatCompletion + + # default case, non OpenAI requests go through here + messages = [{"role": "system", "content": prompt}] + kwargs.pop("prompt", None) + response = completion( + model = model, + messages=messages, + *args, + **kwargs, + **optional_params, + ) + if stream == True or kwargs.get("stream", False) == True: + response = TextCompletionStreamWrapper(completion_stream=response, model=model) + return response + + transformed_logprobs = None + # only supported for TGI models + try: + raw_response = response._hidden_params.get("original_response", None) + transformed_logprobs = litellm.utils.transform_logprobs(raw_response) + except Exception as e: + print_verbose(f"LiteLLM non blocking exception: {e}") + text_completion_response["id"] = response.get("id", None) + text_completion_response["object"] = "text_completion" + text_completion_response["created"] = response.get("created", None) + text_completion_response["model"] = response.get("model", None) + text_choices = TextChoices() + text_choices["text"] = response["choices"][0]["message"]["content"] + text_choices["index"] = response["choices"][0]["index"] + text_choices["logprobs"] = transformed_logprobs + text_choices["finish_reason"] = response["choices"][0]["finish_reason"] + text_completion_response["choices"] = [text_choices] + text_completion_response["usage"] = response.get("usage", None) + return text_completion_response + +##### Moderation ####################### +def moderation(input: str, api_key: Optional[str]=None): + # only supports open ai for now + api_key = ( + api_key or + litellm.api_key or + litellm.openai_key or + get_secret("OPENAI_API_KEY") + ) + openai.api_key = api_key + openai.api_type = "open_ai" # type: ignore + openai.api_version = None + openai.base_url = "https://api.openai.com/v1/" + response = openai.moderations.create(input=input) + return response + +####### HELPER FUNCTIONS ################ +## Set verbose to true -> ```litellm.set_verbose = True``` +def print_verbose(print_statement): + if litellm.set_verbose: + print(print_statement) # noqa + +def config_completion(**kwargs): + if litellm.config_path != None: + config_args = read_config_args(litellm.config_path) + # overwrite any args passed in with config args + return completion(**kwargs, **config_args) + else: + raise ValueError( + "No config path set, please set a config path using `litellm.config_path = 'path/to/config.json'`" + ) + +def stream_chunk_builder(chunks: list, messages: Optional[list]=None): + id = chunks[0]["id"] + object = chunks[0]["object"] + created = chunks[0]["created"] + model = chunks[0]["model"] + system_fingerprint = chunks[0].get("system_fingerprint", None) + role = chunks[0]["choices"][0]["delta"]["role"] + finish_reason = chunks[-1]["choices"][0]["finish_reason"] + + # Initialize the response dictionary + response = { + "id": id, + "object": object, + "created": created, + "model": model, + "system_fingerprint": system_fingerprint, + "choices": [ + { + "index": 0, + "message": { + "role": role, + "content": "" + }, + "finish_reason": finish_reason, + } + ], + "usage": { + "prompt_tokens": 0, # Modify as needed + "completion_tokens": 0, # Modify as needed + "total_tokens": 0 # Modify as needed + } + } + + # Extract the "content" strings from the nested dictionaries within "choices" + content_list = [] + combined_content = "" + combined_arguments = "" + + if "tool_calls" in chunks[0]["choices"][0]["delta"] and chunks[0]["choices"][0]["delta"]["tool_calls"] is not None: + argument_list = [] + delta = chunks[0]["choices"][0]["delta"] + message = response["choices"][0]["message"] + message["tool_calls"] = [] + id = None + name = None + type = None + tool_calls_list = [] + prev_index = 0 + prev_id = None + curr_id = None + curr_index = 0 + for chunk in chunks: + choices = chunk["choices"] + for choice in choices: + delta = choice.get("delta", {}) + tool_calls = delta.get("tool_calls", "") + # Check if a tool call is present + if tool_calls and tool_calls[0].function is not None: + if tool_calls[0].id: + id = tool_calls[0].id + curr_id = id + if prev_id is None: + prev_id = curr_id + if tool_calls[0].index: + curr_index = tool_calls[0].index + if tool_calls[0].function.arguments: + # Now, tool_calls is expected to be a dictionary + arguments = tool_calls[0].function.arguments + argument_list.append(arguments) + if tool_calls[0].function.name: + name = tool_calls[0].function.name + if tool_calls[0].type: + type = tool_calls[0].type + if curr_index != prev_index: # new tool call + combined_arguments = "".join(argument_list) + tool_calls_list.append({"id": prev_id, "index": prev_index, "function": {"arguments": combined_arguments, "name": name}, "type": type}) + argument_list = [] # reset + prev_index = curr_index + prev_id = curr_id + + combined_arguments = "".join(argument_list) + tool_calls_list.append({"id": id, "function": {"arguments": combined_arguments, "name": name}, "type": type}) + response["choices"][0]["message"]["content"] = None + response["choices"][0]["message"]["tool_calls"] = tool_calls_list + elif "function_call" in chunks[0]["choices"][0]["delta"] and chunks[0]["choices"][0]["delta"]["function_call"] is not None: + argument_list = [] + delta = chunks[0]["choices"][0]["delta"] + function_call = delta.get("function_call", "") + function_call_name = function_call.name + + message = response["choices"][0]["message"] + message["function_call"] = {} + message["function_call"]["name"] = function_call_name + + for chunk in chunks: + choices = chunk["choices"] + for choice in choices: + delta = choice.get("delta", {}) + function_call = delta.get("function_call", "") + + # Check if a function call is present + if function_call: + # Now, function_call is expected to be a dictionary + arguments = function_call.arguments + argument_list.append(arguments) + + combined_arguments = "".join(argument_list) + response["choices"][0]["message"]["content"] = None + response["choices"][0]["message"]["function_call"]["arguments"] = combined_arguments + else: + for chunk in chunks: + choices = chunk["choices"] + for choice in choices: + delta = choice.get("delta", {}) + content = delta.get("content", "") + if content == None: + continue # openai v1.0.0 sets content = None for chunks + content_list.append(content) + + # Combine the "content" strings into a single string || combine the 'function' strings into a single string + combined_content = "".join(content_list) + + # Update the "content" field within the response dictionary + response["choices"][0]["message"]["content"] = combined_content + + if len(combined_content) > 0: + completion_output = combined_content + elif len(combined_arguments) > 0: + completion_output = combined_arguments + # # Update usage information if needed + try: + response["usage"]["prompt_tokens"] = token_counter(model=model, messages=messages) + except: # don't allow this failing to block a complete streaming response from being returned + print_verbose(f"token_counter failed, assuming prompt tokens is 0") + response["usage"]["prompt_tokens"] = 0 + response["usage"]["completion_tokens"] = token_counter(model=model, text=completion_output) + response["usage"]["total_tokens"] = response["usage"]["prompt_tokens"] + response["usage"]["completion_tokens"] + return convert_to_model_response_object(response_object=response, model_response_object=litellm.ModelResponse()) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json new file mode 100644 index 0000000000000000000000000000000000000000..2c49dc02db4ced593cb9580b26a1479a0773bc53 --- /dev/null +++ b/litellm/model_prices_and_context_window_backup.json @@ -0,0 +1,623 @@ +{ + "gpt-4": { + "max_tokens": 8192, + "input_cost_per_token": 0.00003, + "output_cost_per_token": 0.00006, + "litellm_provider": "openai", + "mode": "chat" + }, + "gpt-4-0314": { + "max_tokens": 8192, + "input_cost_per_token": 0.00003, + "output_cost_per_token": 0.00006, + "litellm_provider": "openai", + "mode": "chat" + }, + "gpt-4-0613": { + "max_tokens": 8192, + "input_cost_per_token": 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"mode": "chat" + }, + "openrouter/mancer/weaver": { + "max_tokens": 8000, + "input_cost_per_token": 0.000005625, + "output_cost_per_token": 0.000005625, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/gryphe/mythomax-l2-13b": { + "max_tokens": 8192, + "input_cost_per_token": 0.000001875, + "output_cost_per_token": 0.000001875, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/jondurbin/airoboros-l2-70b-2.1": { + "max_tokens": 4096, + "input_cost_per_token": 0.000013875, + "output_cost_per_token": 0.000013875, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/undi95/remm-slerp-l2-13b": { + "max_tokens": 6144, + "input_cost_per_token": 0.000001875, + "output_cost_per_token": 0.000001875, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/pygmalionai/mythalion-13b": { + "max_tokens": 4096, + "input_cost_per_token": 0.000001875, + "output_cost_per_token": 0.000001875, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/mistralai/mistral-7b-instruct": { + "max_tokens": 4096, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "j2-ultra": { + "max_tokens": 8192, + "input_cost_per_token": 0.000015, + "output_cost_per_token": 0.000015, + "litellm_provider": "ai21", + "mode": "completion" + }, + "j2-mid": { + "max_tokens": 8192, + "input_cost_per_token": 0.00001, + "output_cost_per_token": 0.00001, + "litellm_provider": "ai21", + "mode": "completion" + }, + "j2-light": { + "max_tokens": 8192, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000003, + "litellm_provider": "ai21", + "mode": "completion" + }, + "dolphin": { + "max_tokens": 4096, + "input_cost_per_token": 0.00002, + "output_cost_per_token": 0.00002, + "litellm_provider": "nlp_cloud", + "mode": "completion" + }, + "chatdolphin": { + "max_tokens": 4096, + "input_cost_per_token": 0.00002, + "output_cost_per_token": 0.00002, + "litellm_provider": "nlp_cloud", + "mode": "chat" + }, + "luminous-base": { + "max_tokens": 2048, + "input_cost_per_token": 0.00003, + "output_cost_per_token": 0.000033, + "litellm_provider": "aleph_alpha", + "mode": "completion" + }, + "luminous-base-control": { + "max_tokens": 2048, + "input_cost_per_token": 0.0000375, + "output_cost_per_token": 0.00004125, + "litellm_provider": "aleph_alpha", + "mode": "chat" + }, + "luminous-extended": { + "max_tokens": 2048, + "input_cost_per_token": 0.000045, + "output_cost_per_token": 0.0000495, + "litellm_provider": "aleph_alpha", + "mode": "completion" + }, + "luminous-extended-control": { + "max_tokens": 2048, + "input_cost_per_token": 0.00005625, + "output_cost_per_token": 0.000061875, + "litellm_provider": "aleph_alpha", + "mode": "chat" + }, + "luminous-supreme": { + "max_tokens": 2048, + "input_cost_per_token": 0.000175, + "output_cost_per_token": 0.0001925, + "litellm_provider": "aleph_alpha", + "mode": "completion" + }, + "luminous-supreme-control": { + "max_tokens": 2048, + "input_cost_per_token": 0.00021875, + "output_cost_per_token": 0.000240625, + "litellm_provider": "aleph_alpha", + "mode": "chat" + }, + "ai21.j2-mid-v1": { + "max_tokens": 8191, + "input_cost_per_token": 0.0000125, + "output_cost_per_token": 0.0000125, + "litellm_provider": "bedrock", + "mode": "chat" + }, + "ai21.j2-ultra-v1": { + "max_tokens": 8191, + "input_cost_per_token": 0.0000188, + "output_cost_per_token": 0.0000188, + "litellm_provider": "bedrock", + "mode": "chat" + }, + "amazon.titan-text-lite-v1": { + "max_tokens": 8000, + "input_cost_per_token": 0.0000003, + "output_cost_per_token": 0.0000004, + "litellm_provider": "bedrock", + "mode": "chat" + }, + "amazon.titan-text-express-v1": { + "max_tokens": 8000, + "input_cost_per_token": 0.0000013, + "output_cost_per_token": 0.0000017, + "litellm_provider": "bedrock", + "mode": "chat" + }, + "anthropic.claude-v1": { + "max_tokens": 100000, + "input_cost_per_token": 0.00001102, + "output_cost_per_token": 0.00003268, + "litellm_provider": "bedrock", + "mode": "chat" + }, + "anthropic.claude-v2": { + "max_tokens": 100000, + "input_cost_per_token": 0.00001102, + "output_cost_per_token": 0.00003268, + "litellm_provider": "bedrock", + "mode": "chat" + }, + "anthropic.claude-instant-v1": { + "max_tokens": 100000, + "input_cost_per_token": 0.00000163, + "output_cost_per_token": 0.00000551, + "litellm_provider": "bedrock", + "mode": "chat" + }, + "cohere.command-text-v14": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.0000020, + "litellm_provider": "bedrock", + "mode": "chat" + }, + "meta.llama2-13b-chat-v1": { + "max_tokens": 4096, + "input_cost_per_token": 0.00000075, + "output_cost_per_token": 0.000001, + "litellm_provider": "bedrock", + "mode": "chat" + }, + "together-ai-up-to-3b": { + "input_cost_per_token": 0.0000001, + "output_cost_per_token": 0.0000001 + }, + "together-ai-3.1b-7b": { + "input_cost_per_token": 0.0000002, + "output_cost_per_token": 0.0000002 + }, + "together-ai-7.1b-20b": { + "max_tokens": 1000, + "input_cost_per_token": 0.0000004, + "output_cost_per_token": 0.0000004 + }, + "together-ai-20.1b-40b": { + "input_cost_per_token": 0.000001, + "output_cost_per_token": 0.000001 + }, + "together-ai-40.1b-70b": { + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000003 + }, + "ollama/llama2": { + "max_tokens": 4096, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "completion" + }, + "ollama/llama2:13b": { + "max_tokens": 4096, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "completion" + }, + "ollama/llama2:70b": { + "max_tokens": 4096, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "completion" + }, + "ollama/llama2-uncensored": { + "max_tokens": 4096, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "completion" + }, + "deepinfra/meta-llama/Llama-2-70b-chat-hf": { + "max_tokens": 6144, + "input_cost_per_token": 0.000001875, + "output_cost_per_token": 0.000001875, + "litellm_provider": "deepinfra", + "mode": "chat" + }, + "deepinfra/codellama/CodeLlama-34b-Instruct-hf": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000006, + "output_cost_per_token": 0.0000006, + "litellm_provider": "deepinfra", + "mode": "chat" + }, + "deepinfra/meta-llama/Llama-2-13b-chat-hf": { + "max_tokens": 4096, + "input_cost_per_token": 0.00000035, + "output_cost_per_token": 0.00000035, + "litellm_provider": "deepinfra", + "mode": "chat" + }, + "deepinfra/meta-llama/Llama-2-7b-chat-hf": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000002, + "output_cost_per_token": 0.0000002, + "litellm_provider": "deepinfra", + "mode": "chat" + }, + "deepinfra/mistralai/Mistral-7B-Instruct-v0.1": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000002, + "output_cost_per_token": 0.0000002, + "litellm_provider": "deepinfra", + "mode": "chat" + }, + "deepinfra/jondurbin/airoboros-l2-70b-gpt4-1.4.1": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000007, + "output_cost_per_token": 0.00000095, + "litellm_provider": "deepinfra", + "mode": "chat" + } +} diff --git a/litellm/proxy/.gitignore b/litellm/proxy/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..caa4783d9074da6624aae24c2ed0fd95c7111449 --- /dev/null +++ b/litellm/proxy/.gitignore @@ -0,0 +1,2 @@ +.env +secrets.toml \ No newline at end of file diff --git a/litellm/proxy/README.md b/litellm/proxy/README.md new file mode 100644 index 0000000000000000000000000000000000000000..92df6026c2ddfe74c187d964e5d38d34163ff699 --- /dev/null +++ b/litellm/proxy/README.md @@ -0,0 +1,31 @@ +# litellm-proxy + +A local, fast, and lightweight **OpenAI-compatible server** to call 100+ LLM APIs. + +## usage + +```shell +$ pip install litellm +``` +```shell +$ litellm --model ollama/codellama + +#INFO: Ollama running on http://0.0.0.0:8000 +``` + +## replace openai base +```python +import openai # openai v1.0.0+ +client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:8000") # set proxy to base_url +# request sent to model set on litellm proxy, `litellm --model` +response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +]) + +print(response) +``` + +[**See how to call Huggingface,Bedrock,TogetherAI,Anthropic, etc.**](https://docs.litellm.ai/docs/simple_proxy) diff --git a/litellm/proxy/__init__.py b/litellm/proxy/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b9742821a6f164200bc145e7a847382f08778303 --- /dev/null +++ b/litellm/proxy/__init__.py @@ -0,0 +1 @@ +from . import * \ No newline at end of file diff --git a/litellm/proxy/custom_auth.py b/litellm/proxy/custom_auth.py new file mode 100644 index 0000000000000000000000000000000000000000..0cce561ca8bc68625c9fe2f7f57601621fd916e7 --- /dev/null +++ b/litellm/proxy/custom_auth.py @@ -0,0 +1,14 @@ +from litellm.proxy.types import UserAPIKeyAuth +from fastapi import Request +from dotenv import load_dotenv +import os + +load_dotenv() +async def user_api_key_auth(request: Request, api_key: str) -> UserAPIKeyAuth: + try: + modified_master_key = f"{os.getenv('PROXY_MASTER_KEY')}-1234" + if api_key == modified_master_key: + return UserAPIKeyAuth(api_key=api_key) + raise Exception + except: + raise Exception \ No newline at end of file diff --git a/litellm/proxy/custom_callbacks.py b/litellm/proxy/custom_callbacks.py new file mode 100644 index 0000000000000000000000000000000000000000..a991ff1d924a8b2902b1a87dab13df3a0e4e107e --- /dev/null +++ b/litellm/proxy/custom_callbacks.py @@ -0,0 +1,53 @@ +from litellm.integrations.custom_logger import CustomLogger +import litellm + +# This file includes the custom callbacks for LiteLLM Proxy +# Once defined, these can be passed in proxy_config.yaml +class MyCustomHandler(CustomLogger): + def log_pre_api_call(self, model, messages, kwargs): + print(f"Pre-API Call") + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + print(f"Post-API Call") + + def log_stream_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Stream") + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + # log: key, user, model, prompt, response, tokens, cost + print("\nOn Success") + ### Access kwargs passed to litellm.completion() + model = kwargs.get("model", None) + messages = kwargs.get("messages", None) + user = kwargs.get("user", None) + + #### Access litellm_params passed to litellm.completion(), example access `metadata` + litellm_params = kwargs.get("litellm_params", {}) + metadata = litellm_params.get("metadata", {}) # headers passed to LiteLLM proxy, can be found here + ################################################# + + ##### Calculate cost using litellm.completion_cost() ####################### + cost = litellm.completion_cost(completion_response=response_obj) + response = response_obj + # tokens used in response + usage = response_obj["usage"] + + print( + f""" + Model: {model}, + Messages: {messages}, + User: {user}, + Usage: {usage}, + Cost: {cost}, + Response: {response} + Proxy Metadata: {metadata} + """ + ) + return + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Failure") + +proxy_handler_instance = MyCustomHandler() + +# need to set litellm.callbacks = [customHandler] # on the proxy diff --git a/litellm/proxy/example_config_yaml/aliases_config.yaml b/litellm/proxy/example_config_yaml/aliases_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..266f6cf22eef39e2ef0783211ead16de1c5d4576 --- /dev/null +++ b/litellm/proxy/example_config_yaml/aliases_config.yaml @@ -0,0 +1,30 @@ +model_list: + - model_name: text-davinci-003 + litellm_params: + model: ollama/zephyr + - model_name: gpt-4 + litellm_params: + model: ollama/llama2 + - model_name: gpt-3.5-turbo + litellm_params: + model: ollama/llama2 + temperature: 0.1 + max_tokens: 20 + + +# request to gpt-4, response from ollama/llama2 +# curl --location 'http://0.0.0.0:8000/chat/completions' \ +# --header 'Content-Type: application/json' \ +# --data ' { +# "model": "gpt-4", +# "messages": [ +# { +# "role": "user", +# "content": "what llm are you" +# } +# ], +# } +# ' +# + +# {"id":"chatcmpl-27c85cf0-ab09-4bcf-8cb1-0ee950520743","choices":[{"finish_reason":"stop","index":0,"message":{"content":" Hello! I'm just an AI, I don't have personal experiences or emotions like humans do. However, I can help you with any questions or tasks you may have! Is there something specific you'd like to know or discuss?","role":"assistant","_logprobs":null}}],"created":1700094955.373751,"model":"ollama/llama2","object":"chat.completion","system_fingerprint":null,"usage":{"prompt_tokens":12,"completion_tokens":47,"total_tokens":59},"_response_ms":8028.017999999999}% \ No newline at end of file diff --git a/litellm/proxy/example_config_yaml/azure_config.yaml b/litellm/proxy/example_config_yaml/azure_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bd9ff9ac993ef7db74f0334490b48a634bce33ab --- /dev/null +++ b/litellm/proxy/example_config_yaml/azure_config.yaml @@ -0,0 +1,21 @@ +model_list: + - model_name: gpt-4-team1 + litellm_params: + model: azure/chatgpt-v-2 + api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ + api_version: "2023-05-15" + api_key: os.environ/AZURE_API_KEY + tpm: 20_000 + timeout: 5 # 1 second timeout + stream_timeout: 0.5 # 0.5 second timeout for streaming requests + max_retries: 4 + - model_name: gpt-4-team2 + litellm_params: + model: azure/gpt-4 + api_key: os.environ/AZURE_API_KEY + api_base: https://openai-gpt-4-test-v-2.openai.azure.com/ + tpm: 100_000 + timeout: 5 # 1 second timeout + stream_timeout: 0.5 # 0.5 second timeout for streaming requests + max_retries: 4 + diff --git a/litellm/proxy/example_config_yaml/langfuse_config.yaml b/litellm/proxy/example_config_yaml/langfuse_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c2a77b5adc464ca063cb6a5bda3eab6965b400cf --- /dev/null +++ b/litellm/proxy/example_config_yaml/langfuse_config.yaml @@ -0,0 +1,7 @@ +model_list: + - model_name: gpt-3.5-turbo + +litellm_settings: + drop_params: True + success_callback: ["langfuse"] # https://docs.litellm.ai/docs/observability/langfuse_integration + diff --git a/litellm/proxy/example_config_yaml/load_balancer.yaml b/litellm/proxy/example_config_yaml/load_balancer.yaml new file mode 100644 index 0000000000000000000000000000000000000000..502b90ff92af6f381fece2096a3b352f9f0ada91 --- /dev/null +++ b/litellm/proxy/example_config_yaml/load_balancer.yaml @@ -0,0 +1,28 @@ +litellm_settings: + drop_params: True + +# Model-specific settings +model_list: # use the same model_name for using the litellm router. LiteLLM will use the router between gpt-3.5-turbo + - model_name: gpt-3.5-turbo # litellm will + litellm_params: + model: gpt-3.5-turbo + api_key: sk-uj6F + tpm: 20000 # [OPTIONAL] REPLACE with your openai tpm + rpm: 3 # [OPTIONAL] REPLACE with your openai rpm + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + api_key: sk-Imn + tpm: 20000 # [OPTIONAL] REPLACE with your openai tpm + rpm: 3 # [OPTIONAL] REPLACE with your openai rpm + - model_name: gpt-3.5-turbo + litellm_params: + model: openrouter/gpt-3.5-turbo + - model_name: mistral-7b-instruct + litellm_params: + model: mistralai/mistral-7b-instruct + +environment_variables: + REDIS_HOST: localhost + REDIS_PASSWORD: + REDIS_PORT: \ No newline at end of file diff --git a/litellm/proxy/example_config_yaml/opentelemetry_config.yaml b/litellm/proxy/example_config_yaml/opentelemetry_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..92d3454d7d5fdb4e2a896aa47d01a19182043ee7 --- /dev/null +++ b/litellm/proxy/example_config_yaml/opentelemetry_config.yaml @@ -0,0 +1,7 @@ +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + +general_settings: + otel: True # OpenTelemetry Logger this logs OTEL data to your collector diff --git a/litellm/proxy/example_config_yaml/simple_config.yaml b/litellm/proxy/example_config_yaml/simple_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..14b39a12518e83600c24ee86a0cd5405ae48ee3c --- /dev/null +++ b/litellm/proxy/example_config_yaml/simple_config.yaml @@ -0,0 +1,4 @@ +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo \ No newline at end of file diff --git a/litellm/proxy/lambda.py b/litellm/proxy/lambda.py new file mode 100644 index 0000000000000000000000000000000000000000..6b278c4118865c80661cfd31c6f01563f86d2cbb --- /dev/null +++ b/litellm/proxy/lambda.py @@ -0,0 +1,4 @@ +from mangum import Mangum +from litellm.proxy.proxy_server import app + +handler = Mangum(app, lifespan="on") diff --git a/litellm/proxy/openapi.json b/litellm/proxy/openapi.json new file mode 100644 index 0000000000000000000000000000000000000000..95517182667f6032704045cb8b353ca010434479 --- /dev/null +++ b/litellm/proxy/openapi.json @@ -0,0 +1,237 @@ +{ + "openapi": "3.0.0", + "info": { + "version": "1.0.0", + "title": "LiteLLM API", + "description": "API for LiteLLM" + }, + "paths": { + "/chat/completions": { + "post": { + "summary": "Create chat completion for 100+ LLM APIs", + "requestBody": { + "required": true, + "content": { + "application/json": { + "schema": { + "type": "object", + "properties": { + "model": { + "type": "string", + "description": "ID of the model to use" + }, + "messages": { + "type": "array", + "items": { + "type": "object", + "properties": { + "role": { + "type": "string", + "description": "The role of the message's author" + }, + "content": { + "type": "string", + "description": "The contents of the message" + }, + "name": { + "type": "string", + "description": "The name of the author of the message" + }, + "function_call": { + "type": "object", + "description": "The name and arguments of a function that should be called" + } + } + } + }, + "functions": { + "type": "array", + "items": { + "type": "object", + "properties": { + "name": { + "type": "string", + "description": "The name of the function to be called" + }, + "description": { + "type": "string", + "description": "A description explaining what the function does" + }, + "parameters": { + "type": "object", + "description": "The parameters that the function accepts" + }, + "function_call": { + "type": "string", + "description": "Controls how the model responds to function calls" + } + } + } + }, + "temperature": { + "type": "number", + "description": "The sampling temperature to be used" + }, + "top_p": { + "type": "number", + "description": "An alternative to sampling with temperature" + }, + "n": { + "type": "integer", + "description": "The number of chat completion choices to generate for each input message" + }, + "stream": { + "type": "boolean", + "description": "If set to true, it sends partial message deltas" + }, + "stop": { + "type": "array", + "items": { + "type": "string" + }, + "description": "Up to 4 sequences where the API will stop generating further tokens" + }, + "max_tokens": { + "type": "integer", + "description": "The maximum number of tokens to generate in the chat completion" + }, + "presence_penalty": { + "type": "number", + "description": "It is used to penalize new tokens based on their existence in the text so far" + }, + "frequency_penalty": { + "type": "number", + "description": "It is used to penalize new tokens based on their frequency in the text so far" + }, + "logit_bias": { + "type": "object", + "description": "Used to modify the probability of specific tokens appearing in the completion" + }, + "user": { + "type": "string", + "description": "A unique identifier representing your end-user" + } + } + } + } + } + }, + "responses": { + "200": { + "description": "Successful operation", + "content": { + "application/json": { + "schema": { + "type": "object", + "properties": { + "choices": { + "type": "array", + "items": { + "type": "object", + "properties": { + "finish_reason": { + "type": "string" + }, + "index": { + "type": "integer" + }, + "message": { + "type": "object", + "properties": { + "role": { + "type": "string" + }, + "content": { + "type": "string" + } + } + } + } + } + }, + "created": { + "type": "string" + }, + "model": { + "type": "string" + }, + "usage": { + "type": "object", + "properties": { + "prompt_tokens": { + "type": "integer" + }, + "completion_tokens": { + "type": "integer" + }, + "total_tokens": { + "type": "integer" + } + } + } + } + } + } + } + }, + "500": { + "description": "Server error" + } + } + } +}, + + "/completions": { + "post": { + "summary": "Create completion", + "responses": { + "200": { + "description": "Successful operation" + }, + "500": { + "description": "Server error" + } + } + } + }, + "/models": { + "get": { + "summary": "Get models", + "responses": { + "200": { + "description": "Successful operation" + } + } + } + }, + + "/ollama_logs": { + "get": { + "summary": "Retrieve server logs for ollama models", + "responses": { + "200": { + "description": "Successful operation", + "content": { + "application/octet-stream": { + "schema": { + "type": "string", + "format": "binary" + } + } + } + } + } + } + }, + "/": { + "get": { + "summary": "Home", + "responses": { + "200": { + "description": "Successful operation" + } + } + } + } + } +} diff --git a/litellm/proxy/otel_config.yaml b/litellm/proxy/otel_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5b75697b7bd1e83e8f6d566f4de150b54a822d77 --- /dev/null +++ b/litellm/proxy/otel_config.yaml @@ -0,0 +1,30 @@ +receivers: + otlp: + protocols: + grpc: + endpoint: 0.0.0.0:4317 + +processors: + batch: + timeout: 1s + send_batch_size: 1024 + +exporters: + logging: + loglevel: debug + otlphttp/elastic: + endpoint: "https://3f51c0abae7743e2800df44a8a5f0997.apm.us-central1.gcp.cloud.es.io:443" + headers: + Authorization: "Bearer " + +service: + pipelines: + metrics: + receivers: [otlp] + exporters: [logging, otlphttp/elastic] + traces: + receivers: [otlp] + exporters: [logging, otlphttp/elastic] + logs: + receivers: [otlp] + exporters: [logging,otlphttp/elastic] \ No newline at end of file diff --git a/litellm/proxy/proxy_cli.py b/litellm/proxy/proxy_cli.py new file mode 100644 index 0000000000000000000000000000000000000000..a76a49b2cc8ac33e4bbbc2db4aabc90f7f2f1120 --- /dev/null +++ b/litellm/proxy/proxy_cli.py @@ -0,0 +1,238 @@ +import click +import subprocess, traceback, json +import os, sys +import random, appdirs +from datetime import datetime +from dotenv import load_dotenv +import operator +sys.path.append(os.getcwd()) + +config_filename = "litellm.secrets" +# Using appdirs to determine user-specific config path +config_dir = appdirs.user_config_dir("litellm") +user_config_path = os.getenv("LITELLM_CONFIG_PATH", os.path.join(config_dir, config_filename)) + +load_dotenv() +from importlib import resources +import shutil +telemetry = None + +def run_ollama_serve(): + try: + command = ['ollama', 'serve'] + + with open(os.devnull, 'w') as devnull: + process = subprocess.Popen(command, stdout=devnull, stderr=devnull) + except Exception as e: + print(f""" + LiteLLM Warning: proxy started with `ollama` model\n`ollama serve` failed with Exception{e}. \nEnsure you run `ollama serve` + """) + +def clone_subfolder(repo_url, subfolder, destination): + # Clone the full repo + repo_name = repo_url.split('/')[-1] + repo_master = os.path.join(destination, "repo_master") + subprocess.run(['git', 'clone', repo_url, repo_master]) + + # Move into the subfolder + subfolder_path = os.path.join(repo_master, subfolder) + + # Copy subfolder to destination + for file_name in os.listdir(subfolder_path): + source = os.path.join(subfolder_path, file_name) + if os.path.isfile(source): + shutil.copy(source, destination) + else: + dest_path = os.path.join(destination, file_name) + shutil.copytree(source, dest_path) + + # Remove cloned repo folder + subprocess.run(['rm', '-rf', os.path.join(destination, "repo_master")]) + feature_telemetry(feature="create-proxy") + +def is_port_in_use(port): + import socket + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + return s.connect_ex(('localhost', port)) == 0 + +@click.command() +@click.option('--host', default='0.0.0.0', help='Host for the server to listen on.') +@click.option('--port', default=8000, help='Port to bind the server to.') +@click.option('--num_workers', default=1, help='Number of uvicorn workers to spin up') +@click.option('--api_base', default=None, help='API base URL.') +@click.option('--api_version', default="2023-07-01-preview", help='For azure - pass in the api version.') +@click.option('--model', '-m', default=None, help='The model name to pass to litellm expects') +@click.option('--alias', default=None, help='The alias for the model - use this to give a litellm model name (e.g. "huggingface/codellama/CodeLlama-7b-Instruct-hf") a more user-friendly name ("codellama")') +@click.option('--add_key', default=None, help='The model name to pass to litellm expects') +@click.option('--headers', default=None, help='headers for the API call') +@click.option('--save', is_flag=True, type=bool, help='Save the model-specific config') +@click.option('--debug', default=False, is_flag=True, type=bool, help='To debug the input') +@click.option('--use_queue', default=False, is_flag=True, type=bool, help='To use celery workers for async endpoints') +@click.option('--temperature', default=None, type=float, help='Set temperature for the model') +@click.option('--max_tokens', default=None, type=int, help='Set max tokens for the model') +@click.option('--request_timeout', default=600, type=int, help='Set timeout in seconds for completion calls') +@click.option('--drop_params', is_flag=True, help='Drop any unmapped params') +@click.option('--add_function_to_prompt', is_flag=True, help='If function passed but unsupported, pass it as prompt') +@click.option('--config', '-c', default=None, help='Configure Litellm') +@click.option('--file', '-f', help='Path to config file') +@click.option('--max_budget', default=None, type=float, help='Set max budget for API calls - works for hosted models like OpenAI, TogetherAI, Anthropic, etc.`') +@click.option('--telemetry', default=True, type=bool, help='Helps us know if people are using this feature. Turn this off by doing `--telemetry False`') +@click.option('--logs', flag_value=False, type=int, help='Gets the "n" most recent logs. By default gets most recent log.') +@click.option('--health', flag_value=True, help='Make a chat/completions request to all llms in config.yaml') +@click.option('--test', flag_value=True, help='proxy chat completions url to make a test request to') +@click.option('--test_async', default=False, is_flag=True, help='Calls async endpoints /queue/requests and /queue/response') +@click.option('--num_requests', default=10, type=int, help='Number of requests to hit async endpoint with') +@click.option('--local', is_flag=True, default=False, help='for local debugging') +def run_server(host, port, api_base, api_version, model, alias, add_key, headers, save, debug, temperature, max_tokens, request_timeout, drop_params, add_function_to_prompt, config, file, max_budget, telemetry, logs, test, local, num_workers, test_async, num_requests, use_queue, health): + global feature_telemetry + args = locals() + if local: + from proxy_server import app, save_worker_config, usage_telemetry + else: + try: + from .proxy_server import app, save_worker_config, usage_telemetry + except ImportError as e: + from proxy_server import app, save_worker_config, usage_telemetry + feature_telemetry = usage_telemetry + if logs is not None: + if logs == 0: # default to 1 + logs = 1 + try: + with open('api_log.json') as f: + data = json.load(f) + + # convert keys to datetime objects + log_times = {datetime.strptime(k, "%Y%m%d%H%M%S%f"): v for k, v in data.items()} + + # sort by timestamp + sorted_times = sorted(log_times.items(), key=operator.itemgetter(0), reverse=True) + + # get n recent logs + recent_logs = {k.strftime("%Y%m%d%H%M%S%f"): v for k, v in sorted_times[:logs]} + + print(json.dumps(recent_logs, indent=4)) + except: + print("LiteLLM: No logs saved!") + return + if model and "ollama" in model: + run_ollama_serve() + if test_async is True: + import requests, concurrent, time + api_base = f"http://{host}:{port}" + + def _make_openai_completion(): + data = { + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Write a short poem about the moon"}] + } + + response = requests.post("http://0.0.0.0:8000/queue/request", json=data) + + response = response.json() + + while True: + try: + url = response["url"] + polling_url = f"{api_base}{url}" + polling_response = requests.get(polling_url) + polling_response = polling_response.json() + print("\n RESPONSE FROM POLLING JOB", polling_response) + status = polling_response["status"] + if status == "finished": + llm_response = polling_response["result"] + break + print(f"POLLING JOB{polling_url}\nSTATUS: {status}, \n Response {polling_response}") + time.sleep(0.5) + except Exception as e: + print("got exception in polling", e) + break + + # Number of concurrent calls (you can adjust this) + concurrent_calls = num_requests + + # List to store the futures of concurrent calls + futures = [] + start_time = time.time() + # Make concurrent calls + with concurrent.futures.ThreadPoolExecutor(max_workers=concurrent_calls) as executor: + for _ in range(concurrent_calls): + futures.append(executor.submit(_make_openai_completion)) + + # Wait for all futures to complete + concurrent.futures.wait(futures) + + # Summarize the results + successful_calls = 0 + failed_calls = 0 + + for future in futures: + if future.done(): + if future.result() is not None: + successful_calls += 1 + else: + failed_calls += 1 + end_time = time.time() + print(f"Elapsed Time: {end_time-start_time}") + print(f"Load test Summary:") + print(f"Total Requests: {concurrent_calls}") + print(f"Successful Calls: {successful_calls}") + print(f"Failed Calls: {failed_calls}") + return + if health != False: + import requests + print("\nLiteLLM: Health Testing models in config") + response = requests.get(url=f"http://{host}:{port}/health") + print(json.dumps(response.json(), indent=4)) + return + if test != False: + click.echo('\nLiteLLM: Making a test ChatCompletions request to your proxy') + import openai + if test == True: # flag value set + api_base = f"http://{host}:{port}" + else: + api_base = test + client = openai.OpenAI( + api_key="My API Key", + base_url=api_base + ) + + response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } + ], max_tokens=256) + click.echo(f'\nLiteLLM: response from proxy {response}') + + print("\n Making streaming request to proxy") + + response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } + ], + stream=True, + ) + for chunk in response: + click.echo(f'LiteLLM: streaming response from proxy {chunk}') + print("\n making completion request to proxy") + response = client.completions.create(model="gpt-3.5-turbo", prompt='this is a test request, write a short poem') + print(response) + + return + else: + if headers: + headers = json.loads(headers) + save_worker_config(model=model, alias=alias, api_base=api_base, api_version=api_version, debug=debug, temperature=temperature, max_tokens=max_tokens, request_timeout=request_timeout, max_budget=max_budget, telemetry=telemetry, drop_params=drop_params, add_function_to_prompt=add_function_to_prompt, headers=headers, save=save, config=config, use_queue=use_queue) + try: + import uvicorn + except: + raise ImportError("Uvicorn needs to be imported. Run - `pip install uvicorn`") + if port == 8000 and is_port_in_use(port): + port = random.randint(1024, 49152) + uvicorn.run("litellm.proxy.proxy_server:app", host=host, port=port, workers=num_workers) + + +if __name__ == "__main__": + run_server() diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5f4f9fcad709ef435feba277a1a8997dc644b37a --- /dev/null +++ b/litellm/proxy/proxy_config.yaml @@ -0,0 +1,11 @@ +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + +litellm_settings: + # callbacks: custom_callbacks.proxy_handler_instance # sets litellm.callbacks = [proxy_handler_instance] + +general_settings: + # otel: True # OpenTelemetry Logger + # master_key: sk-1234 # [OPTIONAL] Only use this if you to require all calls to contain this key (Authorization: Bearer sk-1234) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py new file mode 100644 index 0000000000000000000000000000000000000000..6f8e0f6ab5c624979e32ce6f6f24006c259702e0 --- /dev/null +++ b/litellm/proxy/proxy_server.py @@ -0,0 +1,1174 @@ +import sys, os, platform, time, copy, re, asyncio +import threading, ast +import shutil, random, traceback, requests +from datetime import datetime, timedelta +from typing import Optional, List +import secrets, subprocess +import hashlib, uuid +import warnings +import importlib +messages: list = [] +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path - for litellm local dev + +try: + import uvicorn + import fastapi + import appdirs + import backoff + import yaml + import rq + import orjson +except ImportError: + import sys + + subprocess.check_call( + [ + sys.executable, + "-m", + "pip", + "install", + "uvicorn", + "fastapi", + "appdirs", + "backoff", + "pyyaml", + "rq", + "orjson" + ] + ) + import uvicorn + import fastapi + import appdirs + import backoff + import yaml + import orjson + + warnings.warn( + "Installed runtime dependencies for proxy server. Specify these dependencies explicitly with `pip install litellm[proxy]`" + ) + +import random + +list_of_messages = [ + "'The thing I wish you improved is...'", + "'A feature I really want is...'", + "'The worst thing about this product is...'", + "'This product would be better if...'", + "'I don't like how this works...'", + "'It would help me if you could add...'", + "'This feature doesn't meet my needs because...'", + "'I get frustrated when the product...'", +] + + +def generate_feedback_box(): + box_width = 60 + + # Select a random message + message = random.choice(list_of_messages) + + print() + print("\033[1;37m" + "#" + "-" * box_width + "#\033[0m") + print("\033[1;37m" + "#" + " " * box_width + "#\033[0m") + print("\033[1;37m" + "# {:^59} #\033[0m".format(message)) + print( + "\033[1;37m" + + "# {:^59} #\033[0m".format("https://github.com/BerriAI/litellm/issues/new") + ) + print("\033[1;37m" + "#" + " " * box_width + "#\033[0m") + print("\033[1;37m" + "#" + "-" * box_width + "#\033[0m") + print() + print(" Thank you for using LiteLLM! - Krrish & Ishaan") + print() + print() + print() + print( + "\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m" + ) + print() + print() + +import litellm +from litellm.proxy.utils import ( + PrismaClient, + get_instance_fn +) +import pydantic +from litellm.proxy.types import * +from litellm.caching import DualCache +litellm.suppress_debug_info = True +from fastapi import FastAPI, Request, HTTPException, status, Depends, BackgroundTasks +from fastapi.routing import APIRouter +from fastapi.security import OAuth2PasswordBearer +from fastapi.encoders import jsonable_encoder +from fastapi.responses import StreamingResponse, FileResponse, ORJSONResponse +from fastapi.middleware.cors import CORSMiddleware +from fastapi.security.api_key import APIKeyHeader +import json +import logging +from typing import Union +# from litellm.proxy.queue import start_rq_worker_in_background + +app = FastAPI(docs_url="/", title="LiteLLM API") +router = APIRouter() +origins = ["*"] + +app.add_middleware( + CORSMiddleware, + allow_origins=origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) +def log_input_output(request, response, custom_logger=None): + if custom_logger is not None: + custom_logger(request, response) + global otel_logging + if otel_logging != True: + return + from opentelemetry import trace + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry.sdk.trace.export import SimpleSpanProcessor + from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter + from opentelemetry.sdk.resources import Resource + + # Initialize OpenTelemetry components + otlp_host = os.environ.get("OTEL_ENDPOINT", "localhost:4317") + otlp_exporter = OTLPSpanExporter(endpoint=otlp_host, insecure=True) + resource = Resource.create({ + "service.name": "LiteLLM Proxy", + }) + trace.set_tracer_provider(TracerProvider(resource=resource)) + tracer = trace.get_tracer(__name__) + span_processor = SimpleSpanProcessor(otlp_exporter) + trace.get_tracer_provider().add_span_processor(span_processor) + with tracer.start_as_current_span("litellm-completion") as current_span: + input_event_attributes = {f"{key}": str(value) for key, value in dict(request).items() if value is not None} + # Log the input event with attributes + current_span.add_event( + name="LiteLLM: Request Input", + attributes=input_event_attributes + ) + event_headers = {f"{key}": str(value) for key, value in dict(request.headers).items() if value is not None} + current_span.add_event( + name="LiteLLM: Request Headers", + attributes=event_headers + ) + + input_event_attributes.update(event_headers) + + input_event_attributes.update({f"{key}": str(value) for key, value in dict(response).items()}) + current_span.add_event( + name="LiteLLM: Request Outpu", + attributes=input_event_attributes + ) + return True + +from typing import Dict + +oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token") +user_api_base = None +user_model = None +user_debug = False +user_max_tokens = None +user_request_timeout = None +user_temperature = None +user_telemetry = True +user_config = None +user_headers = None +user_config_file_path = f"config_{time.time()}.yaml" +local_logging = True # writes logs to a local api_log.json file for debugging +experimental = False +#### GLOBAL VARIABLES #### +llm_router: Optional[litellm.Router] = None +llm_model_list: Optional[list] = None +general_settings: dict = {} +log_file = "api_log.json" +worker_config = None +master_key = None +otel_logging = False +prisma_client: Optional[PrismaClient] = None +user_api_key_cache = DualCache() +user_custom_auth = None +### REDIS QUEUE ### +async_result = None +celery_app_conn = None +celery_fn = None # Redis Queue for handling requests +#### HELPER FUNCTIONS #### +def print_verbose(print_statement): + global user_debug + if user_debug: + print(print_statement) + +def usage_telemetry( + feature: str, +): # helps us know if people are using this feature. Set `litellm --telemetry False` to your cli call to turn this off + if user_telemetry: + data = {"feature": feature} # "local_proxy_server" + threading.Thread( + target=litellm.utils.litellm_telemetry, args=(data,), daemon=True + ).start() + + + +async def user_api_key_auth(request: Request, api_key: str = Depends(oauth2_scheme)) -> UserAPIKeyAuth: + global master_key, prisma_client, llm_model_list, user_custom_auth + try: + ### USER-DEFINED AUTH FUNCTION ### + if user_custom_auth: + response = await user_custom_auth(request=request, api_key=api_key) + return UserAPIKeyAuth.model_validate(response) + + if master_key is None: + if isinstance(api_key, str): + return UserAPIKeyAuth(api_key=api_key.replace("Bearer ", "")) + else: + return UserAPIKeyAuth() + if api_key is None: + raise Exception("No api key passed in.") + route = request.url.path + + # note: never string compare api keys, this is vulenerable to a time attack. Use secrets.compare_digest instead + is_master_key_valid = secrets.compare_digest(api_key, master_key) or secrets.compare_digest(api_key, "Bearer " + master_key) + if is_master_key_valid: + return UserAPIKeyAuth(api_key=master_key) + + if (route == "/key/generate" or route == "/key/delete" or route == "/key/info") and not is_master_key_valid: + raise Exception(f"If master key is set, only master key can be used to generate, delete or get info for new keys") + + if prisma_client: + ## check for cache hit (In-Memory Cache) + valid_token = user_api_key_cache.get_cache(key=api_key) + if valid_token is None and "Bearer " in api_key: + ## check db + cleaned_api_key = api_key[len("Bearer "):] + valid_token = await prisma_client.get_data(token=cleaned_api_key, expires=datetime.utcnow()) + user_api_key_cache.set_cache(key=api_key, value=valid_token, ttl=60) + elif valid_token is not None: + print(f"API Key Cache Hit!") + if valid_token: + litellm.model_alias_map = valid_token.aliases + config = valid_token.config + if config != {}: + model_list = config.get("model_list", []) + llm_model_list = model_list + print("\n new llm router model list", llm_model_list) + if len(valid_token.models) == 0: # assume an empty model list means all models are allowed to be called + return_dict = {"api_key": valid_token.token} + if valid_token.user_id: + return_dict["user_id"] = valid_token.user_id + return UserAPIKeyAuth(**return_dict) + else: + data = await request.json() + model = data.get("model", None) + if model in litellm.model_alias_map: + model = litellm.model_alias_map[model] + if model and model not in valid_token.models: + raise Exception(f"Token not allowed to access model") + return_dict = {"api_key": valid_token.token} + if valid_token.user_id: + return_dict["user_id"] = valid_token.user_id + return UserAPIKeyAuth(**return_dict) + else: + raise Exception(f"Invalid token") + except Exception as e: + print(f"An exception occurred - {traceback.format_exc()}") + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="invalid user key", + ) + +def prisma_setup(database_url: Optional[str]): + global prisma_client + if database_url: + try: + prisma_client = PrismaClient(database_url=database_url) + except Exception as e: + print("Error when initializing prisma, Ensure you run pip install prisma", e) + +def celery_setup(use_queue: bool): + global celery_fn, celery_app_conn, async_result + if use_queue: + from litellm.proxy.queue.celery_worker import start_worker + from litellm.proxy.queue.celery_app import celery_app, process_job + from celery.result import AsyncResult + start_worker(os.getcwd()) + celery_fn = process_job + async_result = AsyncResult + celery_app_conn = celery_app + +def load_from_azure_key_vault(use_azure_key_vault: bool = False): + if use_azure_key_vault is False: + return + + try: + from azure.keyvault.secrets import SecretClient + from azure.identity import ClientSecretCredential + + # Set your Azure Key Vault URI + KVUri = os.getenv("AZURE_KEY_VAULT_URI", None) + + # Set your Azure AD application/client ID, client secret, and tenant ID + client_id = os.getenv("AZURE_CLIENT_ID", None) + client_secret = os.getenv("AZURE_CLIENT_SECRET", None) + tenant_id = os.getenv("AZURE_TENANT_ID", None) + + if KVUri is not None and client_id is not None and client_secret is not None and tenant_id is not None: + # Initialize the ClientSecretCredential + credential = ClientSecretCredential(client_id=client_id, client_secret=client_secret, tenant_id=tenant_id) + + # Create the SecretClient using the credential + client = SecretClient(vault_url=KVUri, credential=credential) + + litellm.secret_manager_client = client + else: + raise Exception(f"Missing KVUri or client_id or client_secret or tenant_id from environment") + except Exception as e: + print("Error when loading keys from Azure Key Vault. Ensure you run `pip install azure-identity azure-keyvault-secrets`") + +def cost_tracking(): + global prisma_client + if prisma_client is not None: + if isinstance(litellm.success_callback, list): + print("setting litellm success callback to track cost") + if (track_cost_callback) not in litellm.success_callback: # type: ignore + litellm.success_callback.append(track_cost_callback) # type: ignore + else: + litellm.success_callback = track_cost_callback # type: ignore + +async def track_cost_callback( + kwargs, # kwargs to completion + completion_response: litellm.ModelResponse, # response from completion + start_time = None, + end_time = None, # start/end time for completion +): + global prisma_client + try: + # check if it has collected an entire stream response + if "complete_streaming_response" in kwargs: + # for tracking streaming cost we pass the "messages" and the output_text to litellm.completion_cost + completion_response=kwargs["complete_streaming_response"] + input_text = kwargs["messages"] + output_text = completion_response["choices"][0]["message"]["content"] + response_cost = litellm.completion_cost( + model = kwargs["model"], + messages = input_text, + completion=output_text + ) + print("streaming response_cost", response_cost) + # for non streaming responses + elif kwargs["stream"] is False: # regular response + input_text = kwargs.get("messages", "") + if isinstance(input_text, list): + input_text = "".join(m["content"] for m in input_text) + print(f"received completion response: {completion_response}") + response_cost = litellm.completion_cost(completion_response=completion_response, completion=input_text) + print("regular response_cost", response_cost) + user_api_key = kwargs["litellm_params"]["metadata"].get("user_api_key", None) + print(f"user_api_key - {user_api_key}; prisma_client - {prisma_client}") + if user_api_key and prisma_client: + await update_prisma_database(token=user_api_key, response_cost=response_cost) + except Exception as e: + print(f"error in tracking cost callback - {str(e)}") + +async def update_prisma_database(token, response_cost): + try: + print(f"Enters prisma db call, token: {token}") + # Fetch the existing cost for the given token + existing_spend_obj = await prisma_client.get_data(token=token) + print(f"existing spend: {existing_spend_obj}") + if existing_spend_obj is None: + existing_spend = 0 + else: + existing_spend = existing_spend_obj.spend + # Calculate the new cost by adding the existing cost and response_cost + new_spend = existing_spend + response_cost + + print(f"new cost: {new_spend}") + # Update the cost column for the given token + await prisma_client.update_data(token=token, data={"spend": new_spend}) + except Exception as e: + print(f"Error updating Prisma database: {traceback.format_exc()}") + pass + +def run_ollama_serve(): + try: + command = ['ollama', 'serve'] + + with open(os.devnull, 'w') as devnull: + process = subprocess.Popen(command, stdout=devnull, stderr=devnull) + except Exception as e: + print(f""" + LiteLLM Warning: proxy started with `ollama` model\n`ollama serve` failed with Exception{e}. \nEnsure you run `ollama serve` + """) + +def load_router_config(router: Optional[litellm.Router], config_file_path: str): + global master_key, user_config_file_path, otel_logging, user_custom_auth + config = {} + try: + if os.path.exists(config_file_path): + user_config_file_path = config_file_path + with open(config_file_path, 'r') as file: + config = yaml.safe_load(file) + else: + raise Exception(f"Path to config does not exist, Current working directory: {os.getcwd()}, 'os.path.exists({config_file_path})' returned False") + except Exception as e: + raise Exception(f"Exception while reading Config: {e}") + + ## PRINT YAML FOR CONFIRMING IT WORKS + printed_yaml = copy.deepcopy(config) + printed_yaml.pop("environment_variables", None) + if "model_list" in printed_yaml: + for model in printed_yaml["model_list"]: + model["litellm_params"].pop("api_key", None) + + print(f"Loaded config YAML (api_key and environment_variables are not shown):\n{json.dumps(printed_yaml, indent=2)}") + + ## ENVIRONMENT VARIABLES + environment_variables = config.get('environment_variables', None) + if environment_variables: + for key, value in environment_variables.items(): + os.environ[key] = value + + ## GENERAL SERVER SETTINGS (e.g. master key,..) + general_settings = config.get("general_settings", {}) + if general_settings is None: + general_settings = {} + if general_settings: + ### LOAD FROM AZURE KEY VAULT ### + use_azure_key_vault = general_settings.get("use_azure_key_vault", False) + load_from_azure_key_vault(use_azure_key_vault=use_azure_key_vault) + ### CONNECT TO DATABASE ### + database_url = general_settings.get("database_url", None) + if database_url and database_url.startswith("os.environ/"): + database_url = litellm.get_secret(database_url) + prisma_setup(database_url=database_url) + ## COST TRACKING ## + cost_tracking() + ### START REDIS QUEUE ### + use_queue = general_settings.get("use_queue", False) + celery_setup(use_queue=use_queue) + ### MASTER KEY ### + master_key = general_settings.get("master_key", None) + if master_key and master_key.startswith("os.environ/"): + master_key = litellm.get_secret(master_key) + #### OpenTelemetry Logging (OTEL) ######## + otel_logging = general_settings.get("otel", False) + if otel_logging == True: + print("\nOpenTelemetry Logging Activated") + ### CUSTOM API KEY AUTH ### + custom_auth = general_settings.get("custom_auth", None) + if custom_auth: + user_custom_auth = get_instance_fn(value=custom_auth, config_file_path=config_file_path) + ## LITELLM MODULE SETTINGS (e.g. litellm.drop_params=True,..) + litellm_settings = config.get('litellm_settings', None) + if litellm_settings: + # ANSI escape code for blue text + blue_color_code = "\033[94m" + reset_color_code = "\033[0m" + for key, value in litellm_settings.items(): + if key == "cache": + print(f"{blue_color_code}\nSetting Cache on Proxy") + from litellm.caching import Cache + cache_type = value["type"] + cache_host = litellm.get_secret("REDIS_HOST") + cache_port = litellm.get_secret("REDIS_PORT") + cache_password = litellm.get_secret("REDIS_PASSWORD") + + # Assuming cache_type, cache_host, cache_port, and cache_password are strings + print(f"{blue_color_code}Cache Type:{reset_color_code} {cache_type}") + print(f"{blue_color_code}Cache Host:{reset_color_code} {cache_host}") + print(f"{blue_color_code}Cache Port:{reset_color_code} {cache_port}") + print(f"{blue_color_code}Cache Password:{reset_color_code} {cache_password}") + print() + + litellm.cache = Cache( + type=cache_type, + host=cache_host, + port=cache_port, + password=cache_password + ) + elif key == "callbacks": + litellm.callbacks = [get_instance_fn(value=value)] + else: + setattr(litellm, key, value) + + ## MODEL LIST + model_list = config.get('model_list', None) + if model_list: + router = litellm.Router(model_list=model_list, num_retries=3) + print(f"\033[32mLiteLLM: Proxy initialized with Config, Set models:\033[0m") + for model in model_list: + print(f"\033[32m {model.get('model_name', '')}\033[0m") + litellm_model_name = model["litellm_params"]["model"] + if "ollama" in litellm_model_name: + run_ollama_serve() + return router, model_list, general_settings + +async def generate_key_helper_fn(duration_str: Optional[str], models: list, aliases: dict, config: dict, spend: float, token: Optional[str]=None, user_id: Optional[str]=None): + global prisma_client + + if prisma_client is None: + raise Exception(f"Connect Proxy to database to generate keys - https://docs.litellm.ai/docs/proxy/virtual_keys ") + + if token is None: + token = f"sk-{secrets.token_urlsafe(16)}" + + + def _duration_in_seconds(duration: str): + match = re.match(r"(\d+)([smhd]?)", duration) + if not match: + raise ValueError("Invalid duration format") + + value, unit = match.groups() + value = int(value) + + if unit == "s": + return value + elif unit == "m": + return value * 60 + elif unit == "h": + return value * 3600 + elif unit == "d": + return value * 86400 + else: + raise ValueError("Unsupported duration unit") + + if duration_str is None: # allow tokens that never expire + expires = None + else: + duration = _duration_in_seconds(duration=duration_str) + expires = datetime.utcnow() + timedelta(seconds=duration) + + aliases_json = json.dumps(aliases) + config_json = json.dumps(config) + user_id = user_id or str(uuid.uuid4()) + try: + # Create a new verification token (you may want to enhance this logic based on your needs) + verification_token_data = { + "token": token, + "expires": expires, + "models": models, + "aliases": aliases_json, + "config": config_json, + "spend": spend, + "user_id": user_id + } + new_verification_token = await prisma_client.insert_data(data=verification_token_data) + except Exception as e: + traceback.print_exc() + raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR) + return {"token": token, "expires": new_verification_token.expires, "user_id": user_id} + +async def delete_verification_token(tokens: List): + global prisma_client + try: + if prisma_client: + # Assuming 'db' is your Prisma Client instance + deleted_tokens = await prisma_client.delete_data(tokens=tokens) + else: + raise Exception + except Exception as e: + traceback.print_exc() + raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR) + return deleted_tokens + +def save_worker_config(**data): + import json + os.environ["WORKER_CONFIG"] = json.dumps(data) + +def initialize( + model, + alias, + api_base, + api_version, + debug, + temperature, + max_tokens, + request_timeout, + max_budget, + telemetry, + drop_params, + add_function_to_prompt, + headers, + save, + config, + use_queue +): + global user_model, user_api_base, user_debug, user_max_tokens, user_request_timeout, user_temperature, user_telemetry, user_headers, experimental, llm_model_list, llm_router, general_settings + generate_feedback_box() + user_model = model + user_debug = debug + dynamic_config = {"general": {}, user_model: {}} + if config: + llm_router, llm_model_list, general_settings = load_router_config(router=llm_router, config_file_path=config) + if headers: # model-specific param + user_headers = headers + dynamic_config[user_model]["headers"] = headers + if api_base: # model-specific param + user_api_base = api_base + dynamic_config[user_model]["api_base"] = api_base + if api_version: + os.environ[ + "AZURE_API_VERSION" + ] = api_version # set this for azure - litellm can read this from the env + if max_tokens: # model-specific param + user_max_tokens = max_tokens + dynamic_config[user_model]["max_tokens"] = max_tokens + if temperature: # model-specific param + user_temperature = temperature + dynamic_config[user_model]["temperature"] = temperature + if request_timeout: + user_request_timeout = request_timeout + dynamic_config[user_model]["request_timeout"] = request_timeout + if alias: # model-specific param + dynamic_config[user_model]["alias"] = alias + if drop_params == True: # litellm-specific param + litellm.drop_params = True + dynamic_config["general"]["drop_params"] = True + if add_function_to_prompt == True: # litellm-specific param + litellm.add_function_to_prompt = True + dynamic_config["general"]["add_function_to_prompt"] = True + if max_budget: # litellm-specific param + litellm.max_budget = max_budget + dynamic_config["general"]["max_budget"] = max_budget + if debug==True: # litellm-specific param + litellm.set_verbose = True + if use_queue: + celery_setup(use_queue=use_queue) + if experimental: + pass + user_telemetry = telemetry + usage_telemetry(feature="local_proxy_server") + curl_command = """ + curl --location 'http://0.0.0.0:8000/chat/completions' \\ + --header 'Content-Type: application/json' \\ + --data ' { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + }' + \n + """ + print() + print(f"\033[1;34mLiteLLM: Test your local proxy with: \"litellm --test\" This runs an openai.ChatCompletion request to your proxy [In a new terminal tab]\033[0m\n") + print(f"\033[1;34mLiteLLM: Curl Command Test for your local proxy\n {curl_command} \033[0m\n") + print("\033[1;34mDocs: https://docs.litellm.ai/docs/simple_proxy\033[0m\n") +# for streaming +def data_generator(response): + print_verbose("inside generator") + for chunk in response: + print_verbose(f"returned chunk: {chunk}") + try: + yield f"data: {json.dumps(chunk.dict())}\n\n" + except: + yield f"data: {json.dumps(chunk)}\n\n" + + +async def async_data_generator(response): + print_verbose("inside generator") + async for chunk in response: + print_verbose(f"returned chunk: {chunk}") + try: + yield f"data: {json.dumps(chunk.dict())}\n\n" + except: + yield f"data: {json.dumps(chunk)}\n\n" + +def litellm_completion(*args, **kwargs): + global user_temperature, user_request_timeout, user_max_tokens, user_api_base + call_type = kwargs.pop("call_type") + # override with user settings, these are params passed via cli + if user_temperature: + kwargs["temperature"] = user_temperature + if user_request_timeout: + kwargs["request_timeout"] = user_request_timeout + if user_max_tokens: + kwargs["max_tokens"] = user_max_tokens + if user_api_base: + kwargs["api_base"] = user_api_base + ## ROUTE TO CORRECT ENDPOINT ## + router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else [] + try: + if llm_router is not None and kwargs["model"] in router_model_names: # model in router model list + if call_type == "chat_completion": + response = llm_router.completion(*args, **kwargs) + elif call_type == "text_completion": + response = llm_router.text_completion(*args, **kwargs) + else: + if call_type == "chat_completion": + response = litellm.completion(*args, **kwargs) + elif call_type == "text_completion": + response = litellm.text_completion(*args, **kwargs) + except Exception as e: + raise e + if 'stream' in kwargs and kwargs['stream'] == True: # use generate_responses to stream responses + return StreamingResponse(data_generator(response), media_type='text/event-stream') + return response + +@router.on_event("startup") +async def startup_event(): + global prisma_client, master_key + import json + worker_config = json.loads(os.getenv("WORKER_CONFIG")) + print(f"worker_config: {worker_config}") + initialize(**worker_config) + print(f"prisma client - {prisma_client}") + if prisma_client: + await prisma_client.connect() + + if prisma_client is not None and master_key is not None: + # add master key to db + await generate_key_helper_fn(duration_str=None, models=[], aliases={}, config={}, spend=0, token=master_key) + +@router.on_event("shutdown") +async def shutdown_event(): + global prisma_client + if prisma_client: + print("Disconnecting from Prisma") + await prisma_client.disconnect() + +#### API ENDPOINTS #### +@router.get("/v1/models", dependencies=[Depends(user_api_key_auth)]) +@router.get("/models", dependencies=[Depends(user_api_key_auth)]) # if project requires model list +def model_list(): + global llm_model_list, general_settings + all_models = [] + if general_settings.get("infer_model_from_keys", False): + all_models = litellm.utils.get_valid_models() + if llm_model_list: + all_models = list(set(all_models + [m["model_name"] for m in llm_model_list])) + if user_model is not None: + all_models += [user_model] + print_verbose(f"all_models: {all_models}") + ### CHECK OLLAMA MODELS ### + try: + response = requests.get("http://0.0.0.0:11434/api/tags") + models = response.json()["models"] + ollama_models = ["ollama/" + m["name"].replace(":latest", "") for m in models] + all_models.extend(ollama_models) + except Exception as e: + pass + return dict( + data=[ + { + "id": model, + "object": "model", + "created": 1677610602, + "owned_by": "openai", + } + for model in all_models + ], + object="list", + ) + +@router.post("/v1/completions", dependencies=[Depends(user_api_key_auth)]) +@router.post("/completions", dependencies=[Depends(user_api_key_auth)]) +@router.post("/engines/{model:path}/completions", dependencies=[Depends(user_api_key_auth)]) +async def completion(request: Request, model: Optional[str] = None, user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth)): + try: + body = await request.body() + body_str = body.decode() + try: + data = ast.literal_eval(body_str) + except: + data = json.loads(body_str) + + data["user"] = user_api_key_dict.user_id + data["model"] = ( + general_settings.get("completion_model", None) # server default + or user_model # model name passed via cli args + or model # for azure deployments + or data["model"] # default passed in http request + ) + if user_model: + data["model"] = user_model + data["call_type"] = "text_completion" + if "metadata" in data: + data["metadata"]["user_api_key"] = user_api_key_dict.api_key + else: + data["metadata"] = {"user_api_key": user_api_key_dict.api_key} + + return litellm_completion( + **data + ) + except Exception as e: + print(f"\033[1;31mAn error occurred: {e}\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`") + error_traceback = traceback.format_exc() + error_msg = f"{str(e)}\n\n{error_traceback}" + try: + status = e.status_code # type: ignore + except: + status = 500 + raise HTTPException( + status_code=status, + detail=error_msg + ) + + +@router.post("/v1/chat/completions", dependencies=[Depends(user_api_key_auth)], tags=["chat/completions"]) +@router.post("/chat/completions", dependencies=[Depends(user_api_key_auth)], tags=["chat/completions"]) +@router.post("/openai/deployments/{model:path}/chat/completions", dependencies=[Depends(user_api_key_auth)], tags=["chat/completions"]) # azure compatible endpoint +async def chat_completion(request: Request, model: Optional[str] = None, user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), background_tasks: BackgroundTasks = BackgroundTasks()): + global general_settings, user_debug + try: + data = {} + data = await request.json() # type: ignore + + print_verbose(f"receiving data: {data}") + data["model"] = ( + general_settings.get("completion_model", None) # server default + or user_model # model name passed via cli args + or model # for azure deployments + or data["model"] # default passed in http request + ) + + # users can pass in 'user' param to /chat/completions. Don't override it + if data.get("user", None) is None: + # if users are using user_api_key_auth, set `user` in `data` + data["user"] = user_api_key_dict.user_id + + if "metadata" in data: + data["metadata"]["user_api_key"] = user_api_key_dict.api_key + data["metadata"]["headers"] = request.headers + else: + data["metadata"] = {"user_api_key": user_api_key_dict.api_key} + data["metadata"]["headers"] = request.headers + global user_temperature, user_request_timeout, user_max_tokens, user_api_base + # override with user settings, these are params passed via cli + if user_temperature: + data["temperature"] = user_temperature + if user_request_timeout: + data["request_timeout"] = user_request_timeout + if user_max_tokens: + data["max_tokens"] = user_max_tokens + if user_api_base: + data["api_base"] = user_api_base + router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else [] + if llm_router is not None and data["model"] in router_model_names: # model in router model list + response = await llm_router.acompletion(**data) + else: + response = await litellm.acompletion(**data) + if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses + return StreamingResponse(async_data_generator(response), media_type='text/event-stream') + background_tasks.add_task(log_input_output, request, response) # background task for logging to OTEL + return response + except Exception as e: + print(f"\033[1;31mAn error occurred: {e}\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`") + router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else [] + if llm_router is not None and data.get("model", "") in router_model_names: + print("Results from router") + print("\nRouter stats") + print("\nTotal Calls made") + for key, value in llm_router.total_calls.items(): + print(f"{key}: {value}") + print("\nSuccess Calls made") + for key, value in llm_router.success_calls.items(): + print(f"{key}: {value}") + print("\nFail Calls made") + for key, value in llm_router.fail_calls.items(): + print(f"{key}: {value}") + if user_debug: + traceback.print_exc() + error_traceback = traceback.format_exc() + error_msg = f"{str(e)}\n\n{error_traceback}" + try: + status = e.status_code # type: ignore + except: + status = 500 + raise HTTPException( + status_code=status, + detail=error_msg + ) + +@router.post("/v1/embeddings", dependencies=[Depends(user_api_key_auth)], response_class=ORJSONResponse) +@router.post("/embeddings", dependencies=[Depends(user_api_key_auth)], response_class=ORJSONResponse) +async def embeddings(request: Request, user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), background_tasks: BackgroundTasks = BackgroundTasks()): + try: + + # Use orjson to parse JSON data, orjson speeds up requests significantly + body = await request.body() + data = orjson.loads(body) + + data["user"] = user_api_key_dict.user_id + data["model"] = ( + general_settings.get("embedding_model", None) # server default + or user_model # model name passed via cli args + or data["model"] # default passed in http request + ) + if user_model: + data["model"] = user_model + if "metadata" in data: + data["metadata"]["user_api_key"] = user_api_key_dict.api_key + else: + data["metadata"] = {"user_api_key": user_api_key_dict.api_key} + + ## ROUTE TO CORRECT ENDPOINT ## + router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else [] + if llm_router is not None and data["model"] in router_model_names: # model in router model list + response = await llm_router.aembedding(**data) + else: + response = await litellm.aembedding(**data) + background_tasks.add_task(log_input_output, request, response) # background task for logging to OTEL + return response + except Exception as e: + traceback.print_exc() + raise e + except Exception as e: + pass + +#### KEY MANAGEMENT #### + +@router.post("/key/generate", tags=["key management"], dependencies=[Depends(user_api_key_auth)], response_model=GenerateKeyResponse) +async def generate_key_fn(request: Request, data: GenerateKeyRequest): + """ + Generate an API key based on the provided data. + + Docs: https://docs.litellm.ai/docs/proxy/virtual_keys + + Parameters: + - duration: Optional[str] - Specify the length of time the token is valid for. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d"). **(Default is set to 1 hour.)** + - models: Optional[list] - Model_name's a user is allowed to call. (if empty, key is allowed to call all models) + - aliases: Optional[dict] - Any alias mappings, on top of anything in the config.yaml model list. - https://docs.litellm.ai/docs/proxy/virtual_keys#managing-auth---upgradedowngrade-models + - config: Optional[dict] - any key-specific configs, overrides config in config.yaml + - spend: Optional[int] - Amount spent by key. Default is 0. Will be updated by proxy whenever key is used. https://docs.litellm.ai/docs/proxy/virtual_keys#managing-auth---tracking-spend + + Returns: + - key: The generated api key + - expires: Datetime object for when key expires. + """ + # data = await request.json() + duration_str = data.duration # Default to 1 hour if duration is not provided + models = data.models # Default to an empty list (meaning allow token to call all models) + aliases = data.aliases # Default to an empty dict (no alias mappings, on top of anything in the config.yaml model_list) + config = data.config + spend = data.spend + user_id = data.user_id + if isinstance(models, list): + response = await generate_key_helper_fn(duration_str=duration_str, models=models, aliases=aliases, config=config, spend=spend, user_id=user_id) + return GenerateKeyResponse(key=response["token"], expires=response["expires"], user_id=response["user_id"]) + else: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail={"error": "models param must be a list"}, + ) + +@router.post("/key/delete", tags=["key management"], dependencies=[Depends(user_api_key_auth)]) +async def delete_key_fn(request: Request, data: DeleteKeyRequest): + try: + keys = data.keys + + deleted_keys = await delete_verification_token(tokens=keys) + assert len(keys) == deleted_keys + return {"deleted_keys": keys} + except Exception as e: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail={"error": str(e)}, + ) + +@router.get("/key/info", tags=["key management"], dependencies=[Depends(user_api_key_auth)]) +async def info_key_fn(key: str = fastapi.Query(..., description="Key in the request parameters")): + global prisma_client + try: + if prisma_client is None: + raise Exception(f"Database not connected. Connect a database to your proxy - https://docs.litellm.ai/docs/simple_proxy#managing-auth---virtual-keys") + key_info = await prisma_client.get_data(token=key) + return {"key": key, "info": key_info} + except Exception as e: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail={"error": str(e)}, + ) + +#### MODEL MANAGEMENT #### + +#### [BETA] - This is a beta endpoint, format might change based on user feedback. - https://github.com/BerriAI/litellm/issues/964 +@router.post("/model/new", description="Allows adding new models to the model list in the config.yaml", tags=["model management"], dependencies=[Depends(user_api_key_auth)]) +async def add_new_model(model_params: ModelParams): + global llm_router, llm_model_list, general_settings, user_config_file_path + try: + # Load existing config + if os.path.exists(f"{user_config_file_path}"): + with open(f"{user_config_file_path}", "r") as config_file: + config = yaml.safe_load(config_file) + else: + config = {"model_list": []} + # Add the new model to the config + config['model_list'].append({ + 'model_name': model_params.model_name, + 'litellm_params': model_params.litellm_params, + 'model_info': model_params.model_info + }) + + # Save the updated config + with open(f"{user_config_file_path}", "w") as config_file: + yaml.dump(config, config_file, default_flow_style=False) + + # update Router + llm_router, llm_model_list, general_settings = load_router_config(router=llm_router, config_file_path=config) + + + return {"message": "Model added successfully"} + + except Exception as e: + raise HTTPException(status_code=500, detail=f"Internal Server Error: {str(e)}") + +#### [BETA] - This is a beta endpoint, format might change based on user feedback. - https://github.com/BerriAI/litellm/issues/933 +@router.get("/model/info", description="Provides more info about each model in /models, including config.yaml descriptions (except api key and api base)", tags=["model management"], dependencies=[Depends(user_api_key_auth)]) +async def model_info(request: Request): + global llm_model_list, general_settings, user_config_file_path + # Load existing config + with open(f"{user_config_file_path}", "r") as config_file: + config = yaml.safe_load(config_file) + all_models = config['model_list'] + + for model in all_models: + # get the model cost map info + ## make an api call + data = copy.deepcopy(model["litellm_params"]) + data["messages"] = [{"role": "user", "content": "Hey, how's it going?"}] + data["max_tokens"] = 10 + print(f"data going to litellm acompletion: {data}") + response = await litellm.acompletion(**data) + response_model = response["model"] + print(f"response model: {response_model}; response - {response}") + litellm_model_info = litellm.get_model_info(response_model) + model_info = model.get("model_info", {}) + for k, v in litellm_model_info.items(): + if k not in model_info: + model_info[k] = v + model["model_info"] = model_info + # don't return the api key + model["litellm_params"].pop("api_key", None) + + # all_models = list(set([m["model_name"] for m in llm_model_list])) + print_verbose(f"all_models: {all_models}") + return dict( + data=[ + { + "id": model, + "object": "model", + "created": 1677610602, + "owned_by": "openai", + } + for model in all_models + ], + object="list", + ) + +#### EXPERIMENTAL QUEUING #### +@router.post("/queue/request", dependencies=[Depends(user_api_key_auth)]) +async def async_queue_request(request: Request): + global celery_fn, llm_model_list + if celery_fn is not None: + body = await request.body() + body_str = body.decode() + try: + data = ast.literal_eval(body_str) + except: + data = json.loads(body_str) + data["model"] = ( + general_settings.get("completion_model", None) # server default + or user_model # model name passed via cli args + or data["model"] # default passed in http request + ) + data["llm_model_list"] = llm_model_list + print(f"data: {data}") + job = celery_fn.apply_async(kwargs=data) + return {"id": job.id, "url": f"/queue/response/{job.id}", "eta": 5, "status": "queued"} + else: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail={"error": "Queue not initialized"}, + ) + +@router.get("/queue/response/{task_id}", dependencies=[Depends(user_api_key_auth)]) +async def async_queue_response(request: Request, task_id: str): + global celery_app_conn, async_result + try: + if celery_app_conn is not None and async_result is not None: + job = async_result(task_id, app=celery_app_conn) + if job.ready(): + return {"status": "finished", "result": job.result} + else: + return {'status': 'queued'} + else: + raise Exception() + except Exception as e: + return {"status": "finished", "result": str(e)} + + +@router.get("/ollama_logs", dependencies=[Depends(user_api_key_auth)]) +async def retrieve_server_log(request: Request): + filepath = os.path.expanduser("~/.ollama/logs/server.log") + return FileResponse(filepath) + + +#### BASIC ENDPOINTS #### + +@router.get("/test") +async def test_endpoint(request: Request): + return {"route": request.url.path} + +@router.get("/health", description="Check the health of all the endpoints in config.yaml", tags=["health"]) +async def health_endpoint(request: Request, model: Optional[str] = fastapi.Query(None, description="Specify the model name (optional)")): + global llm_model_list + healthy_endpoints = [] + unhealthy_endpoints = [] + if llm_model_list: + for model_name in llm_model_list: + try: + if model is None or model == model_name["litellm_params"]["model"]: # if model specified, just call that one. + litellm_params = model_name["litellm_params"] + model_name = litellm.utils.remove_model_id(litellm_params["model"]) # removes, ids set by litellm.router + if model_name not in litellm.all_embedding_models: # filter out embedding models + litellm_params["messages"] = [{"role": "user", "content": "Hey, how's it going?"}] + litellm_params["model"] = model_name + litellm.completion(**litellm_params) + cleaned_params = {} + for key in litellm_params: + if key != "api_key" and key != "messages": + cleaned_params[key] = litellm_params[key] + healthy_endpoints.append(cleaned_params) + except Exception as e: + print("Got Exception", e) + cleaned_params = {} + for key in litellm_params: + if key != "api_key" and key != "messages": + cleaned_params[key] = litellm_params[key] + unhealthy_endpoints.append(cleaned_params) + pass + return { + "healthy_endpoints": healthy_endpoints, + "unhealthy_endpoints": unhealthy_endpoints + } + +@router.get("/") +async def home(request: Request): + return "LiteLLM: RUNNING" + +@router.get("/routes") +async def get_routes(): + """ + Get a list of available routes in the FastAPI application. + """ + routes = [] + for route in app.routes: + route_info = { + "path": route.path, + "methods": route.methods, + "name": route.name, + "endpoint": route.endpoint.__name__ if route.endpoint else None, + } + routes.append(route_info) + + return {"routes": routes} + + +app.include_router(router) diff --git a/litellm/proxy/queue/celery_app.py b/litellm/proxy/queue/celery_app.py new file mode 100644 index 0000000000000000000000000000000000000000..47c9c868fff886d6ea74109d426c52ed6876318f --- /dev/null +++ b/litellm/proxy/queue/celery_app.py @@ -0,0 +1,71 @@ +from dotenv import load_dotenv +load_dotenv() +import json, subprocess +import psutil # Import the psutil library +import atexit +try: + ### OPTIONAL DEPENDENCIES ### - pip install redis and celery only when a user opts into using the async endpoints which require both + from celery import Celery + import redis +except: + import sys + + subprocess.check_call( + [ + sys.executable, + "-m", + "pip", + "install", + "redis", + "celery" + ] + ) + +import time +import sys, os +sys.path.insert( + 0, os.path.abspath("../../..") +) # Adds the parent directory to the system path - for litellm local dev +import litellm + +# Redis connection setup +pool = redis.ConnectionPool(host=os.getenv("REDIS_HOST"), port=os.getenv("REDIS_PORT"), password=os.getenv("REDIS_PASSWORD"), db=0, max_connections=5) +redis_client = redis.Redis(connection_pool=pool) + +# Celery setup +celery_app = Celery('tasks', broker=f"redis://default:{os.getenv('REDIS_PASSWORD')}@{os.getenv('REDIS_HOST')}:{os.getenv('REDIS_PORT')}", backend=f"redis://default:{os.getenv('REDIS_PASSWORD')}@{os.getenv('REDIS_HOST')}:{os.getenv('REDIS_PORT')}") +celery_app.conf.update( + broker_pool_limit = None, + broker_transport_options = {'connection_pool': pool}, + result_backend_transport_options = {'connection_pool': pool}, +) + + +# Celery task +@celery_app.task(name='process_job', max_retries=3) +def process_job(*args, **kwargs): + try: + llm_router: litellm.Router = litellm.Router(model_list=kwargs.pop("llm_model_list")) + response = llm_router.completion(*args, **kwargs) # type: ignore + if isinstance(response, litellm.ModelResponse): + response = response.model_dump_json() + return json.loads(response) + return str(response) + except Exception as e: + raise e + +# Ensure Celery workers are terminated when the script exits +def cleanup(): + try: + # Get a list of all running processes + for process in psutil.process_iter(attrs=['pid', 'name']): + # Check if the process is a Celery worker process + if process.info['name'] == 'celery': + print(f"Terminating Celery worker with PID {process.info['pid']}") + # Terminate the Celery worker process + psutil.Process(process.info['pid']).terminate() + except Exception as e: + print(f"Error during cleanup: {e}") + +# Register the cleanup function to run when the script exits +atexit.register(cleanup) \ No newline at end of file diff --git a/litellm/proxy/queue/celery_worker.py b/litellm/proxy/queue/celery_worker.py new file mode 100644 index 0000000000000000000000000000000000000000..7206723f82088855f4b219785e26b88e7f705b48 --- /dev/null +++ b/litellm/proxy/queue/celery_worker.py @@ -0,0 +1,12 @@ +import os +from multiprocessing import Process + +def run_worker(cwd): + os.chdir(cwd) + os.system("celery -A celery_app.celery_app worker --concurrency=120 --loglevel=info") + +def start_worker(cwd): + cwd += "/queue" + worker_process = Process(target=run_worker, args=(cwd,)) + worker_process.start() + \ No newline at end of file diff --git a/litellm/proxy/queue/rq_worker.py b/litellm/proxy/queue/rq_worker.py new file mode 100644 index 0000000000000000000000000000000000000000..6e8ce29ae9d1de41a9c79a00c5da52400e9e0be6 --- /dev/null +++ b/litellm/proxy/queue/rq_worker.py @@ -0,0 +1,26 @@ +import sys, os +from dotenv import load_dotenv +load_dotenv() +# Add the path to the local folder to sys.path +sys.path.insert( + 0, os.path.abspath("../../..") +) # Adds the parent directory to the system path - for litellm local dev + +def start_rq_worker(): + from rq import Worker, Queue, Connection + from redis import Redis + # Set up RQ connection + redis_conn = Redis(host=os.getenv("REDIS_HOST"), port=os.getenv("REDIS_PORT"), password=os.getenv("REDIS_PASSWORD")) + print(redis_conn.ping()) # Should print True if connected successfully + # Create a worker and add the queue + try: + queue = Queue(connection=redis_conn) + worker = Worker([queue], connection=redis_conn) + except Exception as e: + print(f"Error setting up worker: {e}") + exit() + + with Connection(redis_conn): + worker.work() + +start_rq_worker() \ No newline at end of file diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma new file mode 100644 index 0000000000000000000000000000000000000000..1ce57e2fe25b0860e72efffe92a9e7476eff8c96 --- /dev/null +++ b/litellm/proxy/schema.prisma @@ -0,0 +1,19 @@ +datasource client { + provider = "postgresql" + url = env("DATABASE_URL") +} + +generator client { + provider = "prisma-client-py" +} + +// required for token gen +model LiteLLM_VerificationToken { + token String @unique + spend Float @default(0.0) + expires DateTime? + models String[] + aliases Json @default("{}") + config Json @default("{}") + user_id String? +} \ No newline at end of file diff --git a/litellm/proxy/start.sh b/litellm/proxy/start.sh new file mode 100644 index 0000000000000000000000000000000000000000..44df50aaabfbf3cf0fa38e50d1b2ecc34a5ddecc --- /dev/null +++ b/litellm/proxy/start.sh @@ -0,0 +1,2 @@ +#!/bin/bash +python3 proxy_cli.py \ No newline at end of file diff --git a/litellm/proxy/tests/bursty_load_test_completion.py b/litellm/proxy/tests/bursty_load_test_completion.py new file mode 100644 index 0000000000000000000000000000000000000000..11766c6abe91d67c3eb361b1c5ee9131f5da6a2b --- /dev/null +++ b/litellm/proxy/tests/bursty_load_test_completion.py @@ -0,0 +1,52 @@ +import time, asyncio +from openai import AsyncOpenAI +import uuid +import traceback + + +litellm_client = AsyncOpenAI( + api_key="test", + base_url="http://0.0.0.0:8000" +) + + +async def litellm_completion(): + # Your existing code for litellm_completion goes here + try: + response = await litellm_client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"*180}], # this is about 4k tokens per request + ) + print(response) + return response + + except Exception as e: + # If there's an exception, log the error message + with open("error_log.txt", "a") as error_log: + error_log.write(f"Error during completion: {str(e)}\n") + pass + + + +async def main(): + start = time.time() + n = 60 # Send 60 concurrent requests, each with 4k tokens = 240k Tokens + tasks = [litellm_completion() for _ in range(n)] + + chat_completions = await asyncio.gather(*tasks) + + successful_completions = [c for c in chat_completions if c is not None] + + # Write errors to error_log.txt + with open("error_log.txt", "a") as error_log: + for completion in chat_completions: + if isinstance(completion, str): + error_log.write(completion + "\n") + + print(n, time.time() - start, len(successful_completions)) + +if __name__ == "__main__": + # Blank out contents of error_log.txt + open("error_log.txt", "w").close() + + asyncio.run(main()) diff --git a/litellm/proxy/tests/error_log.txt b/litellm/proxy/tests/error_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/litellm/proxy/tests/load_test_completion.py b/litellm/proxy/tests/load_test_completion.py new file mode 100644 index 0000000000000000000000000000000000000000..73b215020742fdf4538ebab4981a5bf6c2f70b82 --- /dev/null +++ b/litellm/proxy/tests/load_test_completion.py @@ -0,0 +1,51 @@ +import time, asyncio +from openai import AsyncOpenAI +import uuid +import traceback + + +litellm_client = AsyncOpenAI( + api_key="sk-1234", + base_url="http://0.0.0.0:8000" +) + + +async def litellm_completion(): + # Your existing code for litellm_completion goes here + try: + response = await litellm_client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}], + ) + return response + + except Exception as e: + # If there's an exception, log the error message + with open("error_log.txt", "a") as error_log: + error_log.write(f"Error during completion: {str(e)}\n") + pass + + + +async def main(): + start = time.time() + n = 1000 # Number of concurrent tasks + tasks = [litellm_completion() for _ in range(n)] + + chat_completions = await asyncio.gather(*tasks) + + successful_completions = [c for c in chat_completions if c is not None] + + # Write errors to error_log.txt + with open("error_log.txt", "a") as error_log: + for completion in chat_completions: + if isinstance(completion, str): + error_log.write(completion + "\n") + + print(n, time.time() - start, len(successful_completions)) + +if __name__ == "__main__": + # Blank out contents of error_log.txt + open("error_log.txt", "w").close() + + asyncio.run(main()) diff --git a/litellm/proxy/tests/load_test_embedding.py b/litellm/proxy/tests/load_test_embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..097dcf2c543696337f0b4ec9dfc37fd41dfe89a6 --- /dev/null +++ b/litellm/proxy/tests/load_test_embedding.py @@ -0,0 +1,101 @@ +# test time it takes to make 100 concurrent embedding requests to OpenaI + +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest + + +import litellm +litellm.set_verbose=False + + + +question = "embed this very long text" * 100 + + +# make X concurrent calls to litellm.completion(model=gpt-35-turbo, messages=[]), pick a random question in questions array. +# Allow me to tune X concurrent calls.. Log question, output/exception, response time somewhere +# show me a summary of requests made, success full calls, failed calls. For failed calls show me the exceptions + +import concurrent.futures +import random +import time + + +# Function to make concurrent calls to OpenAI API +def make_openai_completion(question): + try: + start_time = time.time() + import openai + client = openai.OpenAI(api_key=os.environ['OPENAI_API_KEY']) #base_url="http://0.0.0.0:8000", + response = client.embeddings.create( + model="text-embedding-ada-002", + input=[question], + ) + print(response) + end_time = time.time() + + # Log the request details + # with open("request_log.txt", "a") as log_file: + # log_file.write( + # f"Question: {question[:100]}\nResponse ID:{response.id} Content:{response.choices[0].message.content[:10]}\nTime: {end_time - start_time:.2f} seconds\n\n" + # ) + + return response + except Exception as e: + # Log exceptions for failed calls + # with open("error_log.txt", "a") as error_log_file: + # error_log_file.write( + # f"\nException: {str(e)}\n\n" + # ) + return None + +start_time = time.time() +# Number of concurrent calls (you can adjust this) +concurrent_calls = 500 + +# List to store the futures of concurrent calls +futures = [] + +# Make concurrent calls +with concurrent.futures.ThreadPoolExecutor(max_workers=concurrent_calls) as executor: + for _ in range(concurrent_calls): + futures.append(executor.submit(make_openai_completion, question)) + +# Wait for all futures to complete +concurrent.futures.wait(futures) + +# Summarize the results +successful_calls = 0 +failed_calls = 0 + +for future in futures: + if future.result() is not None: + successful_calls += 1 + else: + failed_calls += 1 + +end_time = time.time() +# Calculate the duration +duration = end_time - start_time + +print(f"Load test Summary:") +print(f"Total Requests: {concurrent_calls}") +print(f"Successful Calls: {successful_calls}") +print(f"Failed Calls: {failed_calls}") +print(f"Total Time: {duration:.2f} seconds") + +# Display content of the logs +with open("request_log.txt", "r") as log_file: + print("\nRequest Log:\n", log_file.read()) + +with open("error_log.txt", "r") as error_log_file: + print("\nError Log:\n", error_log_file.read()) diff --git a/litellm/proxy/tests/load_test_embedding_100.py b/litellm/proxy/tests/load_test_embedding_100.py new file mode 100644 index 0000000000000000000000000000000000000000..d7d27240544ba25f3141dfa0c9b1608a3e32b84b --- /dev/null +++ b/litellm/proxy/tests/load_test_embedding_100.py @@ -0,0 +1,58 @@ +import time, asyncio +from openai import AsyncOpenAI +import uuid +import traceback + + +litellm_client = AsyncOpenAI( + api_key="test", + base_url="http://0.0.0.0:8000" +) + + + +async def litellm_completion(): + # Your existing code for litellm_completion goes here + try: + print("starting embedding calls") + response = await litellm_client.embeddings.create( + model="text-embedding-ada-002", + input = [ + "hello who are you" * 2000, + "hello who are you tomorrow 1234" * 1000, + "hello who are you tomorrow 1234" * 1000 + ] + ) + print(response) + return response + + except Exception as e: + # If there's an exception, log the error message + with open("error_log.txt", "a") as error_log: + error_log.write(f"Error during completion: {str(e)}\n") + pass + + + +async def main(): + start = time.time() + n = 100 # Number of concurrent tasks + tasks = [litellm_completion() for _ in range(n)] + + chat_completions = await asyncio.gather(*tasks) + + successful_completions = [c for c in chat_completions if c is not None] + + # Write errors to error_log.txt + with open("error_log.txt", "a") as error_log: + for completion in chat_completions: + if isinstance(completion, str): + error_log.write(completion + "\n") + + print(n, time.time() - start, len(successful_completions)) + +if __name__ == "__main__": + # Blank out contents of error_log.txt + open("error_log.txt", "w").close() + + asyncio.run(main()) diff --git a/litellm/proxy/tests/load_test_embedding_proxy.py b/litellm/proxy/tests/load_test_embedding_proxy.py new file mode 100644 index 0000000000000000000000000000000000000000..45136fafc99236b2fea5bd9bbcd72bd73ff8be0b --- /dev/null +++ b/litellm/proxy/tests/load_test_embedding_proxy.py @@ -0,0 +1,101 @@ +# test time it takes to make 100 concurrent embedding requests to OpenaI + +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest + + +import litellm +litellm.set_verbose=False + + + +question = "embed this very long text" * 100 + + +# make X concurrent calls to litellm.completion(model=gpt-35-turbo, messages=[]), pick a random question in questions array. +# Allow me to tune X concurrent calls.. Log question, output/exception, response time somewhere +# show me a summary of requests made, success full calls, failed calls. For failed calls show me the exceptions + +import concurrent.futures +import random +import time + + +# Function to make concurrent calls to OpenAI API +def make_openai_completion(question): + try: + start_time = time.time() + import openai + client = openai.OpenAI(api_key=os.environ['OPENAI_API_KEY'], base_url="http://0.0.0.0:8000") #base_url="http://0.0.0.0:8000", + response = client.embeddings.create( + model="text-embedding-ada-002", + input=[question], + ) + print(response) + end_time = time.time() + + # Log the request details + # with open("request_log.txt", "a") as log_file: + # log_file.write( + # f"Question: {question[:100]}\nResponse ID:{response.id} Content:{response.choices[0].message.content[:10]}\nTime: {end_time - start_time:.2f} seconds\n\n" + # ) + + return response + except Exception as e: + # Log exceptions for failed calls + # with open("error_log.txt", "a") as error_log_file: + # error_log_file.write( + # f"\nException: {str(e)}\n\n" + # ) + return None + +start_time = time.time() +# Number of concurrent calls (you can adjust this) +concurrent_calls = 500 + +# List to store the futures of concurrent calls +futures = [] + +# Make concurrent calls +with concurrent.futures.ThreadPoolExecutor(max_workers=concurrent_calls) as executor: + for _ in range(concurrent_calls): + futures.append(executor.submit(make_openai_completion, question)) + +# Wait for all futures to complete +concurrent.futures.wait(futures) + +# Summarize the results +successful_calls = 0 +failed_calls = 0 + +for future in futures: + if future.result() is not None: + successful_calls += 1 + else: + failed_calls += 1 +end_time = time.time() +# Calculate the duration +duration = end_time - start_time + + +print(f"Load test Summary:") +print(f"Total Requests: {concurrent_calls}") +print(f"Successful Calls: {successful_calls}") +print(f"Failed Calls: {failed_calls}") +print(f"Total Time: {duration:.2f} seconds") + +# # Display content of the logs +# with open("request_log.txt", "r") as log_file: +# print("\nRequest Log:\n", log_file.read()) + +# with open("error_log.txt", "r") as error_log_file: +# print("\nError Log:\n", error_log_file.read()) diff --git a/litellm/proxy/tests/load_test_q.py b/litellm/proxy/tests/load_test_q.py new file mode 100644 index 0000000000000000000000000000000000000000..6b1da1eced32dde1cbee3b906ed15ab19e717cbd --- /dev/null +++ b/litellm/proxy/tests/load_test_q.py @@ -0,0 +1,121 @@ +import requests +import time +import os +from dotenv import load_dotenv +load_dotenv() + + +# Set the base URL as needed +base_url = "https://api.litellm.ai" +# # Uncomment the line below if you want to switch to the local server +# base_url = "http://0.0.0.0:8000" + +# Step 1 Add a config to the proxy, generate a temp key +config = { + "model_list": [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo", + "api_key": os.environ['OPENAI_API_KEY'], + } + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.environ['AZURE_API_KEY'], + "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com/", + "api_version": "2023-07-01-preview" + } + } + ] +} +print("STARTING LOAD TEST Q") +print(os.environ['AZURE_API_KEY']) + +response = requests.post( + url=f"{base_url}/key/generate", + json={ + "config": config, + "duration": "30d" # default to 30d, set it to 30m if you want a temp key + }, + headers={ + "Authorization": "Bearer sk-hosted-litellm" + } +) + +print("\nresponse from generating key", response.text) +print("\n json response from gen key", response.json()) + +generated_key = response.json()["key"] +print("\ngenerated key for proxy", generated_key) + + +# Step 2: Queue 50 requests to the proxy, using your generated_key + +import concurrent.futures + +def create_job_and_poll(request_num): + print(f"Creating a job on the proxy for request {request_num}") + job_response = requests.post( + url=f"{base_url}/queue/request", + json={ + 'model': 'gpt-3.5-turbo', + 'messages': [ + {'role': 'system', 'content': 'write a short poem'}, + ], + }, + headers={ + "Authorization": f"Bearer {generated_key}" + } + ) + print(job_response.status_code) + print(job_response.text) + print("\nResponse from creating job", job_response.text) + job_response = job_response.json() + job_id = job_response["id"] + polling_url = job_response["url"] + polling_url = f"{base_url}{polling_url}" + print(f"\nCreated Job {request_num}, Polling Url {polling_url}") + + # Poll each request + while True: + try: + print(f"\nPolling URL for request {request_num}", polling_url) + polling_response = requests.get( + url=polling_url, + headers={ + "Authorization": f"Bearer {generated_key}" + } + ) + print(f"\nResponse from polling url for request {request_num}", polling_response.text) + polling_response = polling_response.json() + status = polling_response.get("status", None) + if status == "finished": + llm_response = polling_response["result"] + print(f"LLM Response for request {request_num}") + print(llm_response) + # Write the llm_response to load_test_log.txt + try: + with open("load_test_log.txt", "a") as response_file: + response_file.write( + f"Response for request: {request_num}\n{llm_response}\n\n" + ) + except Exception as e: + print("GOT EXCEPTION", e) + break + time.sleep(0.5) + except Exception as e: + print("got exception when polling", e) + +# Number of requests +num_requests = 100 + +# Use ThreadPoolExecutor for parallel execution +with concurrent.futures.ThreadPoolExecutor(max_workers=num_requests) as executor: + # Create and poll each request in parallel + futures = [executor.submit(create_job_and_poll, i) for i in range(num_requests)] + + # Wait for all futures to complete + concurrent.futures.wait(futures) \ No newline at end of file diff --git a/litellm/proxy/tests/request_log.txt b/litellm/proxy/tests/request_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/litellm/proxy/tests/test_async.py b/litellm/proxy/tests/test_async.py new file mode 100644 index 0000000000000000000000000000000000000000..fab10ee08d07e2c5a8da51aa621931270420db7c --- /dev/null +++ b/litellm/proxy/tests/test_async.py @@ -0,0 +1,29 @@ +# # This tests the litelm proxy +# # it makes async Completion requests with streaming +# import openai + +# openai.base_url = "http://0.0.0.0:8000" +# openai.api_key = "temp-key" +# print(openai.base_url) + +# async def test_async_completion(): +# response = await ( +# model="gpt-3.5-turbo", +# prompt='this is a test request, write a short poem', +# ) +# print(response) + +# print("test_streaming") +# response = await openai.chat.completions.create( +# model="gpt-3.5-turbo", +# prompt='this is a test request, write a short poem', +# stream=True +# ) +# print(response) +# async for chunk in response: +# print(chunk) + + +# import asyncio +# asyncio.run(test_async_completion()) + diff --git a/litellm/proxy/tests/test_openai_request.py b/litellm/proxy/tests/test_openai_request.py new file mode 100644 index 0000000000000000000000000000000000000000..f49fce7bb9e7cc82ac469d776a46d18ab26897f1 --- /dev/null +++ b/litellm/proxy/tests/test_openai_request.py @@ -0,0 +1,15 @@ +# import openai +# client = openai.OpenAI( +# api_key="anything", +# base_url="http://0.0.0.0:8000" +# ) + +# # request sent to model set on litellm proxy, `litellm --model` +# response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ +# { +# "role": "user", +# "content": "this is a test request, write a short poem" +# } +# ]) + +# print(response) diff --git a/litellm/proxy/tests/test_proxy_exception_mapping.py b/litellm/proxy/tests/test_proxy_exception_mapping.py new file mode 100644 index 0000000000000000000000000000000000000000..7fb5bedbe70bc0fd610c8aa925c6577adfe94f8f --- /dev/null +++ b/litellm/proxy/tests/test_proxy_exception_mapping.py @@ -0,0 +1,23 @@ +import openai +client = openai.OpenAI( + api_key="anything", + # base_url="http://0.0.0.0:8000", +) + +try: + # request sent to model set on litellm proxy, `litellm --model` + response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + }, + ]) + + print(response) +# except openai.APITimeoutError: +# print("Got openai Timeout Exception. Good job. The proxy mapped to OpenAI exceptions") +except Exception as e: + print("\n the proxy did not map to OpenAI exception. Instead got", e) + print(e.type) # type: ignore + print(e.message) # type: ignore + print(e.code) # type: ignore \ No newline at end of file diff --git a/litellm/proxy/tests/test_q.py b/litellm/proxy/tests/test_q.py new file mode 100644 index 0000000000000000000000000000000000000000..34e713a5d7d484e00e35189c6d5926535179b40b --- /dev/null +++ b/litellm/proxy/tests/test_q.py @@ -0,0 +1,87 @@ +import requests +import time +import os +from dotenv import load_dotenv +load_dotenv() + + +# Set the base URL as needed +base_url = "https://api.litellm.ai" +# Uncomment the line below if you want to switch to the local server +# base_url = "http://0.0.0.0:8000" + +# Step 1 Add a config to the proxy, generate a temp key +config = { + "model_list": [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo", + "api_key": os.environ['OPENAI_API_KEY'], + } + } + ] +} + +response = requests.post( + url=f"{base_url}/key/generate", + json={ + "config": config, + "duration": "30d" # default to 30d, set it to 30m if you want a temp key + }, + headers={ + "Authorization": "Bearer sk-hosted-litellm" + } +) + +print("\nresponse from generating key", response.text) +print("\n json response from gen key", response.json()) + +generated_key = response.json()["key"] +print("\ngenerated key for proxy", generated_key) + +# Step 2: Queue a request to the proxy, using your generated_key +print("Creating a job on the proxy") +job_response = requests.post( + url=f"{base_url}/queue/request", + json={ + 'model': 'gpt-3.5-turbo', + 'messages': [ + {'role': 'system', 'content': f'You are a helpful assistant. What is your name'}, + ], + }, + headers={ + "Authorization": f"Bearer {generated_key}" + } +) +print(job_response.status_code) +print(job_response.text) +print("\nResponse from creating job", job_response.text) +job_response = job_response.json() +job_id = job_response["id"] # type: ignore +polling_url = job_response["url"] # type: ignore +polling_url = f"{base_url}{polling_url}" +print("\nCreated Job, Polling Url", polling_url) + +# Step 3: Poll the request +while True: + try: + print("\nPolling URL", polling_url) + polling_response = requests.get( + url=polling_url, + headers={ + "Authorization": f"Bearer {generated_key}" + } + ) + print("\nResponse from polling url", polling_response.text) + polling_response = polling_response.json() + status = polling_response.get("status", None) # type: ignore + if status == "finished": + llm_response = polling_response["result"] # type: ignore + print("LLM Response") + print(llm_response) + break + time.sleep(0.5) + except Exception as e: + print("got exception in polling", e) + break diff --git a/litellm/proxy/types.py b/litellm/proxy/types.py new file mode 100644 index 0000000000000000000000000000000000000000..fbee0732b925ca2e742f77445a8cc163fc75ba4e --- /dev/null +++ b/litellm/proxy/types.py @@ -0,0 +1,70 @@ +from pydantic import BaseModel +from typing import Optional, List, Union, Dict +from datetime import datetime + +######### Request Class Definition ###### +class ProxyChatCompletionRequest(BaseModel): + model: str + messages: List[Dict[str, str]] + temperature: Optional[float] = None + top_p: Optional[float] = None + n: Optional[int] = None + stream: Optional[bool] = None + stop: Optional[List[str]] = None + max_tokens: Optional[int] = None + presence_penalty: Optional[float] = None + frequency_penalty: Optional[float] = None + logit_bias: Optional[Dict[str, float]] = None + user: Optional[str] = None + response_format: Optional[Dict[str, str]] = None + seed: Optional[int] = None + tools: Optional[List[str]] = None + tool_choice: Optional[str] = None + functions: Optional[List[str]] = None # soon to be deprecated + function_call: Optional[str] = None # soon to be deprecated + + # Optional LiteLLM params + caching: Optional[bool] = None + api_base: Optional[str] = None + api_version: Optional[str] = None + api_key: Optional[str] = None + num_retries: Optional[int] = None + context_window_fallback_dict: Optional[Dict[str, str]] = None + fallbacks: Optional[List[str]] = None + metadata: Optional[Dict[str, str]] = {} + deployment_id: Optional[str] = None + request_timeout: Optional[int] = None + + class Config: + extra='allow' # allow params not defined here, these fall in litellm.completion(**kwargs) + +class ModelParams(BaseModel): + model_name: str + litellm_params: dict + model_info: Optional[dict] + class Config: + protected_namespaces = () + +class GenerateKeyRequest(BaseModel): + duration: str = "1h" + models: list = [] + aliases: dict = {} + config: dict = {} + spend: int = 0 + user_id: Optional[str] = None + +class GenerateKeyResponse(BaseModel): + key: str + expires: datetime + user_id: str + +class _DeleteKeyObject(BaseModel): + key: str + +class DeleteKeyRequest(BaseModel): + keys: List[_DeleteKeyObject] + + +class UserAPIKeyAuth(BaseModel): # the expected response object for user api key auth + api_key: Optional[str] = None + user_id: Optional[str] = None \ No newline at end of file diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9c6a2c17ea02b48ab115caad7fee1c38c4784fd8 --- /dev/null +++ b/litellm/proxy/utils.py @@ -0,0 +1,155 @@ +from typing import Optional, List, Any +import os, subprocess, hashlib, importlib + +### DB CONNECTOR ### +class PrismaClient: + def __init__(self, database_url: str): + print("LiteLLM: DATABASE_URL Set in config, trying to 'pip install prisma'") + os.environ["DATABASE_URL"] = database_url + # Save the current working directory + original_dir = os.getcwd() + # set the working directory to where this script is + abspath = os.path.abspath(__file__) + dname = os.path.dirname(abspath) + os.chdir(dname) + + try: + subprocess.run(['prisma', 'generate']) + subprocess.run(['prisma', 'db', 'push', '--accept-data-loss']) # this looks like a weird edge case when prisma just wont start on render. we need to have the --accept-data-loss + finally: + os.chdir(original_dir) + # Now you can import the Prisma Client + from prisma import Client # type: ignore + self.db = Client() #Client to connect to Prisma db + + def hash_token(self, token: str): + # Hash the string using SHA-256 + hashed_token = hashlib.sha256(token.encode()).hexdigest() + + return hashed_token + + async def get_data(self, token: str, expires: Optional[Any]=None): + hashed_token = self.hash_token(token=token) + if expires: + response = await self.db.litellm_verificationtoken.find_first( + where={ + "token": hashed_token, + "expires": {"gte": expires} # Check if the token is not expired + } + ) + else: + response = await self.db.litellm_verificationtoken.find_unique( + where={ + "token": hashed_token + } + ) + return response + + async def insert_data(self, data: dict): + """ + Add a key to the database. If it already exists, do nothing. + """ + token = data["token"] + hashed_token = self.hash_token(token=token) + data["token"] = hashed_token + print(f"passed in data: {data}; hashed_token: {hashed_token}") + + new_verification_token = await self.db.litellm_verificationtoken.upsert( # type: ignore + where={ + 'token': hashed_token, + }, + data={ + "create": {**data}, #type: ignore + "update": {} # don't do anything if it already exists + } + ) + + return new_verification_token + + async def update_data(self, token: str, data: dict): + """ + Update existing data + """ + hashed_token = self.hash_token(token=token) + data["token"] = hashed_token + await self.db.litellm_verificationtoken.update( + where={ + "token": hashed_token + }, + data={**data} # type: ignore + ) + return {"token": token, "data": data} + + async def delete_data(self, tokens: List): + """ + Allow user to delete a key(s) + """ + hashed_tokens = [self.hash_token(token=token) for token in tokens] + await self.db.litellm_verificationtoken.delete_many( + where={"token": {"in": hashed_tokens}} + ) + return {"deleted_keys": tokens} + + async def connect(self): + await self.db.connect() + + async def disconnect(self): + await self.db.disconnect() + +# ### CUSTOM FILE ### +# def get_instance_fn(value: str, config_file_path: Optional[str]=None): +# try: +# # Split the path by dots to separate module from instance +# parts = value.split(".") +# # The module path is all but the last part, and the instance is the last part +# module_path = ".".join(parts[:-1]) +# instance_name = parts[-1] + +# if config_file_path is not None: +# directory = os.path.dirname(config_file_path) +# module_path = os.path.join(directory, module_path) +# # Dynamically import the module +# module = importlib.import_module(module_path) + +# # Get the instance from the module +# instance = getattr(module, instance_name) + +# return instance +# except ImportError as e: +# print(e) +# raise ImportError(f"Could not import file at {value}") + +def get_instance_fn(value: str, config_file_path: Optional[str] = None) -> Any: + try: + print(f"value: {value}") + # Split the path by dots to separate module from instance + parts = value.split(".") + + # The module path is all but the last part, and the instance_name is the last part + module_name = ".".join(parts[:-1]) + instance_name = parts[-1] + + # If config_file_path is provided, use it to determine the module spec and load the module + if config_file_path is not None: + directory = os.path.dirname(config_file_path) + module_file_path = os.path.join(directory, *module_name.split('.')) + module_file_path += '.py' + + spec = importlib.util.spec_from_file_location(module_name, module_file_path) + if spec is None: + raise ImportError(f"Could not find a module specification for {module_file_path}") + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) # type: ignore + else: + # Dynamically import the module + module = importlib.import_module(module_name) + + # Get the instance from the module + instance = getattr(module, instance_name) + + return instance + except ImportError as e: + # Re-raise the exception with a user-friendly message + raise ImportError(f"Could not import {instance_name} from {module_name}") from e + except Exception as e: + raise e \ No newline at end of file diff --git a/litellm/requirements.txt b/litellm/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..dc833dd4befe4d013346e1b2418e3fe91406a7a8 --- /dev/null +++ b/litellm/requirements.txt @@ -0,0 +1 @@ +../requirements.txt \ No newline at end of file diff --git a/litellm/router.py b/litellm/router.py new file mode 100644 index 0000000000000000000000000000000000000000..edae794c9c1fccdb2e6b1efd5d2c6de6e08e7f7d --- /dev/null +++ b/litellm/router.py @@ -0,0 +1,1124 @@ +# +-----------------------------------------------+ +# | | +# | Give Feedback / Get Help | +# | https://github.com/BerriAI/litellm/issues/new | +# | | +# +-----------------------------------------------+ +# +# Thank you ! We ❤️ you! - Krrish & Ishaan + +from datetime import datetime +from typing import Dict, List, Optional, Union, Literal +import random, threading, time, traceback +import litellm, openai +from litellm.caching import RedisCache, InMemoryCache, DualCache +import logging, asyncio +import inspect, concurrent +from openai import AsyncOpenAI +from collections import defaultdict + +class Router: + """ + Example usage: + ```python + from litellm import Router + model_list = [ + { + "model_name": "azure-gpt-3.5-turbo", # model alias + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/", + "api_key": , + "api_version": , + "api_base": + }, + }, + { + "model_name": "azure-gpt-3.5-turbo", # model alias + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/", + "api_key": , + "api_version": , + "api_base": + }, + }, + { + "model_name": "openai-gpt-3.5-turbo", # model alias + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": , + }, + ] + + router = Router(model_list=model_list, fallbacks=[{"azure-gpt-3.5-turbo": "openai-gpt-3.5-turbo"}]) + ``` + """ + model_names: List = [] + cache_responses: bool = False + default_cache_time_seconds: int = 1 * 60 * 60 # 1 hour + num_retries: int = 0 + tenacity = None + + def __init__(self, + model_list: Optional[list] = None, + redis_host: Optional[str] = None, + redis_port: Optional[int] = None, + redis_password: Optional[str] = None, + cache_responses: bool = False, + num_retries: int = 0, + timeout: Optional[float] = None, + default_litellm_params = {}, # default params for Router.chat.completion.create + set_verbose: bool = False, + fallbacks: List = [], + allowed_fails: Optional[int] = None, + context_window_fallbacks: List = [], + routing_strategy: Literal["simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing"] = "simple-shuffle") -> None: + + self.set_verbose = set_verbose + if model_list: + self.set_model_list(model_list) + self.healthy_deployments: List = self.model_list + self.deployment_latency_map = {} + for m in model_list: + self.deployment_latency_map[m["litellm_params"]["model"]] = 0 + + self.allowed_fails = allowed_fails or litellm.allowed_fails + self.failed_calls = InMemoryCache() # cache to track failed call per deployment, if num failed calls within 1 minute > allowed fails, then add it to cooldown + self.num_retries = num_retries or litellm.num_retries or 0 + self.timeout = timeout or litellm.request_timeout + self.routing_strategy = routing_strategy + self.fallbacks = fallbacks or litellm.fallbacks + self.context_window_fallbacks = context_window_fallbacks or litellm.context_window_fallbacks + self.model_exception_map: dict = {} # dict to store model: list exceptions. self.exceptions = {"gpt-3.5": ["API KEY Error", "Rate Limit Error", "good morning error"]} + self.total_calls: defaultdict = defaultdict(int) # dict to store total calls made to each model + self.fail_calls: defaultdict = defaultdict(int) # dict to store fail_calls made to each model + self.success_calls: defaultdict = defaultdict(int) # dict to store success_calls made to each model + + + # make Router.chat.completions.create compatible for openai.chat.completions.create + self.chat = litellm.Chat(params=default_litellm_params) + + # default litellm args + self.default_litellm_params = default_litellm_params + self.default_litellm_params.setdefault("timeout", timeout) + self.default_litellm_params.setdefault("max_retries", 0) + + + ### HEALTH CHECK THREAD ### + if self.routing_strategy == "least-busy": + self._start_health_check_thread() + ### CACHING ### + redis_cache = None + if redis_host is not None and redis_port is not None and redis_password is not None: + cache_config = { + 'type': 'redis', + 'host': redis_host, + 'port': redis_port, + 'password': redis_password + } + redis_cache = RedisCache(host=redis_host, port=redis_port, password=redis_password) + else: # use an in-memory cache + cache_config = { + "type": "local" + } + if cache_responses: + litellm.cache = litellm.Cache(**cache_config) # use Redis for caching completion requests + self.cache_responses = cache_responses + self.cache = DualCache(redis_cache=redis_cache, in_memory_cache=InMemoryCache()) # use a dual cache (Redis+In-Memory) for tracking cooldowns, usage, etc. + ## USAGE TRACKING ## + if isinstance(litellm.success_callback, list): + litellm.success_callback.append(self.deployment_callback) + else: + litellm.success_callback = [self.deployment_callback] + + if isinstance(litellm.failure_callback, list): + litellm.failure_callback.append(self.deployment_callback_on_failure) + else: + litellm.failure_callback = [self.deployment_callback_on_failure] + self.print_verbose(f"Intialized router with Routing strategy: {self.routing_strategy}\n") + + + ### COMPLETION + EMBEDDING FUNCTIONS + + def completion(self, + model: str, + messages: List[Dict[str, str]], + **kwargs): + """ + Example usage: + response = router.completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey, how's it going?"}] + """ + try: + kwargs["model"] = model + kwargs["messages"] = messages + kwargs["original_function"] = self._completion + timeout = kwargs.get("request_timeout", self.timeout) + kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) + kwargs.setdefault("metadata", {}).update({"model_group": model}) + with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor: + # Submit the function to the executor with a timeout + future = executor.submit(self.function_with_fallbacks, **kwargs) + response = future.result(timeout=timeout) # type: ignore + + return response + except Exception as e: + raise e + + def _completion( + self, + model: str, + messages: List[Dict[str, str]], + **kwargs): + + try: + # pick the one that is available (lowest TPM/RPM) + deployment = self.get_available_deployment(model=model, messages=messages) + kwargs.setdefault("metadata", {}).update({"deployment": deployment["litellm_params"]["model"]}) + data = deployment["litellm_params"].copy() + for k, v in self.default_litellm_params.items(): + if k not in data: # prioritize model-specific params > default router params + data[k] = v + + ########## remove -ModelID-XXXX from model ############## + original_model_string = data["model"] + # Find the index of "ModelID" in the string + self.print_verbose(f"completion model: {original_model_string}") + index_of_model_id = original_model_string.find("-ModelID") + # Remove everything after "-ModelID" if it exists + if index_of_model_id != -1: + data["model"] = original_model_string[:index_of_model_id] + else: + data["model"] = original_model_string + model_client = self._get_client(deployment=deployment, kwargs=kwargs) + return litellm.completion(**{**data, "messages": messages, "caching": self.cache_responses, "client": model_client, **kwargs}) + except Exception as e: + raise e + + async def acompletion(self, + model: str, + messages: List[Dict[str, str]], + **kwargs): + try: + kwargs["model"] = model + kwargs["messages"] = messages + kwargs["original_function"] = self._acompletion + kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) + timeout = kwargs.get("request_timeout", self.timeout) + kwargs.setdefault("metadata", {}).update({"model_group": model}) + # response = await asyncio.wait_for(self.async_function_with_fallbacks(**kwargs), timeout=timeout) + response = await self.async_function_with_fallbacks(**kwargs) + + return response + except Exception as e: + raise e + + async def _acompletion( + self, + model: str, + messages: List[Dict[str, str]], + **kwargs): + try: + self.print_verbose(f"Inside _acompletion()- model: {model}; kwargs: {kwargs}") + original_model_string = None # set a default for this variable + deployment = self.get_available_deployment(model=model, messages=messages) + kwargs.setdefault("metadata", {}).update({"deployment": deployment["litellm_params"]["model"]}) + data = deployment["litellm_params"].copy() + for k, v in self.default_litellm_params.items(): + if k not in data: # prioritize model-specific params > default router params + data[k] = v + ########## remove -ModelID-XXXX from model ############## + original_model_string = data["model"] + # Find the index of "ModelID" in the string + index_of_model_id = original_model_string.find("-ModelID") + # Remove everything after "-ModelID" if it exists + if index_of_model_id != -1: + data["model"] = original_model_string[:index_of_model_id] + else: + data["model"] = original_model_string + model_client = self._get_client(deployment=deployment, kwargs=kwargs, client_type="async") + self.total_calls[original_model_string] +=1 + response = await litellm.acompletion(**{**data, "messages": messages, "caching": self.cache_responses, "client": model_client, **kwargs}) + self.success_calls[original_model_string] +=1 + return response + except Exception as e: + if original_model_string is not None: + self.fail_calls[original_model_string] +=1 + raise e + + def text_completion(self, + model: str, + prompt: str, + is_retry: Optional[bool] = False, + is_fallback: Optional[bool] = False, + is_async: Optional[bool] = False, + **kwargs): + try: + kwargs.setdefault("metadata", {}).update({"model_group": model}) + messages=[{"role": "user", "content": prompt}] + # pick the one that is available (lowest TPM/RPM) + deployment = self.get_available_deployment(model=model, messages=messages) + + data = deployment["litellm_params"].copy() + for k, v in self.default_litellm_params.items(): + if k not in data: # prioritize model-specific params > default router params + data[k] = v + ########## remove -ModelID-XXXX from model ############## + original_model_string = data["model"] + # Find the index of "ModelID" in the string + index_of_model_id = original_model_string.find("-ModelID") + # Remove everything after "-ModelID" if it exists + if index_of_model_id != -1: + data["model"] = original_model_string[:index_of_model_id] + else: + data["model"] = original_model_string + # call via litellm.completion() + return litellm.text_completion(**{**data, "prompt": prompt, "caching": self.cache_responses, **kwargs}) # type: ignore + except Exception as e: + if self.num_retries > 0: + kwargs["model"] = model + kwargs["messages"] = messages + kwargs["original_exception"] = e + kwargs["original_function"] = self.completion + return self.function_with_retries(**kwargs) + else: + raise e + + def embedding(self, + model: str, + input: Union[str, List], + is_async: Optional[bool] = False, + **kwargs) -> Union[List[float], None]: + # pick the one that is available (lowest TPM/RPM) + deployment = self.get_available_deployment(model=model, input=input) + kwargs.setdefault("metadata", {}).update({"deployment": deployment["litellm_params"]["model"]}) + data = deployment["litellm_params"].copy() + for k, v in self.default_litellm_params.items(): + if k not in data: # prioritize model-specific params > default router params + data[k] = v + ########## remove -ModelID-XXXX from model ############## + original_model_string = data["model"] + # Find the index of "ModelID" in the string + index_of_model_id = original_model_string.find("-ModelID") + # Remove everything after "-ModelID" if it exists + if index_of_model_id != -1: + data["model"] = original_model_string[:index_of_model_id] + else: + data["model"] = original_model_string + model_client = self._get_client(deployment=deployment, kwargs=kwargs) + # call via litellm.embedding() + return litellm.embedding(**{**data, "input": input, "caching": self.cache_responses, "client": model_client, **kwargs}) + + async def aembedding(self, + model: str, + input: Union[str, List], + is_async: Optional[bool] = True, + **kwargs) -> Union[List[float], None]: + # pick the one that is available (lowest TPM/RPM) + deployment = self.get_available_deployment(model=model, input=input) + kwargs.setdefault("metadata", {}).update({"deployment": deployment["litellm_params"]["model"]}) + data = deployment["litellm_params"].copy() + for k, v in self.default_litellm_params.items(): + if k not in data: # prioritize model-specific params > default router params + data[k] = v + ########## remove -ModelID-XXXX from model ############## + original_model_string = data["model"] + # Find the index of "ModelID" in the string + index_of_model_id = original_model_string.find("-ModelID") + # Remove everything after "-ModelID" if it exists + if index_of_model_id != -1: + data["model"] = original_model_string[:index_of_model_id] + else: + data["model"] = original_model_string + model_client = self._get_client(deployment=deployment, kwargs=kwargs, client_type="async") + + return await litellm.aembedding(**{**data, "input": input, "caching": self.cache_responses, "client": model_client, **kwargs}) + + async def async_function_with_fallbacks(self, *args, **kwargs): + """ + Try calling the function_with_retries + If it fails after num_retries, fall back to another model group + """ + model_group = kwargs.get("model") + fallbacks = kwargs.get("fallbacks", self.fallbacks) + context_window_fallbacks = kwargs.get("context_window_fallbacks", self.context_window_fallbacks) + try: + response = await self.async_function_with_retries(*args, **kwargs) + self.print_verbose(f'Async Response: {response}') + return response + except Exception as e: + self.print_verbose(f"An exception occurs") + original_exception = e + try: + self.print_verbose(f"Trying to fallback b/w models") + if isinstance(e, litellm.ContextWindowExceededError) and context_window_fallbacks is not None: + fallback_model_group = None + for item in context_window_fallbacks: # [{"gpt-3.5-turbo": ["gpt-4"]}] + if list(item.keys())[0] == model_group: + fallback_model_group = item[model_group] + break + + if fallback_model_group is None: + raise original_exception + + for mg in fallback_model_group: + """ + Iterate through the model groups and try calling that deployment + """ + try: + kwargs["model"] = mg + response = await self.async_function_with_retries(*args, **kwargs) + return response + except Exception as e: + pass + elif fallbacks is not None: + self.print_verbose(f"inside model fallbacks: {fallbacks}") + for item in fallbacks: + if list(item.keys())[0] == model_group: + fallback_model_group = item[model_group] + break + for mg in fallback_model_group: + """ + Iterate through the model groups and try calling that deployment + """ + try: + kwargs["model"] = mg + kwargs["metadata"]["model_group"] = mg + response = await self.async_function_with_retries(*args, **kwargs) + return response + except Exception as e: + raise e + except Exception as e: + self.print_verbose(f"An exception occurred - {str(e)}") + traceback.print_exc() + raise original_exception + + async def async_function_with_retries(self, *args, **kwargs): + self.print_verbose(f"Inside async function with retries: args - {args}; kwargs - {kwargs}") + backoff_factor = 1 + original_function = kwargs.pop("original_function") + fallbacks = kwargs.pop("fallbacks", self.fallbacks) + context_window_fallbacks = kwargs.pop("context_window_fallbacks", self.context_window_fallbacks) + self.print_verbose(f"async function w/ retries: original_function - {original_function}") + num_retries = kwargs.pop("num_retries") + try: + # if the function call is successful, no exception will be raised and we'll break out of the loop + response = await original_function(*args, **kwargs) + return response + except Exception as e: + original_exception = e + ### CHECK IF RATE LIMIT / CONTEXT WINDOW ERROR w/ fallbacks available + if ((isinstance(original_exception, litellm.ContextWindowExceededError) and context_window_fallbacks is None) + or (isinstance(original_exception, openai.RateLimitError) and fallbacks is not None)): + raise original_exception + ### RETRY + #### check if it should retry + back-off if required + if "No models available" in str(e): + timeout = litellm._calculate_retry_after(remaining_retries=num_retries, max_retries=num_retries) + await asyncio.sleep(timeout) + elif hasattr(original_exception, "status_code") and hasattr(original_exception, "response") and litellm._should_retry(status_code=original_exception.status_code): + if hasattr(original_exception.response, "headers"): + timeout = litellm._calculate_retry_after(remaining_retries=num_retries, max_retries=num_retries, response_headers=original_exception.response.headers) + else: + timeout = litellm._calculate_retry_after(remaining_retries=num_retries, max_retries=num_retries) + await asyncio.sleep(timeout) + else: + raise original_exception + + for current_attempt in range(num_retries): + self.print_verbose(f"retrying request. Current attempt - {current_attempt}; num retries: {num_retries}") + try: + # if the function call is successful, no exception will be raised and we'll break out of the loop + response = await original_function(*args, **kwargs) + if inspect.iscoroutinefunction(response): # async errors are often returned as coroutines + response = await response + return response + + except Exception as e: + remaining_retries = num_retries - current_attempt + if "No models available" in str(e): + timeout = litellm._calculate_retry_after(remaining_retries=remaining_retries, max_retries=num_retries, min_timeout=1) + await asyncio.sleep(timeout) + elif hasattr(e, "status_code") and hasattr(e, "response") and litellm._should_retry(status_code=e.status_code): + if hasattr(e.response, "headers"): + timeout = litellm._calculate_retry_after(remaining_retries=remaining_retries, max_retries=num_retries, response_headers=e.response.headers) + else: + timeout = litellm._calculate_retry_after(remaining_retries=remaining_retries, max_retries=num_retries) + await asyncio.sleep(timeout) + else: + raise e + raise original_exception + + def function_with_fallbacks(self, *args, **kwargs): + """ + Try calling the function_with_retries + If it fails after num_retries, fall back to another model group + """ + model_group = kwargs.get("model") + fallbacks = kwargs.get("fallbacks", self.fallbacks) + context_window_fallbacks = kwargs.get("context_window_fallbacks", self.context_window_fallbacks) + try: + response = self.function_with_retries(*args, **kwargs) + return response + except Exception as e: + original_exception = e + self.print_verbose(f"An exception occurs {original_exception}") + try: + self.print_verbose(f"Trying to fallback b/w models. Initial model group: {model_group}") + if isinstance(e, litellm.ContextWindowExceededError) and context_window_fallbacks is not None: + self.print_verbose(f"inside context window fallbacks: {context_window_fallbacks}") + fallback_model_group = None + + for item in context_window_fallbacks: # [{"gpt-3.5-turbo": ["gpt-4"]}] + if list(item.keys())[0] == model_group: + fallback_model_group = item[model_group] + break + + if fallback_model_group is None: + raise original_exception + + for mg in fallback_model_group: + """ + Iterate through the model groups and try calling that deployment + """ + try: + kwargs["model"] = mg + response = self.function_with_fallbacks(*args, **kwargs) + return response + except Exception as e: + pass + elif fallbacks is not None: + self.print_verbose(f"inside model fallbacks: {fallbacks}") + fallback_model_group = None + for item in fallbacks: + if list(item.keys())[0] == model_group: + fallback_model_group = item[model_group] + break + + if fallback_model_group is None: + raise original_exception + + for mg in fallback_model_group: + """ + Iterate through the model groups and try calling that deployment + """ + try: + kwargs["model"] = mg + response = self.function_with_fallbacks(*args, **kwargs) + return response + except Exception as e: + pass + except Exception as e: + raise e + raise original_exception + + def function_with_retries(self, *args, **kwargs): + """ + Try calling the model 3 times. Shuffle between available deployments. + """ + self.print_verbose(f"Inside function with retries: args - {args}; kwargs - {kwargs}") + backoff_factor = 1 + original_function = kwargs.pop("original_function") + num_retries = kwargs.pop("num_retries") + fallbacks = kwargs.pop("fallbacks", self.fallbacks) + context_window_fallbacks = kwargs.pop("context_window_fallbacks", self.context_window_fallbacks) + try: + # if the function call is successful, no exception will be raised and we'll break out of the loop + response = original_function(*args, **kwargs) + return response + except Exception as e: + original_exception = e + self.print_verbose(f"num retries in function with retries: {num_retries}") + ### CHECK IF RATE LIMIT / CONTEXT WINDOW ERROR + if ((isinstance(original_exception, litellm.ContextWindowExceededError) and context_window_fallbacks is None) + or (isinstance(original_exception, openai.RateLimitError) and fallbacks is not None)): + raise original_exception + ### RETRY + for current_attempt in range(num_retries): + self.print_verbose(f"retrying request. Current attempt - {current_attempt}; retries left: {num_retries}") + try: + # if the function call is successful, no exception will be raised and we'll break out of the loop + response = original_function(*args, **kwargs) + return response + + except openai.RateLimitError as e: + if num_retries > 0: + remaining_retries = num_retries - current_attempt + timeout = litellm._calculate_retry_after(remaining_retries=remaining_retries, max_retries=num_retries) + # on RateLimitError we'll wait for an exponential time before trying again + time.sleep(timeout) + else: + raise e + + except Exception as e: + # for any other exception types, immediately retry + if num_retries > 0: + pass + else: + raise e + raise original_exception + + ### HELPER FUNCTIONS + + def deployment_callback( + self, + kwargs, # kwargs to completion + completion_response, # response from completion + start_time, end_time # start/end time + ): + """ + Function LiteLLM submits a callback to after a successful + completion. Purpose of this is to update TPM/RPM usage per model + """ + model_name = kwargs.get('model', None) # i.e. gpt35turbo + custom_llm_provider = kwargs.get("litellm_params", {}).get('custom_llm_provider', None) # i.e. azure + if custom_llm_provider: + model_name = f"{custom_llm_provider}/{model_name}" + if kwargs["stream"] is True: + if kwargs.get("complete_streaming_response"): + total_tokens = kwargs.get("complete_streaming_response")['usage']['total_tokens'] + self._set_deployment_usage(model_name, total_tokens) + else: + total_tokens = completion_response['usage']['total_tokens'] + self._set_deployment_usage(model_name, total_tokens) + + self.deployment_latency_map[model_name] = (end_time - start_time).total_seconds() + + def deployment_callback_on_failure( + self, + kwargs, # kwargs to completion + completion_response, # response from completion + start_time, end_time # start/end time + ): + try: + exception = kwargs.get("exception", None) + exception_type = type(exception) + exception_status = getattr(exception, 'status_code', "") + exception_cause = getattr(exception, '__cause__', "") + exception_message = getattr(exception, 'message', "") + exception_str = str(exception_type) + "Status: " + str(exception_status) + "Message: " + str(exception_cause) + str(exception_message) + "Full exception" + str(exception) + model_name = kwargs.get('model', None) # i.e. gpt35turbo + custom_llm_provider = kwargs.get("litellm_params", {}).get('custom_llm_provider', None) # i.e. azure + metadata = kwargs.get("litellm_params", {}).get('metadata', None) + if metadata: + deployment = metadata.get("deployment", None) + self._set_cooldown_deployments(deployment) + deployment_exceptions = self.model_exception_map.get(deployment, []) + deployment_exceptions.append(exception_str) + self.model_exception_map[deployment] = deployment_exceptions + self.print_verbose("\nEXCEPTION FOR DEPLOYMENTS\n") + self.print_verbose(self.model_exception_map) + for model in self.model_exception_map: + self.print_verbose(f"Model {model} had {len(self.model_exception_map[model])} exception") + if custom_llm_provider: + model_name = f"{custom_llm_provider}/{model_name}" + + except Exception as e: + raise e + + def _set_cooldown_deployments(self, + deployment: str): + """ + Add a model to the list of models being cooled down for that minute, if it exceeds the allowed fails / minute + """ + + current_minute = datetime.now().strftime("%H-%M") + # get current fails for deployment + # update the number of failed calls + # if it's > allowed fails + # cooldown deployment + current_fails = self.failed_calls.get_cache(key=deployment) or 0 + updated_fails = current_fails + 1 + self.print_verbose(f"Attempting to add {deployment} to cooldown list. updated_fails: {updated_fails}; self.allowed_fails: {self.allowed_fails}") + if updated_fails > self.allowed_fails: + # get the current cooldown list for that minute + cooldown_key = f"{current_minute}:cooldown_models" # group cooldown models by minute to reduce number of redis calls + cached_value = self.cache.get_cache(key=cooldown_key) + + self.print_verbose(f"adding {deployment} to cooldown models") + # update value + try: + if deployment in cached_value: + pass + else: + cached_value = cached_value + [deployment] + # save updated value + self.cache.set_cache(value=cached_value, key=cooldown_key, ttl=1) + except: + cached_value = [deployment] + # save updated value + self.cache.set_cache(value=cached_value, key=cooldown_key, ttl=1) + else: + self.failed_calls.set_cache(key=deployment, value=updated_fails, ttl=1) + + def _get_cooldown_deployments(self): + """ + Get the list of models being cooled down for this minute + """ + current_minute = datetime.now().strftime("%H-%M") + # get the current cooldown list for that minute + cooldown_key = f"{current_minute}:cooldown_models" + + # ---------------------- + # Return cooldown models + # ---------------------- + cooldown_models = self.cache.get_cache(key=cooldown_key) or [] + + self.print_verbose(f"retrieve cooldown models: {cooldown_models}") + return cooldown_models + + def get_usage_based_available_deployment(self, + model: str, + messages: Optional[List[Dict[str, str]]] = None, + input: Optional[Union[str, List]] = None): + """ + Returns a deployment with the lowest TPM/RPM usage. + """ + # get list of potential deployments + potential_deployments = [] + for item in self.model_list: + if item["model_name"] == model: + potential_deployments.append(item) + + # get current call usage + token_count = 0 + if messages is not None: + token_count = litellm.token_counter(model=model, messages=messages) + elif input is not None: + if isinstance(input, List): + input_text = "".join(text for text in input) + else: + input_text = input + token_count = litellm.token_counter(model=model, text=input_text) + + # ----------------------- + # Find lowest used model + # ---------------------- + lowest_tpm = float("inf") + deployment = None + + # return deployment with lowest tpm usage + for item in potential_deployments: + item_tpm, item_rpm = self._get_deployment_usage(deployment_name=item["litellm_params"]["model"]) + + if item_tpm == 0: + return item + elif ("tpm" in item and item_tpm + token_count > item["tpm"] + or "rpm" in item and item_rpm + 1 >= item["rpm"]): # if user passed in tpm / rpm in the model_list + continue + elif item_tpm < lowest_tpm: + lowest_tpm = item_tpm + deployment = item + + # if none, raise exception + if deployment is None: + raise ValueError("No models available.") + + # return model + return deployment + + def _get_deployment_usage( + self, + deployment_name: str + ): + # ------------ + # Setup values + # ------------ + current_minute = datetime.now().strftime("%H-%M") + tpm_key = f'{deployment_name}:tpm:{current_minute}' + rpm_key = f'{deployment_name}:rpm:{current_minute}' + + # ------------ + # Return usage + # ------------ + tpm = self.cache.get_cache(key=tpm_key) or 0 + rpm = self.cache.get_cache(key=rpm_key) or 0 + + return int(tpm), int(rpm) + + def increment(self, key: str, increment_value: int): + # get value + cached_value = self.cache.get_cache(key=key) + # update value + try: + cached_value = cached_value + increment_value + except: + cached_value = increment_value + # save updated value + self.cache.set_cache(value=cached_value, key=key, ttl=self.default_cache_time_seconds) + + def _set_deployment_usage( + self, + model_name: str, + total_tokens: int + ): + # ------------ + # Setup values + # ------------ + current_minute = datetime.now().strftime("%H-%M") + tpm_key = f'{model_name}:tpm:{current_minute}' + rpm_key = f'{model_name}:rpm:{current_minute}' + + # ------------ + # Update usage + # ------------ + self.increment(tpm_key, total_tokens) + self.increment(rpm_key, 1) + + def _start_health_check_thread(self): + """ + Starts a separate thread to perform health checks periodically. + """ + health_check_thread = threading.Thread(target=self._perform_health_checks, daemon=True) + health_check_thread.start() + + def _perform_health_checks(self): + """ + Periodically performs health checks on the servers. + Updates the list of healthy servers accordingly. + """ + while True: + self.healthy_deployments = self._health_check() + # Adjust the time interval based on your needs + time.sleep(15) + + def _health_check(self): + """ + Performs a health check on the deployments + Returns the list of healthy deployments + """ + healthy_deployments = [] + for deployment in self.model_list: + litellm_args = deployment["litellm_params"] + try: + start_time = time.time() + litellm.completion(messages=[{"role": "user", "content": ""}], max_tokens=1, **litellm_args) # hit the server with a blank message to see how long it takes to respond + end_time = time.time() + response_time = end_time - start_time + logging.debug(f"response_time: {response_time}") + healthy_deployments.append((deployment, response_time)) + healthy_deployments.sort(key=lambda x: x[1]) + except Exception as e: + pass + return healthy_deployments + + def weighted_shuffle_by_latency(self, items): + # Sort the items by latency + sorted_items = sorted(items, key=lambda x: x[1]) + # Get only the latencies + latencies = [i[1] for i in sorted_items] + # Calculate the sum of all latencies + total_latency = sum(latencies) + # Calculate the weight for each latency (lower latency = higher weight) + weights = [total_latency-latency for latency in latencies] + # Get a weighted random item + if sum(weights) == 0: + chosen_item = random.choice(sorted_items)[0] + else: + chosen_item = random.choices(sorted_items, weights=weights, k=1)[0][0] + return chosen_item + + def set_model_list(self, model_list: list): + self.model_list = model_list + # we add api_base/api_key each model so load balancing between azure/gpt on api_base1 and api_base2 works + import os + for model in self.model_list: + litellm_params = model.get("litellm_params", {}) + model_name = litellm_params.get("model") + #### for OpenAI / Azure we need to initalize the Client for High Traffic ######## + custom_llm_provider = litellm_params.get("custom_llm_provider") + if custom_llm_provider is None: + custom_llm_provider = model_name.split("/",1)[0] + if ( + model_name in litellm.open_ai_chat_completion_models + or custom_llm_provider == "custom_openai" + or custom_llm_provider == "deepinfra" + or custom_llm_provider == "perplexity" + or custom_llm_provider == "anyscale" + or custom_llm_provider == "openai" + or custom_llm_provider == "azure" + or "ft:gpt-3.5-turbo" in model_name + or model_name in litellm.open_ai_embedding_models + ): + # glorified / complicated reading of configs + # user can pass vars directly or they can pas os.environ/AZURE_API_KEY, in which case we will read the env + # we do this here because we init clients for Azure, OpenAI and we need to set the right key + api_key = litellm_params.get("api_key") + if api_key and api_key.startswith("os.environ/"): + api_key_env_name = api_key.replace("os.environ/", "") + api_key = litellm.get_secret(api_key_env_name) + + api_base = litellm_params.get("api_base") + base_url = litellm_params.get("base_url") + api_base = api_base or base_url # allow users to pass in `api_base` or `base_url` for azure + if api_base and api_base.startswith("os.environ/"): + api_base_env_name = api_base.replace("os.environ/", "") + api_base = litellm.get_secret(api_base_env_name) + + api_version = litellm_params.get("api_version") + if api_version and api_version.startswith("os.environ/"): + api_version_env_name = api_version.replace("os.environ/", "") + api_version = litellm.get_secret(api_version_env_name) + + timeout = litellm_params.pop("timeout", None) + if isinstance(timeout, str) and timeout.startswith("os.environ/"): + timeout_env_name = api_version.replace("os.environ/", "") + timeout = litellm.get_secret(timeout_env_name) + + stream_timeout = litellm_params.pop("stream_timeout", timeout) # if no stream_timeout is set, default to timeout + if isinstance(stream_timeout, str) and stream_timeout.startswith("os.environ/"): + stream_timeout_env_name = api_version.replace("os.environ/", "") + stream_timeout = litellm.get_secret(stream_timeout_env_name) + + max_retries = litellm_params.pop("max_retries", 2) + if isinstance(max_retries, str) and max_retries.startswith("os.environ/"): + max_retries_env_name = api_version.replace("os.environ/", "") + max_retries = litellm.get_secret(max_retries_env_name) + + + self.print_verbose(f"Initializing OpenAI Client for {model_name}, {str(api_base)}") + if "azure" in model_name: + self.print_verbose(f"Initializing Azure OpenAI Client for {model_name}, {str(api_base)}, {api_key}") + if api_version is None: + api_version = "2023-07-01-preview" + if "gateway.ai.cloudflare.com" in api_base: + if not api_base.endswith("/"): + api_base += "/" + azure_model = model_name.replace("azure/", "") + api_base += f"{azure_model}" + model["async_client"] = openai.AsyncAzureOpenAI( + api_key=api_key, + base_url=api_base, + api_version=api_version, + timeout=timeout, + max_retries=max_retries + ) + model["client"] = openai.AzureOpenAI( + api_key=api_key, + base_url=api_base, + api_version=api_version, + timeout=timeout, + max_retries=max_retries + ) + + # streaming clients can have diff timeouts + model["stream_async_client"] = openai.AsyncAzureOpenAI( + api_key=api_key, + base_url=api_base, + api_version=api_version, + timeout=stream_timeout, + max_retries=max_retries + ) + model["stream_client"] = openai.AzureOpenAI( + api_key=api_key, + base_url=api_base, + api_version=api_version, + timeout=stream_timeout, + max_retries=max_retries + ) + else: + model["async_client"] = openai.AsyncAzureOpenAI( + api_key=api_key, + azure_endpoint=api_base, + api_version=api_version, + timeout=timeout, + max_retries=max_retries + ) + model["client"] = openai.AzureOpenAI( + api_key=api_key, + azure_endpoint=api_base, + api_version=api_version, + timeout=timeout, + max_retries=max_retries + ) + # streaming clients should have diff timeouts + model["stream_async_client"] = openai.AsyncAzureOpenAI( + api_key=api_key, + base_url=api_base, + api_version=api_version, + timeout=stream_timeout, + max_retries=max_retries + ) + + model["stream_client"] = openai.AzureOpenAI( + api_key=api_key, + base_url=api_base, + api_version=api_version, + timeout=stream_timeout, + max_retries=max_retries + ) + + else: + self.print_verbose(f"Initializing OpenAI Client for {model_name}, {str(api_base)}") + model["async_client"] = openai.AsyncOpenAI( + api_key=api_key, + base_url=api_base, + timeout=timeout, + max_retries=max_retries + ) + model["client"] = openai.OpenAI( + api_key=api_key, + base_url=api_base, + timeout=timeout, + max_retries=max_retries + ) + + # streaming clients should have diff timeouts + model["stream_async_client"] = openai.AsyncOpenAI( + api_key=api_key, + base_url=api_base, + timeout=stream_timeout, + max_retries=max_retries + ) + + # streaming clients should have diff timeouts + model["stream_client"] = openai.OpenAI( + api_key=api_key, + base_url=api_base, + timeout=stream_timeout, + max_retries=max_retries + ) + + ############ End of initializing Clients for OpenAI/Azure ################### + model_id = "" + for key in model["litellm_params"]: + if key != "api_key": + model_id+= str(model["litellm_params"][key]) + model["litellm_params"]["model"] += "-ModelID-" + model_id + + ############ Users can either pass tpm/rpm as a litellm_param or a router param ########### + # for get_available_deployment, we use the litellm_param["rpm"] + # in this snippet we also set rpm to be a litellm_param + if model["litellm_params"].get("rpm") is None and model.get("rpm") is not None: + model["litellm_params"]["rpm"] = model.get("rpm") + if model["litellm_params"].get("tpm") is None and model.get("tpm") is not None: + model["litellm_params"]["tpm"] = model.get("tpm") + + self.model_names = [m["model_name"] for m in model_list] + + def get_model_names(self): + return self.model_names + + def _get_client(self, deployment, kwargs, client_type=None): + """ + Returns the appropriate client based on the given deployment, kwargs, and client_type. + + Parameters: + deployment (dict): The deployment dictionary containing the clients. + kwargs (dict): The keyword arguments passed to the function. + client_type (str): The type of client to return. + + Returns: + The appropriate client based on the given client_type and kwargs. + """ + if client_type == "async": + if kwargs.get("stream") == True: + return deployment["stream_async_client"] + else: + return deployment["async_client"] + else: + if kwargs.get("stream") == True: + return deployment["stream_client"] + else: + return deployment["client"] + + def print_verbose(self, print_statement): + if self.set_verbose or litellm.set_verbose: + print(f"LiteLLM.Router: {print_statement}") # noqa + + def get_available_deployment(self, + model: str, + messages: Optional[List[Dict[str, str]]] = None, + input: Optional[Union[str, List]] = None): + """ + Returns the deployment based on routing strategy + """ + ## get healthy deployments + ### get all deployments + ### filter out the deployments currently cooling down + healthy_deployments = [m for m in self.model_list if m["model_name"] == model] + if len(healthy_deployments) == 0: + # check if the user sent in a deployment name instead + healthy_deployments = [m for m in self.model_list if m["litellm_params"]["model"] == model] + self.print_verbose(f"initial list of deployments: {healthy_deployments}") + deployments_to_remove = [] + cooldown_deployments = self._get_cooldown_deployments() + self.print_verbose(f"cooldown deployments: {cooldown_deployments}") + ### FIND UNHEALTHY DEPLOYMENTS + for deployment in healthy_deployments: + deployment_name = deployment["litellm_params"]["model"] + if deployment_name in cooldown_deployments: + deployments_to_remove.append(deployment) + ### FILTER OUT UNHEALTHY DEPLOYMENTS + for deployment in deployments_to_remove: + healthy_deployments.remove(deployment) + self.print_verbose(f"healthy deployments: length {len(healthy_deployments)} {healthy_deployments}") + if len(healthy_deployments) == 0: + raise ValueError("No models available") + if litellm.model_alias_map and model in litellm.model_alias_map: + model = litellm.model_alias_map[ + model + ] # update the model to the actual value if an alias has been passed in + if self.routing_strategy == "least-busy": + if len(self.healthy_deployments) > 0: + for item in self.healthy_deployments: + if item[0]["model_name"] == model: # first one in queue will be the one with the most availability + return item[0] + else: + raise ValueError("No models available.") + elif self.routing_strategy == "simple-shuffle": + # if users pass rpm or tpm, we do a random weighted pick - based on rpm/tpm + ############## Check if we can do a RPM/TPM based weighted pick ################# + rpm = healthy_deployments[0].get("litellm_params").get("rpm", None) + if rpm is not None: + # use weight-random pick if rpms provided + rpms = [m["litellm_params"].get("rpm", 0) for m in healthy_deployments] + self.print_verbose(f"\nrpms {rpms}") + total_rpm = sum(rpms) + weights = [rpm / total_rpm for rpm in rpms] + self.print_verbose(f"\n weights {weights}") + # Perform weighted random pick + selected_index = random.choices(range(len(rpms)), weights=weights)[0] + self.print_verbose(f"\n selected index, {selected_index}") + deployment = healthy_deployments[selected_index] + return deployment or deployment[0] + ############## Check if we can do a RPM/TPM based weighted pick ################# + tpm = healthy_deployments[0].get("litellm_params").get("tpm", None) + if tpm is not None: + # use weight-random pick if rpms provided + tpms = [m["litellm_params"].get("tpm", 0) for m in healthy_deployments] + self.print_verbose(f"\ntpms {tpms}") + total_tpm = sum(tpms) + weights = [tpm / total_tpm for tpm in tpms] + self.print_verbose(f"\n weights {weights}") + # Perform weighted random pick + selected_index = random.choices(range(len(tpms)), weights=weights)[0] + self.print_verbose(f"\n selected index, {selected_index}") + deployment = healthy_deployments[selected_index] + return deployment or deployment[0] + + ############## No RPM/TPM passed, we do a random pick ################# + item = random.choice(healthy_deployments) + return item or item[0] + elif self.routing_strategy == "latency-based-routing": + returned_item = None + lowest_latency = float('inf') + ### shuffles with priority for lowest latency + # items_with_latencies = [('A', 10), ('B', 20), ('C', 30), ('D', 40)] + items_with_latencies = [] + for item in healthy_deployments: + items_with_latencies.append((item, self.deployment_latency_map[item["litellm_params"]["model"]])) + returned_item = self.weighted_shuffle_by_latency(items_with_latencies) + return returned_item + elif self.routing_strategy == "usage-based-routing": + return self.get_usage_based_available_deployment(model=model, messages=messages, input=input) + + raise ValueError("No models available.") + + def flush_cache(self): + self.cache.flush_cache() + + def reset(self): + ## clean up on close + litellm.success_callback = [] + litellm.failure_callback = [] + self.flush_cache() + \ No newline at end of file diff --git a/litellm/tests/data_map.txt b/litellm/tests/data_map.txt new file mode 100644 index 0000000000000000000000000000000000000000..e8077595f476b6e0ae27fcd3e401b5806ede77af Binary files /dev/null and b/litellm/tests/data_map.txt differ diff --git a/litellm/tests/example_config_yaml/aliases_config.yaml b/litellm/tests/example_config_yaml/aliases_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..266f6cf22eef39e2ef0783211ead16de1c5d4576 --- /dev/null +++ b/litellm/tests/example_config_yaml/aliases_config.yaml @@ -0,0 +1,30 @@ +model_list: + - model_name: text-davinci-003 + litellm_params: + model: ollama/zephyr + - model_name: gpt-4 + litellm_params: + model: ollama/llama2 + - model_name: gpt-3.5-turbo + litellm_params: + model: ollama/llama2 + temperature: 0.1 + max_tokens: 20 + + +# request to gpt-4, response from ollama/llama2 +# curl --location 'http://0.0.0.0:8000/chat/completions' \ +# --header 'Content-Type: application/json' \ +# --data ' { +# "model": "gpt-4", +# "messages": [ +# { +# "role": "user", +# "content": "what llm are you" +# } +# ], +# } +# ' +# + +# {"id":"chatcmpl-27c85cf0-ab09-4bcf-8cb1-0ee950520743","choices":[{"finish_reason":"stop","index":0,"message":{"content":" Hello! I'm just an AI, I don't have personal experiences or emotions like humans do. However, I can help you with any questions or tasks you may have! Is there something specific you'd like to know or discuss?","role":"assistant","_logprobs":null}}],"created":1700094955.373751,"model":"ollama/llama2","object":"chat.completion","system_fingerprint":null,"usage":{"prompt_tokens":12,"completion_tokens":47,"total_tokens":59},"_response_ms":8028.017999999999}% \ No newline at end of file diff --git a/litellm/tests/example_config_yaml/azure_config.yaml b/litellm/tests/example_config_yaml/azure_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fd5865cd7c959503784713c2b9a2d2a7dbbb1307 --- /dev/null +++ b/litellm/tests/example_config_yaml/azure_config.yaml @@ -0,0 +1,15 @@ +model_list: + - model_name: gpt-4-team1 + litellm_params: + model: azure/chatgpt-v-2 + api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ + api_version: "2023-05-15" + api_key: os.environ/AZURE_API_KEY + tpm: 20_000 + - model_name: gpt-4-team2 + litellm_params: + model: azure/gpt-4 + api_key: os.environ/AZURE_API_KEY + api_base: https://openai-gpt-4-test-v-2.openai.azure.com/ + tpm: 100_000 + diff --git a/litellm/tests/example_config_yaml/langfuse_config.yaml b/litellm/tests/example_config_yaml/langfuse_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c2a77b5adc464ca063cb6a5bda3eab6965b400cf --- /dev/null +++ b/litellm/tests/example_config_yaml/langfuse_config.yaml @@ -0,0 +1,7 @@ +model_list: + - model_name: gpt-3.5-turbo + +litellm_settings: + drop_params: True + success_callback: ["langfuse"] # https://docs.litellm.ai/docs/observability/langfuse_integration + diff --git a/litellm/tests/example_config_yaml/load_balancer.yaml b/litellm/tests/example_config_yaml/load_balancer.yaml new file mode 100644 index 0000000000000000000000000000000000000000..502b90ff92af6f381fece2096a3b352f9f0ada91 --- /dev/null +++ b/litellm/tests/example_config_yaml/load_balancer.yaml @@ -0,0 +1,28 @@ +litellm_settings: + drop_params: True + +# Model-specific settings +model_list: # use the same model_name for using the litellm router. LiteLLM will use the router between gpt-3.5-turbo + - model_name: gpt-3.5-turbo # litellm will + litellm_params: + model: gpt-3.5-turbo + api_key: sk-uj6F + tpm: 20000 # [OPTIONAL] REPLACE with your openai tpm + rpm: 3 # [OPTIONAL] REPLACE with your openai rpm + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + api_key: sk-Imn + tpm: 20000 # [OPTIONAL] REPLACE with your openai tpm + rpm: 3 # [OPTIONAL] REPLACE with your openai rpm + - model_name: gpt-3.5-turbo + litellm_params: + model: openrouter/gpt-3.5-turbo + - model_name: mistral-7b-instruct + litellm_params: + model: mistralai/mistral-7b-instruct + +environment_variables: + REDIS_HOST: localhost + REDIS_PASSWORD: + REDIS_PORT: \ No newline at end of file diff --git a/litellm/tests/example_config_yaml/opentelemetry_config.yaml b/litellm/tests/example_config_yaml/opentelemetry_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..92d3454d7d5fdb4e2a896aa47d01a19182043ee7 --- /dev/null +++ b/litellm/tests/example_config_yaml/opentelemetry_config.yaml @@ -0,0 +1,7 @@ +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + +general_settings: + otel: True # OpenTelemetry Logger this logs OTEL data to your collector diff --git a/litellm/tests/example_config_yaml/simple_config.yaml b/litellm/tests/example_config_yaml/simple_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..14b39a12518e83600c24ee86a0cd5405ae48ee3c --- /dev/null +++ b/litellm/tests/example_config_yaml/simple_config.yaml @@ -0,0 +1,4 @@ +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo \ No newline at end of file diff --git a/litellm/tests/litellm_uuid.txt b/litellm/tests/litellm_uuid.txt new file mode 100644 index 0000000000000000000000000000000000000000..e4252e46d0ce1a56b15cd0ad02b39530582bf5d3 --- /dev/null +++ b/litellm/tests/litellm_uuid.txt @@ -0,0 +1 @@ +80888ede-4881-4876-ab3f-765d47282e66 \ No newline at end of file diff --git a/litellm/tests/model_cost.json b/litellm/tests/model_cost.json new file mode 100644 index 0000000000000000000000000000000000000000..e581433952162bc9e08b9affc0dd8890f9669e72 --- /dev/null +++ b/litellm/tests/model_cost.json @@ -0,0 +1,3 @@ +{ + "gpt-3.5-turbo-0613": 7.7e-05 +} \ No newline at end of file diff --git a/litellm/tests/test_acooldowns_router.py b/litellm/tests/test_acooldowns_router.py new file mode 100644 index 0000000000000000000000000000000000000000..afab5944c8cde12472dd4f6c09ec5a5d6a979b68 --- /dev/null +++ b/litellm/tests/test_acooldowns_router.py @@ -0,0 +1,165 @@ +#### What this tests #### +# This tests calling batch_completions by running 100 messages together + +import sys, os, time +import traceback, asyncio +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import Router +import concurrent +from dotenv import load_dotenv +load_dotenv() + +model_list = [{ # list of model deployments + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000 + } +] + +kwargs = {"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Hey, how's it going?"}],} + + +def test_multiple_deployments_sync(): + import concurrent, time + litellm.set_verbose=False + results = [] + router = Router(model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=int(os.getenv("REDIS_PORT")), # type: ignore + routing_strategy="simple-shuffle", + set_verbose=True, + num_retries=1) # type: ignore + try: + for _ in range(3): + response = router.completion(**kwargs) + results.append(response) + print(results) + router.reset() + except Exception as e: + print(f"FAILED TEST!") + pytest.fail(f"An error occurred - {traceback.format_exc()}") + +# test_multiple_deployments_sync() + + +def test_multiple_deployments_parallel(): + litellm.set_verbose = False # Corrected the syntax for setting verbose to False + results = [] + futures = {} + start_time = time.time() + router = Router(model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=int(os.getenv("REDIS_PORT")), # type: ignore + routing_strategy="simple-shuffle", + set_verbose=True, + num_retries=1) # type: ignore + # Assuming you have an executor instance defined somewhere in your code + with concurrent.futures.ThreadPoolExecutor() as executor: + for _ in range(5): + future = executor.submit(router.completion, **kwargs) + futures[future] = future + + # Retrieve the results from the futures + while futures: + done, not_done = concurrent.futures.wait(futures.values(), timeout=10, return_when=concurrent.futures.FIRST_COMPLETED) + for future in done: + try: + result = future.result() + results.append(result) + del futures[future] # Remove the done future + except Exception as e: + print(f"Exception: {e}; traceback: {traceback.format_exc()}") + del futures[future] # Remove the done future with exception + + print(f"Remaining futures: {len(futures)}") + router.reset() + end_time = time.time() + print(results) + print(f"ELAPSED TIME: {end_time - start_time}") + +# Assuming litellm, router, and executor are defined somewhere in your code + +# test_multiple_deployments_parallel() +def test_cooldown_same_model_name(): + # users could have the same model with different api_base + # example + # azure/chatgpt, api_base: 1234 + # azure/chatgpt, api_base: 1235 + # if 1234 fails, it should only cooldown 1234 and then try with 1235 + litellm.set_verbose = False + try: + print("testing cooldown same model name") + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": "BAD_API_BASE", + }, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + }, + ] + + router = Router( + model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=int(os.getenv("REDIS_PORT")), + routing_strategy="simple-shuffle", + set_verbose=True, + num_retries=3 + ) # type: ignore + + response = router.completion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "hello this request will pass" + } + ] + ) + print(router.model_list) + litellm_model_names = [] + for model in router.model_list: + litellm_model_names.append(model["litellm_params"]["model"]) + print("\n litellm model names ", litellm_model_names) + + # example litellm_model_names ['azure/chatgpt-v-2-ModelID-64321', 'azure/chatgpt-v-2-ModelID-63960'] + assert litellm_model_names[0] != litellm_model_names[1] # ensure both models have a uuid added, and they have different names + print("\ngot response\n", response) + except Exception as e: + pytest.fail(f"Got unexpected exception on router! - {e}") + +test_cooldown_same_model_name() diff --git a/litellm/tests/test_add_function_to_prompt.py b/litellm/tests/test_add_function_to_prompt.py new file mode 100644 index 0000000000000000000000000000000000000000..a5ec530629819c6512cdb4abd147a3c291212796 --- /dev/null +++ b/litellm/tests/test_add_function_to_prompt.py @@ -0,0 +1,76 @@ +#### What this tests #### +# Allow the user to map the function to the prompt, if the model doesn't support function calling + +import sys, os, pytest +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm + +## case 1: set_function_to_prompt not set +def test_function_call_non_openai_model(): + try: + model = "claude-instant-1" + messages=[{"role": "user", "content": "what's the weather in sf?"}] + functions = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"] + } + }, + "required": ["location"] + } + } + ] + response = litellm.completion(model=model, messages=messages, functions=functions) + pytest.fail(f'An error occurred') + except Exception as e: + print(e) + pass + +test_function_call_non_openai_model() + +## case 2: add_function_to_prompt set +def test_function_call_non_openai_model_litellm_mod_set(): + litellm.add_function_to_prompt = True + try: + model = "claude-instant-1" + messages=[{"role": "user", "content": "what's the weather in sf?"}] + functions = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"] + } + }, + "required": ["location"] + } + } + ] + response = litellm.completion(model=model, messages=messages, functions=functions) + print(f'response: {response}') + except Exception as e: + pytest.fail(f'An error occurred {e}') + +# test_function_call_non_openai_model_litellm_mod_set() \ No newline at end of file diff --git a/litellm/tests/test_async_fn.py b/litellm/tests/test_async_fn.py new file mode 100644 index 0000000000000000000000000000000000000000..7a722095e61b9eea08b613cba9102590ca028986 --- /dev/null +++ b/litellm/tests/test_async_fn.py @@ -0,0 +1,168 @@ +#### What this tests #### +# This tests the the acompletion function # + +import sys, os +import pytest +import traceback +import asyncio, logging + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import completion, acompletion, acreate +litellm.num_retries = 3 + +def test_sync_response(): + litellm.set_verbose = False + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = completion(model="gpt-3.5-turbo", messages=messages, timeout=5) + print(f"response: {response}") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") +# test_sync_response() + +def test_sync_response_anyscale(): + litellm.set_verbose = False + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = completion(model="anyscale/mistralai/Mistral-7B-Instruct-v0.1", messages=messages, timeout=5) + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") +# test_sync_response_anyscale() + +def test_async_response_openai(): + import asyncio + litellm.set_verbose = True + async def test_get_response(): + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await acompletion(model="gpt-3.5-turbo", messages=messages, timeout=5) + print(f"response: {response}") + print(f"response ms: {response._response_ms}") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + print(e) + + asyncio.run(test_get_response()) + +# test_async_response_openai() + +def test_async_response_azure(): + import asyncio + litellm.set_verbose = True + async def test_get_response(): + user_message = "What do you know?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await acompletion(model="azure/gpt-turbo", messages=messages, base_url=os.getenv("CLOUDFLARE_AZURE_BASE_URL"), api_key=os.getenv("AZURE_FRANCE_API_KEY")) + print(f"response: {response}") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + + asyncio.run(test_get_response()) + +# test_async_response_azure() + + +def test_async_anyscale_response(): + import asyncio + litellm.set_verbose = True + async def test_get_response(): + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await acompletion(model="anyscale/mistralai/Mistral-7B-Instruct-v0.1", messages=messages, timeout=5) + # response = await response + print(f"response: {response}") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + + asyncio.run(test_get_response()) + +# test_async_anyscale_response() + +def test_get_response_streaming(): + import asyncio + async def test_async_call(): + user_message = "write a short poem in one sentence" + messages = [{"content": user_message, "role": "user"}] + try: + litellm.set_verbose = True + response = await acompletion(model="gpt-3.5-turbo", messages=messages, stream=True, timeout=5) + print(type(response)) + + import inspect + + is_async_generator = inspect.isasyncgen(response) + print(is_async_generator) + + output = "" + i = 0 + async for chunk in response: + token = chunk["choices"][0]["delta"].get("content", "") + if token == None: + continue # openai v1.0.0 returns content=None + output += token + assert output is not None, "output cannot be None." + assert isinstance(output, str), "output needs to be of type str" + assert len(output) > 0, "Length of output needs to be greater than 0." + print(f'output: {output}') + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + asyncio.run(test_async_call()) + +# test_get_response_streaming() + +def test_get_response_non_openai_streaming(): + import asyncio + litellm.set_verbose = True + litellm.num_retries = 0 + async def test_async_call(): + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await acompletion(model="anyscale/mistralai/Mistral-7B-Instruct-v0.1", messages=messages, stream=True, timeout=5) + print(type(response)) + + import inspect + + is_async_generator = inspect.isasyncgen(response) + print(is_async_generator) + + output = "" + i = 0 + async for chunk in response: + token = chunk["choices"][0]["delta"].get("content", None) + if token == None: + continue + print(token) + output += token + print(f"output: {output}") + assert output is not None, "output cannot be None." + assert isinstance(output, str), "output needs to be of type str" + assert len(output) > 0, "Length of output needs to be greater than 0." + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + return response + asyncio.run(test_async_call()) + +# test_get_response_non_openai_streaming() \ No newline at end of file diff --git a/litellm/tests/test_bad_params.py b/litellm/tests/test_bad_params.py new file mode 100644 index 0000000000000000000000000000000000000000..749391bd98533f8816d78af922277ad62da14a76 --- /dev/null +++ b/litellm/tests/test_bad_params.py @@ -0,0 +1,139 @@ +#### What this tests #### +# This tests chaos monkeys - if random parts of the system are broken / things aren't sent correctly - what happens. +# Expect to add more edge cases to this over time. + +import sys, os +import traceback +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import embedding, completion +from litellm.utils import Message + + +# litellm.set_verbose = True +user_message = "Hello, how are you?" +messages = [{"content": user_message, "role": "user"}] +model_val = None + +def test_completion_with_no_model(): + # test on empty + with pytest.raises(ValueError): + response = completion(messages=messages) + + +def test_completion_with_empty_model(): + # test on empty + try: + response = completion(model=model_val, messages=messages) + except Exception as e: + print(f"error occurred: {e}") + pass + +# def test_completion_catch_nlp_exception(): +# TEMP commented out NLP cloud API is unstable +# try: +# response = completion(model="dolphin", messages=messages, functions=[ +# { +# "name": "get_current_weather", +# "description": "Get the current weather in a given location", +# "parameters": { +# "type": "object", +# "properties": { +# "location": { +# "type": "string", +# "description": "The city and state, e.g. San Francisco, CA" +# }, +# "unit": { +# "type": "string", +# "enum": ["celsius", "fahrenheit"] +# } +# }, +# "required": ["location"] +# } +# } +# ]) + +# except Exception as e: +# if "Function calling is not supported by nlp_cloud" in str(e): +# pass +# else: +# pytest.fail(f'An error occurred {e}') + +# test_completion_catch_nlp_exception() + +def test_completion_invalid_param_cohere(): + try: + response = completion(model="command-nightly", messages=messages, top_p=1) + print(f"response: {response}") + except Exception as e: + if "Unsupported parameters passed: top_p" in str(e): + pass + else: + pytest.fail(f'An error occurred {e}') + +# test_completion_invalid_param_cohere() + +def test_completion_function_call_cohere(): + try: + response = completion(model="command-nightly", messages=messages, functions=["TEST-FUNCTION"]) + pytest.fail(f'An error occurred {e}') + except Exception as e: + print(e) + pass + + +# test_completion_function_call_cohere() + +def test_completion_function_call_openai(): + try: + messages = [{"role": "user", "content": "What is the weather like in Boston?"}] + response = completion(model="gpt-3.5-turbo", messages=messages, functions=[ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"] + } + }, + "required": ["location"] + } + } + ]) + print(f"response: {response}") + except: + pass + +# test_completion_function_call_openai() + +def test_completion_with_no_provider(): + # test on empty + try: + model = "cerebras/btlm-3b-8k-base" + response = completion(model=model, messages=messages) + except Exception as e: + print(f"error occurred: {e}") + pass + +# test_completion_with_no_provider() +# # bad key +# temp_key = os.environ.get("OPENAI_API_KEY") +# os.environ["OPENAI_API_KEY"] = "bad-key" +# # test on openai completion call +# try: +# response = completion(model="gpt-3.5-turbo", messages=messages) +# print(f"response: {response}") +# except: +# print(f"error occurred: {traceback.format_exc()}") +# pass +# os.environ["OPENAI_API_KEY"] = str(temp_key) # this passes linting#5 \ No newline at end of file diff --git a/litellm/tests/test_batch_completions.py b/litellm/tests/test_batch_completions.py new file mode 100644 index 0000000000000000000000000000000000000000..8f149f3fac4a321f3a0947b70d8a734a0164ed27 --- /dev/null +++ b/litellm/tests/test_batch_completions.py @@ -0,0 +1,65 @@ +#### What this tests #### +# This tests calling batch_completions by running 100 messages together + +import sys, os +import traceback +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +from openai import APITimeoutError as Timeout +import litellm +litellm.num_retries = 0 +from litellm import batch_completion, batch_completion_models, completion, batch_completion_models_all_responses +# litellm.set_verbose=True + +def test_batch_completions(): + messages = [[{"role": "user", "content": "write a short poem"}] for _ in range(3)] + model = "j2-mid" + litellm.set_verbose = True + try: + result = batch_completion( + model=model, + messages=messages, + max_tokens=10, + temperature=0.2, + request_timeout=1 + ) + print(result) + print(len(result)) + assert(len(result)==3) + except Timeout as e: + print(f"IN TIMEOUT") + pass + except Exception as e: + pytest.fail(f"An error occurred: {e}") +test_batch_completions() + +def test_batch_completions_models(): + try: + result = batch_completion_models( + models=["gpt-3.5-turbo", "gpt-3.5-turbo", "gpt-3.5-turbo"], + messages=[{"role": "user", "content": "Hey, how's it going"}] + ) + print(result) + except Timeout as e: + pass + except Exception as e: + pytest.fail(f"An error occurred: {e}") +# test_batch_completions_models() + +def test_batch_completion_models_all_responses(): + try: + responses = batch_completion_models_all_responses( + models=["j2-light", "claude-instant-1.2"], + messages=[{"role": "user", "content": "write a poem"}], + max_tokens=10 + ) + print(responses) + assert(len(responses) == 2) + except Timeout as e: + pass + except Exception as e: + pytest.fail(f"An error occurred: {e}") +# test_batch_completion_models_all_responses() + diff --git a/litellm/tests/test_budget_manager.py b/litellm/tests/test_budget_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..5b4f4e6b343c0a97a917d2e2aff30a3f826b462e --- /dev/null +++ b/litellm/tests/test_budget_manager.py @@ -0,0 +1,130 @@ +# #### What this tests #### +# # This tests calling batch_completions by running 100 messages together + +# import sys, os, json +# import traceback +# import pytest + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import litellm +# litellm.set_verbose = True +# from litellm import completion, BudgetManager + +# budget_manager = BudgetManager(project_name="test_project", client_type="hosted") + +# ## Scenario 1: User budget enough to make call +# def test_user_budget_enough(): +# try: +# user = "1234" +# # create a budget for a user +# budget_manager.create_budget(total_budget=10, user=user, duration="daily") + +# # check if a given call can be made +# data = { +# "model": "gpt-3.5-turbo", +# "messages": [{"role": "user", "content": "Hey, how's it going?"}] +# } +# if budget_manager.get_current_cost(user=user) <= budget_manager.get_total_budget(user): +# response = completion(**data) +# print(budget_manager.update_cost(completion_obj=response, user=user)) +# else: +# response = "Sorry - no budget!" + +# print(f"response: {response}") +# except Exception as e: +# pytest.fail(f"An error occurred - {str(e)}") + +# ## Scenario 2: User budget not enough to make call +# def test_user_budget_not_enough(): +# try: +# user = "12345" +# # create a budget for a user +# budget_manager.create_budget(total_budget=0, user=user, duration="daily") + +# # check if a given call can be made +# data = { +# "model": "gpt-3.5-turbo", +# "messages": [{"role": "user", "content": "Hey, how's it going?"}] +# } +# model = data["model"] +# messages = data["messages"] +# if budget_manager.get_current_cost(user=user) < budget_manager.get_total_budget(user=user): +# response = completion(**data) +# print(budget_manager.update_cost(completion_obj=response, user=user)) +# else: +# response = "Sorry - no budget!" + +# print(f"response: {response}") +# except: +# pytest.fail(f"An error occurred") + +# ## Scenario 3: Saving budget to client +# def test_save_user_budget(): +# try: +# response = budget_manager.save_data() +# if response["status"] == "error": +# raise Exception(f"An error occurred - {json.dumps(response)}") +# print(response) +# except Exception as e: +# pytest.fail(f"An error occurred: {str(e)}") + +# test_save_user_budget() +# ## Scenario 4: Getting list of users +# def test_get_users(): +# try: +# response = budget_manager.get_users() +# print(response) +# except: +# pytest.fail(f"An error occurred") + + +# ## Scenario 5: Reset budget at the end of duration +# def test_reset_on_duration(): +# try: +# # First, set a short duration budget for a user +# user = "123456" +# budget_manager.create_budget(total_budget=10, user=user, duration="daily") + +# # Use some of the budget +# data = { +# "model": "gpt-3.5-turbo", +# "messages": [{"role": "user", "content": "Hello!"}] +# } +# if budget_manager.get_current_cost(user=user) <= budget_manager.get_total_budget(user=user): +# response = litellm.completion(**data) +# print(budget_manager.update_cost(completion_obj=response, user=user)) + +# assert budget_manager.get_current_cost(user) > 0, f"Test setup failed: Budget did not decrease after completion" + +# # Now, we need to simulate the passing of time. Since we don't want our tests to actually take days, we're going +# # to cheat a little -- we'll manually adjust the "created_at" time so it seems like a day has passed. +# # In a real-world testing scenario, we might instead use something like the `freezegun` library to mock the system time. +# one_day_in_seconds = 24 * 60 * 60 +# budget_manager.user_dict[user]["last_updated_at"] -= one_day_in_seconds + +# # Now the duration should have expired, so our budget should reset +# budget_manager.update_budget_all_users() + +# # Make sure the budget was actually reset +# assert budget_manager.get_current_cost(user) == 0, "Budget didn't reset after duration expired" +# except Exception as e: +# pytest.fail(f"An error occurred - {str(e)}") + +# ## Scenario 6: passing in text: +# def test_input_text_on_completion(): +# try: +# user = "12345" +# budget_manager.create_budget(total_budget=10, user=user, duration="daily") + +# input_text = "hello world" +# output_text = "it's a sunny day in san francisco" +# model = "gpt-3.5-turbo" + +# budget_manager.update_cost(user=user, model=model, input_text=input_text, output_text=output_text) +# print(budget_manager.get_current_cost(user)) +# except Exception as e: +# pytest.fail(f"An error occurred - {str(e)}") + +# test_input_text_on_completion() \ No newline at end of file diff --git a/litellm/tests/test_caching.py b/litellm/tests/test_caching.py new file mode 100644 index 0000000000000000000000000000000000000000..713f97b3e2ed52575526181ea772854c0d7c99d5 --- /dev/null +++ b/litellm/tests/test_caching.py @@ -0,0 +1,351 @@ +import sys, os +import time +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import embedding, completion +from litellm.caching import Cache +import random +# litellm.set_verbose=True + +messages = [{"role": "user", "content": "who is ishaan Github? "}] +# comment + +messages = [{"role": "user", "content": "who is ishaan 5222"}] +def test_caching_v2(): # test in memory cache + try: + litellm.cache = Cache() + response1 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) + response2 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) + print(f"response1: {response1}") + print(f"response2: {response2}") + litellm.cache = None # disable cache + if response2['choices'][0]['message']['content'] != response1['choices'][0]['message']['content']: + print(f"response1: {response1}") + print(f"response2: {response2}") + pytest.fail(f"Error occurred: {e}") + except Exception as e: + print(f"error occurred: {traceback.format_exc()}") + pytest.fail(f"Error occurred: {e}") + +# test_caching_v2() + + + +def test_caching_with_models_v2(): + messages = [{"role": "user", "content": "who is ishaan CTO of litellm from litellm 2023"}] + litellm.cache = Cache() + print("test2 for caching") + response1 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) + response2 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) + response3 = completion(model="command-nightly", messages=messages, caching=True) + print(f"response1: {response1}") + print(f"response2: {response2}") + print(f"response3: {response3}") + litellm.cache = None + if response3['choices'][0]['message']['content'] == response2['choices'][0]['message']['content']: + # if models are different, it should not return cached response + print(f"response2: {response2}") + print(f"response3: {response3}") + pytest.fail(f"Error occurred:") + if response1['choices'][0]['message']['content'] != response2['choices'][0]['message']['content']: + print(f"response1: {response1}") + print(f"response2: {response2}") + pytest.fail(f"Error occurred:") +# test_caching_with_models_v2() + +embedding_large_text = """ +small text +""" * 5 + +# # test_caching_with_models() +def test_embedding_caching(): + import time + litellm.cache = Cache() + text_to_embed = [embedding_large_text] + start_time = time.time() + embedding1 = embedding(model="text-embedding-ada-002", input=text_to_embed, caching=True) + end_time = time.time() + print(f"Embedding 1 response time: {end_time - start_time} seconds") + + time.sleep(1) + start_time = time.time() + embedding2 = embedding(model="text-embedding-ada-002", input=text_to_embed, caching=True) + end_time = time.time() + print(f"embedding2: {embedding2}") + print(f"Embedding 2 response time: {end_time - start_time} seconds") + + litellm.cache = None + assert end_time - start_time <= 0.1 # ensure 2nd response comes in in under 0.1 s + if embedding2['data'][0]['embedding'] != embedding1['data'][0]['embedding']: + print(f"embedding1: {embedding1}") + print(f"embedding2: {embedding2}") + pytest.fail("Error occurred: Embedding caching failed") + +# test_embedding_caching() + + +def test_embedding_caching_azure(): + print("Testing azure embedding caching") + import time + litellm.cache = Cache() + text_to_embed = [embedding_large_text] + + api_key = os.environ['AZURE_API_KEY'] + api_base = os.environ['AZURE_API_BASE'] + api_version = os.environ['AZURE_API_VERSION'] + + os.environ['AZURE_API_VERSION'] = "" + os.environ['AZURE_API_BASE'] = "" + os.environ['AZURE_API_KEY'] = "" + + + start_time = time.time() + print("AZURE CONFIGS") + print(api_version) + print(api_key) + print(api_base) + embedding1 = embedding( + model="azure/azure-embedding-model", + input=["good morning from litellm", "this is another item"], + api_key=api_key, + api_base=api_base, + api_version=api_version, + caching=True + ) + end_time = time.time() + print(f"Embedding 1 response time: {end_time - start_time} seconds") + + time.sleep(1) + start_time = time.time() + embedding2 = embedding( + model="azure/azure-embedding-model", + input=["good morning from litellm", "this is another item"], + api_key=api_key, + api_base=api_base, + api_version=api_version, + caching=True + ) + end_time = time.time() + print(f"Embedding 2 response time: {end_time - start_time} seconds") + + litellm.cache = None + assert end_time - start_time <= 0.1 # ensure 2nd response comes in in under 0.1 s + if embedding2['data'][0]['embedding'] != embedding1['data'][0]['embedding']: + print(f"embedding1: {embedding1}") + print(f"embedding2: {embedding2}") + pytest.fail("Error occurred: Embedding caching failed") + + os.environ['AZURE_API_VERSION'] = api_version + os.environ['AZURE_API_BASE'] = api_base + os.environ['AZURE_API_KEY'] = api_key + +# test_embedding_caching_azure() + + +def test_redis_cache_completion(): + litellm.set_verbose = False + + random_number = random.randint(1, 100000) # add a random number to ensure it's always adding / reading from cache + messages = [{"role": "user", "content": f"write a one sentence poem about: {random_number}"}] + litellm.cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) + print("test2 for caching") + response1 = completion(model="gpt-3.5-turbo", messages=messages, caching=True, max_tokens=10, seed=1222) + response2 = completion(model="gpt-3.5-turbo", messages=messages, caching=True, max_tokens=10, seed=1222) + response3 = completion(model="gpt-3.5-turbo", messages=messages, caching=True, temperature=1) + response4 = completion(model="command-nightly", messages=messages, caching=True) + + print("\nresponse 1", response1) + print("\nresponse 2", response2) + print("\nresponse 3", response3) + print("\nresponse 4", response4) + litellm.cache = None + + """ + 1 & 2 should be exactly the same + 1 & 3 should be different, since input params are diff + 1 & 4 should be diff, since models are diff + """ + if response1['choices'][0]['message']['content'] != response2['choices'][0]['message']['content']: # 1 and 2 should be the same + # 1&2 have the exact same input params. This MUST Be a CACHE HIT + print(f"response1: {response1}") + print(f"response2: {response2}") + pytest.fail(f"Error occurred:") + if response1['choices'][0]['message']['content'] == response3['choices'][0]['message']['content']: + # if input params like seed, max_tokens are diff it should NOT be a cache hit + print(f"response1: {response1}") + print(f"response3: {response3}") + pytest.fail(f"Response 1 == response 3. Same model, diff params shoudl not cache Error occurred:") + if response1['choices'][0]['message']['content'] == response4['choices'][0]['message']['content']: + # if models are different, it should not return cached response + print(f"response1: {response1}") + print(f"response4: {response4}") + pytest.fail(f"Error occurred:") + +# test_redis_cache_completion() + +# redis cache with custom keys +def custom_get_cache_key(*args, **kwargs): + # return key to use for your cache: + key = kwargs.get("model", "") + str(kwargs.get("messages", "")) + str(kwargs.get("temperature", "")) + str(kwargs.get("logit_bias", "")) + return key + +def test_custom_redis_cache_with_key(): + messages = [{"role": "user", "content": "write a one line story"}] + litellm.cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) + litellm.cache.get_cache_key = custom_get_cache_key + + local_cache = {} + + def set_cache(key, value): + local_cache[key] = value + + def get_cache(key): + if key in local_cache: + return local_cache[key] + + litellm.cache.cache.set_cache = set_cache + litellm.cache.cache.get_cache = get_cache + + # patch this redis cache get and set call + + response1 = completion(model="gpt-3.5-turbo", messages=messages, temperature=1, caching=True, num_retries=3) + response2 = completion(model="gpt-3.5-turbo", messages=messages, temperature=1, caching=True, num_retries=3) + response3 = completion(model="gpt-3.5-turbo", messages=messages, temperature=1, caching=False, num_retries=3) + + print(f"response1: {response1}") + print(f"response2: {response2}") + print(f"response3: {response3}") + + if response3['choices'][0]['message']['content'] == response2['choices'][0]['message']['content']: + pytest.fail(f"Error occurred:") + litellm.cache = None + +# test_custom_redis_cache_with_key() + + +def test_custom_redis_cache_params(): + # test if we can init redis with **kwargs + try: + litellm.cache = Cache( + type="redis", + host=os.environ['REDIS_HOST'], + port=os.environ['REDIS_PORT'], + password=os.environ['REDIS_PASSWORD'], + db = 0, + ssl=True, + ssl_certfile="./redis_user.crt", + ssl_keyfile="./redis_user_private.key", + ssl_ca_certs="./redis_ca.pem", + ) + + print(litellm.cache.cache.redis_client) + litellm.cache = None + except Exception as e: + pytest.fail(f"Error occurred:", e) + +# test_custom_redis_cache_params() + +# def test_redis_cache_with_ttl(): +# cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) +# sample_model_response_object_str = """{ +# "choices": [ +# { +# "finish_reason": "stop", +# "index": 0, +# "message": { +# "role": "assistant", +# "content": "I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." +# } +# } +# ], +# "created": 1691429984.3852863, +# "model": "claude-instant-1", +# "usage": { +# "prompt_tokens": 18, +# "completion_tokens": 23, +# "total_tokens": 41 +# } +# }""" +# sample_model_response_object = { +# "choices": [ +# { +# "finish_reason": "stop", +# "index": 0, +# "message": { +# "role": "assistant", +# "content": "I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." +# } +# } +# ], +# "created": 1691429984.3852863, +# "model": "claude-instant-1", +# "usage": { +# "prompt_tokens": 18, +# "completion_tokens": 23, +# "total_tokens": 41 +# } +# } +# cache.add_cache(cache_key="test_key", result=sample_model_response_object_str, ttl=1) +# cached_value = cache.get_cache(cache_key="test_key") +# print(f"cached-value: {cached_value}") +# assert cached_value['choices'][0]['message']['content'] == sample_model_response_object['choices'][0]['message']['content'] +# time.sleep(2) +# assert cache.get_cache(cache_key="test_key") is None + +# # test_redis_cache_with_ttl() + +# def test_in_memory_cache_with_ttl(): +# cache = Cache(type="local") +# sample_model_response_object_str = """{ +# "choices": [ +# { +# "finish_reason": "stop", +# "index": 0, +# "message": { +# "role": "assistant", +# "content": "I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." +# } +# } +# ], +# "created": 1691429984.3852863, +# "model": "claude-instant-1", +# "usage": { +# "prompt_tokens": 18, +# "completion_tokens": 23, +# "total_tokens": 41 +# } +# }""" +# sample_model_response_object = { +# "choices": [ +# { +# "finish_reason": "stop", +# "index": 0, +# "message": { +# "role": "assistant", +# "content": "I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." +# } +# } +# ], +# "created": 1691429984.3852863, +# "model": "claude-instant-1", +# "usage": { +# "prompt_tokens": 18, +# "completion_tokens": 23, +# "total_tokens": 41 +# } +# } +# cache.add_cache(cache_key="test_key", result=sample_model_response_object_str, ttl=1) +# cached_value = cache.get_cache(cache_key="test_key") +# assert cached_value['choices'][0]['message']['content'] == sample_model_response_object['choices'][0]['message']['content'] +# time.sleep(2) +# assert cache.get_cache(cache_key="test_key") is None +# # test_in_memory_cache_with_ttl() \ No newline at end of file diff --git a/litellm/tests/test_class.py b/litellm/tests/test_class.py new file mode 100644 index 0000000000000000000000000000000000000000..aa7f862425804cfb79c80966e660701c3ce5b2c6 --- /dev/null +++ b/litellm/tests/test_class.py @@ -0,0 +1,74 @@ +# #### What this tests #### +# # This tests the LiteLLM Class + +# import sys, os +# import traceback +# import pytest +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import litellm +# import asyncio + +# litellm.set_verbose = True +# from litellm import Router +# import instructor +# from pydantic import BaseModel + +# # This enables response_model keyword +# # # from client.chat.completions.create +# # client = instructor.patch(Router(model_list=[{ +# # "model_name": "gpt-3.5-turbo", # openai model name +# # "litellm_params": { # params for litellm completion/embedding call +# # "model": "azure/chatgpt-v-2", +# # "api_key": os.getenv("AZURE_API_KEY"), +# # "api_version": os.getenv("AZURE_API_VERSION"), +# # "api_base": os.getenv("AZURE_API_BASE") +# # } +# # }])) + +# # class UserDetail(BaseModel): +# # name: str +# # age: int + +# # user = client.chat.completions.create( +# # model="gpt-3.5-turbo", +# # response_model=UserDetail, +# # messages=[ +# # {"role": "user", "content": "Extract Jason is 25 years old"}, +# # ] +# # ) +# # assert isinstance(model, UserExtract) + +# # assert isinstance(user, UserDetail) +# # assert user.name == "Jason" +# # assert user.age == 25 + +# # print(f"user: {user}") +# import instructor +# from openai import AsyncOpenAI + +# aclient = instructor.apatch(Router(model_list=[{ +# "model_name": "gpt-3.5-turbo", # openai model name +# "litellm_params": { # params for litellm completion/embedding call +# "model": "azure/chatgpt-v-2", +# "api_key": os.getenv("AZURE_API_KEY"), +# "api_version": os.getenv("AZURE_API_VERSION"), +# "api_base": os.getenv("AZURE_API_BASE") +# } +# }], default_litellm_params={"acompletion": True})) + +# class UserExtract(BaseModel): +# name: str +# age: int +# async def main(): +# model = await aclient.chat.completions.create( +# model="gpt-3.5-turbo", +# response_model=UserExtract, +# messages=[ +# {"role": "user", "content": "Extract jason is 25 years old"}, +# ], +# ) +# print(f"model: {model}") + +# asyncio.run(main()) \ No newline at end of file diff --git a/litellm/tests/test_completion.py b/litellm/tests/test_completion.py new file mode 100644 index 0000000000000000000000000000000000000000..15b547a8551188cd81f9508643f4594224b5451d --- /dev/null +++ b/litellm/tests/test_completion.py @@ -0,0 +1,1642 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError +litellm.num_retries = 3 +litellm.cache = None +user_message = "Write a short poem about the sky" +messages = [{"content": user_message, "role": "user"}] + +def logger_fn(user_model_dict): + print(f"user_model_dict: {user_model_dict}") + + +def test_completion_custom_provider_model_name(): + try: + litellm.cache = None + response = completion( + model="together_ai/togethercomputer/llama-2-70b-chat", + messages=messages, + logger_fn=logger_fn, + ) + # Add any assertions here to check the response + print(response) + print(response['choices'][0]['finish_reason']) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_custom_provider_model_name() + + +def test_completion_claude(): + litellm.set_verbose = True + litellm.cache = None + litellm.AnthropicConfig(max_tokens_to_sample=200, metadata={"user_id": "1224"}) + messages = [{"role": "system", "content": """You are an upbeat, enthusiastic personal fitness coach named Sam. Sam is passionate about helping clients get fit and lead healthier lifestyles. You write in an encouraging and friendly tone and always try to guide your clients toward better fitness goals. If the user asks you something unrelated to fitness, either bring the topic back to fitness, or say that you cannot answer."""},{"content": user_message, "role": "user"}] + try: + # test without max tokens + response = completion( + model="claude-instant-1", messages=messages, request_timeout=10, + ) + # Add any assertions here to check the response + print(response) + print(response.usage) + print(response.usage.completion_tokens) + print(response["usage"]["completion_tokens"]) + # print("new cost tracking") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_claude() + +def test_completion_claude2_1(): + try: + print("claude2.1 test request") + # test without max tokens + response = completion( + model="claude-2.1", + messages=messages, + request_timeout=10, + max_tokens=10 + ) + # Add any assertions here to check the response + print(response) + print(response.usage) + print(response.usage.completion_tokens) + print(response["usage"]["completion_tokens"]) + # print("new cost tracking") + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_claude2_1() + +# def test_completion_oobabooga(): +# try: +# response = completion( +# model="oobabooga/vicuna-1.3b", messages=messages, api_base="http://127.0.0.1:5000" +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_oobabooga() +# aleph alpha +# def test_completion_aleph_alpha(): +# try: +# response = completion( +# model="luminous-base", messages=messages, logger_fn=logger_fn +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_completion_aleph_alpha() + + +# def test_completion_aleph_alpha_control_models(): +# try: +# response = completion( +# model="luminous-base-control", messages=messages, logger_fn=logger_fn +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_completion_aleph_alpha_control_models() + +import openai +def test_completion_gpt4_turbo(): + try: + response = completion( + model="gpt-4-1106-preview", + messages=messages, + max_tokens=10, + ) + print(response) + except openai.RateLimitError: + print("got a rate liimt error") + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_gpt4_turbo() + +def test_completion_gpt4_vision(): + try: + litellm.set_verbose=True + response = completion( + model="gpt-4-vision-preview", + messages=[ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "Whats in this image?" + }, + { + "type": "image_url", + "image_url": { + "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" + } + } + ] + } + ], + ) + print(response) + except openai.RateLimitError: + print("got a rate liimt error") + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_gpt4_vision() + +def test_completion_perplexity_api(): + try: + # litellm.set_verbose=True + messages=[{ + "role": "system", + "content": "You're a good bot" + },{ + "role": "user", + "content": "Hey", + },{ + "role": "user", + "content": "Hey", + }] + response = completion( + model="mistral-7b-instruct", + messages=messages, + api_base="https://api.perplexity.ai") + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_perplexity_api() + +def test_completion_perplexity_api_2(): + try: + # litellm.set_verbose=True + messages=[{ + "role": "system", + "content": "You're a good bot" + },{ + "role": "user", + "content": "Hey", + },{ + "role": "user", + "content": "Hey", + }] + response = completion( + model="perplexity/mistral-7b-instruct", + messages=messages + ) + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_perplexity_api_2() + +# commenting out as this is a flaky test on circle ci +# def test_completion_nlp_cloud(): +# try: +# messages = [ +# {"role": "system", "content": "You are a helpful assistant."}, +# { +# "role": "user", +# "content": "how does a court case get to the Supreme Court?", +# }, +# ] +# response = completion(model="dolphin", messages=messages, logger_fn=logger_fn) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_nlp_cloud() + +######### HUGGING FACE TESTS ######################## +##################################################### +""" +HF Tests we should pass +- TGI: + - Pro Inference API + - Deployed Endpoint +- Coversational + - Free Inference API + - Deployed Endpoint +- Neither TGI or Coversational + - Free Inference API + - Deployed Endpoint +""" +##################################################### +##################################################### +# Test util to sort models to TGI, conv, None +def test_get_hf_task_for_model(): + model = "glaiveai/glaive-coder-7b" + model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) + print(f"model:{model}, model type: {model_type}") + assert(model_type == "text-generation-inference") + + model = "meta-llama/Llama-2-7b-hf" + model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) + print(f"model:{model}, model type: {model_type}") + assert(model_type == "text-generation-inference") + + model = "facebook/blenderbot-400M-distill" + model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) + print(f"model:{model}, model type: {model_type}") + assert(model_type == "conversational") + + model = "facebook/blenderbot-3B" + model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) + print(f"model:{model}, model type: {model_type}") + assert(model_type == "conversational") + + # neither Conv or None + model = "roneneldan/TinyStories-3M" + model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) + print(f"model:{model}, model type: {model_type}") + assert(model_type == None) + +# test_get_hf_task_for_model() +# litellm.set_verbose=False +# ################### Hugging Face TGI models ######################## +# # TGI model +# # this is a TGI model https://huggingface.co/glaiveai/glaive-coder-7b +def hf_test_completion_tgi(): + # litellm.set_verbose=True + try: + response = completion( + model = 'huggingface/HuggingFaceH4/zephyr-7b-beta', + messages = [{ "content": "Hello, how are you?","role": "user"}], + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# hf_test_completion_tgi() + +# ################### Hugging Face Conversational models ######################## +# def hf_test_completion_conv(): +# try: +# response = litellm.completion( +# model="huggingface/facebook/blenderbot-3B", +# messages=[{ "content": "Hello, how are you?","role": "user"}], +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# hf_test_completion_conv() + +# ################### Hugging Face Neither TGI or Conversational models ######################## +# # Neither TGI or Conversational +# def hf_test_completion_none_task(): +# try: +# user_message = "My name is Merve and my favorite" +# messages = [{ "content": user_message,"role": "user"}] +# response = completion( +# model="huggingface/roneneldan/TinyStories-3M", +# messages=messages, +# api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud", +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# hf_test_completion_none_task() +########################### End of Hugging Face Tests ############################################## +# def test_completion_hf_api(): +# # failing on circle ci commenting out +# try: +# user_message = "write some code to find the sum of two numbers" +# messages = [{ "content": user_message,"role": "user"}] +# api_base = "https://a8l9e3ucxinyl3oj.us-east-1.aws.endpoints.huggingface.cloud" +# response = completion(model="huggingface/meta-llama/Llama-2-7b-chat-hf", messages=messages, api_base=api_base) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# if "loading" in str(e): +# pass +# pytest.fail(f"Error occurred: {e}") + +# test_completion_hf_api() + +# def test_completion_hf_api_best_of(): +# # failing on circle ci commenting out +# try: +# user_message = "write some code to find the sum of two numbers" +# messages = [{ "content": user_message,"role": "user"}] +# api_base = "https://a8l9e3ucxinyl3oj.us-east-1.aws.endpoints.huggingface.cloud" +# response = completion(model="huggingface/meta-llama/Llama-2-7b-chat-hf", messages=messages, api_base=api_base, n=2) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# if "loading" in str(e): +# pass +# pytest.fail(f"Error occurred: {e}") + +# test_completion_hf_api_best_of() + +# def test_completion_hf_deployed_api(): +# try: +# user_message = "There's a llama in my garden 😱 What should I do?" +# messages = [{ "content": user_message,"role": "user"}] +# response = completion(model="huggingface/https://ji16r2iys9a8rjk2.us-east-1.aws.endpoints.huggingface.cloud", messages=messages, logger_fn=logger_fn) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +# this should throw an exception, to trigger https://logs.litellm.ai/ +# def hf_test_error_logs(): +# try: +# litellm.set_verbose=True +# user_message = "My name is Merve and my favorite" +# messages = [{ "content": user_message,"role": "user"}] +# response = completion( +# model="huggingface/roneneldan/TinyStories-3M", +# messages=messages, +# api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud", + +# ) +# # Add any assertions here to check the response +# print(response) + +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# hf_test_error_logs() + +def test_completion_cohere(): # commenting for now as the cohere endpoint is being flaky + try: + litellm.CohereConfig(max_tokens=1000, stop_sequences=["a"]) + response = completion( + model="command-nightly", + messages=messages, + logger_fn=logger_fn + ) + # Add any assertions here to check the response + print(response) + response_str = response["choices"][0]["message"]["content"] + response_str_2 = response.choices[0].message.content + if type(response_str) != str: + pytest.fail(f"Error occurred: {e}") + if type(response_str_2) != str: + pytest.fail(f"Error occurred: {e}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_cohere() + + +def test_completion_openai(): + try: + litellm.set_verbose=True + print(f"api key: {os.environ['OPENAI_API_KEY']}") + litellm.api_key = os.environ['OPENAI_API_KEY'] + response = completion( + model="gpt-3.5-turbo", + messages=messages, + max_tokens=10, + request_timeout=1, + metadata = {"hi": "bye"} + ) + print("This is the response object\n", response) + + + response_str = response["choices"][0]["message"]["content"] + response_str_2 = response.choices[0].message.content + + cost = completion_cost(completion_response=response) + print("Cost for completion call with gpt-3.5-turbo: ", f"${float(cost):.10f}") + assert response_str == response_str_2 + assert type(response_str) == str + assert len(response_str) > 1 + + litellm.api_key = None + except Timeout as e: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_openai() + +def test_completion_text_openai(): + try: + # litellm.set_verbose = True + response = completion(model="gpt-3.5-turbo-instruct", messages=messages) + print(response["choices"][0]["message"]["content"]) + except Exception as e: + print(e) + pytest.fail(f"Error occurred: {e}") +# test_completion_text_openai() + +def custom_callback( + kwargs, # kwargs to completion + completion_response, # response from completion + start_time, end_time # start/end time +): + # Your custom code here + try: + print("LITELLM: in custom callback function") + print("\nkwargs\n", kwargs) + model = kwargs["model"] + messages = kwargs["messages"] + user = kwargs.get("user") + + ################################################# + + print( + f""" + Model: {model}, + Messages: {messages}, + User: {user}, + Seed: {kwargs["seed"]}, + temperature: {kwargs["temperature"]}, + """ + ) + + assert kwargs["user"] == "ishaans app" + assert kwargs["model"] == "gpt-3.5-turbo-1106" + assert kwargs["seed"] == 12 + assert kwargs["temperature"] == 0.5 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +def test_completion_openai_with_optional_params(): + # [Proxy PROD TEST] WARNING: DO NOT DELETE THIS TEST + # assert that `user` gets passed to the completion call + # Note: This tests that we actually send the optional params to the completion call + # We use custom callbacks to test this + try: + litellm.set_verbose = True + litellm.success_callback = [custom_callback] + response = completion( + model="gpt-3.5-turbo-1106", + messages=[ + { + "role": "user", + "content": "respond in valid, json - what is the day" + } + ], + temperature=0.5, + top_p=0.1, + seed=12, + response_format={ "type": "json_object" }, + logit_bias=None, + user = "ishaans app" + ) + # Add any assertions here to check the response + + print(response) + litellm.success_callback = [] # unset callbacks + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_openai_with_optional_params() + +def test_completion_openai_litellm_key(): + try: + litellm.set_verbose = True + litellm.num_retries = 0 + litellm.api_key = os.environ['OPENAI_API_KEY'] + + # ensure key is set to None in .env and in openai.api_key + os.environ['OPENAI_API_KEY'] = "" + import openai + openai.api_key = "" + ########################################################## + + response = completion( + model="gpt-3.5-turbo", + messages=messages, + temperature=0.5, + top_p=0.1, + max_tokens=10, + user="ishaan_dev@berri.ai", + ) + # Add any assertions here to check the response + print(response) + + ###### reset environ key + os.environ['OPENAI_API_KEY'] = litellm.api_key + + ##### unset litellm var + litellm.api_key = None + except Timeout as e: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_openai_litellm_key() + +def test_completion_openrouter1(): + try: + response = completion( + model="openrouter/google/palm-2-chat-bison", + messages=messages, + max_tokens=5, + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_openrouter1() + +def test_completion_hf_model_no_provider(): + try: + response = completion( + model="WizardLM/WizardLM-70B-V1.0", + messages=messages, + max_tokens=5, + ) + # Add any assertions here to check the response + print(response) + pytest.fail(f"Error occurred: {e}") + except Exception as e: + pass + +# test_completion_hf_model_no_provider() + +# def test_completion_openai_azure_with_functions(): +# function1 = [ +# { +# "name": "get_current_weather", +# "description": "Get the current weather in a given location", +# "parameters": { +# "type": "object", +# "properties": { +# "location": { +# "type": "string", +# "description": "The city and state, e.g. San Francisco, CA", +# }, +# "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, +# }, +# "required": ["location"], +# }, +# } +# ] +# try: +# messages = [{"role": "user", "content": "What is the weather like in Boston?"}] +# response = completion( +# model="azure/chatgpt-functioncalling", messages=messages, functions=function1 +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_completion_openai_azure_with_functions() + +def test_completion_azure_key_completion_arg(): + # this tests if we can pass api_key to completion, when it's not in the env + # DO NOT REMOVE THIS TEST. No MATTER WHAT Happens. + # If you want to remove it, speak to Ishaan! + # Ishaan will be very disappointed if this test is removed -> this is a standard way to pass api_key + the router + proxy use this + old_key = os.environ["AZURE_API_KEY"] + os.environ.pop("AZURE_API_KEY", None) + try: + print("azure gpt-3.5 test\n\n") + litellm.set_verbose=False + ## Test azure call + response = completion( + model="azure/chatgpt-v-2", + messages=messages, + api_key=old_key, + max_tokens=10, + ) + print(f"response: {response}") + os.environ["AZURE_API_KEY"] = old_key + except Exception as e: + os.environ["AZURE_API_KEY"] = old_key + pytest.fail(f"Error occurred: {e}") +# test_completion_azure_key_completion_arg() + + +async def test_re_use_azure_async_client(): + try: + print("azure gpt-3.5 ASYNC with clie nttest\n\n") + litellm.set_verbose=True + import openai + client = openai.AsyncAzureOpenAI( + azure_endpoint=os.environ['AZURE_API_BASE'], + api_key=os.environ["AZURE_API_KEY"], + api_version="2023-07-01-preview", + ) + ## Test azure call + for _ in range(3): + response = await litellm.acompletion( + model="azure/chatgpt-v-2", + messages=messages, + client=client + ) + print(f"response: {response}") + except Exception as e: + pytest.fail("got Exception", e) + +# import asyncio +# asyncio.run( +# test_re_use_azure_async_client() +# ) + + +def test_re_use_openaiClient(): + try: + print("gpt-3.5 with client test\n\n") + litellm.set_verbose=True + import openai + client = openai.OpenAI( + api_key=os.environ["OPENAI_API_KEY"], + ) + ## Test OpenAI call + for _ in range(2): + response = litellm.completion( + model="gpt-3.5-turbo", + messages=messages, + client=client + ) + print(f"response: {response}") + except Exception as e: + pytest.fail("got Exception", e) +# test_re_use_openaiClient() + +def test_completion_azure(): + try: + print("azure gpt-3.5 test\n\n") + litellm.set_verbose=False + ## Test azure call + response = completion( + model="azure/chatgpt-v-2", + messages=messages, + api_key="os.environ/AZURE_API_KEY" + ) + print(f"response: {response}") + ## Test azure flag for backwards compatibility + # response = completion( + # model="chatgpt-v-2", + # messages=messages, + # azure=True, + # max_tokens=10 + # ) + # Add any assertions here to check the response + print(response) + + cost = completion_cost(completion_response=response) + print("Cost for azure completion request", cost) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_azure() + +def test_azure_openai_ad_token(): + # this tests if the azure ad token is set in the request header + # the request can fail since azure ad tokens expire after 30 mins, but the header MUST have the azure ad token + # we use litellm.input_callbacks for this test + def tester( + kwargs, # kwargs to completion + ): + print(kwargs["additional_args"]) + if kwargs["additional_args"]["headers"]["Authorization"] != 'Bearer gm': + pytest.fail("AZURE AD TOKEN Passed but not set in request header") + return + litellm.input_callback = [tester] + try: + response = litellm.completion( + model="azure/chatgpt-v-2", # e.g. gpt-35-instant + messages=[ + { + "role": "user", + "content": "what is your name", + }, + ], + azure_ad_token="gm" + ) + print("azure ad token respoonse\n") + print(response) + litellm.input_callback = [] + except: + litellm.input_callback = [] + pass +# test_azure_openai_ad_token() + + +# test_completion_azure() +def test_completion_azure2(): + # test if we can pass api_base, api_version and api_key in compleition() + try: + print("azure gpt-3.5 test\n\n") + litellm.set_verbose=False + api_base = os.environ["AZURE_API_BASE"] + api_key = os.environ["AZURE_API_KEY"] + api_version = os.environ["AZURE_API_VERSION"] + + os.environ["AZURE_API_BASE"] = "" + os.environ["AZURE_API_VERSION"] = "" + os.environ["AZURE_API_KEY"] = "" + + + ## Test azure call + response = completion( + model="azure/chatgpt-v-2", + messages=messages, + api_base = api_base, + api_key = api_key, + api_version = api_version, + max_tokens=10, + ) + + # Add any assertions here to check the response + print(response) + + os.environ["AZURE_API_BASE"] = api_base + os.environ["AZURE_API_VERSION"] = api_version + os.environ["AZURE_API_KEY"] = api_key + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_azure2() + +def test_completion_azure3(): + # test if we can pass api_base, api_version and api_key in compleition() + try: + print("azure gpt-3.5 test\n\n") + litellm.set_verbose=True + litellm.api_base = os.environ["AZURE_API_BASE"] + litellm.api_key = os.environ["AZURE_API_KEY"] + litellm.api_version = os.environ["AZURE_API_VERSION"] + + os.environ["AZURE_API_BASE"] = "" + os.environ["AZURE_API_VERSION"] = "" + os.environ["AZURE_API_KEY"] = "" + + + ## Test azure call + response = completion( + model="azure/chatgpt-v-2", + messages=messages, + max_tokens=10, + ) + + # Add any assertions here to check the response + print(response) + + os.environ["AZURE_API_BASE"] = litellm.api_base + os.environ["AZURE_API_VERSION"] = litellm.api_version + os.environ["AZURE_API_KEY"] = litellm.api_key + + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_azure3() + +# new azure test for using litellm. vars, +# use the following vars in this test and make an azure_api_call +# litellm.api_type = self.azure_api_type +# litellm.api_base = self.azure_api_base +# litellm.api_version = self.azure_api_version +# litellm.api_key = self.api_key +def test_completion_azure_with_litellm_key(): + try: + print("azure gpt-3.5 test\n\n") + import openai + + + #### set litellm vars + litellm.api_type = "azure" + litellm.api_base = os.environ['AZURE_API_BASE'] + litellm.api_version = os.environ['AZURE_API_VERSION'] + litellm.api_key = os.environ['AZURE_API_KEY'] + + ######### UNSET ENV VARs for this ################ + os.environ['AZURE_API_BASE'] = "" + os.environ['AZURE_API_VERSION'] = "" + os.environ['AZURE_API_KEY'] = "" + + ######### UNSET OpenAI vars for this ############## + openai.api_type = "" + openai.api_base = "gm" + openai.api_version = "333" + openai.api_key = "ymca" + + response = completion( + model="azure/chatgpt-v-2", + messages=messages, + ) + # Add any assertions here to check the response + print(response) + + + ######### RESET ENV VARs for this ################ + os.environ['AZURE_API_BASE'] = litellm.api_base + os.environ['AZURE_API_VERSION'] = litellm.api_version + os.environ['AZURE_API_KEY'] = litellm.api_key + + ######### UNSET litellm vars + litellm.api_type = None + litellm.api_base = None + litellm.api_version = None + litellm.api_key = None + + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_azure() + + +def test_completion_azure_deployment_id(): + try: + litellm.set_verbose = True + response = completion( + deployment_id="chatgpt-v-2", + model="gpt-3.5-turbo", + messages=messages, + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_azure_deployment_id() + +# Only works for local endpoint +# def test_completion_anthropic_openai_proxy(): +# try: +# response = completion( +# model="custom_openai/claude-2", +# messages=messages, +# api_base="http://0.0.0.0:8000" +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_anthropic_openai_proxy() + +def test_completion_replicate_vicuna(): + print("TESTING REPLICATE") + litellm.set_verbose=True + model_name = "replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b" + try: + response = completion( + model=model_name, + messages=messages, + temperature=0.5, + top_k=20, + repetition_penalty=1, + min_tokens=1, + seed=-1, + max_tokens=20, + ) + print(response) + # Add any assertions here to check the response + response_str = response["choices"][0]["message"]["content"] + print("RESPONSE STRING\n", response_str) + if type(response_str) != str: + pytest.fail(f"Error occurred: {e}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_replicate_vicuna() +# commenting out - flaky test +# def test_completion_replicate_llama2_stream(): +# litellm.set_verbose=False +# model_name = "replicate/meta/llama-2-7b-chat:13c3cdee13ee059ab779f0291d29054dab00a47dad8261375654de5540165fb0" +# try: +# response = completion( +# model=model_name, +# messages=[ +# { +# "role": "user", +# "content": "what is yc write 1 paragraph", +# } +# ], +# stream=True, +# max_tokens=20, +# num_retries=3 +# ) +# print(f"response: {response}") +# # Add any assertions here to check the response +# complete_response = "" +# for i, chunk in enumerate(response): +# complete_response += chunk.choices[0].delta["content"] +# # if i == 0: +# # assert len(chunk.choices[0].delta["content"]) > 2 +# # print(chunk) +# assert len(complete_response) > 5 +# print(f"complete_response: {complete_response}") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_completion_replicate_llama2_stream() + +def test_replicate_custom_prompt_dict(): + litellm.set_verbose = True + model_name = "replicate/meta/llama-2-7b-chat:13c3cdee13ee059ab779f0291d29054dab00a47dad8261375654de5540165fb0" + litellm.register_prompt_template( + model="replicate/meta/llama-2-7b-chat:13c3cdee13ee059ab779f0291d29054dab00a47dad8261375654de5540165fb0", + initial_prompt_value="You are a good assistant", # [OPTIONAL] + roles={ + "system": { + "pre_message": "[INST] <>\n", # [OPTIONAL] + "post_message": "\n<>\n [/INST]\n" # [OPTIONAL] + }, + "user": { + "pre_message": "[INST] ", # [OPTIONAL] + "post_message": " [/INST]" # [OPTIONAL] + }, + "assistant": { + "pre_message": "\n", # [OPTIONAL] + "post_message": "\n" # [OPTIONAL] + } + }, + final_prompt_value="Now answer as best you can:" # [OPTIONAL] + ) + response = completion( + model=model_name, + messages=[ + { + "role": "user", + "content": "what is yc write 1 paragraph", + } + ], + num_retries=3 + ) + print(f"response: {response}") + litellm.custom_prompt_dict = {} # reset + +# test_replicate_custom_prompt_dict() + +# commenthing this out since we won't be always testing a custom replicate deployment +# def test_completion_replicate_deployments(): +# print("TESTING REPLICATE") +# litellm.set_verbose=False +# model_name = "replicate/deployments/ishaan-jaff/ishaan-mistral" +# try: +# response = completion( +# model=model_name, +# messages=messages, +# temperature=0.5, +# seed=-1, +# ) +# print(response) +# # Add any assertions here to check the response +# response_str = response["choices"][0]["message"]["content"] +# print("RESPONSE STRING\n", response_str) +# if type(response_str) != str: +# pytest.fail(f"Error occurred: {e}") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_completion_replicate_deployments() + + +######## Test TogetherAI ######## +def test_completion_together_ai(): + model_name = "together_ai/togethercomputer/llama-2-70b-chat" + try: + response = completion(model=model_name, messages=messages, max_tokens=256, n=1, logger_fn=logger_fn) + # Add any assertions here to check the response + print(response) + cost = completion_cost(completion_response=response) + print("Cost for completion call together-computer/llama-2-70b: ", f"${float(cost):.10f}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_together_ai() +def test_customprompt_together_ai(): + try: + litellm.set_verbose = False + litellm.num_retries = 0 + response = completion( + model="together_ai/togethercomputer/llama-2-70b-chat", + messages=messages, + roles={"system":{"pre_message":"<|im_start|>system\n", "post_message":"<|im_end|>"}, "assistant":{"pre_message":"<|im_start|>assistant\n","post_message":"<|im_end|>"}, "user":{"pre_message":"<|im_start|>user\n","post_message":"<|im_end|>"}} + ) + print(response) + except litellm.exceptions.Timeout as e: + print(f"Timeout Error") + litellm.num_retries = 3 # reset retries + pass + except Exception as e: + print(f"ERROR TYPE {type(e)}") + pytest.fail(f"Error occurred: {e}") + +# test_customprompt_together_ai() + +def test_completion_sagemaker(): + try: + print("testing sagemaker") + litellm.set_verbose=True + response = completion( + model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", + messages=messages, + temperature=0.2, + max_tokens=80, + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_sagemaker() + +def test_completion_chat_sagemaker(): + try: + print("testing sagemaker") + litellm.set_verbose=True + response = completion( + model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b-f", + messages=messages, + stream=True, + ) + # Add any assertions here to check the response + print(response) + for chunk in response: + print(chunk) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_chat_sagemaker() + +def test_completion_bedrock_titan(): + try: + response = completion( + model="bedrock/amazon.titan-tg1-large", + messages=messages, + temperature=0.2, + max_tokens=200, + top_p=0.8, + logger_fn=logger_fn + ) + # Add any assertions here to check the response + print(response) + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_bedrock_titan() + +def test_completion_bedrock_claude(): + print("calling claude") + try: + response = completion( + model="anthropic.claude-instant-v1", + messages=messages, + max_tokens=10, + temperature=0.1, + logger_fn=logger_fn + ) + # Add any assertions here to check the response + print(response) + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_bedrock_claude() + +def test_completion_bedrock_cohere(): + print("calling bedrock cohere") + litellm.set_verbose = True + try: + response = completion( + model="bedrock/cohere.command-text-v14", + messages=[{"role": "user", "content": "hi"}], + temperature=0.1, + max_tokens=10, + stream=True + ) + # Add any assertions here to check the response + print(response) + for chunk in response: + print(chunk) + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_bedrock_cohere() + + +def test_completion_bedrock_claude_completion_auth(): + print("calling bedrock claude completion params auth") + import os + + aws_access_key_id = os.environ["AWS_ACCESS_KEY_ID"] + aws_secret_access_key = os.environ["AWS_SECRET_ACCESS_KEY"] + aws_region_name = os.environ["AWS_REGION_NAME"] + + os.environ["AWS_ACCESS_KEY_ID"] = "" + os.environ["AWS_SECRET_ACCESS_KEY"] = "" + os.environ["AWS_REGION_NAME"] = "" + + + try: + response = completion( + model="bedrock/anthropic.claude-instant-v1", + messages=messages, + max_tokens=10, + temperature=0.1, + logger_fn=logger_fn, + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + aws_region_name=aws_region_name, + ) + # Add any assertions here to check the response + print(response) + + os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id + os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key + os.environ["AWS_REGION_NAME"] = aws_region_name + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_bedrock_claude_completion_auth() + +# def test_completion_bedrock_claude_external_client_auth(): +# print("calling bedrock claude external client auth") +# import os + +# aws_access_key_id = os.environ["AWS_ACCESS_KEY_ID"] +# aws_secret_access_key = os.environ["AWS_SECRET_ACCESS_KEY"] +# aws_region_name = os.environ["AWS_REGION_NAME"] + +# os.environ["AWS_ACCESS_KEY_ID"] = "" +# os.environ["AWS_SECRET_ACCESS_KEY"] = "" +# os.environ["AWS_REGION_NAME"] = "" + +# try: +# import boto3 +# bedrock = boto3.client( +# service_name="bedrock-runtime", +# region_name=aws_region_name, +# aws_access_key_id=aws_access_key_id, +# aws_secret_access_key=aws_secret_access_key, +# endpoint_url=f"https://bedrock-runtime.{aws_region_name}.amazonaws.com" +# ) + +# response = completion( +# model="bedrock/anthropic.claude-instant-v1", +# messages=messages, +# max_tokens=10, +# temperature=0.1, +# logger_fn=logger_fn, +# aws_bedrock_client=bedrock, +# ) +# # Add any assertions here to check the response +# print(response) + +# os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id +# os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key +# os.environ["AWS_REGION_NAME"] = aws_region_name +# except RateLimitError: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_completion_bedrock_claude_external_client_auth() + +# def test_completion_bedrock_claude_stream(): +# print("calling claude") +# litellm.set_verbose = False +# try: +# response = completion( +# model="bedrock/anthropic.claude-instant-v1", +# messages=messages, +# stream=True +# ) +# # Add any assertions here to check the response +# print(response) +# for chunk in response: +# print(chunk) +# except RateLimitError: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_completion_bedrock_claude_stream() + +# def test_completion_bedrock_ai21(): +# try: +# litellm.set_verbose = False +# response = completion( +# model="bedrock/ai21.j2-mid", +# messages=messages, +# temperature=0.2, +# top_p=0.2, +# max_tokens=20 +# ) +# # Add any assertions here to check the response +# print(response) +# except RateLimitError: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +######## Test VLLM ######## +# def test_completion_vllm(): +# try: +# response = completion( +# model="vllm/facebook/opt-125m", +# messages=messages, +# temperature=0.2, +# max_tokens=80, +# ) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_vllm() + +# def test_completion_hosted_chatCompletion(): +# # this tests calling a server where vllm is hosted +# # this should make an openai.Completion() call to the specified api_base +# # send a request to this proxy server: https://replit.com/@BerriAI/openai-proxy#main.py +# # it checks if model == facebook/opt-125m and returns test passed +# try: +# litellm.set_verbose = True +# response = completion( +# model="facebook/opt-125m", +# messages=messages, +# temperature=0.2, +# max_tokens=80, +# api_base="https://openai-proxy.berriai.repl.co", +# custom_llm_provider="openai" +# ) +# print(response) + +# if response['choices'][0]['message']['content'] != "passed": +# # see https://replit.com/@BerriAI/openai-proxy#main.py +# pytest.fail(f"Error occurred: proxy server did not respond") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_hosted_chatCompletion() + +# def test_completion_custom_api_base(): +# try: +# response = completion( +# model="custom/meta-llama/Llama-2-13b-hf", +# messages=messages, +# temperature=0.2, +# max_tokens=10, +# api_base="https://api.autoai.dev/inference", +# request_timeout=300, +# ) +# # Add any assertions here to check the response +# print("got response\n", response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_custom_api_base() + +# def test_vertex_ai(): +# test_models = ["codechat-bison"] + litellm.vertex_chat_models + litellm.vertex_code_chat_models + litellm.vertex_text_models + litellm.vertex_code_text_models +# # test_models = ["chat-bison"] +# for model in test_models: +# try: +# if model in ["code-gecko@001", "code-gecko@latest"]: +# # our account does not have access to this model +# continue +# print("making request", model) +# response = completion(model=model, messages=[{'role': 'user', 'content': 'hi'}]) +# print(response) + +# print(response.usage.completion_tokens) +# print(response['usage']['completion_tokens']) +# assert type(response.choices[0].message.content) == str +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_vertex_ai() + +# def test_vertex_ai_stream(): +# litellm.set_verbose=False +# test_models = litellm.vertex_chat_models + litellm.vertex_code_chat_models + litellm.vertex_text_models + litellm.vertex_code_text_models +# for model in test_models: +# try: +# if model in ["code-gecko@001", "code-gecko@latest"]: +# # our account does not have access to this model +# continue +# print("making request", model) +# response = completion(model=model, messages=[{"role": "user", "content": "write 100 line code code for saying hi"}], stream=True) +# for chunk in response: +# print(chunk) +# # pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_vertex_ai_stream() + + +def test_completion_with_fallbacks(): + print(f"RUNNING TEST COMPLETION WITH FALLBACKS - test_completion_with_fallbacks") + fallbacks = ["gpt-3.5-turbo", "gpt-3.5-turbo", "command-nightly"] + try: + response = completion( + model="bad-model", messages=messages, force_timeout=120, fallbacks=fallbacks + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_with_fallbacks() +def test_completion_anyscale_api(): + try: + # litellm.set_verbose=True + messages=[{ + "role": "system", + "content": "You're a good bot" + },{ + "role": "user", + "content": "Hey", + },{ + "role": "user", + "content": "Hey", + }] + response = completion( + model="anyscale/meta-llama/Llama-2-7b-chat-hf", + messages=messages,) + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_anyscale_api() + +def test_azure_cloudflare_api(): + try: + messages = [ + { + "role": "user", + "content": "How do I output all files in a directory using Python?", + }, + ] + response = completion(model="azure/gpt-turbo", messages=messages, base_url=os.getenv("CLOUDFLARE_AZURE_BASE_URL"), api_key=os.getenv("AZURE_FRANCE_API_KEY")) + print(f"response: {response}") + except Exception as e: + traceback.print_exc() + pass + +# test_azure_cloudflare_api() + +def test_completion_anyscale_2(): + try: + # litellm.set_verbose=True + messages=[{ + "role": "system", + "content": "You're a good bot" + },{ + "role": "user", + "content": "Hey", + },{ + "role": "user", + "content": "Hey", + }] + response = completion( + model="anyscale/meta-llama/Llama-2-7b-chat-hf", + messages=messages + ) + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +def test_mistral_anyscale_stream(): + litellm.set_verbose=False + response = completion( + model = 'anyscale/mistralai/Mistral-7B-Instruct-v0.1', + messages = [{ "content": "hello, good morning","role": "user"}], + stream=True, + ) + for chunk in response: + # print(chunk) + print(chunk["choices"][0]["delta"].get("content", ""), end="") +# test_mistral_anyscale_stream() +# test_completion_anyscale_2() +# def test_completion_with_fallbacks_multiple_keys(): +# print(f"backup key 1: {os.getenv('BACKUP_OPENAI_API_KEY_1')}") +# print(f"backup key 2: {os.getenv('BACKUP_OPENAI_API_KEY_2')}") +# backup_keys = [{"api_key": os.getenv("BACKUP_OPENAI_API_KEY_1")}, {"api_key": os.getenv("BACKUP_OPENAI_API_KEY_2")}] +# try: +# api_key = "bad-key" +# response = completion( +# model="gpt-3.5-turbo", messages=messages, force_timeout=120, fallbacks=backup_keys, api_key=api_key +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# error_str = traceback.format_exc() +# pytest.fail(f"Error occurred: {error_str}") + +# test_completion_with_fallbacks_multiple_keys() +# def test_petals(): +# try: +# response = completion(model="petals-team/StableBeluga2", messages=messages) +# # Add any assertions here to check the response +# print(response) + +# response = completion(model="petals-team/StableBeluga2", messages=messages) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# def test_baseten(): +# try: + +# response = completion(model="baseten/7qQNLDB", messages=messages, logger_fn=logger_fn) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_baseten() +# def test_baseten_falcon_7bcompletion(): +# model_name = "qvv0xeq" +# try: +# response = completion(model=model_name, messages=messages, custom_llm_provider="baseten") +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_baseten_falcon_7bcompletion() + +# def test_baseten_falcon_7bcompletion_withbase(): +# model_name = "qvv0xeq" +# litellm.api_base = "https://app.baseten.co" +# try: +# response = completion(model=model_name, messages=messages) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# litellm.api_base = None + +# test_baseten_falcon_7bcompletion_withbase() + + +# def test_baseten_wizardLMcompletion_withbase(): +# model_name = "q841o8w" +# litellm.api_base = "https://app.baseten.co" +# try: +# response = completion(model=model_name, messages=messages) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_baseten_wizardLMcompletion_withbase() + +# def test_baseten_mosaic_ML_completion_withbase(): +# model_name = "31dxrj3" +# litellm.api_base = "https://app.baseten.co" +# try: +# response = completion(model=model_name, messages=messages) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +#### Test A121 ################### +def test_completion_ai21(): + print("running ai21 j2light test") + litellm.set_verbose=True + model_name = "j2-light" + try: + response = completion(model=model_name, messages=messages, max_tokens=100, temperature=0.8) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_ai21() +# test_completion_ai21() +## test deep infra +def test_completion_deep_infra(): + litellm.set_verbose = False + model_name = "deepinfra/meta-llama/Llama-2-70b-chat-hf" + try: + response = completion( + model=model_name, + messages=messages, + temperature=0, + max_tokens=10 + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_deep_infra() + +def test_completion_deep_infra_mistral(): + print("deep infra test with temp=0") + model_name = "deepinfra/mistralai/Mistral-7B-Instruct-v0.1" + try: + response = completion( + model=model_name, + messages=messages, + temperature=0.01, # mistrail fails with temperature=0 + max_tokens=10 + ) + # Add any assertions here to check the response + print(response) + except litellm.exceptions.Timeout as e: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_deep_infra_mistral() + +# Palm tests +def test_completion_palm(): + litellm.set_verbose = True + model_name = "palm/chat-bison" + messages = [{"role": "user", "content": "Hey, how's it going?"}] + try: + response = completion(model=model_name, messages=messages) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_palm() + +# test palm with streaming +def test_completion_palm_stream(): + # litellm.set_verbose = True + model_name = "palm/chat-bison" + try: + response = completion( + model=model_name, + messages=messages, + stop=["stop"], + stream=True, + max_tokens=20 + ) + # Add any assertions here to check the response + for chunk in response: + print(chunk) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_palm_stream() + +# test_completion_deep_infra() +# test_completion_ai21() +# test config file with completion # +# def test_completion_openai_config(): +# try: +# litellm.config_path = "../config.json" +# litellm.set_verbose = True +# response = litellm.config_completion(messages=messages) +# # Add any assertions here to check the response +# print(response) +# litellm.config_path = None +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +# def test_maritalk(): +# messages = [{"role": "user", "content": "Hey"}] +# try: +# response = completion("maritalk", messages=messages) +# print(f"response: {response}") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_maritalk() + +def test_completion_together_ai_stream(): + user_message = "Write 1pg about YC & litellm" + messages = [{ "content": user_message,"role": "user"}] + try: + response = completion( + model="together_ai/togethercomputer/llama-2-70b-chat", + messages=messages, stream=True, + max_tokens=5 + ) + print(response) + for chunk in response: + print(chunk) + # print(string_response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_together_ai_stream() + + +# async def get_response(generator): +# async for elem in generator: +# print(elem) +# return + +# test_completion_together_ai_stream() + +def test_moderation(): + import openai + openai.api_type = "azure" + openai.api_version = "GM" + response = litellm.moderation(input="i'm ishaan cto of litellm") + print(response) + output = response.results[0] + print(output) + return output + +# test_moderation() \ No newline at end of file diff --git a/litellm/tests/test_completion_with_retries.py b/litellm/tests/test_completion_with_retries.py new file mode 100644 index 0000000000000000000000000000000000000000..416051e3839f09093d41d60648ac37c1191e6720 --- /dev/null +++ b/litellm/tests/test_completion_with_retries.py @@ -0,0 +1,73 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import openai +import litellm +from litellm import completion_with_retries, completion +from litellm import ( + AuthenticationError, + BadRequestError, + RateLimitError, + ServiceUnavailableError, + OpenAIError, +) + +user_message = "Hello, whats the weather in San Francisco??" +messages = [{"content": user_message, "role": "user"}] + + +def logger_fn(user_model_dict): + # print(f"user_model_dict: {user_model_dict}") + pass + +# normal call +def test_completion_custom_provider_model_name(): + try: + response = completion_with_retries( + model="together_ai/togethercomputer/llama-2-70b-chat", + messages=messages, + logger_fn=logger_fn, + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# completion with num retries + impact on exception mapping +def test_completion_with_num_retries(): + try: + response = completion(model="j2-ultra", messages=[{"messages": "vibe", "bad": "message"}], num_retries=2) + pytest.fail(f"Unmapped exception occurred") + except Exception as e: + pass + +# test_completion_with_num_retries() +def test_completion_with_0_num_retries(): + try: + litellm.set_verbose=False + print("making request") + + # Use the completion function + response = completion( + model="gpt-3.5-turbo", + messages=[{"gm": "vibe", "role": "user"}], + max_retries=4 + ) + + print(response) + + # print(response) + except Exception as e: + print("exception", e) + pass + +# Call the test function +test_completion_with_0_num_retries() \ No newline at end of file diff --git a/litellm/tests/test_config.py b/litellm/tests/test_config.py new file mode 100644 index 0000000000000000000000000000000000000000..73e719cad91d7db4270b2f8c327b686ff67b2f7d --- /dev/null +++ b/litellm/tests/test_config.py @@ -0,0 +1,91 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import completion_with_config + +config = { + "default_fallback_models": ["gpt-3.5-turbo", "claude-instant-1", "j2-ultra"], + "model": { + "claude-instant-1": { + "needs_moderation": True + }, + "gpt-3.5-turbo": { + "error_handling": { + "ContextWindowExceededError": {"fallback_model": "gpt-3.5-turbo-16k"} + } + } + } +} + +def test_config_context_window_exceeded(): + try: + sample_text = "how does a court case get to the Supreme Court?" * 1000 + messages = [{"content": sample_text, "role": "user"}] + response = completion_with_config(model="gpt-3.5-turbo", messages=messages, config=config) + print(response) + except Exception as e: + print(f"Exception: {e}") + pytest.fail(f"An exception occurred: {e}") + +# test_config_context_window_exceeded() + +def test_config_context_moderation(): + try: + messages=[{"role": "user", "content": "I want to kill them."}] + response = completion_with_config(model="claude-instant-1", messages=messages, config=config) + print(response) + except Exception as e: + print(f"Exception: {e}") + pytest.fail(f"An exception occurred: {e}") + +# test_config_context_moderation() + +def test_config_context_default_fallback(): + try: + messages=[{"role": "user", "content": "Hey, how's it going?"}] + response = completion_with_config(model="claude-instant-1", messages=messages, config=config, api_key="bad-key") + print(response) + except Exception as e: + print(f"Exception: {e}") + pytest.fail(f"An exception occurred: {e}") + +# test_config_context_default_fallback() + + +config = { + "default_fallback_models": ["gpt-3.5-turbo", "claude-instant-1", "j2-ultra"], + "available_models": ["gpt-3.5-turbo", "gpt-3.5-turbo-0301", "gpt-3.5-turbo-0613", "gpt-4", "gpt-4-0314", "gpt-4-0613", + "j2-ultra", "command-nightly", "togethercomputer/llama-2-70b-chat", "chat-bison", "chat-bison@001", "claude-2"], + "adapt_to_prompt_size": True, # type: ignore + "model": { + "claude-instant-1": { + "needs_moderation": True + }, + "gpt-3.5-turbo": { + "error_handling": { + "ContextWindowExceededError": {"fallback_model": "gpt-3.5-turbo-16k"} + } + } + } +} + +def test_config_context_adapt_to_prompt(): + try: + sample_text = "how does a court case get to the Supreme Court?" * 1000 + messages = [{"content": sample_text, "role": "user"}] + response = completion_with_config(model="gpt-3.5-turbo", messages=messages, config=config) + print(response) + except Exception as e: + print(f"Exception: {e}") + pytest.fail(f"An exception occurred: {e}") + +test_config_context_adapt_to_prompt() \ No newline at end of file diff --git a/litellm/tests/test_configs/custom_auth.py b/litellm/tests/test_configs/custom_auth.py new file mode 100644 index 0000000000000000000000000000000000000000..f9de3a97abd78c4bde497a73237377468d5cc15a --- /dev/null +++ b/litellm/tests/test_configs/custom_auth.py @@ -0,0 +1,14 @@ +from litellm.proxy.types import UserAPIKeyAuth +from fastapi import Request +from dotenv import load_dotenv +import os + +load_dotenv() +async def user_api_key_auth(request: Request, api_key: str) -> UserAPIKeyAuth: + try: + print(f"api_key: {api_key}") + if api_key == f"{os.getenv('PROXY_MASTER_KEY')}-1234": + return UserAPIKeyAuth(api_key=api_key) + raise Exception + except: + raise Exception \ No newline at end of file diff --git a/litellm/tests/test_configs/test_config.yaml b/litellm/tests/test_configs/test_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..34b3d928a41c0093b02f9f7311cbec75054ee11c --- /dev/null +++ b/litellm/tests/test_configs/test_config.yaml @@ -0,0 +1,24 @@ +model_list: + - model_name: "azure-model" + litellm_params: + model: "azure/gpt-35-turbo" + api_key: "os.environ/AZURE_EUROPE_API_KEY" + api_base: "https://my-endpoint-europe-berri-992.openai.azure.com/" + - model_name: "azure-model" + litellm_params: + model: "azure/gpt-35-turbo" + api_key: "os.environ/AZURE_CANADA_API_KEY" + api_base: "https://my-endpoint-canada-berri992.openai.azure.com" + - model_name: "azure-model" + litellm_params: + model: "azure/gpt-turbo" + api_key: "os.environ/AZURE_FRANCE_API_KEY" + api_base: "https://openai-france-1234.openai.azure.com" + +litellm_settings: + drop_params: True + set_verbose: True + +general_settings: + master_key: "os.environ/PROXY_MASTER_KEY" + database_url: "os.environ/PROXY_DATABASE_URL" # [OPTIONAL] use for token-based auth to proxy diff --git a/litellm/tests/test_configs/test_config_custom_auth.yaml b/litellm/tests/test_configs/test_config_custom_auth.yaml new file mode 100644 index 0000000000000000000000000000000000000000..33088bd1ce11683f3e689df1576d17bea2986516 --- /dev/null +++ b/litellm/tests/test_configs/test_config_custom_auth.yaml @@ -0,0 +1,11 @@ +model_list: + - model_name: "openai-model" + litellm_params: + model: "gpt-3.5-turbo" + +litellm_settings: + drop_params: True + set_verbose: True + +general_settings: + custom_auth: custom_auth.user_api_key_auth \ No newline at end of file diff --git a/litellm/tests/test_custom_logger.py b/litellm/tests/test_custom_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..f88bc6868d105a8935fb6f0d64496490308d718b --- /dev/null +++ b/litellm/tests/test_custom_logger.py @@ -0,0 +1,79 @@ +### What this tests #### +import sys, os, time, inspect, asyncio +import pytest +sys.path.insert(0, os.path.abspath('../..')) + +from litellm import completion, embedding +import litellm +from litellm.integrations.custom_logger import CustomLogger + +async_success = False +class MyCustomHandler(CustomLogger): + success: bool = False + failure: bool = False + + def log_pre_api_call(self, model, messages, kwargs): + print(f"Pre-API Call") + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + print(f"Post-API Call") + + def log_stream_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Stream") + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Success") + self.success = True + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Failure") + self.failure = True + + +async def async_test_logging_fn(kwargs, completion_obj, start_time, end_time): + global async_success + print(f"ON ASYNC LOGGING") + async_success = True + +@pytest.mark.asyncio +async def test_chat_openai(): + try: + # litellm.set_verbose = True + litellm.success_callback = [async_test_logging_fn] + response = await litellm.acompletion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm openai" + }], + stream=True) + async for chunk in response: + continue + assert async_success == True + except Exception as e: + print(e) + pytest.fail(f"An error occurred - {str(e)}") + +def test_completion_azure_stream_moderation_failure(): + try: + customHandler = MyCustomHandler() + litellm.callbacks = [customHandler] + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how do i kill someone", + }, + ] + try: + response = completion( + model="azure/chatgpt-v-2", messages=messages, stream=True + ) + for chunk in response: + print(f"chunk: {chunk}") + continue + except Exception as e: + print(e) + time.sleep(1) + assert customHandler.failure == True + except Exception as e: + pytest.fail(f"Error occurred: {e}") diff --git a/litellm/tests/test_embedding.py b/litellm/tests/test_embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..628d2ed047043550383057cd0df9aef0be5c2fd0 --- /dev/null +++ b/litellm/tests/test_embedding.py @@ -0,0 +1,225 @@ +import sys, os +import traceback +import pytest +from dotenv import load_dotenv +import openai + +load_dotenv() + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import embedding, completion + +litellm.set_verbose = False + +def test_openai_embedding(): + try: + litellm.set_verbose=True + response = embedding( + model="text-embedding-ada-002", + input=["good morning from litellm", "this is another item"], + metadata = {"anything": "good day"} + ) + litellm_response = dict(response) + litellm_response_keys = set(litellm_response.keys()) + litellm_response_keys.discard('_response_ms') + + print(litellm_response_keys) + print("LiteLLM Response\n") + # print(litellm_response) + + # same request with OpenAI 1.0+ + import openai + client = openai.OpenAI(api_key=os.environ['OPENAI_API_KEY']) + response = client.embeddings.create( + model="text-embedding-ada-002", input=["good morning from litellm", "this is another item"] + ) + + response = dict(response) + openai_response_keys = set(response.keys()) + print(openai_response_keys) + assert litellm_response_keys == openai_response_keys # ENSURE the Keys in litellm response is exactly what the openai package returns + assert len(litellm_response["data"]) == 2 # expect two embedding responses from litellm_response since input had two + print(openai_response_keys) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_openai_embedding() + +def test_openai_azure_embedding_simple(): + try: + response = embedding( + model="azure/azure-embedding-model", + input=["good morning from litellm"], + ) + print(response) + response_keys = set(dict(response).keys()) + response_keys.discard('_response_ms') + assert set(["usage", "model", "object", "data"]) == set(response_keys) #assert litellm response has expected keys from OpenAI embedding response + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_openai_azure_embedding_simple() + + +def test_openai_azure_embedding_timeouts(): + try: + response = embedding( + model="azure/azure-embedding-model", + input=["good morning from litellm"], + timeout=0.00001 + ) + print(response) + except openai.APITimeoutError: + print("Good job got timeout error!") + pass + except Exception as e: + pytest.fail(f"Expected timeout error, did not get the correct error. Instead got {e}") + +# test_openai_azure_embedding_timeouts() + +def test_openai_embedding_timeouts(): + try: + response = embedding( + model="text-embedding-ada-002", + input=["good morning from litellm"], + timeout=0.00001 + ) + print(response) + except openai.APITimeoutError: + print("Good job got OpenAI timeout error!") + pass + except Exception as e: + pytest.fail(f"Expected timeout error, did not get the correct error. Instead got {e}") +# test_openai_embedding_timeouts() + +def test_openai_azure_embedding(): + try: + api_key = os.environ['AZURE_API_KEY'] + api_base = os.environ['AZURE_API_BASE'] + api_version = os.environ['AZURE_API_VERSION'] + + os.environ['AZURE_API_VERSION'] = "" + os.environ['AZURE_API_BASE'] = "" + os.environ['AZURE_API_KEY'] = "" + + response = embedding( + model="azure/azure-embedding-model", + input=["good morning from litellm", "this is another item"], + api_key=api_key, + api_base=api_base, + api_version=api_version, + ) + print(response) + + + os.environ['AZURE_API_VERSION'] = api_version + os.environ['AZURE_API_BASE'] = api_base + os.environ['AZURE_API_KEY'] = api_key + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_openai_azure_embedding() + +# test_openai_embedding() + +def test_cohere_embedding(): + try: + # litellm.set_verbose=True + response = embedding( + model="embed-english-v2.0", input=["good morning from litellm", "this is another item"] + ) + print(f"response:", response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_cohere_embedding() + +def test_cohere_embedding3(): + try: + litellm.set_verbose=True + response = embedding( + model="embed-english-v3.0", + input=["good morning from litellm", "this is another item"], + ) + print(f"response:", response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_cohere_embedding3() + +def test_bedrock_embedding(): + try: + response = embedding( + model="amazon.titan-embed-text-v1", input=["good morning from litellm, attempting to embed data", + "lets test a second string for good measure"] + ) + print(f"response:", response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_bedrock_embedding() + +# comment out hf tests - since hf endpoints are unstable +def test_hf_embedding(): + try: + # huggingface/microsoft/codebert-base + # huggingface/facebook/bart-large + response = embedding( + model="huggingface/sentence-transformers/all-MiniLM-L6-v2", input=["good morning from litellm", "this is another item"] + ) + print(f"response:", response) + except Exception as e: + # Note: Huggingface inference API is unstable and fails with "model loading errors all the time" + pass +# test_hf_embedding() + +# test async embeddings +def test_aembedding(): + try: + import asyncio + async def embedding_call(): + try: + response = await litellm.aembedding( + model="text-embedding-ada-002", + input=["good morning from litellm", "this is another item"] + ) + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + asyncio.run(embedding_call()) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_aembedding() + + +def test_aembedding_azure(): + try: + import asyncio + async def embedding_call(): + try: + response = await litellm.aembedding( + model="azure/azure-embedding-model", + input=["good morning from litellm", "this is another item"] + ) + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + asyncio.run(embedding_call()) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_aembedding_azure() + +# def test_custom_openai_embedding(): +# litellm.set_verbose=True +# response = embedding( +# model="openai/custom_embedding", +# input=["good morning from litellm"], +# api_base="http://0.0.0.0:8000/" +# ) +# print(response) +# test_custom_openai_embedding() diff --git a/litellm/tests/test_exceptions.py b/litellm/tests/test_exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..67298f019b7e4f0c0a4ed764e159796fb7143d05 --- /dev/null +++ b/litellm/tests/test_exceptions.py @@ -0,0 +1,304 @@ +from openai import AuthenticationError, BadRequestError, RateLimitError, OpenAIError +import os +import sys +import traceback +import subprocess + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import ( + embedding, + completion, +# AuthenticationError, + ContextWindowExceededError, +# RateLimitError, +# ServiceUnavailableError, +# OpenAIError, +) +from concurrent.futures import ThreadPoolExecutor +import pytest +litellm.vertex_project = "pathrise-convert-1606954137718" +litellm.vertex_location = "us-central1" + +# litellm.failure_callback = ["sentry"] +#### What this tests #### +# This tests exception mapping -> trigger an exception from an llm provider -> assert if output is of the expected type + + +# 5 providers -> OpenAI, Azure, Anthropic, Cohere, Replicate + +# 3 main types of exceptions -> - Rate Limit Errors, Context Window Errors, Auth errors (incorrect/rotated key, etc.) + +# Approach: Run each model through the test -> assert if the correct error (always the same one) is triggered + +models = ["command-nightly"] + +# Test 1: Context Window Errors +@pytest.mark.parametrize("model", models) +def test_context_window(model): + sample_text = "Say error 50 times" * 1000000 + messages = [{"content": sample_text, "role": "user"}] + try: + litellm.set_verbose = False + response = completion(model=model, messages=messages) + print(f"response: {response}") + print("FAILED!") + pytest.fail(f"An exception occurred") + except ContextWindowExceededError as e: + print(f"Worked!") + except RateLimitError: + print("RateLimited!") + except Exception as e: + print(f"{e}") + pytest.fail(f"An error occcurred - {e}") + +@pytest.mark.parametrize("model", models) +def test_context_window_with_fallbacks(model): + ctx_window_fallback_dict = {"command-nightly": "claude-2", "gpt-3.5-turbo-instruct": "gpt-3.5-turbo-16k", "azure/chatgpt-v-2": "gpt-3.5-turbo-16k"} + sample_text = "how does a court case get to the Supreme Court?" * 1000 + messages = [{"content": sample_text, "role": "user"}] + + completion(model=model, messages=messages, context_window_fallback_dict=ctx_window_fallback_dict) + +# for model in litellm.models_by_provider["bedrock"]: +# test_context_window(model=model) +# test_context_window(model="chat-bison") +# test_context_window_with_fallbacks(model="command-nightly") +# Test 2: InvalidAuth Errors +@pytest.mark.parametrize("model", models) +def invalid_auth(model): # set the model key to an invalid key, depending on the model + messages = [{"content": "Hello, how are you?", "role": "user"}] + temporary_key = None + try: + if model == "gpt-3.5-turbo" or model == "gpt-3.5-turbo-instruct": + temporary_key = os.environ["OPENAI_API_KEY"] + os.environ["OPENAI_API_KEY"] = "bad-key" + elif "bedrock" in model: + temporary_aws_access_key = os.environ["AWS_ACCESS_KEY_ID"] + os.environ["AWS_ACCESS_KEY_ID"] = "bad-key" + temporary_aws_region_name = os.environ["AWS_REGION_NAME"] + os.environ["AWS_REGION_NAME"] = "bad-key" + temporary_secret_key = os.environ["AWS_SECRET_ACCESS_KEY"] + os.environ["AWS_SECRET_ACCESS_KEY"] = "bad-key" + elif model == "azure/chatgpt-v-2": + temporary_key = os.environ["AZURE_API_KEY"] + os.environ["AZURE_API_KEY"] = "bad-key" + elif model == "claude-instant-1": + temporary_key = os.environ["ANTHROPIC_API_KEY"] + os.environ["ANTHROPIC_API_KEY"] = "bad-key" + elif model == "command-nightly": + temporary_key = os.environ["COHERE_API_KEY"] + os.environ["COHERE_API_KEY"] = "bad-key" + elif "j2" in model: + temporary_key = os.environ["AI21_API_KEY"] + os.environ["AI21_API_KEY"] = "bad-key" + elif "togethercomputer" in model: + temporary_key = os.environ["TOGETHERAI_API_KEY"] + os.environ["TOGETHERAI_API_KEY"] = "84060c79880fc49df126d3e87b53f8a463ff6e1c6d27fe64207cde25cdfcd1f24a" + elif model in litellm.openrouter_models: + temporary_key = os.environ["OPENROUTER_API_KEY"] + os.environ["OPENROUTER_API_KEY"] = "bad-key" + elif model in litellm.aleph_alpha_models: + temporary_key = os.environ["ALEPH_ALPHA_API_KEY"] + os.environ["ALEPH_ALPHA_API_KEY"] = "bad-key" + elif model in litellm.nlp_cloud_models: + temporary_key = os.environ["NLP_CLOUD_API_KEY"] + os.environ["NLP_CLOUD_API_KEY"] = "bad-key" + elif ( + model + == "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1" + ): + temporary_key = os.environ["REPLICATE_API_KEY"] + os.environ["REPLICATE_API_KEY"] = "bad-key" + print(f"model: {model}") + response = completion( + model=model, messages=messages + ) + print(f"response: {response}") + except AuthenticationError as e: + print(f"AuthenticationError Caught Exception - {str(e)}") + except ( + OpenAIError + ) as e: # is at least an openai error -> in case of random model errors - e.g. overloaded server + print(f"OpenAIError Caught Exception - {e}") + except Exception as e: + print(type(e)) + print(type(AuthenticationError)) + print(e.__class__.__name__) + print(f"Uncaught Exception - {e}") + pytest.fail(f"Error occurred: {e}") + if temporary_key != None: # reset the key + if model == "gpt-3.5-turbo": + os.environ["OPENAI_API_KEY"] = temporary_key + elif model == "chatgpt-test": + os.environ["AZURE_API_KEY"] = temporary_key + azure = True + elif model == "claude-instant-1": + os.environ["ANTHROPIC_API_KEY"] = temporary_key + elif model == "command-nightly": + os.environ["COHERE_API_KEY"] = temporary_key + elif ( + model + == "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1" + ): + os.environ["REPLICATE_API_KEY"] = temporary_key + elif "j2" in model: + os.environ["AI21_API_KEY"] = temporary_key + elif ("togethercomputer" in model): + os.environ["TOGETHERAI_API_KEY"] = temporary_key + elif model in litellm.aleph_alpha_models: + os.environ["ALEPH_ALPHA_API_KEY"] = temporary_key + elif model in litellm.nlp_cloud_models: + os.environ["NLP_CLOUD_API_KEY"] = temporary_key + elif "bedrock" in model: + os.environ["AWS_ACCESS_KEY_ID"] = temporary_aws_access_key + os.environ["AWS_REGION_NAME"] = temporary_aws_region_name + os.environ["AWS_SECRET_ACCESS_KEY"] = temporary_secret_key + return + +# for model in litellm.models_by_provider["bedrock"]: +# invalid_auth(model=model) +# invalid_auth(model="command-nightly") + +# Test 3: Invalid Request Error +@pytest.mark.parametrize("model", models) +def test_invalid_request_error(model): + messages = [{"content": "hey, how's it going?", "role": "user"}] + + with pytest.raises(BadRequestError): + completion(model=model, messages=messages, max_tokens="hello world") + + + +def test_completion_azure_exception(): + try: + import openai + print("azure gpt-3.5 test\n\n") + litellm.set_verbose=False + ## Test azure call + old_azure_key = os.environ["AZURE_API_KEY"] + os.environ["AZURE_API_KEY"] = "good morning" + response = completion( + model="azure/chatgpt-v-2", + messages=[ + { + "role": "user", + "content": "hello" + } + ], + ) + print(f"response: {response}") + print(response) + except openai.AuthenticationError as e: + os.environ["AZURE_API_KEY"] = old_azure_key + print("good job got the correct error for azure when key not set") + except Exception as e: + pytest.fail(f"Error occurred: {e}") +test_completion_azure_exception() + +async def asynctest_completion_azure_exception(): + try: + import openai + import litellm + print("azure gpt-3.5 test\n\n") + litellm.set_verbose=False + ## Test azure call + old_azure_key = os.environ["AZURE_API_KEY"] + os.environ["AZURE_API_KEY"] = "good morning" + response = await litellm.acompletion( + model="azure/chatgpt-v-2", + messages=[ + { + "role": "user", + "content": "hello" + } + ], + ) + print(f"response: {response}") + print(response) + except openai.AuthenticationError as e: + os.environ["AZURE_API_KEY"] = old_azure_key + print("good job got the correct error for azure when key not set") + print(e) + except Exception as e: + print("Got wrong exception") + print("exception", e) + pytest.fail(f"Error occurred: {e}") + +# import asyncio +# asyncio.run( +# asynctest_completion_azure_exception() +# ) + + +def test_completion_openai_exception(): + # test if openai:gpt raises openai.AuthenticationError + try: + import openai + print("openai gpt-3.5 test\n\n") + litellm.set_verbose=False + ## Test azure call + old_azure_key = os.environ["OPENAI_API_KEY"] + os.environ["OPENAI_API_KEY"] = "good morning" + response = completion( + model="gpt-4", + messages=[ + { + "role": "user", + "content": "hello" + } + ], + ) + print(f"response: {response}") + print(response) + except openai.AuthenticationError as e: + os.environ["OPENAI_API_KEY"] = old_azure_key + print("good job got the correct error for openai when key not set") + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_openai_exception() + + + + +# # test_invalid_request_error(model="command-nightly") +# # Test 3: Rate Limit Errors +# def test_model_call(model): +# try: +# sample_text = "how does a court case get to the Supreme Court?" +# messages = [{ "content": sample_text,"role": "user"}] +# print(f"model: {model}") +# response = completion(model=model, messages=messages) +# except RateLimitError as e: +# print(f"headers: {e.response.headers}") +# return True +# # except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server +# # return True +# except Exception as e: +# print(f"Uncaught Exception {model}: {type(e).__name__} - {e}") +# traceback.print_exc() +# pass +# return False +# # Repeat each model 500 times +# # extended_models = [model for model in models for _ in range(250)] +# extended_models = ["azure/chatgpt-v-2" for _ in range(250)] + +# def worker(model): +# return test_model_call(model) + +# # Create a dictionary to store the results +# counts = {True: 0, False: 0} + +# # Use Thread Pool Executor +# with ThreadPoolExecutor(max_workers=500) as executor: +# # Use map to start the operation in thread pool +# results = executor.map(worker, extended_models) + +# # Iterate over results and count True/False +# for result in results: +# counts[result] += 1 + +# accuracy_score = counts[True]/(counts[True] + counts[False]) +# print(f"accuracy_score: {accuracy_score}") diff --git a/litellm/tests/test_function_calling.py b/litellm/tests/test_function_calling.py new file mode 100644 index 0000000000000000000000000000000000000000..a7f0225d4fd30552f6b93ff104b95cefefe90fa0 --- /dev/null +++ b/litellm/tests/test_function_calling.py @@ -0,0 +1,197 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError +import pytest +litellm.num_retries = 0 +litellm.cache = None +# litellm.set_verbose=True +import json + +# litellm.success_callback = ["langfuse"] + +def get_current_weather(location, unit="fahrenheit"): + """Get the current weather in a given location""" + if "tokyo" in location.lower(): + return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"}) + elif "san francisco" in location.lower(): + return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}) + elif "paris" in location.lower(): + return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"}) + else: + return json.dumps({"location": location, "temperature": "unknown"}) + +# Example dummy function hard coded to return the same weather +# In production, this could be your backend API or an external API +def test_parallel_function_call(): + try: + # Step 1: send the conversation and available functions to the model + messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}] + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } + ] + response = litellm.completion( + model="gpt-3.5-turbo-1106", + messages=messages, + tools=tools, + tool_choice="auto", # auto is default, but we'll be explicit + ) + print("Response\n", response) + response_message = response.choices[0].message + tool_calls = response_message.tool_calls + + print("length of tool calls", len(tool_calls)) + print("Expecting there to be 3 tool calls") + assert len(tool_calls) > 1 # this has to call the function for SF, Tokyo and parise + + # Step 2: check if the model wanted to call a function + if tool_calls: + # Step 3: call the function + # Note: the JSON response may not always be valid; be sure to handle errors + available_functions = { + "get_current_weather": get_current_weather, + } # only one function in this example, but you can have multiple + messages.append(response_message) # extend conversation with assistant's reply + print("Response message\n", response_message) + # Step 4: send the info for each function call and function response to the model + for tool_call in tool_calls: + function_name = tool_call.function.name + function_to_call = available_functions[function_name] + function_args = json.loads(tool_call.function.arguments) + function_response = function_to_call( + location=function_args.get("location"), + unit=function_args.get("unit"), + ) + messages.append( + { + "tool_call_id": tool_call.id, + "role": "tool", + "name": function_name, + "content": function_response, + } + ) # extend conversation with function response + print(f"messages: {messages}") + second_response = litellm.completion( + model="gpt-3.5-turbo-1106", + messages=messages, + temperature=0.2, + seed=22 + ) # get a new response from the model where it can see the function response + print("second response\n", second_response) + return second_response + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +test_parallel_function_call() + + + + +def test_parallel_function_call_stream(): + try: + # Step 1: send the conversation and available functions to the model + messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}] + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } + ] + response = litellm.completion( + model="gpt-3.5-turbo-1106", + messages=messages, + tools=tools, + stream=True, + tool_choice="auto", # auto is default, but we'll be explicit + complete_response = True + ) + print("Response\n", response) + # for chunk in response: + # print(chunk) + response_message = response.choices[0].message + tool_calls = response_message.tool_calls + + print("length of tool calls", len(tool_calls)) + print("Expecting there to be 3 tool calls") + assert len(tool_calls) > 1 # this has to call the function for SF, Tokyo and parise + + # Step 2: check if the model wanted to call a function + if tool_calls: + # Step 3: call the function + # Note: the JSON response may not always be valid; be sure to handle errors + available_functions = { + "get_current_weather": get_current_weather, + } # only one function in this example, but you can have multiple + messages.append(response_message) # extend conversation with assistant's reply + print("Response message\n", response_message) + # Step 4: send the info for each function call and function response to the model + for tool_call in tool_calls: + function_name = tool_call.function.name + function_to_call = available_functions[function_name] + function_args = json.loads(tool_call.function.arguments) + function_response = function_to_call( + location=function_args.get("location"), + unit=function_args.get("unit"), + ) + messages.append( + { + "tool_call_id": tool_call.id, + "role": "tool", + "name": function_name, + "content": function_response, + } + ) # extend conversation with function response + print(f"messages: {messages}") + second_response = litellm.completion( + model="gpt-3.5-turbo-1106", + messages=messages, + temperature=0.2, + seed=22 + ) # get a new response from the model where it can see the function response + print("second response\n", second_response) + return second_response + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +test_parallel_function_call_stream() \ No newline at end of file diff --git a/litellm/tests/test_get_model_cost_map.py b/litellm/tests/test_get_model_cost_map.py new file mode 100644 index 0000000000000000000000000000000000000000..b7763da12acaa553d87bb8f201f33dabb718fc90 --- /dev/null +++ b/litellm/tests/test_get_model_cost_map.py @@ -0,0 +1,85 @@ +import sys, os +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import time +import litellm +from litellm import get_max_tokens, model_cost, open_ai_chat_completion_models +import pytest + +def test_get_gpt3_tokens(): + max_tokens = get_max_tokens("gpt-3.5-turbo") + print(max_tokens) + assert max_tokens==4097 + # print(results) +test_get_gpt3_tokens() + +def test_get_palm_tokens(): + # # 🦄🦄🦄🦄🦄🦄🦄🦄 + max_tokens = get_max_tokens("palm/chat-bison") + assert max_tokens == 4096 + print(max_tokens) +test_get_palm_tokens() + +def test_zephyr_hf_tokens(): + max_tokens = get_max_tokens("huggingface/HuggingFaceH4/zephyr-7b-beta") + print(max_tokens) + assert max_tokens == 32768 + +test_zephyr_hf_tokens() + +def test_cost_ft_gpt_35(): + try: + # this tests if litellm.completion_cost can calculate cost for ft:gpt-3.5-turbo:my-org:custom_suffix:id + # it needs to lookup ft:gpt-3.5-turbo in the litellm model_cost map to get the correct cost + from litellm import ModelResponse, Choices, Message + from litellm.utils import Usage + resp = ModelResponse( + id='chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac', + choices=[Choices(finish_reason=None, index=0, + message=Message(content=' Sure! Here is a short poem about the sky:\n\nA canvas of blue, a', role='assistant'))], + created=1700775391, + model='ft:gpt-3.5-turbo:my-org:custom_suffix:id', + object='chat.completion', system_fingerprint=None, + usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38) + ) + + cost = litellm.completion_cost(completion_response=resp) + print("\n Calculated Cost for ft:gpt-3.5", cost) + input_cost = model_cost["ft:gpt-3.5-turbo"]["input_cost_per_token"] + output_cost = model_cost["ft:gpt-3.5-turbo"]["output_cost_per_token"] + print(input_cost, output_cost) + expected_cost = (input_cost*resp.usage.prompt_tokens) + (output_cost*resp.usage.completion_tokens) + print("\n Excpected cost", expected_cost) + assert cost == expected_cost + except Exception as e: + pytest.fail(f"Cost Calc failed for ft:gpt-3.5. Expected {expected_cost}, Calculated cost {cost}") +test_cost_ft_gpt_35() + +def test_cost_azure_gpt_35(): + try: + # this tests if litellm.completion_cost can calculate cost for azure/chatgpt-deployment-2 which maps to azure/gpt-3.5-turbo + # for this test we check if passing `model` to completion_cost overrides the completion cost + from litellm import ModelResponse, Choices, Message + from litellm.utils import Usage + resp = ModelResponse( + id='chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac', + choices=[Choices(finish_reason=None, index=0, + message=Message(content=' Sure! Here is a short poem about the sky:\n\nA canvas of blue, a', role='assistant'))], + model='azure/gpt-35-turbo', # azure always has model written like this + usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38) + ) + + cost = litellm.completion_cost(completion_response=resp, model="azure/gpt-3.5-turbo") + print("\n Calculated Cost for azure/gpt-3.5-turbo", cost) + input_cost = model_cost["azure/gpt-3.5-turbo"]["input_cost_per_token"] + output_cost = model_cost["azure/gpt-3.5-turbo"]["output_cost_per_token"] + expected_cost = (input_cost*resp.usage.prompt_tokens) + (output_cost*resp.usage.completion_tokens) + print("\n Excpected cost", expected_cost) + assert cost == expected_cost + except Exception as e: + pytest.fail(f"Cost Calc failed for azure/gpt-3.5-turbo. Expected {expected_cost}, Calculated cost {cost}") +test_cost_azure_gpt_35() + diff --git a/litellm/tests/test_get_model_file.py b/litellm/tests/test_get_model_file.py new file mode 100644 index 0000000000000000000000000000000000000000..820465273c7f2c50a3126d433cad8ea5162b000c --- /dev/null +++ b/litellm/tests/test_get_model_file.py @@ -0,0 +1,12 @@ +import os, sys, traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +import pytest + +try: + print(litellm.get_model_cost_map(url="fake-url")) +except Exception as e: + pytest.fail(f"An exception occurred: {e}") \ No newline at end of file diff --git a/litellm/tests/test_get_model_list.py b/litellm/tests/test_get_model_list.py new file mode 100644 index 0000000000000000000000000000000000000000..7663eebf5213f05df200953f2462e542a7bccdcb --- /dev/null +++ b/litellm/tests/test_get_model_list.py @@ -0,0 +1,11 @@ +import os, sys, traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import get_model_list + +print(get_model_list()) +print(get_model_list()) +# print(litellm.model_list) diff --git a/litellm/tests/test_helicone_integration.py b/litellm/tests/test_helicone_integration.py new file mode 100644 index 0000000000000000000000000000000000000000..82669d09206d35ed254df516efa40c98ebdf6b48 --- /dev/null +++ b/litellm/tests/test_helicone_integration.py @@ -0,0 +1,30 @@ +# #### What this tests #### +# # This tests if logging to the helicone integration actually works + +# import sys, os +# import traceback +# import pytest + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import litellm +# from litellm import embedding, completion + +# litellm.success_callback = ["helicone"] + +# litellm.set_verbose = True + +# user_message = "Hello, how are you?" +# messages = [{"content": user_message, "role": "user"}] + + +# # openai call +# response = completion( +# model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}] +# ) + +# # cohere call +# response = completion( +# model="command-nightly", messages=[{"role": "user", "content": "Hi 👋 - i'm cohere"}] +# ) diff --git a/litellm/tests/test_hf_prompt_templates.py b/litellm/tests/test_hf_prompt_templates.py new file mode 100644 index 0000000000000000000000000000000000000000..9f1aedcff3adea70b231a0e8a863dad292a51d80 --- /dev/null +++ b/litellm/tests/test_hf_prompt_templates.py @@ -0,0 +1,48 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +from litellm.llms.prompt_templates.factory import prompt_factory + +def test_prompt_formatting(): + try: + prompt = prompt_factory(model="mistralai/Mistral-7B-Instruct-v0.1", messages=[{"role": "system", "content": "Be a good bot"}, {"role": "user", "content": "Hello world"}]) + assert prompt == "[INST] Be a good bot [/INST] [INST] Hello world [/INST]" + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") +# def logger_fn(user_model_dict): +# return +# print(f"user_model_dict: {user_model_dict}") + +# messages=[{"role": "user", "content": "Write me a function to print hello world"}] + +# # test if the first-party prompt templates work +# def test_huggingface_supported_models(): +# model = "huggingface/WizardLM/WizardCoder-Python-34B-V1.0" +# response = completion(model=model, messages=messages, max_tokens=256, api_base="https://ji16r2iys9a8rjk2.us-east-1.aws.endpoints.huggingface.cloud", logger_fn=logger_fn) +# print(response['choices'][0]['message']['content']) +# return response + +# test_huggingface_supported_models() + +# # test if a custom prompt template works +# litellm.register_prompt_template( +# model="togethercomputer/LLaMA-2-7B-32K", +# roles={"system":"", "assistant":"Assistant:", "user":"User:"}, +# pre_message_sep= "\n", +# post_message_sep= "\n" +# ) +# def test_huggingface_custom_model(): +# model = "huggingface/togethercomputer/LLaMA-2-7B-32K" +# response = completion(model=model, messages=messages, api_base="https://ecd4sb5n09bo4ei2.us-east-1.aws.endpoints.huggingface.cloud", logger_fn=logger_fn) +# print(response['choices'][0]['message']['content']) +# return response + +# test_huggingface_custom_model() \ No newline at end of file diff --git a/litellm/tests/test_langchain_ChatLiteLLM.py b/litellm/tests/test_langchain_ChatLiteLLM.py new file mode 100644 index 0000000000000000000000000000000000000000..46fb33150ba38e566d456981228a45ca0d1e4232 --- /dev/null +++ b/litellm/tests/test_langchain_ChatLiteLLM.py @@ -0,0 +1,107 @@ +# import os +# import sys, os +# import traceback +# from dotenv import load_dotenv + +# load_dotenv() +# import os, io + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import pytest +# import litellm +# from litellm import embedding, completion, text_completion, completion_cost + +# from langchain.chat_models import ChatLiteLLM +# from langchain.prompts.chat import ( +# ChatPromptTemplate, +# SystemMessagePromptTemplate, +# AIMessagePromptTemplate, +# HumanMessagePromptTemplate, +# ) +# from langchain.schema import AIMessage, HumanMessage, SystemMessage + +# def test_chat_gpt(): +# try: +# chat = ChatLiteLLM(model="gpt-3.5-turbo", max_tokens=10) +# messages = [ +# HumanMessage( +# content="what model are you" +# ) +# ] +# resp = chat(messages) + +# print(resp) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # test_chat_gpt() + + +# def test_claude(): +# try: +# chat = ChatLiteLLM(model="claude-2", max_tokens=10) +# messages = [ +# HumanMessage( +# content="what model are you" +# ) +# ] +# resp = chat(messages) + +# print(resp) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # test_claude() + +# def test_palm(): +# try: +# chat = ChatLiteLLM(model="palm/chat-bison", max_tokens=10) +# messages = [ +# HumanMessage( +# content="what model are you" +# ) +# ] +# resp = chat(messages) + +# print(resp) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # test_palm() + + +# # def test_openai_with_params(): +# # try: +# # api_key = os.environ["OPENAI_API_KEY"] +# # os.environ.pop("OPENAI_API_KEY") +# # print("testing openai with params") +# # llm = ChatLiteLLM( +# # model="gpt-3.5-turbo", +# # openai_api_key=api_key, +# # # Prefer using None which is the default value, endpoint could be empty string +# # openai_api_base= None, +# # max_tokens=20, +# # temperature=0.5, +# # request_timeout=10, +# # model_kwargs={ +# # "frequency_penalty": 0, +# # "presence_penalty": 0, +# # }, +# # verbose=True, +# # max_retries=0, +# # ) +# # messages = [ +# # HumanMessage( +# # content="what model are you" +# # ) +# # ] +# # resp = llm(messages) + +# # print(resp) +# # except Exception as e: +# # pytest.fail(f"Error occurred: {e}") + +# # test_openai_with_params() + diff --git a/litellm/tests/test_langfuse.py b/litellm/tests/test_langfuse.py new file mode 100644 index 0000000000000000000000000000000000000000..017d7e1e2e366da3ba41909380c7b87dde37b06e --- /dev/null +++ b/litellm/tests/test_langfuse.py @@ -0,0 +1,140 @@ +import sys +import os +import io, asyncio +# import logging +# logging.basicConfig(level=logging.DEBUG) +sys.path.insert(0, os.path.abspath('../..')) + +from litellm import completion +import litellm +litellm.num_retries = 3 +litellm.success_callback = ["langfuse"] +# litellm.set_verbose = True +import time +import pytest + +def test_langfuse_logging_async(): + try: + litellm.set_verbose = True + async def _test_langfuse(): + return await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content":"This is a test"}], + max_tokens=1000, + temperature=0.7, + timeout=5, + ) + response = asyncio.run(_test_langfuse()) + print(f"response: {response}") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred - {e}") + +# test_langfuse_logging_async() + +def test_langfuse_logging(): + try: + # litellm.set_verbose = True + response = completion(model="claude-instant-1.2", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm claude" + }], + max_tokens=10, + temperature=0.2, + metadata={"langfuse/key": "foo"} + ) + print(response) + except litellm.Timeout as e: + pass + except Exception as e: + print(e) + +test_langfuse_logging() + + +def test_langfuse_logging_stream(): + try: + litellm.set_verbose=True + response = completion(model="anyscale/meta-llama/Llama-2-7b-chat-hf", + messages=[{ + "role": "user", + "content": "this is a streaming test for llama2 + langfuse" + }], + max_tokens=20, + temperature=0.2, + stream=True + ) + print(response) + for chunk in response: + pass + # print(chunk) + except litellm.Timeout as e: + pass + except Exception as e: + print(e) + +# test_langfuse_logging_stream() + +def test_langfuse_logging_custom_generation_name(): + try: + litellm.set_verbose=True + response = completion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm claude" + }], + max_tokens=10, + metadata = { + "langfuse/foo": "bar", + "langsmith/fizz": "buzz", + "prompt_hash": "asdf98u0j9131123" + } + ) + print(response) + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred - {e}") + print(e) + +test_langfuse_logging_custom_generation_name() + +def test_langfuse_logging_function_calling(): + function1 = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + } + ] + try: + response = completion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "what's the weather in boston" + }], + temperature=0.1, + functions=function1, + ) + print(response) + except litellm.Timeout as e: + pass + except Exception as e: + print(e) + +# test_langfuse_logging_function_calling() + + + diff --git a/litellm/tests/test_langsmith.py b/litellm/tests/test_langsmith.py new file mode 100644 index 0000000000000000000000000000000000000000..a99ee3a32fc514bd971d7d1dcc2ddc86083493ac --- /dev/null +++ b/litellm/tests/test_langsmith.py @@ -0,0 +1,50 @@ +import sys +import os +import io + +sys.path.insert(0, os.path.abspath('../..')) + +from litellm import completion +import litellm + +litellm.success_callback = ["langsmith"] +# litellm.set_verbose = True +import time + + +def test_langsmith_logging(): + try: + response = completion(model="claude-instant-1.2", + messages=[{ + "role": "user", + "content": "what llm are u" + }], + max_tokens=10, + temperature=0.2 + ) + print(response) + except Exception as e: + print(e) + +test_langsmith_logging() + + +def test_langsmith_logging_with_metadata(): + try: + response = completion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "what llm are u" + }], + max_tokens=10, + temperature=0.2, + metadata={ + "run_name": "litellmRUN", + "project_name": "litellm-completion", + } + ) + print(response) + except Exception as e: + print(e) + +test_langsmith_logging_with_metadata() diff --git a/litellm/tests/test_litellm_max_budget.py b/litellm/tests/test_litellm_max_budget.py new file mode 100644 index 0000000000000000000000000000000000000000..0e933c6041ea184387e42c422618a4fc708ad9eb --- /dev/null +++ b/litellm/tests/test_litellm_max_budget.py @@ -0,0 +1,31 @@ +# #### What this tests #### +# # This tests calling litellm.max_budget by making back-to-back gpt-4 calls +# # commenting out this test for circle ci, as it causes other tests to fail, since litellm.max_budget would impact other litellm imports +# import sys, os, json +# import traceback +# import pytest + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import litellm +# # litellm.set_verbose = True +# from litellm import completion, BudgetExceededError + +# def test_max_budget(): +# try: +# litellm.max_budget = 0.001 # sets a max budget of $0.001 + +# messages = [{"role": "user", "content": "Hey, how's it going"}] +# response = completion(model="gpt-4", messages=messages, stream=True) +# for chunk in response: +# continue +# print(litellm._current_cost) +# completion(model="gpt-4", messages=messages, stream=True) +# litellm.max_budget = float('inf') +# except BudgetExceededError as e: +# pass +# except Exception as e: +# pytest.fail(f"An error occured: {str(e)}") + + diff --git a/litellm/tests/test_llmonitor_integration.py b/litellm/tests/test_llmonitor_integration.py new file mode 100644 index 0000000000000000000000000000000000000000..e88995f3b44418030233d0082208fad7cf0fdcd3 --- /dev/null +++ b/litellm/tests/test_llmonitor_integration.py @@ -0,0 +1,76 @@ +# #### What this tests #### +# # This tests if logging to the llmonitor integration actually works +# # Adds the parent directory to the system path +# import sys +# import os + +# sys.path.insert(0, os.path.abspath("../..")) + +# from litellm import completion, embedding +# import litellm + +# litellm.success_callback = ["llmonitor"] +# litellm.failure_callback = ["llmonitor"] + +# litellm.set_verbose = True + + +# def test_chat_openai(): +# try: +# response = completion( +# model="gpt-3.5-turbo", +# messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], +# user="ishaan_from_litellm" +# ) + +# print(response) + +# except Exception as e: +# print(e) + + +# def test_embedding_openai(): +# try: +# response = embedding(model="text-embedding-ada-002", input=["test"]) +# # Add any assertions here to check the response +# print(f"response: {str(response)[:50]}") +# except Exception as e: +# print(e) + + +# test_chat_openai() +# # test_embedding_openai() + + +# def test_llmonitor_logging_function_calling(): +# function1 = [ +# { +# "name": "get_current_weather", +# "description": "Get the current weather in a given location", +# "parameters": { +# "type": "object", +# "properties": { +# "location": { +# "type": "string", +# "description": "The city and state, e.g. San Francisco, CA", +# }, +# "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, +# }, +# "required": ["location"], +# }, +# } +# ] +# try: +# response = completion(model="gpt-3.5-turbo", +# messages=[{ +# "role": "user", +# "content": "what's the weather in boston" +# }], +# temperature=0.1, +# functions=function1, +# ) +# print(response) +# except Exception as e: +# print(e) + +# # test_llmonitor_logging_function_calling() diff --git a/litellm/tests/test_loadtest_router.py b/litellm/tests/test_loadtest_router.py new file mode 100644 index 0000000000000000000000000000000000000000..da031be69c89b0deb2c2b50149663c34c62d63ff --- /dev/null +++ b/litellm/tests/test_loadtest_router.py @@ -0,0 +1,69 @@ +# import sys, os +# import traceback +# from dotenv import load_dotenv +# import copy + +# load_dotenv() +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import asyncio +# from litellm import Router, Timeout + + +# async def call_acompletion(semaphore, router: Router, input_data): +# async with semaphore: +# try: +# # Use asyncio.wait_for to set a timeout for the task +# response = await router.acompletion(**input_data) +# # Handle the response as needed +# return response +# except Timeout: +# print(f"Task timed out: {input_data}") +# return None # You may choose to return something else or raise an exception + + +# async def main(): +# # Initialize the Router +# model_list= [{ +# "model_name": "gpt-3.5-turbo", +# "litellm_params": { +# "model": "gpt-3.5-turbo", +# "api_key": os.getenv("OPENAI_API_KEY"), +# }, +# }, { +# "model_name": "gpt-3.5-turbo", +# "litellm_params": { +# "model": "azure/chatgpt-v-2", +# "api_key": os.getenv("AZURE_API_KEY"), +# "api_base": os.getenv("AZURE_API_BASE"), +# "api_version": os.getenv("AZURE_API_VERSION") +# }, +# }, { +# "model_name": "gpt-3.5-turbo", +# "litellm_params": { +# "model": "azure/chatgpt-functioncalling", +# "api_key": os.getenv("AZURE_API_KEY"), +# "api_base": os.getenv("AZURE_API_BASE"), +# "api_version": os.getenv("AZURE_API_VERSION") +# }, +# }] +# router = Router(model_list=model_list, num_retries=3, timeout=10) + +# # Create a semaphore with a capacity of 100 +# semaphore = asyncio.Semaphore(100) + +# # List to hold all task references +# tasks = [] + +# # Launch 1000 tasks +# for _ in range(1000): +# task = asyncio.create_task(call_acompletion(semaphore, router, {"model": "gpt-3.5-turbo", "messages": [{"role":"user", "content": "Hey, how's it going?"}]})) +# tasks.append(task) + +# # Wait for all tasks to complete +# responses = await asyncio.gather(*tasks) +# # Process responses as needed +# print(f"NUMBER OF COMPLETED TASKS: {len(responses)}") +# # Run the main function +# asyncio.run(main()) diff --git a/litellm/tests/test_logging.py b/litellm/tests/test_logging.py new file mode 100644 index 0000000000000000000000000000000000000000..d8557d4c9961cbcd7e7e7d38329d3a6d3fc7cffd --- /dev/null +++ b/litellm/tests/test_logging.py @@ -0,0 +1,382 @@ +# #### What this tests #### +# # This tests error logging (with custom user functions) for the raw `completion` + `embedding` endpoints + +# # Test Scenarios (test across completion, streaming, embedding) +# ## 1: Pre-API-Call +# ## 2: Post-API-Call +# ## 3: On LiteLLM Call success +# ## 4: On LiteLLM Call failure + +# import sys, os, io +# import traceback, logging +# import pytest +# import dotenv +# dotenv.load_dotenv() + +# # Create logger +# logger = logging.getLogger(__name__) +# logger.setLevel(logging.DEBUG) + +# # Create a stream handler +# stream_handler = logging.StreamHandler(sys.stdout) +# logger.addHandler(stream_handler) + +# # Create a function to log information +# def logger_fn(message): +# logger.info(message) + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import litellm +# from litellm import embedding, completion +# from openai.error import AuthenticationError +# litellm.set_verbose = True + +# score = 0 + +# user_message = "Hello, how are you?" +# messages = [{"content": user_message, "role": "user"}] + +# # 1. On Call Success +# # normal completion +# # test on openai completion call +# def test_logging_success_completion(): +# global score +# try: +# # Redirect stdout +# old_stdout = sys.stdout +# sys.stdout = new_stdout = io.StringIO() + +# response = completion(model="gpt-3.5-turbo", messages=messages) +# # Restore stdout +# sys.stdout = old_stdout +# output = new_stdout.getvalue().strip() + +# if "Logging Details Pre-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details Post-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details LiteLLM-Success Call" not in output: +# raise Exception("Required log message not found!") +# score += 1 +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# pass + +# # ## test on non-openai completion call +# # def test_logging_success_completion_non_openai(): +# # global score +# # try: +# # # Redirect stdout +# # old_stdout = sys.stdout +# # sys.stdout = new_stdout = io.StringIO() + +# # response = completion(model="claude-instant-1", messages=messages) + +# # # Restore stdout +# # sys.stdout = old_stdout +# # output = new_stdout.getvalue().strip() + +# # if "Logging Details Pre-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details Post-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details LiteLLM-Success Call" not in output: +# # raise Exception("Required log message not found!") +# # score += 1 +# # except Exception as e: +# # pytest.fail(f"Error occurred: {e}") +# # pass + +# # streaming completion +# ## test on openai completion call +# def test_logging_success_streaming_openai(): +# global score +# try: +# # litellm.set_verbose = False +# def custom_callback( +# kwargs, # kwargs to completion +# completion_response, # response from completion +# start_time, end_time # start/end time +# ): +# if "complete_streaming_response" in kwargs: +# print(f"Complete Streaming Response: {kwargs['complete_streaming_response']}") + +# # Assign the custom callback function +# litellm.success_callback = [custom_callback] + +# # Redirect stdout +# old_stdout = sys.stdout +# sys.stdout = new_stdout = io.StringIO() + +# response = completion(model="gpt-3.5-turbo", messages=messages, stream=True) +# for chunk in response: +# pass + +# # Restore stdout +# sys.stdout = old_stdout +# output = new_stdout.getvalue().strip() + +# if "Logging Details Pre-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details Post-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details LiteLLM-Success Call" not in output: +# raise Exception("Required log message not found!") +# elif "Complete Streaming Response:" not in output: +# raise Exception("Required log message not found!") +# score += 1 +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# pass + +# # test_logging_success_streaming_openai() + +# ## test on non-openai completion call +# def test_logging_success_streaming_non_openai(): +# global score +# try: +# # litellm.set_verbose = False +# def custom_callback( +# kwargs, # kwargs to completion +# completion_response, # response from completion +# start_time, end_time # start/end time +# ): +# # print(f"streaming response: {completion_response}") +# if "complete_streaming_response" in kwargs: +# print(f"Complete Streaming Response: {kwargs['complete_streaming_response']}") + +# # Assign the custom callback function +# litellm.success_callback = [custom_callback] + +# # Redirect stdout +# old_stdout = sys.stdout +# sys.stdout = new_stdout = io.StringIO() + +# response = completion(model="claude-instant-1", messages=messages, stream=True) +# for idx, chunk in enumerate(response): +# pass + +# # Restore stdout +# sys.stdout = old_stdout +# output = new_stdout.getvalue().strip() + +# if "Logging Details Pre-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details Post-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details LiteLLM-Success Call" not in output: +# raise Exception("Required log message not found!") +# elif "Complete Streaming Response:" not in output: +# raise Exception(f"Required log message not found! {output}") +# score += 1 +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# pass + +# # test_logging_success_streaming_non_openai() +# # embedding + +# def test_logging_success_embedding_openai(): +# try: +# # Redirect stdout +# old_stdout = sys.stdout +# sys.stdout = new_stdout = io.StringIO() + +# response = embedding(model="text-embedding-ada-002", input=["good morning from litellm"]) + +# # Restore stdout +# sys.stdout = old_stdout +# output = new_stdout.getvalue().strip() + +# if "Logging Details Pre-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details Post-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details LiteLLM-Success Call" not in output: +# raise Exception("Required log message not found!") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # ## 2. On LiteLLM Call failure +# # ## TEST BAD KEY + +# # # normal completion +# # ## test on openai completion call +# # try: +# # temporary_oai_key = os.environ["OPENAI_API_KEY"] +# # os.environ["OPENAI_API_KEY"] = "bad-key" + +# # temporary_anthropic_key = os.environ["ANTHROPIC_API_KEY"] +# # os.environ["ANTHROPIC_API_KEY"] = "bad-key" + + +# # # Redirect stdout +# # old_stdout = sys.stdout +# # sys.stdout = new_stdout = io.StringIO() + +# # try: +# # response = completion(model="gpt-3.5-turbo", messages=messages) +# # except AuthenticationError: +# # print(f"raised auth error") +# # pass +# # # Restore stdout +# # sys.stdout = old_stdout +# # output = new_stdout.getvalue().strip() + +# # print(output) + +# # if "Logging Details Pre-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details Post-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details LiteLLM-Failure Call" not in output: +# # raise Exception("Required log message not found!") + +# # os.environ["OPENAI_API_KEY"] = temporary_oai_key +# # os.environ["ANTHROPIC_API_KEY"] = temporary_anthropic_key + +# # score += 1 +# # except Exception as e: +# # print(f"exception type: {type(e).__name__}") +# # pytest.fail(f"Error occurred: {e}") +# # pass + +# # ## test on non-openai completion call +# # try: +# # temporary_oai_key = os.environ["OPENAI_API_KEY"] +# # os.environ["OPENAI_API_KEY"] = "bad-key" + +# # temporary_anthropic_key = os.environ["ANTHROPIC_API_KEY"] +# # os.environ["ANTHROPIC_API_KEY"] = "bad-key" +# # # Redirect stdout +# # old_stdout = sys.stdout +# # sys.stdout = new_stdout = io.StringIO() + +# # try: +# # response = completion(model="claude-instant-1", messages=messages) +# # except AuthenticationError: +# # pass + +# # if "Logging Details Pre-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details Post-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details LiteLLM-Failure Call" not in output: +# # raise Exception("Required log message not found!") +# # os.environ["OPENAI_API_KEY"] = temporary_oai_key +# # os.environ["ANTHROPIC_API_KEY"] = temporary_anthropic_key +# # score += 1 +# # except Exception as e: +# # print(f"exception type: {type(e).__name__}") +# # # Restore stdout +# # sys.stdout = old_stdout +# # output = new_stdout.getvalue().strip() + +# # print(output) +# # pytest.fail(f"Error occurred: {e}") + + +# # # streaming completion +# # ## test on openai completion call +# # try: +# # temporary_oai_key = os.environ["OPENAI_API_KEY"] +# # os.environ["OPENAI_API_KEY"] = "bad-key" + +# # temporary_anthropic_key = os.environ["ANTHROPIC_API_KEY"] +# # os.environ["ANTHROPIC_API_KEY"] = "bad-key" +# # # Redirect stdout +# # old_stdout = sys.stdout +# # sys.stdout = new_stdout = io.StringIO() + +# # try: +# # response = completion(model="gpt-3.5-turbo", messages=messages) +# # except AuthenticationError: +# # pass + +# # # Restore stdout +# # sys.stdout = old_stdout +# # output = new_stdout.getvalue().strip() + +# # print(output) + +# # if "Logging Details Pre-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details Post-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details LiteLLM-Failure Call" not in output: +# # raise Exception("Required log message not found!") + +# # os.environ["OPENAI_API_KEY"] = temporary_oai_key +# # os.environ["ANTHROPIC_API_KEY"] = temporary_anthropic_key +# # score += 1 +# # except Exception as e: +# # print(f"exception type: {type(e).__name__}") +# # pytest.fail(f"Error occurred: {e}") + +# # ## test on non-openai completion call +# # try: +# # temporary_oai_key = os.environ["OPENAI_API_KEY"] +# # os.environ["OPENAI_API_KEY"] = "bad-key" + +# # temporary_anthropic_key = os.environ["ANTHROPIC_API_KEY"] +# # os.environ["ANTHROPIC_API_KEY"] = "bad-key" +# # # Redirect stdout +# # old_stdout = sys.stdout +# # sys.stdout = new_stdout = io.StringIO() + +# # try: +# # response = completion(model="claude-instant-1", messages=messages) +# # except AuthenticationError: +# # pass + +# # # Restore stdout +# # sys.stdout = old_stdout +# # output = new_stdout.getvalue().strip() + +# # print(output) + +# # if "Logging Details Pre-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details Post-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details LiteLLM-Failure Call" not in output: +# # raise Exception("Required log message not found!") +# # score += 1 +# # except Exception as e: +# # print(f"exception type: {type(e).__name__}") +# # pytest.fail(f"Error occurred: {e}") + +# # # embedding + +# # try: +# # temporary_oai_key = os.environ["OPENAI_API_KEY"] +# # os.environ["OPENAI_API_KEY"] = "bad-key" + +# # temporary_anthropic_key = os.environ["ANTHROPIC_API_KEY"] +# # os.environ["ANTHROPIC_API_KEY"] = "bad-key" +# # # Redirect stdout +# # old_stdout = sys.stdout +# # sys.stdout = new_stdout = io.StringIO() + +# # try: +# # response = embedding(model="text-embedding-ada-002", input=["good morning from litellm"]) +# # except AuthenticationError: +# # pass + +# # # Restore stdout +# # sys.stdout = old_stdout +# # output = new_stdout.getvalue().strip() + +# # print(output) + +# # if "Logging Details Pre-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details Post-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details LiteLLM-Failure Call" not in output: +# # raise Exception("Required log message not found!") +# # except Exception as e: +# # print(f"exception type: {type(e).__name__}") +# # pytest.fail(f"Error occurred: {e}") \ No newline at end of file diff --git a/litellm/tests/test_longer_context_fallback.py b/litellm/tests/test_longer_context_fallback.py new file mode 100644 index 0000000000000000000000000000000000000000..8f8942897a12da6bff3a1fcbd9779da6c2dc8fb1 --- /dev/null +++ b/litellm/tests/test_longer_context_fallback.py @@ -0,0 +1,13 @@ +#### What this tests #### +# This tests context fallback dict + +import sys, os +import traceback +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import longer_context_model_fallback_dict + +print(longer_context_model_fallback_dict) \ No newline at end of file diff --git a/litellm/tests/test_mock_request.py b/litellm/tests/test_mock_request.py new file mode 100644 index 0000000000000000000000000000000000000000..82eb3092676763b12898be04fd6a58db96f0cc6a --- /dev/null +++ b/litellm/tests/test_mock_request.py @@ -0,0 +1,36 @@ +#### What this tests #### +# This tests mock request calls to litellm + +import sys, os +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm + +def test_mock_request(): + try: + model = "gpt-3.5-turbo" + messages = [{"role": "user", "content": "Hey, I'm a mock request"}] + response = litellm.mock_completion(model=model, messages=messages, stream=False) + print(response) + print(type(response)) + except: + traceback.print_exc() + +# test_mock_request() +def test_streaming_mock_request(): + try: + model = "gpt-3.5-turbo" + messages = [{"role": "user", "content": "Hey, I'm a mock request"}] + response = litellm.mock_completion(model=model, messages=messages, stream=True) + complete_response = "" + for chunk in response: + complete_response += chunk["choices"][0]["delta"]["content"] + if complete_response == "": + raise Exception("Empty response received") + except: + traceback.print_exc() + +test_streaming_mock_request() \ No newline at end of file diff --git a/litellm/tests/test_model_alias_map.py b/litellm/tests/test_model_alias_map.py new file mode 100644 index 0000000000000000000000000000000000000000..f4647fe7cb1db090db4d4713d0b069b5c3a11e7f --- /dev/null +++ b/litellm/tests/test_model_alias_map.py @@ -0,0 +1,37 @@ +#### What this tests #### +# This tests the model alias mapping - if user passes in an alias, and has set an alias, set it to the actual value + +import sys, os +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import embedding, completion +import pytest + +litellm.set_verbose = True + +model_alias_map = { + "good-model": "anyscale/meta-llama/Llama-2-7b-chat-hf" +} + +litellm.model_alias_map = model_alias_map + +def test_model_alias_map(): + try: + response = completion( + "good-model", + messages=[{"role": "user", "content": "Hey, how's it going?"}], + top_p=0.1, + temperature=0.01, + max_tokens=10, + ) + print(response.model) + assert "Llama-2-7b-chat-hf" in response.model + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +test_model_alias_map() \ No newline at end of file diff --git a/litellm/tests/test_model_response_typing/server.py b/litellm/tests/test_model_response_typing/server.py new file mode 100644 index 0000000000000000000000000000000000000000..80dbc33affd86c91fdcccf297c41aafb214958be --- /dev/null +++ b/litellm/tests/test_model_response_typing/server.py @@ -0,0 +1,23 @@ +# #### What this tests #### +# # This tests if the litellm model response type is returnable in a flask app + +# import sys, os +# import traceback +# from flask import Flask, request, jsonify, abort, Response +# sys.path.insert(0, os.path.abspath('../../..')) # Adds the parent directory to the system path + +# import litellm +# from litellm import completion + +# litellm.set_verbose = False + +# app = Flask(__name__) + +# @app.route('/') +# def hello(): +# data = request.json +# return completion(**data) + +# if __name__ == '__main__': +# from waitress import serve +# serve(app, host='localhost', port=8080, threads=10) diff --git a/litellm/tests/test_model_response_typing/test.py b/litellm/tests/test_model_response_typing/test.py new file mode 100644 index 0000000000000000000000000000000000000000..95d40480988c6054b0351153d269f34f34b8204d --- /dev/null +++ b/litellm/tests/test_model_response_typing/test.py @@ -0,0 +1,14 @@ +# import requests, json + +# BASE_URL = 'http://localhost:8080' + +# def test_hello_route(): +# data = {"model": "claude-instant-1", "messages": [{"role": "user", "content": "hey, how's it going?"}]} +# headers = {'Content-Type': 'application/json'} +# response = requests.get(BASE_URL, headers=headers, data=json.dumps(data)) +# print(response.text) +# assert response.status_code == 200 +# print("Hello route test passed!") + +# if __name__ == '__main__': +# test_hello_route() diff --git a/litellm/tests/test_multiple_deployments.py b/litellm/tests/test_multiple_deployments.py new file mode 100644 index 0000000000000000000000000000000000000000..e4feb88e3441dccf0088bac2d79f5da9882901a0 --- /dev/null +++ b/litellm/tests/test_multiple_deployments.py @@ -0,0 +1,53 @@ +#### What this tests #### +# This tests error handling + logging (esp. for sentry breadcrumbs) + +import sys, os +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import completion + +messages=[{"role": "user", "content": "Hey, how's it going?"}] + +## All your mistral deployments ## +model_list = [{ + "model_name": "mistral-7b-instruct", + "litellm_params": { # params for litellm completion/embedding call + "model": "replicate/mistralai/mistral-7b-instruct-v0.1:83b6a56e7c828e667f21fd596c338fd4f0039b46bcfa18d973e8e70e455fda70", + "api_key": os.getenv("REPLICATE_API_KEY"), + } +}, { + "model_name": "mistral-7b-instruct", + "litellm_params": { # params for litellm completion/embedding call + "model": "together_ai/mistralai/Mistral-7B-Instruct-v0.1", + "api_key": os.getenv("TOGETHERAI_API_KEY"), + } +}, { + "model_name": "mistral-7b-instruct", + "litellm_params": { # params for litellm completion/embedding call + "model": "mistral-7b-instruct", + "api_base": "https://api.perplexity.ai", + "api_key": os.getenv("PERPLEXITYAI_API_KEY") + } +}, { + "model_name": "mistral-7b-instruct", + "litellm_params": { + "model": "deepinfra/mistralai/Mistral-7B-Instruct-v0.1", + "api_key": os.getenv("DEEPINFRA_API_KEY") + } +}] + +def test_multiple_deployments(): + try: + ## LiteLLM completion call ## returns first response + response = completion(model="mistral-7b-instruct", messages=messages, model_list=model_list) + print(f"response: {response}") + except Exception as e: + traceback.print_exc() + pytest.fail(f"An exception occurred: {e}") + +test_multiple_deployments() \ No newline at end of file diff --git a/litellm/tests/test_ollama.py b/litellm/tests/test_ollama.py new file mode 100644 index 0000000000000000000000000000000000000000..b3635b6a9403dd2fa6b736f8a0914f3114b37b0f --- /dev/null +++ b/litellm/tests/test_ollama.py @@ -0,0 +1,100 @@ +##### THESE TESTS CAN ONLY RUN LOCALLY WITH THE OLLAMA SERVER RUNNING ###### +# import aiohttp +# import json +# import asyncio +# import requests +# +# async def get_ollama_response_stream(api_base="http://localhost:11434", model="llama2", prompt="Why is the sky blue?"): +# session = aiohttp.ClientSession() +# url = f'{api_base}/api/generate' +# data = { +# "model": model, +# "prompt": prompt, +# } + +# response = "" + +# try: +# async with session.post(url, json=data) as resp: +# async for line in resp.content.iter_any(): +# if line: +# try: +# json_chunk = line.decode("utf-8") +# chunks = json_chunk.split("\n") +# for chunk in chunks: +# if chunk.strip() != "": +# j = json.loads(chunk) +# if "response" in j: +# print(j["response"]) +# yield { +# "role": "assistant", +# "content": j["response"] +# } +# # self.responses.append(j["response"]) +# # yield "blank" +# except Exception as e: +# print(f"Error decoding JSON: {e}") +# finally: +# await session.close() + +# async def get_ollama_response_no_stream(api_base="http://localhost:11434", model="llama2", prompt="Why is the sky blue?"): +# generator = get_ollama_response_stream(api_base="http://localhost:11434", model="llama2", prompt="Why is the sky blue?") +# response = "" +# async for elem in generator: +# print(elem) +# response += elem["content"] +# return response + +# #generator = get_ollama_response_stream() + +# result = asyncio.run(get_ollama_response_no_stream()) +# print(result) + +# # return this generator to the client for streaming requests + + +# async def get_response(): +# global generator +# async for elem in generator: +# print(elem) + +# asyncio.run(get_response()) + + + +##### latest implementation of making raw http post requests to local ollama server + +# import requests +# import json +# def get_ollama_response_stream(api_base="http://localhost:11434", model="llama2", prompt="Why is the sky blue?"): +# url = f"{api_base}/api/generate" +# data = { +# "model": model, +# "prompt": prompt, +# } +# session = requests.Session() + +# with session.post(url, json=data, stream=True) as resp: +# for line in resp.iter_lines(): +# if line: +# try: +# json_chunk = line.decode("utf-8") +# chunks = json_chunk.split("\n") +# for chunk in chunks: +# if chunk.strip() != "": +# j = json.loads(chunk) +# if "response" in j: +# completion_obj = { +# "role": "assistant", +# "content": "", +# } +# completion_obj["content"] = j["response"] +# yield {"choices": [{"delta": completion_obj}]} +# except Exception as e: +# print(f"Error decoding JSON: {e}") +# session.close() + +# response = get_ollama_response_stream() + +# for chunk in response: +# print(chunk['choices'][0]['delta']) diff --git a/litellm/tests/test_ollama_local.py b/litellm/tests/test_ollama_local.py new file mode 100644 index 0000000000000000000000000000000000000000..05dd9c646b20346b5f02b955e5a65fe65a21aeed --- /dev/null +++ b/litellm/tests/test_ollama_local.py @@ -0,0 +1,161 @@ +# ##### THESE TESTS CAN ONLY RUN LOCALLY WITH THE OLLAMA SERVER RUNNING ###### +# # https://ollama.ai/ + +# import sys, os +# import traceback +# from dotenv import load_dotenv +# load_dotenv() +# import os +# sys.path.insert(0, os.path.abspath('../..')) # Adds the parent directory to the system path +# import pytest +# import litellm +# from litellm import embedding, completion +# import asyncio + + +# user_message = "respond in 20 words. who are you?" +# messages = [{ "content": user_message,"role": "user"}] + +# def test_completion_ollama(): +# try: +# response = completion( +# model="ollama/llama2", +# messages=messages, +# max_tokens=200, +# request_timeout = 10, + +# ) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_ollama() + +# def test_completion_ollama_with_api_base(): +# try: +# response = completion( +# model="ollama/llama2", +# messages=messages, +# api_base="http://localhost:11434" +# ) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_ollama_with_api_base() + + +# def test_completion_ollama_custom_prompt_template(): +# user_message = "what is litellm?" +# litellm.register_prompt_template( +# model="ollama/llama2", +# roles={ +# "system": {"pre_message": "System: "}, +# "user": {"pre_message": "User: "}, +# "assistant": {"pre_message": "Assistant: "} +# } +# ) +# messages = [{ "content": user_message,"role": "user"}] +# litellm.set_verbose = True +# try: +# response = completion( +# model="ollama/llama2", +# messages=messages, +# stream=True +# ) +# print(response) +# for chunk in response: +# print(chunk) +# # print(chunk['choices'][0]['delta']) + +# except Exception as e: +# traceback.print_exc() +# pytest.fail(f"Error occurred: {e}") + +# test_completion_ollama_custom_prompt_template() + +# async def test_completion_ollama_async_stream(): +# user_message = "what is the weather" +# messages = [{ "content": user_message,"role": "user"}] +# try: +# response = await litellm.acompletion( +# model="ollama/llama2", +# messages=messages, +# api_base="http://localhost:11434", +# stream=True +# ) +# async for chunk in response: +# print(chunk['choices'][0]['delta']) + + +# print("TEST ASYNC NON Stream") +# response = await litellm.acompletion( +# model="ollama/llama2", +# messages=messages, +# api_base="http://localhost:11434", +# ) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# import asyncio +# asyncio.run(test_completion_ollama_async_stream()) + + + +# def prepare_messages_for_chat(text: str) -> list: +# messages = [ +# {"role": "user", "content": text}, +# ] +# return messages + + +# async def ask_question(): +# params = { +# "messages": prepare_messages_for_chat("What is litellm? tell me 10 things about it who is sihaan.write an essay"), +# "api_base": "http://localhost:11434", +# "model": "ollama/llama2", +# "stream": True, +# } +# response = await litellm.acompletion(**params) +# return response + +# async def main(): +# response = await ask_question() +# async for chunk in response: +# print(chunk) + +# print("test async completion without streaming") +# response = await litellm.acompletion( +# model="ollama/llama2", +# messages=prepare_messages_for_chat("What is litellm? respond in 2 words"), +# ) +# print("response", response) + + +# def test_completion_expect_error(): +# # this tests if we can exception map correctly for ollama +# print("making ollama request") +# # litellm.set_verbose=True +# user_message = "what is litellm?" +# messages = [{ "content": user_message,"role": "user"}] +# try: +# response = completion( +# model="ollama/invalid", +# messages=messages, +# stream=True +# ) +# print(response) +# for chunk in response: +# print(chunk) +# # print(chunk['choices'][0]['delta']) + +# except Exception as e: +# pass +# pytest.fail(f"Error occurred: {e}") + +# test_completion_expect_error() + +# if __name__ == "__main__": +# import asyncio +# asyncio.run(main()) diff --git a/litellm/tests/test_profiling_router.py b/litellm/tests/test_profiling_router.py new file mode 100644 index 0000000000000000000000000000000000000000..48ed9cb0eb437173048123ba3a3f7b1500298a47 --- /dev/null +++ b/litellm/tests/test_profiling_router.py @@ -0,0 +1,152 @@ +# #### What this tests #### +# # This profiles a router call to find where calls are taking the most time. + +# import sys, os, time, logging +# import traceback, asyncio, uuid +# import pytest +# import cProfile +# from pstats import Stats +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import litellm +# from litellm import Router +# from concurrent.futures import ThreadPoolExecutor +# from dotenv import load_dotenv +# from aiodebug import log_slow_callbacks # Import the aiodebug utility for logging slow callbacks + +# # litellm.telemetry = False + +# load_dotenv() + +# logging.basicConfig( +# level=logging.DEBUG, +# format='%(asctime)s %(levelname)s: %(message)s', +# datefmt='%I:%M:%S %p', +# filename='aiologs.log', # Name of the log file where logs will be written +# filemode='w' # 'w' to overwrite the log file on each run, use 'a' to append +# ) + +# # Dictionary to store exception types and their counts +# exception_counts = {} +# exception_data = [] + +# litellm.telemetry = False + +# num_task_cancelled_errors = 0 + +# model_list = [{ +# "model_name": "azure-model", +# "litellm_params": { +# "model": "azure/gpt-turbo", +# "api_key": "os.environ/AZURE_FRANCE_API_KEY", +# "api_base": "https://openai-france-1234.openai.azure.com", +# "rpm": 1440, +# } +# }, { +# "model_name": "azure-model", +# "litellm_params": { +# "model": "azure/gpt-35-turbo", +# "api_key": "os.environ/AZURE_EUROPE_API_KEY", +# "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com", +# "rpm": 6 +# } +# }, { +# "model_name": "azure-model", +# "litellm_params": { +# "model": "azure/gpt-35-turbo", +# "api_key": "os.environ/AZURE_CANADA_API_KEY", +# "api_base": "https://my-endpoint-canada-berri992.openai.azure.com", +# "rpm": 6 +# } +# }] + +# router = Router(model_list=model_list, set_verbose=False, num_retries=3) + +# async def router_completion(): +# global num_task_cancelled_errors, exception_counts +# try: +# messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}] +# response = await router.acompletion(model="azure-model", messages=messages) +# return response +# except asyncio.exceptions.CancelledError: +# exception_type = "CancelledError" +# exception_counts[exception_type] = exception_counts.get(exception_type, 0) + 1 +# print("Task was cancelled") +# num_task_cancelled_errors += 1 +# exception_data.append({ +# "type": exception_type, +# "traceback": None +# }) +# return None +# except Exception as e: +# exception_type = type(e).__name__ +# exception_counts[exception_type] = exception_counts.get(exception_type, 0) + 1 +# exception_data.append({ +# "type": exception_type, +# "traceback": traceback.format_exc() +# }) +# return None + +# async def loadtest_fn(n = 1452): +# global num_task_cancelled_errors, exception_counts +# start = time.time() +# tasks = [router_completion() for _ in range(n)] +# chat_completions = await asyncio.gather(*tasks) +# successful_completions = [c for c in chat_completions if c is not None] +# print(n, time.time() - start, len(successful_completions)) + +# # Print exception breakdown +# print("Exception Breakdown:") +# for exception_type, count in exception_counts.items(): +# print(f"{exception_type}: {count}") + +# # Store exception_data in a file +# with open('exception_data.txt', 'w') as file: +# for data in exception_data: +# file.write(f"Type: {data['type']}\n") +# if data['traceback']: +# file.write(f"Traceback:\n{data['traceback']}\n\n") + +# loop = asyncio.get_event_loop() +# loop.set_debug(True) +# log_slow_callbacks.enable(0.05) # Log callbacks slower than 0.05 seconds + +# # Excute the load testing function within the asyncio event loop +# loop.run_until_complete(loadtest_fn()) + +# # ### SUSTAINED LOAD TESTS ### +# # import time, asyncio +# # async def make_requests(n): +# # tasks = [router_completion() for _ in range(n)] +# # print(f"num tasks: {len(tasks)}") +# # chat_completions = await asyncio.gather(*tasks) +# # successful_completions = [c for c in chat_completions if c is not None] +# # print(f"successful_completions: {len(successful_completions)}") +# # return successful_completions + +# # async def main(): +# # start_time = time.time() +# # total_successful_requests = 0 +# # request_limit = 1000 +# # batches = 2 # batches of 1k requests +# # start = time.time() +# # tasks = [] # list to hold all tasks + +# # async def request_loop(): +# # nonlocal tasks +# # for _ in range(batches): +# # # Make 1,000 requests +# # task = asyncio.create_task(make_requests(request_limit)) +# # tasks.append(task) + +# # # Introduce a delay to achieve 1,000 requests per second +# # await asyncio.sleep(1) + +# # await request_loop() +# # results = await asyncio.gather(*tasks) +# # total_successful_requests = sum(len(res) for res in results) + +# # print(request_limit*batches, time.time() - start, total_successful_requests) + +# # asyncio.run(main()) \ No newline at end of file diff --git a/litellm/tests/test_prompt_factory.py b/litellm/tests/test_prompt_factory.py new file mode 100644 index 0000000000000000000000000000000000000000..11ebbb4246e6d7ee668c4c7097db13928e5b1da7 --- /dev/null +++ b/litellm/tests/test_prompt_factory.py @@ -0,0 +1,23 @@ +#### What this tests #### +# This tests if prompts are being correctly formatted +import sys +import os +import io + +sys.path.insert(0, os.path.abspath('../..')) + +# from litellm.llms.prompt_templates.factory import prompt_factory +from litellm import completion + +def codellama_prompt_format(): + model = "huggingface/codellama/CodeLlama-7b-Instruct-hf" + messages = [{"role": "system", "content": "You are a good bot"}, {"role": "user", "content": "Hey, how's it going?"}] + expected_response = """[INST] <> +You are a good bot +<> + [/INST] +[INST] Hey, how's it going? [/INST]""" + response = completion(model=model, messages=messages) + print(response) + +# codellama_prompt_format() \ No newline at end of file diff --git a/litellm/tests/test_promptlayer_integration.py b/litellm/tests/test_promptlayer_integration.py new file mode 100644 index 0000000000000000000000000000000000000000..8c919bb1e8e680e927eb53ed6869aa0aa2b20fd2 --- /dev/null +++ b/litellm/tests/test_promptlayer_integration.py @@ -0,0 +1,86 @@ +import sys +import os +import io + +sys.path.insert(0, os.path.abspath('../..')) + +from litellm import completion +import litellm + +litellm.success_callback = ["promptlayer"] +litellm.set_verbose = True +import time + + + +# def test_promptlayer_logging(): +# try: +# # Redirect stdout +# old_stdout = sys.stdout +# sys.stdout = new_stdout = io.StringIO() + + +# response = completion(model="claude-instant-1.2", +# messages=[{ +# "role": "user", +# "content": "Hi 👋 - i'm claude" +# }]) + +# # Restore stdout +# time.sleep(1) +# sys.stdout = old_stdout +# output = new_stdout.getvalue().strip() +# print(output) +# if "LiteLLM: Prompt Layer Logging: success" not in output: +# raise Exception("Required log message not found!") + +# except Exception as e: +# print(e) + +# test_promptlayer_logging() + + +def test_promptlayer_logging_with_metadata(): + try: + # Redirect stdout + old_stdout = sys.stdout + sys.stdout = new_stdout = io.StringIO() + + response = completion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm ai21" + }], + temperature=0.2, + max_tokens=20, + metadata={"model": "ai21"}) + + # Restore stdout + time.sleep(1) + sys.stdout = old_stdout + output = new_stdout.getvalue().strip() + print(output) + if "LiteLLM: Prompt Layer Logging: success" not in output: + raise Exception("Required log message not found!") + + except Exception as e: + print(e) + +test_promptlayer_logging_with_metadata() + + + + +# def test_chat_openai(): +# try: +# response = completion(model="replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1", +# messages=[{ +# "role": "user", +# "content": "Hi 👋 - i'm openai" +# }]) + +# print(response) +# except Exception as e: +# print(e) + +# test_chat_openai() diff --git a/litellm/tests/test_provider_specific_config.py b/litellm/tests/test_provider_specific_config.py new file mode 100644 index 0000000000000000000000000000000000000000..e3a7aa82909cc969aefa5e64b387542b4561a685 --- /dev/null +++ b/litellm/tests/test_provider_specific_config.py @@ -0,0 +1,538 @@ +#### What this tests #### +# This tests setting provider specific configs across providers +# There are 2 types of tests - changing config dynamically or by setting class variables + +import sys, os +import traceback +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import completion +from litellm import RateLimitError + +# Huggingface - Expensive to deploy models and keep them running. Maybe we can try doing this via baseten?? +# def hf_test_completion_tgi(): +# litellm.HuggingfaceConfig(max_new_tokens=200) +# litellm.set_verbose=True +# try: +# # OVERRIDE WITH DYNAMIC MAX TOKENS +# response_1 = litellm.completion( +# model="huggingface/mistralai/Mistral-7B-Instruct-v0.1", +# messages=[{ "content": "Hello, how are you?","role": "user"}], +# api_base="https://n9ox93a8sv5ihsow.us-east-1.aws.endpoints.huggingface.cloud", +# max_tokens=10 +# ) +# # Add any assertions here to check the response +# print(response_1) +# response_1_text = response_1.choices[0].message.content + +# # USE CONFIG TOKENS +# response_2 = litellm.completion( +# model="huggingface/mistralai/Mistral-7B-Instruct-v0.1", +# messages=[{ "content": "Hello, how are you?","role": "user"}], +# api_base="https://n9ox93a8sv5ihsow.us-east-1.aws.endpoints.huggingface.cloud", +# ) +# # Add any assertions here to check the response +# print(response_2) +# response_2_text = response_2.choices[0].message.content + +# assert len(response_2_text) > len(response_1_text) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# hf_test_completion_tgi() + +#Anthropic + +def claude_test_completion(): + litellm.AnthropicConfig(max_tokens_to_sample=200) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="claude-instant-1", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + # Add any assertions here to check the response + print(response_1) + response_1_text = response_1.choices[0].message.content + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="claude-instant-1", + messages=[{ "content": "Hello, how are you?","role": "user"}], + ) + # Add any assertions here to check the response + print(response_2) + response_2_text = response_2.choices[0].message.content + + assert len(response_2_text) > len(response_1_text) + + try: + response_3 = litellm.completion(model="claude-instant-1", + messages=[{ "content": "Hello, how are you?","role": "user"}], + n=2) + + except Exception as e: + print(e) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# claude_test_completion() + +# Replicate + +def replicate_test_completion(): + litellm.ReplicateConfig(max_new_tokens=200) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + # Add any assertions here to check the response + print(response_1) + response_1_text = response_1.choices[0].message.content + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", + messages=[{ "content": "Hello, how are you?","role": "user"}], + ) + # Add any assertions here to check the response + print(response_2) + response_2_text = response_2.choices[0].message.content + + assert len(response_2_text) > len(response_1_text) + try: + response_3 = litellm.completion(model="meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", + messages=[{ "content": "Hello, how are you?","role": "user"}], + n=2) + except: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# replicate_test_completion() + +# Cohere + +def cohere_test_completion(): + # litellm.CohereConfig(max_tokens=200) + litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="command-nightly", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + response_1_text = response_1.choices[0].message.content + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="command-nightly", + messages=[{ "content": "Hello, how are you?","role": "user"}], + ) + response_2_text = response_2.choices[0].message.content + + assert len(response_2_text) > len(response_1_text) + + response_3 = litellm.completion(model="command-nightly", + messages=[{ "content": "Hello, how are you?","role": "user"}], + n=2) + assert len(response_3.choices) > 1 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# cohere_test_completion() + +# AI21 + +def ai21_test_completion(): + litellm.AI21Config(maxTokens=10) + litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="j2-mid", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + max_tokens=100 + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="j2-mid", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + + response_3 = litellm.completion(model="j2-light", + messages=[{ "content": "Hello, how are you?","role": "user"}], + n=2) + assert len(response_3.choices) > 1 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# ai21_test_completion() + +# TogetherAI + +def togetherai_test_completion(): + litellm.TogetherAIConfig(max_tokens=10) + litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="together_ai/togethercomputer/llama-2-70b-chat", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + max_tokens=100 + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="together_ai/togethercomputer/llama-2-70b-chat", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + + try: + response_3 = litellm.completion(model="together_ai/togethercomputer/llama-2-70b-chat", + messages=[{ "content": "Hello, how are you?","role": "user"}], + n=2) + pytest.fail(f"Error not raised when n=2 passed to provider") + except: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# togetherai_test_completion() + +# Palm + +def palm_test_completion(): + litellm.PalmConfig(max_output_tokens=10, temperature=0.9) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="palm/chat-bison", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + max_tokens=100 + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="palm/chat-bison", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + + response_3 = litellm.completion(model="palm/chat-bison", + messages=[{ "content": "Hello, how are you?","role": "user"}], + n=2) + assert len(response_3.choices) > 1 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# palm_test_completion() + +# NLP Cloud + +def nlp_cloud_test_completion(): + litellm.NLPCloudConfig(max_length=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="dolphin", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + max_tokens=100 + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="dolphin", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + + try: + response_3 = litellm.completion(model="dolphin", + messages=[{ "content": "Hello, how are you?","role": "user"}], + n=2) + pytest.fail(f"Error not raised when n=2 passed to provider") + except: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# nlp_cloud_test_completion() + +# AlephAlpha + +def aleph_alpha_test_completion(): + litellm.AlephAlphaConfig(maximum_tokens=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="luminous-base", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + max_tokens=100 + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="luminous-base", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + + response_3 = litellm.completion(model="luminous-base", + messages=[{ "content": "Hello, how are you?","role": "user"}], + n=2) + + assert len(response_3.choices) > 1 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# aleph_alpha_test_completion() + +# Petals - calls are too slow, will cause circle ci to fail due to delay. Test locally. +# def petals_completion(): +# litellm.PetalsConfig(max_new_tokens=10) +# # litellm.set_verbose=True +# try: +# # OVERRIDE WITH DYNAMIC MAX TOKENS +# response_1 = litellm.completion( +# model="petals/petals-team/StableBeluga2", +# messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], +# api_base="https://chat.petals.dev/api/v1/generate", +# max_tokens=100 +# ) +# response_1_text = response_1.choices[0].message.content +# print(f"response_1_text: {response_1_text}") + +# # USE CONFIG TOKENS +# response_2 = litellm.completion( +# model="petals/petals-team/StableBeluga2", +# api_base="https://chat.petals.dev/api/v1/generate", +# messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], +# ) +# response_2_text = response_2.choices[0].message.content +# print(f"response_2_text: {response_2_text}") + +# assert len(response_2_text) < len(response_1_text) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# petals_completion() + +# VertexAI +# We don't have vertex ai configured for circle ci yet -- need to figure this out. +# def vertex_ai_test_completion(): +# litellm.VertexAIConfig(max_output_tokens=10) +# # litellm.set_verbose=True +# try: +# # OVERRIDE WITH DYNAMIC MAX TOKENS +# response_1 = litellm.completion( +# model="chat-bison", +# messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], +# max_tokens=100 +# ) +# response_1_text = response_1.choices[0].message.content +# print(f"response_1_text: {response_1_text}") + +# # USE CONFIG TOKENS +# response_2 = litellm.completion( +# model="chat-bison", +# messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], +# ) +# response_2_text = response_2.choices[0].message.content +# print(f"response_2_text: {response_2_text}") + +# assert len(response_2_text) < len(response_1_text) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# vertex_ai_test_completion() + +# Sagemaker + +def sagemaker_test_completion(): + litellm.SagemakerConfig(max_new_tokens=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + max_tokens=100 + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# sagemaker_test_completion() + +# Bedrock + + +def bedrock_test_completion(): + litellm.AmazonCohereConfig(max_tokens=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="bedrock/cohere.command-text-v14", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + max_tokens=100 + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="bedrock/cohere.command-text-v14", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# bedrock_test_completion() + +# OpenAI Chat Completion +def openai_test_completion(): + litellm.OpenAIConfig(max_tokens=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="gpt-3.5-turbo", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + max_tokens=100 + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="gpt-3.5-turbo", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# openai_test_completion() + +# OpenAI Text Completion +def openai_text_completion_test(): + litellm.OpenAITextCompletionConfig(max_tokens=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="text-davinci-003", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + max_tokens=100 + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="text-davinci-003", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + + response_3 = litellm.completion(model="text-davinci-003", + messages=[{ "content": "Hello, how are you?","role": "user"}], + n=2) + assert len(response_3.choices) > 1 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# openai_text_completion_test() + +# Azure OpenAI +def azure_openai_test_completion(): + litellm.AzureOpenAIConfig(max_tokens=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="azure/chatgpt-v-2", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + max_tokens=100 + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="azure/chatgpt-v-2", + messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# azure_openai_test_completion() \ No newline at end of file diff --git a/litellm/tests/test_proxy_custom_auth.py b/litellm/tests/test_proxy_custom_auth.py new file mode 100644 index 0000000000000000000000000000000000000000..fa1b5f6dd7aad0d45f598286ac3d4bbfd44eacaa --- /dev/null +++ b/litellm/tests/test_proxy_custom_auth.py @@ -0,0 +1,63 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +# this file is to test litellm/proxy + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError + +# test /chat/completion request to the proxy +from fastapi.testclient import TestClient +from fastapi import FastAPI +from litellm.proxy.proxy_server import router, save_worker_config, startup_event # Replace with the actual module where your FastAPI router is defined +filepath = os.path.dirname(os.path.abspath(__file__)) +config_fp = f"{filepath}/test_configs/test_config_custom_auth.yaml" +save_worker_config(config=config_fp, model=None, alias=None, api_base=None, api_version=None, debug=False, temperature=None, max_tokens=None, request_timeout=600, max_budget=None, telemetry=False, drop_params=True, add_function_to_prompt=False, headers=None, save=False, use_queue=False) +app = FastAPI() +app.include_router(router) # Include your router in the test app +@app.on_event("startup") +async def wrapper_startup_event(): + await startup_event() + +# Here you create a fixture that will be used by your tests +# Make sure the fixture returns TestClient(app) +@pytest.fixture(autouse=True) +def client(): + with TestClient(app) as client: + yield client + +def test_custom_auth(client): + try: + # Your test data + test_data = { + "model": "openai-model", + "messages": [ + { + "role": "user", + "content": "hi" + }, + ], + "max_tokens": 10, + } + # Your bearer token + token = os.getenv("PROXY_MASTER_KEY") + + headers = { + "Authorization": f"Bearer {token}" + } + response = client.post("/chat/completions", json=test_data, headers=headers) + print(f"response: {response.text}") + assert response.status_code == 401 + result = response.json() + print(f"Received response: {result}") + except Exception as e: + pytest.fail("LiteLLM Proxy test failed. Exception", e) \ No newline at end of file diff --git a/litellm/tests/test_proxy_server.py b/litellm/tests/test_proxy_server.py new file mode 100644 index 0000000000000000000000000000000000000000..a525f01bf00c27d2e7caea8726e4eb323a9b3fa8 --- /dev/null +++ b/litellm/tests/test_proxy_server.py @@ -0,0 +1,193 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +# this file is to test litellm/proxy + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError + +# test /chat/completion request to the proxy +from fastapi.testclient import TestClient +from fastapi import FastAPI +from litellm.proxy.proxy_server import router # Replace with the actual module where your FastAPI router is defined +app = FastAPI() +app.include_router(router) # Include your router in the test app +client = TestClient(app) +def test_chat_completion(): + try: + # Your test data + test_data = { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "hi" + }, + ], + "max_tokens": 10, + } + print("testing proxy server") + response = client.post("/v1/chat/completions", json=test_data) + + assert response.status_code == 200 + result = response.json() + print(f"Received response: {result}") + except Exception as e: + pytest.fail("LiteLLM Proxy test failed. Exception", e) + +# Run the test +# test_chat_completion() + + +def test_chat_completion_azure(): + try: + # Your test data + test_data = { + "model": "azure/chatgpt-v-2", + "messages": [ + { + "role": "user", + "content": "write 1 sentence poem" + }, + ], + "max_tokens": 10, + } + print("testing proxy server with Azure Request") + response = client.post("/v1/chat/completions", json=test_data) + + assert response.status_code == 200 + result = response.json() + print(f"Received response: {result}") + assert len(result["choices"][0]["message"]["content"]) > 0 + except Exception as e: + pytest.fail("LiteLLM Proxy test failed. Exception", e) + +# Run the test +# test_chat_completion_azure() + + +def test_embedding(): + try: + test_data = { + "model": "azure/azure-embedding-model", + "input": ["good morning from litellm"], + } + print("testing proxy server with OpenAI embedding") + response = client.post("/v1/embeddings", json=test_data) + + assert response.status_code == 200 + result = response.json() + print(len(result["data"][0]["embedding"])) + assert len(result["data"][0]["embedding"]) > 10 # this usually has len==1536 so + except Exception as e: + pytest.fail("LiteLLM Proxy test failed. Exception", e) + +# Run the test +# test_embedding() + + +def test_add_new_model(): + try: + test_data = { + "model_name": "test_openai_models", + "litellm_params": { + "model": "gpt-3.5-turbo", + }, + "model_info": { + "description": "this is a test openai model" + } + } + client.post("/model/new", json=test_data) + response = client.get("/model/info") + assert response.status_code == 200 + result = response.json() + print(f"response: {result}") + model_info = None + for m in result["data"]: + if m["id"]["model_name"] == "test_openai_models": + model_info = m["id"]["model_info"] + assert model_info["description"] == "this is a test openai model" + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + +# test_add_new_model() + +from litellm.integrations.custom_logger import CustomLogger +class MyCustomHandler(CustomLogger): + def log_pre_api_call(self, model, messages, kwargs): + print(f"Pre-API Call") + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Success") + assert kwargs["user"] == "proxy-user" + assert kwargs["model"] == "gpt-3.5-turbo" + assert kwargs["max_tokens"] == 10 + +customHandler = MyCustomHandler() + + +def test_chat_completion_optional_params(): + # [PROXY: PROD TEST] - DO NOT DELETE + # This tests if all the /chat/completion params are passed to litellm + + try: + # Your test data + litellm.set_verbose=True + test_data = { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "hi" + }, + ], + "max_tokens": 10, + "user": "proxy-user" + } + + litellm.callbacks = [customHandler] + print("testing proxy server: optional params") + response = client.post("/v1/chat/completions", json=test_data) + assert response.status_code == 200 + result = response.json() + print(f"Received response: {result}") + except Exception as e: + pytest.fail("LiteLLM Proxy test failed. Exception", e) + +# Run the test +# test_chat_completion_optional_params() + +# Test Reading config.yaml file +from litellm.proxy.proxy_server import load_router_config + +def test_load_router_config(): + try: + print("testing reading config") + # this is a basic config.yaml with only a model + filepath = os.path.dirname(os.path.abspath(__file__)) + result = load_router_config(router=None, config_file_path=f"{filepath}/example_config_yaml/simple_config.yaml") + print(result) + assert len(result[1]) == 1 + + # this is a load balancing config yaml + result = load_router_config(router=None, config_file_path=f"{filepath}/example_config_yaml/azure_config.yaml") + print(result) + assert len(result[1]) == 2 + + # config with general settings - custom callbacks + result = load_router_config(router=None, config_file_path=f"{filepath}/example_config_yaml/azure_config.yaml") + print(result) + assert len(result[1]) == 2 + + except Exception as e: + pytest.fail("Proxy: Got exception reading config", e) +# test_load_router_config() diff --git a/litellm/tests/test_proxy_server_cost.py b/litellm/tests/test_proxy_server_cost.py new file mode 100644 index 0000000000000000000000000000000000000000..b127e72e3b8fd7dacd54f9a49e5b70e76554748f --- /dev/null +++ b/litellm/tests/test_proxy_server_cost.py @@ -0,0 +1,138 @@ +# #### What this tests #### +# # This tests the cost tracking function works with consecutive calls (~10 consecutive calls) + +# import sys, os, asyncio +# import traceback +# import pytest +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import dotenv +# dotenv.load_dotenv() +# import litellm +# from fastapi.testclient import TestClient +# from fastapi import FastAPI +# from litellm.proxy.proxy_server import router, save_worker_config, startup_event # Replace with the actual module where your FastAPI router is defined +# filepath = os.path.dirname(os.path.abspath(__file__)) +# config_fp = f"{filepath}/test_config.yaml" +# save_worker_config(config=config_fp, model=None, alias=None, api_base=None, api_version=None, debug=True, temperature=None, max_tokens=None, request_timeout=600, max_budget=None, telemetry=False, drop_params=True, add_function_to_prompt=False, headers=None, save=False, use_queue=False) +# app = FastAPI() +# app.include_router(router) # Include your router in the test app +# @app.on_event("startup") +# async def wrapper_startup_event(): +# await startup_event() + +# # Here you create a fixture that will be used by your tests +# # Make sure the fixture returns TestClient(app) +# @pytest.fixture(autouse=True) +# def client(): +# with TestClient(app) as client: +# yield client + +# @pytest.mark.asyncio +# async def test_proxy_cost_tracking(client): +# """ +# Get min cost. +# Create new key. +# Run 10 parallel calls. +# Check cost for key at the end. +# assert it's > min cost. +# """ +# model = "gpt-3.5-turbo" +# messages = [{"role": "user", "content": "Hey, how's it going?"}] +# number_of_calls = 1 +# min_cost = litellm.completion_cost(model=model, messages=messages) * number_of_calls +# try: +# ### CREATE NEW KEY ### +# test_data = { +# "models": ["azure-model"], +# } +# # Your bearer token +# token = os.getenv("PROXY_MASTER_KEY") + +# headers = { +# "Authorization": f"Bearer {token}" +# } +# create_new_key = client.post("/key/generate", json=test_data, headers=headers) +# key = create_new_key.json()["key"] +# print(f"received key: {key}") +# ### MAKE PARALLEL CALLS ### +# async def test_chat_completions(): +# # Your test data +# test_data = { +# "model": "azure-model", +# "messages": messages +# } + +# tmp_headers = { +# "Authorization": f"Bearer {key}" +# } + +# response = client.post("/v1/chat/completions", json=test_data, headers=tmp_headers) + +# assert response.status_code == 200 +# result = response.json() +# print(f"Received response: {result}") +# tasks = [test_chat_completions() for _ in range(number_of_calls)] +# chat_completions = await asyncio.gather(*tasks) +# ### CHECK SPEND ### +# get_key_spend = client.get(f"/key/info?key={key}", headers=headers) + +# assert get_key_spend.json()["info"]["spend"] > min_cost +# # print(f"chat_completions: {chat_completions}") +# # except Exception as e: +# # pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") + +# #### JUST TEST LOCAL PROXY SERVER + +# import requests, os +# from concurrent.futures import ThreadPoolExecutor +# import dotenv +# dotenv.load_dotenv() + +# api_url = "http://0.0.0.0:8000/chat/completions" + +# def make_api_call(api_url): +# # Your test data +# test_data = { +# "model": "azure-model", +# "messages": [ +# { +# "role": "user", +# "content": "hi" +# }, +# ], +# "max_tokens": 10, +# } +# # Your bearer token +# token = os.getenv("PROXY_MASTER_KEY") + +# headers = { +# "Authorization": f"Bearer {token}" +# } +# print("testing proxy server") +# response = requests.post(api_url, json=test_data, headers=headers) +# return response.json() + +# # Number of parallel API calls +# num_parallel_calls = 3 + +# # List to store results +# results = [] + +# # Create a ThreadPoolExecutor +# with ThreadPoolExecutor() as executor: +# # Submit the API calls concurrently +# futures = [executor.submit(make_api_call, api_url) for _ in range(num_parallel_calls)] + +# # Gather the results as they become available +# for future in futures: +# try: +# result = future.result() +# results.append(result) +# except Exception as e: +# print(f"Error: {e}") + +# # Print the results +# for idx, result in enumerate(results, start=1): +# print(f"Result {idx}: {result}") diff --git a/litellm/tests/test_proxy_server_keys.py b/litellm/tests/test_proxy_server_keys.py new file mode 100644 index 0000000000000000000000000000000000000000..fb0ec2f3c77417b1fc43ef6b8ce381500cafdf59 --- /dev/null +++ b/litellm/tests/test_proxy_server_keys.py @@ -0,0 +1,67 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +# this file is to test litellm/proxy + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest, logging +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError +# Configure logging +logging.basicConfig( + level=logging.DEBUG, # Set the desired logging level + format="%(asctime)s - %(levelname)s - %(message)s", +) + +# test /chat/completion request to the proxy +from fastapi.testclient import TestClient +from fastapi import FastAPI +from litellm.proxy.proxy_server import router, save_worker_config, startup_event # Replace with the actual module where your FastAPI router is defined +filepath = os.path.dirname(os.path.abspath(__file__)) +config_fp = f"{filepath}/test_configs/test_config.yaml" +save_worker_config(config=config_fp, model=None, alias=None, api_base=None, api_version=None, debug=False, temperature=None, max_tokens=None, request_timeout=600, max_budget=None, telemetry=False, drop_params=True, add_function_to_prompt=False, headers=None, save=False, use_queue=False) +app = FastAPI() +app.include_router(router) # Include your router in the test app +@app.on_event("startup") +async def wrapper_startup_event(): + await startup_event() + +# Here you create a fixture that will be used by your tests +# Make sure the fixture returns TestClient(app) +@pytest.fixture(autouse=True) +def client(): + with TestClient(app) as client: + yield client + +def test_add_new_key(client): + try: + # Your test data + test_data = { + "models": ["gpt-3.5-turbo", "gpt-4", "claude-2"], + "aliases": {"mistral-7b": "gpt-3.5-turbo"}, + "duration": "20m" + } + print("testing proxy server") + # Your bearer token + token = os.getenv("PROXY_MASTER_KEY") + + headers = { + "Authorization": f"Bearer {token}" + } + response = client.post("/key/generate", json=test_data, headers=headers) + print(f"response: {response.text}") + assert response.status_code == 200 + result = response.json() + assert result["key"].startswith("sk-") + print(f"Received response: {result}") + except Exception as e: + pytest.fail("LiteLLM Proxy test failed. Exception", e) + +# # Run the test - only runs via pytest \ No newline at end of file diff --git a/litellm/tests/test_register_model.py b/litellm/tests/test_register_model.py new file mode 100644 index 0000000000000000000000000000000000000000..185e96c20197448b0529f5bb399a45def07b9203 --- /dev/null +++ b/litellm/tests/test_register_model.py @@ -0,0 +1,47 @@ +#### What this tests #### +# This tests calling batch_completions by running 100 messages together + +import sys, os +import traceback +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm + +def test_update_model_cost(): + try: + litellm.register_model({ + "gpt-4": { + "max_tokens": 8192, + "input_cost_per_token": 0.00002, + "output_cost_per_token": 0.00006, + "litellm_provider": "openai", + "mode": "chat" + }, + }) + assert litellm.model_cost["gpt-4"]["input_cost_per_token"] == 0.00002 + except Exception as e: + pytest.fail(f"An error occurred: {e}") + +# test_update_model_cost() + +def test_update_model_cost_map_url(): + try: + litellm.register_model(model_cost="https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json") + assert litellm.model_cost["gpt-4"]["input_cost_per_token"] == 0.00003 + except Exception as e: + pytest.fail(f"An error occurred: {e}") + +# test_update_model_cost_map_url() + +def test_update_model_cost_via_completion(): + try: + response = litellm.completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey, how's it going?"}], input_cost_per_token=0.3, output_cost_per_token=0.4) + print(f"litellm.model_cost for gpt-3.5-turbo: {litellm.model_cost['gpt-3.5-turbo']}") + assert litellm.model_cost["gpt-3.5-turbo"]["input_cost_per_token"] == 0.3 + assert litellm.model_cost["gpt-3.5-turbo"]["output_cost_per_token"] == 0.4 + except Exception as e: + pytest.fail(f"An error occurred: {e}") + +test_update_model_cost_via_completion() \ No newline at end of file diff --git a/litellm/tests/test_router.py b/litellm/tests/test_router.py new file mode 100644 index 0000000000000000000000000000000000000000..8024c9dd2d3bbc6b071b5fcdb21f3fd182700691 --- /dev/null +++ b/litellm/tests/test_router.py @@ -0,0 +1,332 @@ +#### What this tests #### +#This tests litellm router + +import sys, os, time +import traceback, asyncio +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import Router +from concurrent.futures import ThreadPoolExecutor +from collections import defaultdict +from dotenv import load_dotenv +load_dotenv() + +def test_exception_raising(): + # this tests if the router raises an exception when invalid params are set + # in this test both deployments have bad keys - Keep this test. It validates if the router raises the most recent exception + litellm.set_verbose=True + import openai + try: + print("testing if router raises an exception") + old_api_key = os.environ["AZURE_API_KEY"] + os.environ["AZURE_API_KEY"] = "" + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # + "model": "gpt-3.5-turbo", + "api_key": "bad-key", + }, + "tpm": 240000, + "rpm": 1800 + } + ] + router = Router(model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=int(os.getenv("REDIS_PORT")), + routing_strategy="simple-shuffle", + set_verbose=False, + num_retries=1) # type: ignore + response = router.completion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "hello this request will fail" + } + ] + ) + os.environ["AZURE_API_KEY"] = old_api_key + pytest.fail(f"Should have raised an Auth Error") + except openai.AuthenticationError: + print("Test Passed: Caught an OPENAI AUTH Error, Good job. This is what we needed!") + os.environ["AZURE_API_KEY"] = old_api_key + router.reset() + except Exception as e: + os.environ["AZURE_API_KEY"] = old_api_key + print("Got unexpected exception on router!", e) +# test_exception_raising() + + +def test_reading_key_from_model_list(): + # this tests if the router raises an exception when invalid params are set + # DO NOT REMOVE THIS TEST. It's an IMP ONE. Speak to Ishaan, if you are tring to remove this + litellm.set_verbose=False + import openai + try: + print("testing if router raises an exception") + old_api_key = os.environ["AZURE_API_KEY"] + os.environ.pop("AZURE_API_KEY", None) + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": old_api_key, + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + } + ] + + router = Router(model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=int(os.getenv("REDIS_PORT")), + routing_strategy="simple-shuffle", + set_verbose=True, + num_retries=1) # type: ignore + response = router.completion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "hello this request will fail" + } + ] + ) + os.environ["AZURE_API_KEY"] = old_api_key + router.reset() + except Exception as e: + os.environ["AZURE_API_KEY"] = old_api_key + print(f"FAILED TEST") + pytest.fail(f"Got unexpected exception on router! - {e}") +# test_reading_key_from_model_list() + + +### FUNCTION CALLING + +def test_function_calling(): + model_list = [ + { + "model_name": "gpt-3.5-turbo-0613", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 100000, + "rpm": 10000, + }, + ] + + messages = [ + {"role": "user", "content": "What is the weather like in Boston?"} + ] + functions = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"] + } + }, + "required": ["location"] + } + } + ] + + router = Router(model_list=model_list, routing_strategy="latency-based-routing") + response = router.completion(model="gpt-3.5-turbo-0613", messages=messages, functions=functions) + router.reset() + print(response) + +def test_acompletion_on_router(): + try: + litellm.set_verbose = False + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 100000, + "rpm": 10000, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION") + }, + "tpm": 100000, + "rpm": 10000, + } + ] + + messages = [ + {"role": "user", "content": f"write a one sentence poem {time.time()}?"} + ] + start_time = time.time() + router = Router(model_list=model_list, + redis_host=os.environ["REDIS_HOST"], + redis_password=os.environ["REDIS_PASSWORD"], + redis_port=os.environ["REDIS_PORT"], + cache_responses=True, + timeout=30, + routing_strategy="simple-shuffle") + async def get_response(): + response1 = await router.acompletion(model="gpt-3.5-turbo", messages=messages) + print(f"response1: {response1}") + response2 = await router.acompletion(model="gpt-3.5-turbo", messages=messages) + print(f"response2: {response2}") + assert response1.id == response2.id + assert len(response1.choices[0].message.content) > 0 + assert response1.choices[0].message.content == response2.choices[0].message.content + asyncio.run(get_response()) + router.reset() + except litellm.Timeout as e: + end_time = time.time() + print(f"timeout error occurred: {end_time - start_time}") + pass + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + +# test_acompletion_on_router() + +def test_function_calling_on_router(): + try: + litellm.set_verbose = True + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + }, + ] + function1 = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + } + ] + router = Router( + model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=os.getenv("REDIS_PORT") + ) + messages=[ + { + "role": "user", + "content": "what's the weather in boston" + } + ] + response = router.completion(model="gpt-3.5-turbo", messages=messages, functions=function1) + print(f"final returned response: {response}") + router.reset() + assert isinstance(response["choices"][0]["message"]["function_call"], dict) + except Exception as e: + print(f"An exception occurred: {e}") + +# test_function_calling_on_router() + +def test_aembedding_on_router(): + litellm.set_verbose = True + try: + model_list = [ + { + "model_name": "text-embedding-ada-002", + "litellm_params": { + "model": "text-embedding-ada-002", + }, + "tpm": 100000, + "rpm": 10000, + }, + ] + + async def embedding_call(): + router = Router(model_list=model_list) + response = await router.aembedding( + model="text-embedding-ada-002", + input=["good morning from litellm", "this is another item"], + ) + print(response) + router.reset() + asyncio.run(embedding_call()) + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") +# test_aembedding_on_router() + + +def test_azure_aembedding_on_router(): + litellm.set_verbose = True + try: + model_list = [ + { + "model_name": "text-embedding-ada-002", + "litellm_params": { + "model": "azure/azure-embedding-model", + "api_key":os.environ['AZURE_API_KEY'], + "api_base": os.environ['AZURE_API_BASE'] + }, + "tpm": 100000, + "rpm": 10000, + }, + ] + + async def embedding_call(): + router = Router(model_list=model_list) + response = await router.aembedding( + model="text-embedding-ada-002", + input=["good morning from litellm"] + ) + print(response) + router.reset() + asyncio.run(embedding_call()) + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") +# test_azure_aembedding_on_router() \ No newline at end of file diff --git a/litellm/tests/test_router_fallbacks.py b/litellm/tests/test_router_fallbacks.py new file mode 100644 index 0000000000000000000000000000000000000000..0501ea8a2fda95b3155a526cc2bbd4db0dbb2110 --- /dev/null +++ b/litellm/tests/test_router_fallbacks.py @@ -0,0 +1,155 @@ +#### What this tests #### +# This tests calling router with fallback models + +import sys, os, time +import traceback, asyncio +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path + +import litellm +from litellm import Router + +model_list = [ + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + { + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000 + }, + { + "model_name": "gpt-3.5-turbo-16k", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-16k", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000 + } +] + + + +router = Router(model_list=model_list, + fallbacks=[{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}], + context_window_fallbacks=[{"azure/gpt-3.5-turbo-context-fallback": ["gpt-3.5-turbo-16k"]}, {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}], + set_verbose=True) + +kwargs = {"model": "azure/gpt-3.5-turbo", "messages": [{"role": "user", "content":"Hey, how's it going?"}]} + +def test_sync_fallbacks(): + try: + litellm.set_verbose = True + response = router.completion(**kwargs) + print(f"response: {response}") + router.flush_cache() + except Exception as e: + print(e) +# test_sync_fallbacks() + +def test_async_fallbacks(): + litellm.set_verbose = False + async def test_get_response(): + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await router.acompletion(**kwargs) + # response = await response + print(f"response: {response}") + router.flush_cache() + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + + asyncio.run(test_get_response()) + +# test_async_fallbacks() + +def test_sync_context_window_fallbacks(): + try: + sample_text = "Say error 50 times" * 10000 + kwargs["model"] = "azure/gpt-3.5-turbo-context-fallback" + kwargs["messages"] = [{"role": "user", "content": sample_text}] + response = router.completion(**kwargs) + print(f"response: {response}") + router.reset() + except Exception as e: + print(e) + +# test_sync_context_window_fallbacks() + +def test_dynamic_fallbacks_sync(): + """ + Allow setting the fallback in the router.completion() call. + """ + try: + router = Router(model_list=model_list, set_verbose=True) + kwargs = {} + kwargs["model"] = "azure/gpt-3.5-turbo" + kwargs["messages"] = [{"role": "user", "content": "Hey, how's it going?"}] + kwargs["fallbacks"] = [{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}] + response = router.completion(**kwargs) + print(f"response: {response}") + router.reset() + except Exception as e: + pytest.fail(f"An exception occurred - {e}") + +# test_dynamic_fallbacks_sync() + +def test_dynamic_fallbacks_async(): + """ + Allow setting the fallback in the router.completion() call. + """ + async def test_get_response(): + try: + router = Router(model_list=model_list, set_verbose=True) + kwargs = {} + kwargs["model"] = "azure/gpt-3.5-turbo" + kwargs["messages"] = [{"role": "user", "content": "Hey, how's it going?"}] + kwargs["fallbacks"] = [{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}] + response = await router.acompletion(**kwargs) + print(f"response: {response}") + router.reset() + except Exception as e: + pytest.fail(f"An exception occurred - {e}") + asyncio.run(test_get_response()) + +# test_dynamic_fallbacks_async() \ No newline at end of file diff --git a/litellm/tests/test_router_get_deployments.py b/litellm/tests/test_router_get_deployments.py new file mode 100644 index 0000000000000000000000000000000000000000..d180cc6d4f54b3ed2b8e12f4dcf38ba9896b341c --- /dev/null +++ b/litellm/tests/test_router_get_deployments.py @@ -0,0 +1,287 @@ +# Tests for router.get_available_deployment +# specifically test if it can pick the correct LLM when rpm/tpm set +# These are fast Tests, and make no API calls +import sys, os, time +import traceback, asyncio +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import Router +from concurrent.futures import ThreadPoolExecutor +from collections import defaultdict +from dotenv import load_dotenv +load_dotenv() + +def test_weighted_selection_router(): + # this tests if load balancing works based on the provided rpms in the router + # it's a fast test, only tests get_available_deployment + # users can pass rpms as a litellm_param + try: + litellm.set_verbose = False + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + "rpm": 6, + }, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + "rpm": 1440, + }, + } + ] + router = Router( + model_list=model_list, + ) + selection_counts = defaultdict(int) + + # call get_available_deployment 1k times, it should pick azure/chatgpt-v-2 about 90% of the time + for _ in range(1000): + selected_model = router.get_available_deployment("gpt-3.5-turbo") + selected_model_id = selected_model["litellm_params"]["model"] + selected_model_name = litellm.utils.remove_model_id(selected_model_id) + selection_counts[selected_model_name] +=1 + print(selection_counts) + + total_requests = sum(selection_counts.values()) + + # Assert that 'azure/chatgpt-v-2' has about 90% of the total requests + assert selection_counts['azure/chatgpt-v-2'] / total_requests > 0.89, f"Assertion failed: 'azure/chatgpt-v-2' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}" + + + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") +# test_weighted_selection_router() + +def test_weighted_selection_router_tpm(): + # this tests if load balancing works based on the provided tpms in the router + # it's a fast test, only tests get_available_deployment + # users can pass rpms as a litellm_param + try: + print("\ntest weighted selection based on TPM\n") + litellm.set_verbose = False + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + "tpm": 5, + }, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + "tpm": 90, + }, + } + ] + router = Router( + model_list=model_list, + ) + selection_counts = defaultdict(int) + + # call get_available_deployment 1k times, it should pick azure/chatgpt-v-2 about 90% of the time + for _ in range(1000): + selected_model = router.get_available_deployment("gpt-3.5-turbo") + selected_model_id = selected_model["litellm_params"]["model"] + selected_model_name = litellm.utils.remove_model_id(selected_model_id) + selection_counts[selected_model_name] +=1 + print(selection_counts) + + total_requests = sum(selection_counts.values()) + + # Assert that 'azure/chatgpt-v-2' has about 90% of the total requests + assert selection_counts['azure/chatgpt-v-2'] / total_requests > 0.89, f"Assertion failed: 'azure/chatgpt-v-2' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}" + + + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") +# test_weighted_selection_router_tpm() + + +def test_weighted_selection_router_tpm_as_router_param(): + # this tests if load balancing works based on the provided tpms in the router + # it's a fast test, only tests get_available_deployment + # users can pass rpms as a litellm_param + try: + print("\ntest weighted selection based on TPM\n") + litellm.set_verbose = False + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 5, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + }, + "tpm": 90, + } + ] + router = Router( + model_list=model_list, + ) + selection_counts = defaultdict(int) + + # call get_available_deployment 1k times, it should pick azure/chatgpt-v-2 about 90% of the time + for _ in range(1000): + selected_model = router.get_available_deployment("gpt-3.5-turbo") + selected_model_id = selected_model["litellm_params"]["model"] + selected_model_name = litellm.utils.remove_model_id(selected_model_id) + selection_counts[selected_model_name] +=1 + print(selection_counts) + + total_requests = sum(selection_counts.values()) + + # Assert that 'azure/chatgpt-v-2' has about 90% of the total requests + assert selection_counts['azure/chatgpt-v-2'] / total_requests > 0.89, f"Assertion failed: 'azure/chatgpt-v-2' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}" + + + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") +test_weighted_selection_router_tpm_as_router_param() + + + +def test_weighted_selection_router_rpm_as_router_param(): + # this tests if load balancing works based on the provided tpms in the router + # it's a fast test, only tests get_available_deployment + # users can pass rpms as a litellm_param + try: + print("\ntest weighted selection based on RPM\n") + litellm.set_verbose = False + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "rpm": 5, + "tpm": 5, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + }, + "rpm": 90, + "tpm": 90, + } + ] + router = Router( + model_list=model_list, + ) + selection_counts = defaultdict(int) + + # call get_available_deployment 1k times, it should pick azure/chatgpt-v-2 about 90% of the time + for _ in range(1000): + selected_model = router.get_available_deployment("gpt-3.5-turbo") + selected_model_id = selected_model["litellm_params"]["model"] + selected_model_name = litellm.utils.remove_model_id(selected_model_id) + selection_counts[selected_model_name] +=1 + print(selection_counts) + + total_requests = sum(selection_counts.values()) + + # Assert that 'azure/chatgpt-v-2' has about 90% of the total requests + assert selection_counts['azure/chatgpt-v-2'] / total_requests > 0.89, f"Assertion failed: 'azure/chatgpt-v-2' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}" + + + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") +# test_weighted_selection_router_tpm_as_router_param() + + + +def test_weighted_selection_router_no_rpm_set(): + # this tests if we can do selection when no rpm is provided too + # it's a fast test, only tests get_available_deployment + # users can pass rpms as a litellm_param + try: + litellm.set_verbose = False + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + "rpm": 6, + }, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + "rpm": 1440, + }, + }, + { + "model_name": "claude-1", + "litellm_params": { + "model": "bedrock/claude1.2", + "rpm": 1440, + }, + } + ] + router = Router( + model_list=model_list, + ) + selection_counts = defaultdict(int) + + # call get_available_deployment 1k times, it should pick azure/chatgpt-v-2 about 90% of the time + for _ in range(1000): + selected_model = router.get_available_deployment("claude-1") + selected_model_id = selected_model["litellm_params"]["model"] + selected_model_name = litellm.utils.remove_model_id(selected_model_id) + selection_counts[selected_model_name] +=1 + print(selection_counts) + + total_requests = sum(selection_counts.values()) + + # Assert that 'azure/chatgpt-v-2' has about 90% of the total requests + assert selection_counts['bedrock/claude1.2'] / total_requests == 1, f"Assertion failed: Selection counts {selection_counts}" + + + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") +test_weighted_selection_router_no_rpm_set() \ No newline at end of file diff --git a/litellm/tests/test_router_init.py b/litellm/tests/test_router_init.py new file mode 100644 index 0000000000000000000000000000000000000000..4d861365eb83ee495b72a82ac678e325f705ae5e --- /dev/null +++ b/litellm/tests/test_router_init.py @@ -0,0 +1,190 @@ +# this tests if the router is initialized correctly +import sys, os, time +import traceback, asyncio +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import Router +from concurrent.futures import ThreadPoolExecutor +from collections import defaultdict +from dotenv import load_dotenv +load_dotenv() + +# every time we load the router we should have 4 clients: +# Async +# Sync +# Async + Stream +# Sync + Stream + +def test_init_clients(): + litellm.set_verbose = True + try: + print("testing init 4 clients with diff timeouts") + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "timeout": 0.01, + "stream_timeout": 0.000_001, + "max_retries": 7 + }, + }, + ] + router = Router(model_list=model_list) + for elem in router.model_list: + assert elem["client"] is not None + assert elem["async_client"] is not None + assert elem["stream_client"] is not None + assert elem["stream_async_client"] is not None + + # check if timeout for stream/non stream clients is set correctly + async_client = elem["async_client"] + stream_async_client = elem["stream_async_client"] + + assert async_client.timeout == 0.01 + assert stream_async_client.timeout == 0.000_001 + print("PASSED !") + + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + +# test_init_clients() + + +def test_init_clients_basic(): + litellm.set_verbose = True + try: + print("Test basic client init") + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + }, + ] + router = Router(model_list=model_list) + for elem in router.model_list: + assert elem["client"] is not None + assert elem["async_client"] is not None + assert elem["stream_client"] is not None + assert elem["stream_async_client"] is not None + print("PASSED !") + + # see if we can init clients without timeout or max retries set + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + +# test_init_clients_basic() + + +def test_timeouts_router(): + """ + Test the timeouts of the router with multiple clients. This HASas to raise a timeout error + """ + import openai + litellm.set_verbose = True + try: + print("testing init 4 clients with diff timeouts") + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "timeout": 0.000001, + "stream_timeout": 0.000_001, + }, + }, + ] + router = Router(model_list=model_list) + + print("PASSED !") + async def test(): + try: + await router.acompletion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "hello, write a 20 pg essay" + } + ], + ) + except Exception as e: + raise e + asyncio.run(test()) + except openai.APITimeoutError as e: + print("Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e) + print(type(e)) + pass + except Exception as e: + pytest.fail(f"Did not raise error `openai.APITimeoutError`. Instead raised error type: {type(e)}, Error: {e}") + +# test_timeouts_router() + + +def test_stream_timeouts_router(): + """ + Test the stream timeouts router. See if it selected the correct client with stream timeout + """ + import openai + + litellm.set_verbose = True + try: + print("testing init 4 clients with diff timeouts") + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "timeout": 200, # regular calls will not timeout, stream calls will + "stream_timeout": 0.000_001, + }, + }, + ] + router = Router(model_list=model_list) + + print("PASSED !") + selected_client = router._get_client( + deployment=router.model_list[0], + kwargs={ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "hello, write a 20 pg essay" + } + ], + "stream": True + }, + client_type=None + ) + print("Select client timeout", selected_client.timeout) + assert selected_client.timeout == 0.000_001 + except openai.APITimeoutError as e: + print("Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e) + print(type(e)) + pass + except Exception as e: + pytest.fail(f"Did not raise error `openai.APITimeoutError`. Instead raised error type: {type(e)}, Error: {e}") + +test_stream_timeouts_router() + + diff --git a/litellm/tests/test_rules.py b/litellm/tests/test_rules.py new file mode 100644 index 0000000000000000000000000000000000000000..7905babc5709b18ac696eb0ade67e67ee684da23 --- /dev/null +++ b/litellm/tests/test_rules.py @@ -0,0 +1,77 @@ +#### What this tests #### +# This tests setting rules before / after making llm api calls +import sys, os, time +import traceback, asyncio +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import completion, acompletion + +def my_pre_call_rule(input: str): + print(f"input: {input}") + print(f"INSIDE MY PRE CALL RULE, len(input) - {len(input)}") + if len(input) > 10: + return False + return True + +def my_post_call_rule(input: str): + input = input.lower() + print(f"input: {input}") + print(f"INSIDE MY POST CALL RULE, len(input) - {len(input)}") + if "sorry" in input: + return False + return True + +## Test 1: Pre-call rule +def test_pre_call_rule(): + try: + litellm.pre_call_rules = [my_pre_call_rule] + ### completion + response = completion(model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "say something inappropriate"}]) + pytest.fail(f"Completion call should have been failed. ") + except: + pass + ### async completion + async def test_async_response(): + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await acompletion(model="gpt-3.5-turbo", messages=messages) + pytest.fail(f"acompletion call should have been failed. ") + except Exception as e: + pass + asyncio.run(test_async_response()) + litellm.pre_call_rules = [] + +# test_pre_call_rule() +## Test 2: Post-call rule +# commenting out of ci/cd since llm's have variable output which was causing our pipeline to fail erratically. +# def test_post_call_rule(): +# try: +# litellm.pre_call_rules = [] +# litellm.post_call_rules = [my_post_call_rule] +# ### completion +# response = completion(model="gpt-3.5-turbo", +# messages=[{"role": "user", "content": "say sorry"}], +# fallbacks=["deepinfra/Gryphe/MythoMax-L2-13b"]) +# pytest.fail(f"Completion call should have been failed. ") +# except: +# pass +# print(f"MAKING ACOMPLETION CALL") +# # litellm.set_verbose = True +# ### async completion +# async def test_async_response(): +# messages=[{"role": "user", "content": "say sorry"}] +# try: +# response = await acompletion(model="gpt-3.5-turbo", messages=messages) +# pytest.fail(f"acompletion call should have been failed.") +# except Exception as e: +# pass +# asyncio.run(test_async_response()) +# litellm.pre_call_rules = [] +# litellm.post_call_rules = [] + +# test_post_call_rule() \ No newline at end of file diff --git a/litellm/tests/test_stream_chunk_builder.py b/litellm/tests/test_stream_chunk_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..23f67a2e8bb8c240adecae51cd71f272072b1054 --- /dev/null +++ b/litellm/tests/test_stream_chunk_builder.py @@ -0,0 +1,135 @@ +import sys, os, time +import traceback, asyncio +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +from litellm import completion, stream_chunk_builder +import litellm +import os, dotenv +from openai import OpenAI +import pytest +dotenv.load_dotenv() + +user_message = "What is the current weather in Boston?" +messages = [{"content": user_message, "role": "user"}] + +function_schema = { + "name": "get_weather", + "description": + "gets the current weather", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": + "The city and state, e.g. San Francisco, CA" + }, + }, + "required": ["location"] + }, +} + + +tools_schema = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"] + } + }, + "required": ["location"] + } + } + } + ] + +# def test_stream_chunk_builder_tools(): +# try: +# litellm.set_verbose = False +# response = client.chat.completions.create( +# model="gpt-3.5-turbo", +# messages=messages, +# tools=tools_schema, +# # stream=True, +# # complete_response=True # runs stream_chunk_builder under-the-hood +# ) + +# print(f"response: {response}") +# print(f"response usage: {response.usage}") +# except Exception as e: +# pytest.fail(f"An exception occurred - {str(e)}") + +# test_stream_chunk_builder_tools() + +def test_stream_chunk_builder_litellm_function_call(): + try: + litellm.set_verbose = False + response = litellm.completion( + model="gpt-3.5-turbo", + messages=messages, + functions=[function_schema], + # stream=True, + # complete_response=True # runs stream_chunk_builder under-the-hood + ) + + print(f"response: {response}") + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + +# test_stream_chunk_builder_litellm_function_call() + +def test_stream_chunk_builder_litellm_tool_call(): + try: + litellm.set_verbose = False + response = litellm.completion( + model="azure/gpt-4-nov-release", + messages=messages, + tools=tools_schema, + stream=True, + api_key="os.environ/AZURE_FRANCE_API_KEY", + api_base="https://openai-france-1234.openai.azure.com", + complete_response = True + ) + + print(f"complete response: {response}") + print(f"complete response usage: {response.usage}") + assert response.system_fingerprint is not None + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + +# test_stream_chunk_builder_litellm_tool_call() + +def test_stream_chunk_builder_litellm_tool_call_regular_message(): + try: + messages = [{"role": "user", "content": "Hey, how's it going?"}] + litellm.set_verbose = False + response = litellm.completion( + model="azure/gpt-4-nov-release", + messages=messages, + tools=tools_schema, + stream=True, + api_key="os.environ/AZURE_FRANCE_API_KEY", + api_base="https://openai-france-1234.openai.azure.com", + complete_response = True + ) + + print(f"complete response: {response}") + print(f"complete response usage: {response.usage}") + assert response.system_fingerprint is not None + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + +test_stream_chunk_builder_litellm_tool_call_regular_message() diff --git a/litellm/tests/test_streaming.py b/litellm/tests/test_streaming.py new file mode 100644 index 0000000000000000000000000000000000000000..38af89631e8d7f549c76eae2b940a7347f5caa87 --- /dev/null +++ b/litellm/tests/test_streaming.py @@ -0,0 +1,1323 @@ +#### What this tests #### +# This tests streaming for the completion endpoint + +import sys, os, asyncio +import traceback +import time, pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +from dotenv import load_dotenv +load_dotenv() +import litellm +from litellm import completion, acompletion, AuthenticationError, BadRequestError, RateLimitError, ModelResponse + +litellm.logging = False +litellm.set_verbose = True +litellm.num_retries = 3 +litellm.cache = None + +score = 0 + + +def logger_fn(model_call_object: dict): + print(f"model call details: {model_call_object}") + + +user_message = "Hello, how are you?" +messages = [{"content": user_message, "role": "user"}] + + +first_openai_chunk_example = { + "id": "chatcmpl-7zSKLBVXnX9dwgRuDYVqVVDsgh2yp", + "object": "chat.completion.chunk", + "created": 1694881253, + "model": "gpt-4-0613", + "choices": [ + { + "index": 0, + "delta": { + "role": "assistant", + "content": "" + }, + "finish_reason": None # it's null + } + ] +} + +def validate_first_format(chunk): + # write a test to make sure chunk follows the same format as first_openai_chunk_example + assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary." + assert isinstance(chunk['id'], str), "'id' should be a string." + assert isinstance(chunk['object'], str), "'object' should be a string." + assert isinstance(chunk['created'], int), "'created' should be an integer." + assert isinstance(chunk['model'], str), "'model' should be a string." + assert isinstance(chunk['choices'], list), "'choices' should be a list." + + for choice in chunk['choices']: + assert isinstance(choice['index'], int), "'index' should be an integer." + assert isinstance(choice['delta']['role'], str), "'role' should be a string." + # openai v1.0.0 returns content as None + assert (choice['finish_reason'] is None) or isinstance(choice['finish_reason'], str), "'finish_reason' should be None or a string." + +second_openai_chunk_example = { + "id": "chatcmpl-7zSKLBVXnX9dwgRuDYVqVVDsgh2yp", + "object": "chat.completion.chunk", + "created": 1694881253, + "model": "gpt-4-0613", + "choices": [ + { + "index": 0, + "delta": { + "content": "Hello" + }, + "finish_reason": None # it's null + } + ] +} + +def validate_second_format(chunk): + assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary." + assert isinstance(chunk['id'], str), "'id' should be a string." + assert isinstance(chunk['object'], str), "'object' should be a string." + assert isinstance(chunk['created'], int), "'created' should be an integer." + assert isinstance(chunk['model'], str), "'model' should be a string." + assert isinstance(chunk['choices'], list), "'choices' should be a list." + + for choice in chunk['choices']: + assert isinstance(choice['index'], int), "'index' should be an integer." + # openai v1.0.0 returns content as None + assert (choice['finish_reason'] is None) or isinstance(choice['finish_reason'], str), "'finish_reason' should be None or a string." + +last_openai_chunk_example = { + "id": "chatcmpl-7zSKLBVXnX9dwgRuDYVqVVDsgh2yp", + "object": "chat.completion.chunk", + "created": 1694881253, + "model": "gpt-4-0613", + "choices": [ + { + "index": 0, + "delta": {}, + "finish_reason": "stop" + } + ] +} + +def validate_last_format(chunk): + assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary." + assert isinstance(chunk['id'], str), "'id' should be a string." + assert isinstance(chunk['object'], str), "'object' should be a string." + assert isinstance(chunk['created'], int), "'created' should be an integer." + assert isinstance(chunk['model'], str), "'model' should be a string." + assert isinstance(chunk['choices'], list), "'choices' should be a list." + + for choice in chunk['choices']: + assert isinstance(choice['index'], int), "'index' should be an integer." + assert isinstance(choice['finish_reason'], str), "'finish_reason' should be a string." + +def streaming_format_tests(idx, chunk): + extracted_chunk = "" + finished = False + print(f"chunk: {chunk}") + if idx == 0: # ensure role assistant is set + validate_first_format(chunk=chunk) + role = chunk["choices"][0]["delta"]["role"] + assert role == "assistant" + elif idx == 1: # second chunk + validate_second_format(chunk=chunk) + if idx != 0: # ensure no role + if "role" in chunk["choices"][0]["delta"]: + pass # openai v1.0.0+ passes role = None + if chunk["choices"][0]["finish_reason"]: # ensure finish reason is only in last chunk + validate_last_format(chunk=chunk) + finished = True + if "content" in chunk["choices"][0]["delta"] and chunk["choices"][0]["delta"]["content"] is not None: + extracted_chunk = chunk["choices"][0]["delta"]["content"] + print(f"extracted chunk: {extracted_chunk}") + return extracted_chunk, finished + +tools_schema = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"] + } + }, + "required": ["location"] + } + } + } + ] + +# def test_completion_cohere_stream(): +# # this is a flaky test due to the cohere API endpoint being unstable +# try: +# messages = [ +# {"role": "system", "content": "You are a helpful assistant."}, +# { +# "role": "user", +# "content": "how does a court case get to the Supreme Court?", +# }, +# ] +# response = completion( +# model="command-nightly", messages=messages, stream=True, max_tokens=50, +# ) +# complete_response = "" +# # Add any assertions here to check the response +# has_finish_reason = False +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finish_reason = finished +# if finished: +# break +# complete_response += chunk +# if has_finish_reason is False: +# raise Exception("Finish reason not in final chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# print(f"completion_response: {complete_response}") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_cohere_stream() + +def test_completion_cohere_stream_bad_key(): + try: + litellm.cache = None + api_key = "bad-key" + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + response = completion( + model="command-nightly", messages=messages, stream=True, max_tokens=50, api_key=api_key + ) + complete_response = "" + # Add any assertions here to check the response + has_finish_reason = False + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + has_finish_reason = finished + if finished: + break + complete_response += chunk + if has_finish_reason is False: + raise Exception("Finish reason not in final chunk") + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except AuthenticationError as e: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_cohere_stream_bad_key() + +def test_completion_azure_stream(): + try: + litellm.set_verbose = True + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + response = completion( + model="azure/chatgpt-v-2", messages=messages, stream=True, max_tokens=50 + ) + complete_response = "" + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_azure_stream() + +def test_completion_azure_function_calling_stream(): + try: + litellm.set_verbose = False + user_message = "What is the current weather in Boston?" + messages = [{"content": user_message, "role": "user"}] + response = completion( + model="azure/chatgpt-functioncalling", messages=messages, stream=True, tools=tools_schema + ) + # Add any assertions here to check the response + for chunk in response: + print(chunk) + if chunk["choices"][0]["finish_reason"] == "stop": + break + print(chunk["choices"][0]["finish_reason"]) + print(chunk["choices"][0]["delta"]["content"]) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_azure_function_calling_stream() + +def test_completion_claude_stream(): + try: + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + response = completion( + model="claude-instant-1", messages=messages, stream=True, max_tokens=50 + ) + complete_response = "" + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_claude_stream() + + +def test_completion_palm_stream(): + try: + litellm.set_verbose=False + print("Streaming palm response") + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + print("testing palm streaming") + response = completion( + model="palm/chat-bison", messages=messages, stream=True + ) + + complete_response = "" + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + print(chunk) + # print(chunk.choices[0].delta) + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_palm_stream() + +def test_completion_deep_infra_stream(): + # deep infra currently includes role in the 2nd chunk + # waiting for them to make a fix on this + try: + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + print("testing deep infra streaming") + response = completion( + model="deepinfra/meta-llama/Llama-2-70b-chat-hf", messages=messages, stream=True, max_tokens=80 + ) + + complete_response = "" + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_deep_infra_stream() + +def test_completion_nlp_cloud_stream(): + try: + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + print("testing nlp cloud streaming") + response = completion( + model="nlp_cloud/finetuned-llama-2-70b", messages=messages, stream=True, max_tokens=20 + ) + + complete_response = "" + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + complete_response += chunk + if finished: + break + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except Exception as e: + print(f"Error occurred: {e}") + pytest.fail(f"Error occurred: {e}") +# test_completion_nlp_cloud_stream() + +def test_completion_claude_stream_bad_key(): + try: + litellm.cache = None + litellm.set_verbose = True + api_key = "bad-key" + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + response = completion( + model="claude-instant-1", messages=messages, stream=True, max_tokens=50, api_key=api_key + ) + complete_response = "" + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"1234completion_response: {complete_response}") + raise Exception("Auth error not raised") + except AuthenticationError as e: + print("Auth Error raised") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_claude_stream_bad_key() +# test_completion_replicate_stream() + +# def test_completion_vertexai_stream(): +# try: +# import os +# os.environ["VERTEXAI_PROJECT"] = "pathrise-convert-1606954137718" +# os.environ["VERTEXAI_LOCATION"] = "us-central1" +# messages = [ +# {"role": "system", "content": "You are a helpful assistant."}, +# { +# "role": "user", +# "content": "how does a court case get to the Supreme Court?", +# }, +# ] +# response = completion( +# model="vertex_ai/chat-bison", messages=messages, stream=True, max_tokens=50 +# ) +# complete_response = "" +# has_finish_reason = False +# # Add any assertions here to check the response +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finish_reason = finished +# if finished: +# break +# complete_response += chunk +# if has_finish_reason is False: +# raise Exception("finish reason not set for last chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# print(f"completion_response: {complete_response}") +# except InvalidRequestError as e: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_vertexai_stream() + + +# def test_completion_vertexai_stream_bad_key(): +# try: +# import os +# messages = [ +# {"role": "system", "content": "You are a helpful assistant."}, +# { +# "role": "user", +# "content": "how does a court case get to the Supreme Court?", +# }, +# ] +# response = completion( +# model="vertex_ai/chat-bison", messages=messages, stream=True, max_tokens=50 +# ) +# complete_response = "" +# has_finish_reason = False +# # Add any assertions here to check the response +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finish_reason = finished +# if finished: +# break +# complete_response += chunk +# if has_finish_reason is False: +# raise Exception("finish reason not set for last chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# print(f"completion_response: {complete_response}") +# except InvalidRequestError as e: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_vertexai_stream_bad_key() + +# def test_completion_replicate_stream(): +# TEMP Commented out - replicate throwing an auth error +# try: +# litellm.set_verbose = True +# messages = [ +# {"role": "system", "content": "You are a helpful assistant."}, +# { +# "role": "user", +# "content": "how does a court case get to the Supreme Court?", +# }, +# ] +# response = completion( +# model="replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", messages=messages, stream=True, max_tokens=50 +# ) +# complete_response = "" +# has_finish_reason = False +# # Add any assertions here to check the response +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finish_reason = finished +# if finished: +# break +# complete_response += chunk +# if has_finish_reason is False: +# raise Exception("finish reason not set for last chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# print(f"completion_response: {complete_response}") +# except InvalidRequestError as e: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +def test_completion_replicate_stream_bad_key(): + try: + api_key = "bad-key" + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + response = completion( + model="replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", + messages=messages, + stream=True, + max_tokens=50, + api_key=api_key + ) + complete_response = "" + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except AuthenticationError as e: + # this is an auth error with a bad key + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_replicate_stream_bad_key() + +def test_completion_bedrock_claude_stream(): + try: + litellm.set_verbose=False + response = completion( + model="bedrock/anthropic.claude-instant-v1", + messages=[{"role": "user", "content": "Be as verbose as possible and give as many details as possible, how does a court case get to the Supreme Court?"}], + temperature=1, + max_tokens=20, + stream=True, + ) + print(response) + complete_response = "" + has_finish_reason = False + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + # print + chunk, finished = streaming_format_tests(idx, chunk) + has_finish_reason = finished + complete_response += chunk + if finished: + break + if has_finish_reason is False: + raise Exception("finish reason not set for last chunk") + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_bedrock_claude_stream() + +def test_completion_bedrock_ai21_stream(): + try: + litellm.set_verbose=False + response = completion( + model="bedrock/ai21.j2-mid-v1", + messages=[{"role": "user", "content": "Be as verbose as possible and give as many details as possible, how does a court case get to the Supreme Court?"}], + temperature=1, + max_tokens=20, + stream=True, + ) + print(response) + complete_response = "" + has_finish_reason = False + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + # print + chunk, finished = streaming_format_tests(idx, chunk) + has_finish_reason = finished + complete_response += chunk + if finished: + break + if has_finish_reason is False: + raise Exception("finish reason not set for last chunk") + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_completion_bedrock_ai21_stream() + +# def test_completion_sagemaker_stream(): +# try: +# response = completion( +# model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", +# messages=messages, +# temperature=0.2, +# max_tokens=80, +# stream=True, +# ) +# complete_response = "" +# has_finish_reason = False +# # Add any assertions here to check the response +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finish_reason = finished +# if finished: +# break +# complete_response += chunk +# if has_finish_reason is False: +# raise Exception("finish reason not set for last chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# except InvalidRequestError as e: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_sagemaker_stream() + + +# def test_maritalk_streaming(): +# messages = [{"role": "user", "content": "Hey"}] +# try: +# response = completion("maritalk", messages=messages, stream=True) +# complete_response = "" +# start_time = time.time() +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# complete_response += chunk +# if finished: +# break +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# except: +# pytest.fail(f"error occurred: {traceback.format_exc()}") +# test_maritalk_streaming() +# test on openai completion call + +# # test on ai21 completion call +def ai21_completion_call(): + try: + messages=[{ + "role": "system", + "content": "You are an all-knowing oracle", + }, + { + "role": "user", + "content": "What is the meaning of the Universe?" + }] + response = completion( + model="j2-ultra", messages=messages, stream=True, max_tokens=500 + ) + print(f"response: {response}") + has_finished = False + complete_response = "" + start_time = time.time() + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + has_finished = finished + complete_response += chunk + if finished: + break + if has_finished is False: + raise Exception("finished reason missing from final chunk") + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except: + pytest.fail(f"error occurred: {traceback.format_exc()}") + +# ai21_completion_call() + +def ai21_completion_call_bad_key(): + try: + api_key = "bad-key" + response = completion( + model="j2-ultra", messages=messages, stream=True, api_key=api_key + ) + print(f"response: {response}") + complete_response = "" + start_time = time.time() + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except: + pytest.fail(f"error occurred: {traceback.format_exc()}") + +# ai21_completion_call_bad_key() + +def hf_test_completion_tgi_stream(): + try: + response = completion( + model = 'huggingface/HuggingFaceH4/zephyr-7b-beta', + messages = [{ "content": "Hello, how are you?","role": "user"}], + stream=True + ) + # Add any assertions here to check the response + print(f"response: {response}") + complete_response = "" + start_time = time.time() + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + complete_response += chunk + if finished: + break + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# hf_test_completion_tgi_stream() + +# def test_completion_aleph_alpha(): +# try: +# response = completion( +# model="luminous-base", messages=messages, stream=True +# ) +# # Add any assertions here to check the response +# has_finished = False +# complete_response = "" +# start_time = time.time() +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finished = finished +# complete_response += chunk +# if finished: +# break +# if has_finished is False: +# raise Exception("finished reason missing from final chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # test_completion_aleph_alpha() + +# def test_completion_aleph_alpha_bad_key(): +# try: +# api_key = "bad-key" +# response = completion( +# model="luminous-base", messages=messages, stream=True, api_key=api_key +# ) +# # Add any assertions here to check the response +# has_finished = False +# complete_response = "" +# start_time = time.time() +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finished = finished +# complete_response += chunk +# if finished: +# break +# if has_finished is False: +# raise Exception("finished reason missing from final chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# except InvalidRequestError as e: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_aleph_alpha_bad_key() + +# test on openai completion call +def test_openai_chat_completion_call(): + try: + litellm.set_verbose = False + print(f"making openai chat completion call") + response = completion( + model="gpt-3.5-turbo", messages=messages, stream=True + ) + complete_response = "" + start_time = time.time() + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + print(f"outside chunk: {chunk}") + if finished: + break + complete_response += chunk + # print(f'complete_chunk: {complete_response}') + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"complete response: {complete_response}") + except: + print(f"error occurred: {traceback.format_exc()}") + pass + +# test_openai_chat_completion_call() + +def test_openai_chat_completion_complete_response_call(): + try: + complete_response = completion( + model="gpt-3.5-turbo", messages=messages, stream=True, complete_response=True + ) + print(f"complete response: {complete_response}") + except: + print(f"error occurred: {traceback.format_exc()}") + pass + +# test_openai_chat_completion_complete_response_call() + +def test_openai_text_completion_call(): + try: + litellm.set_verbose = True + response = completion( + model="gpt-3.5-turbo-instruct", messages=messages, stream=True + ) + complete_response = "" + start_time = time.time() + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + print(f"chunk: {chunk}") + complete_response += chunk + if finished: + break + # print(f'complete_chunk: {complete_response}') + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"complete response: {complete_response}") + except: + print(f"error occurred: {traceback.format_exc()}") + pass + +# test_openai_text_completion_call() + +# # test on together ai completion call - starcoder +def test_together_ai_completion_call_starcoder(): + try: + start_time = time.time() + response = completion( + model="together_ai/bigcode/starcoder", + messages=messages, + logger_fn=logger_fn, + stream=True, + ) + complete_response = "" + print(f"returned response object: {response}") + has_finish_reason = False + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + has_finish_reason = finished + if finished: + break + complete_response += chunk + if has_finish_reason is False: + raise Exception("Finish reason not set for last chunk") + if complete_response == "": + raise Exception("Empty response received") + print(f"complete response: {complete_response}") + except: + print(f"error occurred: {traceback.format_exc()}") + pass + +# test_together_ai_completion_call_starcoder() + +def test_together_ai_completion_call_starcoder_bad_key(): + try: + api_key = "bad-key" + start_time = time.time() + response = completion( + model="together_ai/bigcode/starcoder", + messages=messages, + stream=True, + api_key=api_key + ) + complete_response = "" + has_finish_reason = False + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + has_finish_reason = finished + if finished: + break + complete_response += chunk + if has_finish_reason is False: + raise Exception("Finish reason not set for last chunk") + if complete_response == "": + raise Exception("Empty response received") + print(f"complete response: {complete_response}") + except BadRequestError as e: + pass + except: + print(f"error occurred: {traceback.format_exc()}") + pass + +# test_together_ai_completion_call_starcoder_bad_key() +#### Test Function calling + streaming #### + +def test_completion_openai_with_functions(): + function1 = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + } + ] + try: + litellm.set_verbose=False + response = completion( + model="gpt-3.5-turbo-1106", + messages=[ + { + "role": "user", + "content": "what's the weather in SF" + } + ], + functions=function1, + stream=True, + ) + # Add any assertions here to check the response + print(response) + for chunk in response: + print(chunk) + if chunk["choices"][0]["finish_reason"] == "stop": + break + print(chunk["choices"][0]["finish_reason"]) + print(chunk["choices"][0]["delta"]["content"]) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_openai_with_functions() +#### Test Async streaming #### + +# # test on ai21 completion call +async def ai21_async_completion_call(): + try: + response = completion( + model="j2-ultra", messages=messages, stream=True, logger_fn=logger_fn + ) + print(f"response: {response}") + complete_response = "" + start_time = time.time() + # Change for loop to async for loop + idx = 0 + async for chunk in response: + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + idx += 1 + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"complete response: {complete_response}") + except: + print(f"error occurred: {traceback.format_exc()}") + pass + +# asyncio.run(ai21_async_completion_call()) + +async def completion_call(): + try: + response = completion( + model="gpt-3.5-turbo", messages=messages, stream=True, logger_fn=logger_fn, max_tokens=10 + ) + print(f"response: {response}") + complete_response = "" + start_time = time.time() + # Change for loop to async for loop + idx = 0 + async for chunk in response: + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + idx += 1 + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"complete response: {complete_response}") + except: + print(f"error occurred: {traceback.format_exc()}") + pass + +# asyncio.run(completion_call()) + +#### Test Function Calling + Streaming #### + +final_openai_function_call_example = { + "id": "chatcmpl-7zVNA4sXUftpIg6W8WlntCyeBj2JY", + "object": "chat.completion", + "created": 1694892960, + "model": "gpt-3.5-turbo-0613", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": None, + "function_call": { + "name": "get_current_weather", + "arguments": "{\n \"location\": \"Boston, MA\"\n}" + } + }, + "finish_reason": "function_call" + } + ], + "usage": { + "prompt_tokens": 82, + "completion_tokens": 18, + "total_tokens": 100 + } +} + +function_calling_output_structure = { + "id": str, + "object": str, + "created": int, + "model": str, + "choices": [ + { + "index": int, + "message": { + "role": str, + "content": (type(None), str), + "function_call": { + "name": str, + "arguments": str + } + }, + "finish_reason": str + } + ], + "usage": { + "prompt_tokens": int, + "completion_tokens": int, + "total_tokens": int + } + } + +def validate_final_structure(item, structure=function_calling_output_structure): + if isinstance(item, list): + if not all(validate_final_structure(i, structure[0]) for i in item): + return Exception("Function calling final output doesn't match expected output format") + elif isinstance(item, dict): + if not all(k in item and validate_final_structure(item[k], v) for k, v in structure.items()): + return Exception("Function calling final output doesn't match expected output format") + else: + if not isinstance(item, structure): + return Exception("Function calling final output doesn't match expected output format") + return True + + +first_openai_function_call_example = { + "id": "chatcmpl-7zVRoE5HjHYsCMaVSNgOjzdhbS3P0", + "object": "chat.completion.chunk", + "created": 1694893248, + "model": "gpt-3.5-turbo-0613", + "choices": [ + { + "index": 0, + "delta": { + "role": "assistant", + "content": None, + "function_call": { + "name": "get_current_weather", + "arguments": "" + } + }, + "finish_reason": None + } + ] +} + +def validate_first_function_call_chunk_structure(item): + if not isinstance(item, dict): + raise Exception("Incorrect format") + + required_keys = {"id", "object", "created", "model", "choices"} + for key in required_keys: + if key not in item: + raise Exception("Incorrect format") + + if not isinstance(item["choices"], list) or not item["choices"]: + raise Exception("Incorrect format") + + required_keys_in_choices_array = {"index", "delta", "finish_reason"} + for choice in item["choices"]: + if not isinstance(choice, dict): + raise Exception("Incorrect format") + for key in required_keys_in_choices_array: + if key not in choice: + raise Exception("Incorrect format") + + if not isinstance(choice["delta"], dict): + raise Exception("Incorrect format") + + required_keys_in_delta = {"role", "content", "function_call"} + for key in required_keys_in_delta: + if key not in choice["delta"]: + raise Exception("Incorrect format") + + if not isinstance(choice["delta"]["function_call"], dict): + raise Exception("Incorrect format") + + required_keys_in_function_call = {"name", "arguments"} + for key in required_keys_in_function_call: + if key not in choice["delta"]["function_call"]: + raise Exception("Incorrect format") + + return True + +second_function_call_chunk_format = { + "id": "chatcmpl-7zVRoE5HjHYsCMaVSNgOjzdhbS3P0", + "object": "chat.completion.chunk", + "created": 1694893248, + "model": "gpt-3.5-turbo-0613", + "choices": [ + { + "index": 0, + "delta": { + "function_call": { + "arguments": "{\n" + } + }, + "finish_reason": None + } + ] +} + + +def validate_second_function_call_chunk_structure(data): + if not isinstance(data, dict): + raise Exception("Incorrect format") + + required_keys = {"id", "object", "created", "model", "choices"} + for key in required_keys: + if key not in data: + raise Exception("Incorrect format") + + if not isinstance(data["choices"], list) or not data["choices"]: + raise Exception("Incorrect format") + + required_keys_in_choices_array = {"index", "delta", "finish_reason"} + for choice in data["choices"]: + if not isinstance(choice, dict): + raise Exception("Incorrect format") + for key in required_keys_in_choices_array: + if key not in choice: + raise Exception("Incorrect format") + + if "function_call" not in choice["delta"] or "arguments" not in choice["delta"]["function_call"]: + raise Exception("Incorrect format") + + return True + + +final_function_call_chunk_example = { + "id": "chatcmpl-7zVRoE5HjHYsCMaVSNgOjzdhbS3P0", + "object": "chat.completion.chunk", + "created": 1694893248, + "model": "gpt-3.5-turbo-0613", + "choices": [ + { + "index": 0, + "delta": {}, + "finish_reason": "function_call" + } + ] +} + + +def validate_final_function_call_chunk_structure(data): + if not isinstance(data, dict): + raise Exception("Incorrect format") + + required_keys = {"id", "object", "created", "model", "choices"} + for key in required_keys: + if key not in data: + raise Exception("Incorrect format") + + if not isinstance(data["choices"], list) or not data["choices"]: + raise Exception("Incorrect format") + + required_keys_in_choices_array = {"index", "delta", "finish_reason"} + for choice in data["choices"]: + if not isinstance(choice, dict): + raise Exception("Incorrect format") + for key in required_keys_in_choices_array: + if key not in choice: + raise Exception("Incorrect format") + + return True + +def streaming_and_function_calling_format_tests(idx, chunk): + extracted_chunk = "" + finished = False + print(f"idx: {idx}") + print(f"chunk: {chunk}") + decision = False + if idx == 0: # ensure role assistant is set + decision = validate_first_function_call_chunk_structure(chunk) + role = chunk["choices"][0]["delta"]["role"] + assert role == "assistant" + elif idx != 0: # second chunk + try: + decision = validate_second_function_call_chunk_structure(data=chunk) + except: # check if it's the last chunk (returns an empty delta {} ) + decision = validate_final_function_call_chunk_structure(data=chunk) + finished = True + if "content" in chunk["choices"][0]["delta"]: + extracted_chunk = chunk["choices"][0]["delta"]["content"] + if decision == False: + raise Exception("incorrect format") + return extracted_chunk, finished + +# def test_openai_streaming_and_function_calling(): +# function1 = [ +# { +# "name": "get_current_weather", +# "description": "Get the current weather in a given location", +# "parameters": { +# "type": "object", +# "properties": { +# "location": { +# "type": "string", +# "description": "The city and state, e.g. San Francisco, CA", +# }, +# "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, +# }, +# "required": ["location"], +# }, +# } +# ] +# messages=[{"role": "user", "content": "What is the weather like in Boston?"}] +# try: +# response = completion( +# model="gpt-3.5-turbo", functions=function1, messages=messages, stream=True, +# ) +# # Add any assertions here to check the response +# for idx, chunk in enumerate(response): +# streaming_and_function_calling_format_tests(idx=idx, chunk=chunk) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# raise e + +# test_openai_streaming_and_function_calling() + +def test_success_callback_streaming(): + def success_callback(kwargs, completion_response, start_time, end_time): + print( + { + "success": True, + "input": kwargs, + "output": completion_response, + "start_time": start_time, + "end_time": end_time, + } + ) + + + litellm.success_callback = [success_callback] + + messages = [{"role": "user", "content": "hello"}] + print("TESTING LITELLM COMPLETION CALL") + response = litellm.completion( + model="j2-light", + messages=messages, stream=True, + max_tokens=5, + ) + print(response) + + + for chunk in response: + print(chunk["choices"][0]) + +# test_success_callback_streaming() \ No newline at end of file diff --git a/litellm/tests/test_supabase_integration.py b/litellm/tests/test_supabase_integration.py new file mode 100644 index 0000000000000000000000000000000000000000..b07505c2383e186a0d3a98f4744d2b0fc46dbaed --- /dev/null +++ b/litellm/tests/test_supabase_integration.py @@ -0,0 +1,75 @@ +#### What this tests #### +# This tests if logging to the supabase integration actually works +import sys, os +import traceback +import pytest + +sys.path.insert(0, os.path.abspath('../..')) # Adds the parent directory to the system path +import litellm +from litellm import embedding, completion + +litellm.input_callback = ["supabase"] +litellm.success_callback = ["supabase"] +litellm.failure_callback = ["supabase"] + + +litellm.set_verbose = False + +def test_supabase_logging(): + try: + response = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello tell me hi"}], + user="ishaanRegular", + max_tokens=10, + ) + print(response) + except Exception as e: + print(e) + +# test_supabase_logging() + +def test_acompletion_sync(): + import asyncio + import time + async def completion_call(): + try: + response = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "write a poem"}], + max_tokens=10, + stream=True, + user="ishaanStreamingUser", + timeout=5 + ) + complete_response = "" + start_time = time.time() + async for chunk in response: + chunk_time = time.time() + #print(chunk) + complete_response += chunk["choices"][0]["delta"].get("content", "") + #print(complete_response) + #print(f"time since initial request: {chunk_time - start_time:.5f}") + + if chunk["choices"][0].get("finish_reason", None) != None: + print("🤗🤗🤗 DONE") + return + + except litellm.Timeout as e: + pass + except: + print(f"error occurred: {traceback.format_exc()}") + pass + + asyncio.run(completion_call()) +# test_acompletion_sync() + + +# reset callbacks +litellm.input_callback = [] +litellm.success_callback = [] +litellm.failure_callback = [] + + + + diff --git a/litellm/tests/test_text_completion.py b/litellm/tests/test_text_completion.py new file mode 100644 index 0000000000000000000000000000000000000000..660367651cca22bdcd7d33904b7aa2db61d5fecf --- /dev/null +++ b/litellm/tests/test_text_completion.py @@ -0,0 +1,170 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import embedding, completion, text_completion, completion_cost +from litellm import RateLimitError + + +token_prompt = [[32, 2043, 32, 329, 4585, 262, 1644, 14, 34, 3705, 319, 616, 47551, 30, 930, 19219, 284, 1949, 284, 787, 428, 355, 1790, 355, 1744, 981, 1390, 3307, 2622, 13, 220, 198, 198, 40, 423, 587, 351, 616, 41668, 32682, 329, 718, 812, 13, 376, 666, 32682, 468, 281, 4697, 6621, 11, 356, 1183, 869, 607, 25737, 11, 508, 318, 2579, 290, 468, 257, 642, 614, 1468, 1200, 13, 314, 373, 612, 262, 1110, 25737, 373, 287, 4827, 290, 14801, 373, 4642, 11, 673, 318, 616, 41803, 13, 2399, 2104, 1641, 468, 6412, 284, 502, 355, 465, 38074, 494, 1201, 1110, 352, 13, 314, 716, 407, 2910, 475, 356, 389, 1641, 11, 673, 3848, 502, 38074, 494, 290, 356, 423, 3993, 13801, 11, 26626, 11864, 11, 3503, 13, 220, 198, 198, 17, 812, 2084, 25737, 373, 287, 14321, 422, 2563, 13230, 13, 21051, 11, 2356, 25542, 11, 290, 47482, 897, 547, 607, 1517, 13, 1375, 550, 257, 5110, 14608, 290, 262, 1641, 7723, 1637, 284, 3758, 607, 284, 14321, 290, 477, 8389, 257, 7269, 284, 1011, 1337, 286, 14801, 13, 383, 5156, 338, 9955, 11, 25737, 338, 13850, 11, 468, 257, 47973, 14, 9979, 2762, 1693, 290, 373, 503, 286, 3240, 329, 362, 1933, 523, 339, 2492, 470, 612, 329, 477, 286, 428, 13, 220, 198, 198, 3347, 10667, 5223, 503, 706, 513, 1528, 11, 23630, 673, 373, 366, 38125, 290, 655, 2622, 257, 3338, 8399, 1911, 314, 2298, 607, 510, 11, 1011, 607, 284, 607, 2156, 11, 290, 673, 3393, 2925, 284, 7523, 20349, 290, 4144, 257, 6099, 13, 314, 836, 470, 892, 20349, 318, 257, 2563, 290, 716, 845, 386, 12, 66, 1236, 571, 292, 3584, 314, 836, 470, 7523, 11, 475, 326, 373, 407, 5035, 6402, 314, 655, 6497, 607, 510, 422, 14321, 13, 220, 198, 198, 32, 1285, 1568, 673, 373, 6294, 329, 3013, 24707, 287, 262, 12436, 1539, 819, 5722, 329, 852, 604, 1933, 2739, 11, 39398, 607, 1097, 5059, 981, 1029, 290, 318, 852, 16334, 329, 720, 1120, 74, 422, 15228, 278, 656, 257, 2156, 11, 290, 373, 12165, 503, 286, 376, 666, 32682, 338, 584, 6621, 338, 2156, 329, 32012, 262, 14595, 373, 30601, 510, 290, 2491, 357, 7091, 373, 1029, 8, 290, 262, 2104, 34624, 373, 46432, 1268, 1961, 422, 1660, 2465, 780, 8168, 2073, 1625, 1363, 329, 807, 2250, 13, 720, 1238, 11, 830, 286, 2465, 290, 5875, 5770, 511, 2156, 5096, 5017, 340, 13, 220, 198, 198, 2504, 373, 477, 938, 614, 13, 1119, 1053, 587, 287, 511, 649, 2156, 319, 511, 898, 329, 546, 718, 1933, 13, 554, 3389, 673, 1444, 34020, 290, 531, 511, 8744, 373, 4423, 572, 780, 673, 1422, 470, 423, 262, 1637, 780, 41646, 338, 37751, 1392, 32621, 510, 290, 1422, 470, 467, 832, 13, 679, 3432, 511, 2739, 8744, 9024, 492, 257, 2472, 286, 720, 4059, 13, 314, 1807, 340, 373, 13678, 306, 5789, 475, 4030, 616, 5422, 4423, 13, 1439, 468, 587, 5897, 1201, 13, 220, 198, 198, 7571, 2745, 2084, 11, 673, 1965, 502, 284, 8804, 617, 1637, 284, 651, 38464, 329, 399, 8535, 13, 3226, 1781, 314, 1101, 407, 1016, 284, 1309, 616, 41803, 393, 6621, 467, 14720, 11, 645, 2300, 644, 318, 1016, 319, 4306, 11, 523, 314, 910, 314, 1183, 307, 625, 379, 642, 13, 314, 1392, 572, 670, 1903, 290, 651, 612, 379, 362, 25, 2231, 13, 314, 1282, 287, 1262, 616, 13952, 1994, 11, 2513, 287, 11, 766, 399, 8535, 2712, 351, 36062, 287, 262, 5228, 11, 25737, 3804, 503, 319, 262, 18507, 11, 290, 16914, 319, 262, 6891, 3084, 13, 8989, 2406, 422, 257, 1641, 47655, 351, 13230, 11, 314, 760, 644, 16914, 3073, 588, 13, 314, 836, 470, 760, 703, 881, 340, 373, 11, 475, 314, 714, 423, 23529, 276, 340, 510, 290, 5901, 616, 18057, 351, 340, 13, 314, 6810, 19772, 2024, 8347, 287, 262, 2166, 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print(response.choices[0]) + #print(response.choices[0].text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_openai_prompt() + +def test_completion_openai_engine_and_model(): + try: + print("\n text 003 test\n") + litellm.set_verbose=True + response = text_completion( + model="text-davinci-003", engine="anything", prompt="What's the weather in SF?", max_tokens=5 + ) + print(response) + response_str = response["choices"][0]["text"] + # print(response.choices[0]) + #print(response.choices[0].text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_openai_engine_and_model() + +def test_completion_openai_engine(): + try: + print("\n text 003 test\n") + litellm.set_verbose=True + response = text_completion( + engine="text-davinci-003", prompt="What's the weather in SF?", max_tokens=5 + ) + print(response) + response_str = response["choices"][0]["text"] + # print(response.choices[0]) + #print(response.choices[0].text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +test_completion_openai_engine() + + +def test_completion_chatgpt_prompt(): + try: + print("\n gpt3.5 test\n") + response = text_completion( + model="gpt-3.5-turbo", prompt="What's the weather in SF?" + ) + print(response) + response_str = response["choices"][0]["text"] + print("\n", response.choices) + print("\n", response.choices[0]) + #print(response.choices[0].text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_chatgpt_prompt() + + +def test_text_completion_basic(): + try: + print("\n test 003 with echo and logprobs \n") + litellm.set_verbose=False + response = text_completion( + model="text-davinci-003", prompt="good morning", max_tokens=10, logprobs=10, echo=True + ) + print(response) + print(response.choices) + print(response.choices[0]) + #print(response.choices[0].text) + response_str = response["choices"][0]["text"] + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_text_completion_basic() + + +def test_completion_text_003_prompt_array(): + try: + litellm.set_verbose=False + response = text_completion( + model="text-davinci-003", + prompt=token_prompt, # token prompt is a 2d list + ) + print("\n\n response") + + print(response) + # response_str = response["choices"][0]["text"] + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_text_003_prompt_array() + + +# not including this in our ci cd pipeline, since we don't want to fail tests due to an unstable replit +# def test_text_completion_with_proxy(): +# try: +# litellm.set_verbose=True +# response = text_completion( +# model="facebook/opt-125m", +# prompt='Write a tagline for a traditional bavarian tavern', +# api_base="https://openai-proxy.berriai.repl.co/v1", +# custom_llm_provider="openai", +# temperature=0, +# max_tokens=10, +# ) +# print("\n\n response") + +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_text_completion_with_proxy() + +##### hugging face tests +def test_completion_hf_prompt_array(): + try: + litellm.set_verbose=True + print("\n testing hf mistral\n") + response = text_completion( + model="huggingface/mistralai/Mistral-7B-v0.1", + prompt=token_prompt, # token prompt is a 2d list, + max_tokens=0, + temperature=0.0, + echo=True, + ) + print("\n\n response") + + print(response) + print(response.choices) + assert(len(response.choices)==2) + # response_str = response["choices"][0]["text"] + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_hf_prompt_array() + +def test_text_completion_stream(): + try: + response = text_completion( + model="huggingface/mistralai/Mistral-7B-v0.1", + prompt="good morning", + stream=True, + max_tokens=10, + ) + for chunk in response: + print(chunk) + except Exception as e: + pytest.fail(f"GOT exception for HF In streaming{e}") + +test_text_completion_stream() diff --git a/litellm/tests/test_timeout.py b/litellm/tests/test_timeout.py new file mode 100644 index 0000000000000000000000000000000000000000..64c30e51ed1477a45931b8b7df6f6371804bf392 --- /dev/null +++ b/litellm/tests/test_timeout.py @@ -0,0 +1,63 @@ +#### What this tests #### +# This tests the timeout decorator + +import sys, os +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import time +import litellm +import openai +import pytest + + +def test_timeout(): + # this Will Raise a timeout + litellm.set_verbose=False + try: + response = litellm.completion( + model="gpt-3.5-turbo", + timeout=0.01, + messages=[ + { + "role": "user", + "content": "hello, write a 20 pg essay" + } + ] + ) + except openai.APITimeoutError as e: + print("Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e) + print(type(e)) + pass + except Exception as e: + pytest.fail(f"Did not raise error `openai.APITimeoutError`. Instead raised error type: {type(e)}, Error: {e}") +# test_timeout() + + + +def test_timeout_streaming(): + # this Will Raise a timeout + litellm.set_verbose=False + try: + response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "hello, write a 20 pg essay" + } + ], + timeout=0.0001, + stream=True, + ) + for chunk in response: + print(chunk) + except openai.APITimeoutError as e: + print("Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e) + print(type(e)) + pass + except Exception as e: + pytest.fail(f"Did not raise error `openai.APITimeoutError`. Instead raised error type: {type(e)}, Error: {e}") +test_timeout_streaming() \ No newline at end of file diff --git a/litellm/tests/test_token_counter.py b/litellm/tests/test_token_counter.py new file mode 100644 index 0000000000000000000000000000000000000000..b30e1126d90cf6d1f69a8fc87033d42e597b0a0e --- /dev/null +++ b/litellm/tests/test_token_counter.py @@ -0,0 +1,89 @@ +#### What this tests #### +# This tests litellm.token_counter() function + +import sys, os +import traceback +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import time +from litellm import token_counter, encode, decode + + +def test_token_counter_normal_plus_function_calling(): + try: + messages = [ + {'role': 'system', 'content': "System prompt"}, + {'role': 'user', 'content': 'content1'}, + {'role': 'assistant', 'content': 'content2'}, + {'role': 'user', 'content': 'conten3'}, + {'role': 'assistant', 'content': None, 'tool_calls': [{'id': 'call_E0lOb1h6qtmflUyok4L06TgY', 'function': {'arguments': '{"query":"search query","domain":"google.ca","gl":"ca","hl":"en"}', 'name': 'SearchInternet'}, 'type': 'function'}]}, + {'tool_call_id': 'call_E0lOb1h6qtmflUyok4L06TgY', 'role': 'tool', 'name': 'SearchInternet', 'content': 'tool content'} + ] + tokens = token_counter(model="gpt-3.5-turbo", messages=messages) + print(f"tokens: {tokens}") + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + +test_token_counter_normal_plus_function_calling() + +def test_tokenizers(): + try: + ### test the openai, claude, cohere and llama2 tokenizers. + ### The tokenizer value should be different for all + sample_text = "Hellö World, this is my input string!" + + # openai tokenizer + openai_tokens = token_counter(model="gpt-3.5-turbo", text=sample_text) + + # claude tokenizer + claude_tokens = token_counter(model="claude-instant-1", text=sample_text) + + # cohere tokenizer + cohere_tokens = token_counter(model="command-nightly", text=sample_text) + + # llama2 tokenizer + llama2_tokens = token_counter(model="meta-llama/Llama-2-7b-chat", text=sample_text) + + print(f"openai tokens: {openai_tokens}; claude tokens: {claude_tokens}; cohere tokens: {cohere_tokens}; llama2 tokens: {llama2_tokens}") + + # assert that all token values are different + assert openai_tokens != cohere_tokens != llama2_tokens, "Token values are not different." + + print("test tokenizer: It worked!") + except Exception as e: + pytest.fail(f'An exception occured: {e}') + +# test_tokenizers() + +def test_encoding_and_decoding(): + try: + sample_text = "Hellö World, this is my input string!" + # openai encoding + decoding + openai_tokens = encode(model="gpt-3.5-turbo", text=sample_text) + openai_text = decode(model="gpt-3.5-turbo", tokens=openai_tokens) + + assert openai_text == sample_text + + # claude encoding + decoding + claude_tokens = encode(model="claude-instant-1", text=sample_text) + claude_text = decode(model="claude-instant-1", tokens=claude_tokens.ids) + + assert claude_text == sample_text + + # cohere encoding + decoding + cohere_tokens = encode(model="command-nightly", text=sample_text) + cohere_text = decode(model="command-nightly", tokens=cohere_tokens.ids) + + assert cohere_text == sample_text + + # llama2 encoding + decoding + llama2_tokens = encode(model="meta-llama/Llama-2-7b-chat", text=sample_text) + llama2_text = decode(model="meta-llama/Llama-2-7b-chat", tokens=llama2_tokens.ids) + + assert llama2_text == sample_text + except Exception as e: + pytest.fail(f'An exception occured: {e}') + +# test_encoding_and_decoding() \ No newline at end of file diff --git a/litellm/tests/test_utils.py b/litellm/tests/test_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..394682fd14969829a65acfaa1a56422a37446f17 --- /dev/null +++ b/litellm/tests/test_utils.py @@ -0,0 +1,236 @@ +import sys, os +from dotenv import load_dotenv +import copy + +load_dotenv() +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm.utils import trim_messages, get_token_count, get_valid_models, check_valid_key, validate_environment, function_to_dict, token_counter + +# Assuming your trim_messages, shorten_message_to_fit_limit, and get_token_count functions are all in a module named 'message_utils' + +# Test 1: Check trimming of normal message +def test_basic_trimming(): + messages = [{"role": "user", "content": "This is a long message that definitely exceeds the token limit."}] + trimmed_messages = trim_messages(messages, model="claude-2", max_tokens=8) + print("trimmed messages") + print(trimmed_messages) + # print(get_token_count(messages=trimmed_messages, model="claude-2")) + assert (get_token_count(messages=trimmed_messages, model="claude-2")) <= 8 +# test_basic_trimming() + +def test_basic_trimming_no_max_tokens_specified(): + messages = [{"role": "user", "content": "This is a long message that is definitely under the token limit."}] + trimmed_messages = trim_messages(messages, model="gpt-4") + print("trimmed messages for gpt-4") + print(trimmed_messages) + # print(get_token_count(messages=trimmed_messages, model="claude-2")) + assert (get_token_count(messages=trimmed_messages, model="gpt-4")) <= litellm.model_cost['gpt-4']['max_tokens'] +# test_basic_trimming_no_max_tokens_specified() + +def test_multiple_messages_trimming(): + messages = [ + {"role": "user", "content": "This is a long message that will exceed the token limit."}, + {"role": "user", "content": "This is another long message that will also exceed the limit."} + ] + trimmed_messages = trim_messages(messages=messages, model="gpt-3.5-turbo", max_tokens=20) + # print(get_token_count(messages=trimmed_messages, model="gpt-3.5-turbo")) + assert(get_token_count(messages=trimmed_messages, model="gpt-3.5-turbo")) <= 20 +# test_multiple_messages_trimming() + +def test_multiple_messages_no_trimming(): + messages = [ + {"role": "user", "content": "This is a long message that will exceed the token limit."}, + {"role": "user", "content": "This is another long message that will also exceed the limit."} + ] + trimmed_messages = trim_messages(messages=messages, model="gpt-3.5-turbo", max_tokens=100) + print("Trimmed messages") + print(trimmed_messages) + assert(messages==trimmed_messages) + +# test_multiple_messages_no_trimming() + + +def test_large_trimming_multiple_messages(): + messages = [{"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."}, {"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."},{"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."},{"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."},{"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."}] + trimmed_messages = trim_messages(messages, max_tokens=20, model="gpt-4-0613") + print("trimmed messages") + print(trimmed_messages) + assert(get_token_count(messages=trimmed_messages, model="gpt-4-0613")) <= 20 +# test_large_trimming() + +def test_large_trimming_single_message(): + messages = [{"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."}] + trimmed_messages = trim_messages(messages, max_tokens=5, model="gpt-4-0613") + assert(get_token_count(messages=trimmed_messages, model="gpt-4-0613")) <= 5 + assert(get_token_count(messages=trimmed_messages, model="gpt-4-0613")) > 0 + + +def test_trimming_with_system_message_within_max_tokens(): + # This message is 33 tokens long + messages = [{"role": "system", "content": "This is a short system message"}, {"role": "user", "content": "This is a medium normal message, let's say litellm is awesome."}] + trimmed_messages = trim_messages(messages, max_tokens=30, model="gpt-4-0613") # The system message should fit within the token limit + assert len(trimmed_messages) == 2 + assert trimmed_messages[0]["content"] == "This is a short system message" + + +def test_trimming_with_system_message_exceeding_max_tokens(): + # This message is 33 tokens long. The system message is 13 tokens long. + messages = [{"role": "system", "content": "This is a short system message"}, {"role": "user", "content": "This is a medium normal message, let's say litellm is awesome."}] + trimmed_messages = trim_messages(messages, max_tokens=12, model="gpt-4-0613") + assert len(trimmed_messages) == 1 + +def test_trimming_should_not_change_original_messages(): + messages = [{"role": "system", "content": "This is a short system message"}, {"role": "user", "content": "This is a medium normal message, let's say litellm is awesome."}] + messages_copy = copy.deepcopy(messages) + trimmed_messages = trim_messages(messages, max_tokens=12, model="gpt-4-0613") + assert(messages==messages_copy) + +def test_get_valid_models(): + old_environ = os.environ + os.environ = {'OPENAI_API_KEY': 'temp'} # mock set only openai key in environ + + valid_models = get_valid_models() + print(valid_models) + + # list of openai supported llms on litellm + expected_models = litellm.open_ai_chat_completion_models + litellm.open_ai_text_completion_models + + assert(valid_models == expected_models) + + # reset replicate env key + os.environ = old_environ + +# test_get_valid_models() + +def test_bad_key(): + key = "bad-key" + response = check_valid_key(model="gpt-3.5-turbo", api_key=key) + print(response, key) + assert(response == False) + +def test_good_key(): + key = os.environ['OPENAI_API_KEY'] + response = check_valid_key(model="gpt-3.5-turbo", api_key=key) + assert(response == True) + +# test validate environment + +def test_validate_environment_empty_model(): + api_key = validate_environment() + if api_key is None: + raise Exception() + +# test_validate_environment_empty_model() + +def test_function_to_dict(): + print("testing function to dict for get current weather") + def get_current_weather(location: str, unit: str): + """Get the current weather in a given location + + Parameters + ---------- + location : str + The city and state, e.g. San Francisco, CA + unit : {'celsius', 'fahrenheit'} + Temperature unit + + Returns + ------- + str + a sentence indicating the weather + """ + if location == "Boston, MA": + return "The weather is 12F" + function_json = litellm.utils.function_to_dict(get_current_weather) + print(function_json) + + expected_output = { + 'name': 'get_current_weather', + 'description': 'Get the current weather in a given location', + 'parameters': { + 'type': 'object', + 'properties': { + 'location': {'type': 'string', 'description': 'The city and state, e.g. San Francisco, CA'}, + 'unit': {'type': 'string', 'description': 'Temperature unit', 'enum': "['fahrenheit', 'celsius']"} + }, + 'required': ['location', 'unit'] + } + } + print(expected_output) + + assert function_json['name'] == expected_output["name"] + assert function_json["description"] == expected_output["description"] + assert function_json["parameters"]["type"] == expected_output["parameters"]["type"] + assert function_json["parameters"]["properties"]["location"] == expected_output["parameters"]["properties"]["location"] + + # the enum can change it can be - which is why we don't assert on unit + # {'type': 'string', 'description': 'Temperature unit', 'enum': "['fahrenheit', 'celsius']"} + # {'type': 'string', 'description': 'Temperature unit', 'enum': "['celsius', 'fahrenheit']"} + + assert function_json["parameters"]["required"] == expected_output["parameters"]["required"] + + print("passed") +# test_function_to_dict() + + +def test_token_counter(): + try: + messages = [ + { + "role": "user", + "content": "hi how are you what time is it" + } + ] + tokens = token_counter( + model = "gpt-3.5-turbo", + messages=messages + ) + print("gpt-35-turbo") + print(tokens) + assert tokens > 0 + + tokens = token_counter( + model = "claude-2", + messages=messages + ) + print("claude-2") + print(tokens) + assert tokens > 0 + + tokens = token_counter( + model = "palm/chat-bison", + messages=messages + ) + print("palm/chat-bison") + print(tokens) + assert tokens > 0 + + tokens = token_counter( + model = "ollama/llama2", + messages=messages + ) + print("ollama/llama2") + print(tokens) + assert tokens > 0 + + tokens = token_counter( + model = "anthropic.claude-instant-v1", + messages=messages + ) + print("anthropic.claude-instant-v1") + print(tokens) + assert tokens > 0 + except Exception as e: + pytest.fail(f"Error occurred: {e}") +test_token_counter() + + + + + diff --git a/litellm/tests/test_validate_environment.py b/litellm/tests/test_validate_environment.py new file mode 100644 index 0000000000000000000000000000000000000000..2b60c7fe8d0f2bf661622156907011947df21321 --- /dev/null +++ b/litellm/tests/test_validate_environment.py @@ -0,0 +1,13 @@ +#### What this tests #### +# This tests the validate environment function + +import sys, os +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import time +import litellm + +print(litellm.validate_environment("openai/gpt-3.5-turbo")) \ No newline at end of file diff --git a/litellm/tests/test_wandb.py b/litellm/tests/test_wandb.py new file mode 100644 index 0000000000000000000000000000000000000000..fe10b3e61654701918d87482595e0312b6f1ecdf --- /dev/null +++ b/litellm/tests/test_wandb.py @@ -0,0 +1,58 @@ +import sys +import os +import io, asyncio +# import logging +# logging.basicConfig(level=logging.DEBUG) +sys.path.insert(0, os.path.abspath('../..')) + +from litellm import completion +import litellm +litellm.num_retries = 3 +litellm.success_callback = ["wandb"] +import time +import pytest + +def test_wandb_logging_async(): + try: + litellm.set_verbose = False + async def _test_langfuse(): + from litellm import Router + model_list = [{ # list of model deployments + "model_name": "gpt-3.5-turbo", + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + } + }] + + router = Router(model_list=model_list) + + # openai.ChatCompletion.create replacement + response = await router.acompletion(model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "this is a test with litellm router ?"}]) + print(response) + response = asyncio.run(_test_langfuse()) + print(f"response: {response}") + except litellm.Timeout as e: + pass + except Exception as e: + pass +test_wandb_logging_async() + +def test_wandb_logging(): + try: + response = completion(model="claude-instant-1.2", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm claude" + }], + max_tokens=10, + temperature=0.2 + ) + print(response) + except litellm.Timeout as e: + pass + except Exception as e: + print(e) + +# test_wandb_logging() diff --git a/litellm/tests/user_cost.json b/litellm/tests/user_cost.json new file mode 100644 index 0000000000000000000000000000000000000000..b3411c777cd5eed30989740cbe08afedbbd70e08 --- /dev/null +++ b/litellm/tests/user_cost.json @@ -0,0 +1,12 @@ +{ + "1234": { + "total_budget": 10, + "current_cost": 7.3e-05, + "model_cost": { + "gpt-3.5-turbo-0613": 7.3e-05 + } + }, + "12345": { + "total_budget": 0 + } +} \ No newline at end of file diff --git a/litellm/timeout.py b/litellm/timeout.py new file mode 100644 index 0000000000000000000000000000000000000000..9007c309e0bc994fd27382ad4ef8bc853de69ffa --- /dev/null +++ b/litellm/timeout.py @@ -0,0 +1,111 @@ +# +-----------------------------------------------+ +# | | +# | Give Feedback / Get Help | +# | https://github.com/BerriAI/litellm/issues/new | +# | | +# +-----------------------------------------------+ +# +# Thank you users! We ❤️ you! - Krrish & Ishaan + +""" +Module containing "timeout" decorator for sync and async callables. +""" + +import asyncio + +from concurrent import futures +from inspect import iscoroutinefunction +from functools import wraps +from threading import Thread +from litellm.exceptions import Timeout + + +def timeout(timeout_duration: float = 0.0, exception_to_raise=Timeout): + """ + Wraps a function to raise the specified exception if execution time + is greater than the specified timeout. + + Works with both synchronous and asynchronous callables, but with synchronous ones will introduce + some overhead due to the backend use of threads and asyncio. + + :param float timeout_duration: Timeout duration in seconds. If none callable won't time out. + :param OpenAIError exception_to_raise: Exception to raise when the callable times out. + Defaults to TimeoutError. + :return: The decorated function. + :rtype: callable + """ + + def decorator(func): + @wraps(func) + def wrapper(*args, **kwargs): + async def async_func(): + return func(*args, **kwargs) + + thread = _LoopWrapper() + thread.start() + future = asyncio.run_coroutine_threadsafe(async_func(), thread.loop) + local_timeout_duration = timeout_duration + if "force_timeout" in kwargs and kwargs["force_timeout"] is not None: + local_timeout_duration = kwargs["force_timeout"] + elif "request_timeout" in kwargs and kwargs["request_timeout"] is not None: + local_timeout_duration = kwargs["request_timeout"] + try: + result = future.result(timeout=local_timeout_duration) + except futures.TimeoutError: + thread.stop_loop() + model = args[0] if len(args) > 0 else kwargs["model"] + raise exception_to_raise( + f"A timeout error occurred. The function call took longer than {local_timeout_duration} second(s).", + model=model, # [TODO]: replace with logic for parsing out llm provider from model name + llm_provider="openai" + ) + thread.stop_loop() + return result + + @wraps(func) + async def async_wrapper(*args, **kwargs): + local_timeout_duration = timeout_duration + if "force_timeout" in kwargs: + local_timeout_duration = kwargs["force_timeout"] + elif "request_timeout" in kwargs and kwargs["request_timeout"] is not None: + local_timeout_duration = kwargs["request_timeout"] + try: + value = await asyncio.wait_for( + func(*args, **kwargs), timeout=timeout_duration + ) + return value + except asyncio.TimeoutError: + model = args[0] if len(args) > 0 else kwargs["model"] + raise exception_to_raise( + f"A timeout error occurred. The function call took longer than {local_timeout_duration} second(s).", + model=model, # [TODO]: replace with logic for parsing out llm provider from model name + llm_provider="openai" + ) + + if iscoroutinefunction(func): + return async_wrapper + return wrapper + + return decorator + + +class _LoopWrapper(Thread): + def __init__(self): + super().__init__(daemon=True) + self.loop = asyncio.new_event_loop() + + def run(self) -> None: + try: + self.loop.run_forever() + self.loop.call_soon_threadsafe(self.loop.close) + except Exception as e: + # Log exception here + pass + finally: + self.loop.close() + asyncio.set_event_loop(None) + + def stop_loop(self): + for task in asyncio.all_tasks(self.loop): + task.cancel() + self.loop.call_soon_threadsafe(self.loop.stop) diff --git a/litellm/utils.py b/litellm/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..892fc010c1c9dbacabf4d0b262cf1e8bff4e0b5d --- /dev/null +++ b/litellm/utils.py @@ -0,0 +1,5937 @@ +# +-----------------------------------------------+ +# | | +# | Give Feedback / Get Help | +# | https://github.com/BerriAI/litellm/issues/new | +# | | +# +-----------------------------------------------+ +# +# Thank you users! We ❤️ you! - Krrish & Ishaan + +import sys, re +import dotenv, json, traceback, threading +import subprocess, os +import litellm, openai +import itertools +import random, uuid, requests +import datetime, time +import tiktoken +import uuid +import aiohttp +import logging +import asyncio, httpx, inspect +import copy +from tokenizers import Tokenizer +from dataclasses import ( + dataclass, + field, +) # for storing API inputs, outputs, and metadata +encoding = tiktoken.get_encoding("cl100k_base") +import importlib.metadata +from .integrations.traceloop import TraceloopLogger +from .integrations.helicone import HeliconeLogger +from .integrations.aispend import AISpendLogger +from .integrations.berrispend import BerriSpendLogger +from .integrations.supabase import Supabase +from .integrations.llmonitor import LLMonitorLogger +from .integrations.prompt_layer import PromptLayerLogger +from .integrations.langsmith import LangsmithLogger +from .integrations.weights_biases import WeightsBiasesLogger +from .integrations.custom_logger import CustomLogger +from .integrations.langfuse import LangFuseLogger +from .integrations.litedebugger import LiteDebugger +from openai import OpenAIError as OriginalError +from openai._models import BaseModel as OpenAIObject +from .exceptions import ( + AuthenticationError, + BadRequestError, + RateLimitError, + ServiceUnavailableError, + OpenAIError, + ContextWindowExceededError, + Timeout, + APIConnectionError, + APIError, + BudgetExceededError +) +from typing import cast, List, Dict, Union, Optional, Literal +from .caching import Cache +from concurrent.futures import ThreadPoolExecutor +####### ENVIRONMENT VARIABLES #################### +# Adjust to your specific application needs / system capabilities. +MAX_THREADS = 100 + +# Create a ThreadPoolExecutor +executor = ThreadPoolExecutor(max_workers=MAX_THREADS) +dotenv.load_dotenv() # Loading env variables using dotenv +sentry_sdk_instance = None +capture_exception = None +add_breadcrumb = None +posthog = None +slack_app = None +alerts_channel = None +heliconeLogger = None +promptLayerLogger = None +langsmithLogger = None +weightsBiasesLogger = None +customLogger = None +langFuseLogger = None +llmonitorLogger = None +aispendLogger = None +berrispendLogger = None +supabaseClient = None +liteDebuggerClient = None +callback_list: Optional[List[str]] = [] +user_logger_fn = None +additional_details: Optional[Dict[str, str]] = {} +local_cache: Optional[Dict[str, str]] = {} +last_fetched_at = None +last_fetched_at_keys = None +######## Model Response ######################### +# All liteLLM Model responses will be in this format, Follows the OpenAI Format +# https://docs.litellm.ai/docs/completion/output +# { +# 'choices': [ +# { +# 'finish_reason': 'stop', +# 'index': 0, +# 'message': { +# 'role': 'assistant', +# 'content': " I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." +# } +# } +# ], +# 'created': 1691429984.3852863, +# 'model': 'claude-instant-1', +# 'usage': {'prompt_tokens': 18, 'completion_tokens': 23, 'total_tokens': 41} +# } + +class UnsupportedParamsError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url=" https://openai.api.com/v1/") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +def _generate_id(): # private helper function + return 'chatcmpl-' + str(uuid.uuid4()) + +def map_finish_reason(finish_reason: str): # openai supports 5 stop sequences - 'stop', 'length', 'function_call', 'content_filter', 'null' + # anthropic mapping + if finish_reason == "stop_sequence": + return "stop" + return finish_reason + +class FunctionCall(OpenAIObject): + arguments: str + name: str + +class Function(OpenAIObject): + arguments: str + name: str + +class ChatCompletionMessageToolCall(OpenAIObject): + id: str + function: Function + type: str + +class Message(OpenAIObject): + def __init__(self, content="default", role="assistant", logprobs=None, function_call=None, tool_calls=None, **params): + super(Message, self).__init__(**params) + self.content = content + self.role = role + if function_call is not None: + self.function_call = FunctionCall(**function_call) + if tool_calls is not None: + self.tool_calls = [] + for tool_call in tool_calls: + self.tool_calls.append( + ChatCompletionMessageToolCall(**tool_call) + ) + if logprobs is not None: + self._logprobs = logprobs + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + +class Delta(OpenAIObject): + def __init__(self, content=None, role=None, **params): + super(Delta, self).__init__(**params) + self.content = content + self.role = role + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + + +class Choices(OpenAIObject): + def __init__(self, finish_reason=None, index=0, message=None, **params): + super(Choices, self).__init__(**params) + self.finish_reason = map_finish_reason(finish_reason) # set finish_reason for all responses + self.index = index + if message is None: + self.message = Message(content=None) + else: + self.message = message + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + +class Usage(OpenAIObject): + def __init__(self, prompt_tokens=None, completion_tokens=None, total_tokens=None, **params): + super(Usage, self).__init__(**params) + if prompt_tokens: + self.prompt_tokens = prompt_tokens + if completion_tokens: + self.completion_tokens = completion_tokens + if total_tokens: + self.total_tokens = total_tokens + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + +class StreamingChoices(OpenAIObject): + def __init__(self, finish_reason=None, index=0, delta: Optional[Delta]=None, **params): + super(StreamingChoices, self).__init__(**params) + if finish_reason: + self.finish_reason = finish_reason + else: + self.finish_reason = None + self.index = index + if delta: + self.delta = delta + else: + self.delta = Delta() + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + +class ModelResponse(OpenAIObject): + id: str + """A unique identifier for the completion.""" + + choices: List[Union[Choices, StreamingChoices]] + """The list of completion choices the model generated for the input prompt.""" + + created: int + """The Unix timestamp (in seconds) of when the completion was created.""" + + model: Optional[str] = None + """The model used for completion.""" + + object: str + """The object type, which is always "text_completion" """ + + system_fingerprint: Optional[str] = None + """This fingerprint represents the backend configuration that the model runs with. + + Can be used in conjunction with the `seed` request parameter to understand when + backend changes have been made that might impact determinism. + """ + + usage: Optional[Usage] = None + """Usage statistics for the completion request.""" + + _hidden_params: dict = {} + + def __init__(self, id=None, choices=None, created=None, model=None, object=None, system_fingerprint=None, usage=None, stream=False, response_ms=None, hidden_params=None, **params): + if stream: + object = "chat.completion.chunk" + choices = [StreamingChoices()] + else: + if model in litellm.open_ai_embedding_models: + object = "embedding" + else: + object = "chat.completion" + choices = [Choices()] + if id is None: + id = _generate_id() + else: + id = id + if created is None: + created = int(time.time()) + else: + created = created + model = model + if usage: + usage = usage + else: + usage = Usage() + if hidden_params: + self._hidden_params = hidden_params + super().__init__(id=id, choices=choices, created=created, model=model, object=object, system_fingerprint=system_fingerprint, usage=usage, **params) + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + +class Embedding(OpenAIObject): + embedding: list = [] + index: int + object: str + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + +class EmbeddingResponse(OpenAIObject): + model: Optional[str] = None + """The model used for embedding.""" + + data: Optional[List] = None + """The actual embedding value""" + + object: str + """The object type, which is always "embedding" """ + + usage: Optional[Usage] = None + """Usage statistics for the embedding request.""" + + def __init__(self, model=None, usage=None, stream=False, response_ms=None, data=None): + object = "list" + if response_ms: + _response_ms = response_ms + else: + _response_ms = None + if data: + data = data + else: + data = None + + if usage: + usage = usage + else: + usage = Usage() + + model = model + super().__init__(model=model, object=object, data=data, usage=usage) + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + +class TextChoices(OpenAIObject): + def __init__(self, finish_reason=None, index=0, text=None, logprobs=None, **params): + super(TextChoices, self).__init__(**params) + if finish_reason: + self.finish_reason = map_finish_reason(finish_reason) + else: + self.finish_reason = "stop" + self.index = index + if text: + self.text = text + else: + self.text = None + if logprobs: + self.logprobs = [] + else: + self.logprobs = logprobs + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + +class TextCompletionResponse(OpenAIObject): + """ + { + "id": response["id"], + "object": "text_completion", + "created": response["created"], + "model": response["model"], + "choices": [ + { + "text": response["choices"][0]["message"]["content"], + "index": response["choices"][0]["index"], + "logprobs": transformed_logprobs, + "finish_reason": response["choices"][0]["finish_reason"] + } + ], + "usage": response["usage"] + } + """ + def __init__(self, id=None, choices=None, created=None, model=None, usage=None, stream=False, response_ms=None, **params): + super(TextCompletionResponse, self).__init__(**params) + if stream: + self.object = "text_completion.chunk" + self.choices = [TextChoices()] + else: + self.object = "text_completion" + self.choices = [TextChoices()] + if id is None: + self.id = _generate_id() + else: + self.id = id + if created is None: + self.created = int(time.time()) + else: + self.created = created + if response_ms: + self._response_ms = response_ms + else: + self._response_ms = None + self.model = model + if usage: + self.usage = usage + else: + self.usage = Usage() + self._hidden_params = {} # used in case users want to access the original model response + + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + +############################################################ +def print_verbose(print_statement): + if litellm.set_verbose: + print(print_statement) # noqa + +####### LOGGING ################### +from enum import Enum + +class CallTypes(Enum): + embedding = 'embedding' + completion = 'completion' + acompletion = 'acompletion' + +# Logging function -> log the exact model details + what's being sent | Non-Blocking +class Logging: + global supabaseClient, liteDebuggerClient, promptLayerLogger, weightsBiasesLogger, langsmithLogger, capture_exception, add_breadcrumb, llmonitorLogger + + def __init__(self, model, messages, stream, call_type, start_time, litellm_call_id, function_id): + if call_type not in [item.value for item in CallTypes]: + allowed_values = ", ".join([item.value for item in CallTypes]) + raise ValueError(f"Invalid call_type {call_type}. Allowed values: {allowed_values}") + self.model = model + self.messages = messages + self.stream = stream + self.start_time = start_time # log the call start time + self.call_type = call_type + self.litellm_call_id = litellm_call_id + self.function_id = function_id + self.streaming_chunks = [] # for generating complete stream response + + def update_environment_variables(self, model, user, optional_params, litellm_params): + self.optional_params = optional_params + self.model = model + self.user = user + self.litellm_params = litellm_params + self.logger_fn = litellm_params["logger_fn"] + print_verbose(f"self.optional_params: {self.optional_params}") + self.model_call_details = { + "model": self.model, + "messages": self.messages, + "optional_params": self.optional_params, + "litellm_params": self.litellm_params, + "start_time": self.start_time, + "stream": self.stream, + **self.optional_params + } + + def pre_call(self, input, api_key, model=None, additional_args={}): + # Log the exact input to the LLM API + litellm.error_logs['PRE_CALL'] = locals() + try: + # print_verbose(f"logging pre call for model: {self.model} with call type: {self.call_type}") + self.model_call_details["input"] = input + self.model_call_details["api_key"] = api_key + self.model_call_details["additional_args"] = additional_args + self.model_call_details["log_event_type"] = "pre_api_call" + if ( + model + ): # if model name was changes pre-call, overwrite the initial model call name with the new one + self.model_call_details["model"] = model + + # User Logging -> if you pass in a custom logging function + headers = additional_args.get("headers", {}) + if headers is None: + headers = {} + data = additional_args.get("complete_input_dict", {}) + api_base = additional_args.get("api_base", "") + masked_headers = {k: (v[:-20] + '*' * 20) if (isinstance(v, str) and len(v) > 20) else v for k, v in headers.items()} + formatted_headers = " ".join([f"-H '{k}: {v}'" for k, v in masked_headers.items()]) + + print_verbose(f"PRE-API-CALL ADDITIONAL ARGS: {additional_args}") + + curl_command = "\n\nPOST Request Sent from LiteLLM:\n" + curl_command += "curl -X POST \\\n" + curl_command += f"{api_base} \\\n" + curl_command += f"{formatted_headers} \\\n" if formatted_headers.strip() != "" else "" + curl_command += f"-d '{str(data)}'\n" + if additional_args.get("request_str", None) is not None: + # print the sagemaker / bedrock client request + curl_command = "\nRequest Sent from LiteLLM:\n" + curl_command += additional_args.get("request_str", None) + elif api_base == "": + curl_command = self.model_call_details + print_verbose(f"\033[92m{curl_command}\033[0m\n") + if self.logger_fn and callable(self.logger_fn): + try: + self.logger_fn( + self.model_call_details + ) # Expectation: any logger function passed in by the user should accept a dict object + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}" + ) + + if litellm.max_budget and self.stream: + start_time = self.start_time + end_time = self.start_time # no time has passed as the call hasn't been made yet + time_diff = (end_time - start_time).total_seconds() + float_diff = float(time_diff) + litellm._current_cost += litellm.completion_cost(model=self.model, prompt="".join(message["content"] for message in self.messages), completion="", total_time=float_diff) + + # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made + for callback in litellm.input_callback: + try: + if callback == "supabase": + print_verbose("reaches supabase for logging!") + model = self.model_call_details["model"] + messages = self.model_call_details["input"] + print_verbose(f"supabaseClient: {supabaseClient}") + supabaseClient.input_log_event( + model=model, + messages=messages, + end_user=self.model_call_details.get("user", "default"), + litellm_call_id=self.litellm_params["litellm_call_id"], + print_verbose=print_verbose, + ) + + elif callback == "lite_debugger": + print_verbose(f"reaches litedebugger for logging! - model_call_details {self.model_call_details}") + model = self.model_call_details["model"] + messages = self.model_call_details["input"] + print_verbose(f"liteDebuggerClient: {liteDebuggerClient}") + liteDebuggerClient.input_log_event( + model=model, + messages=messages, + end_user=self.model_call_details.get("user", "default"), + litellm_call_id=self.litellm_params["litellm_call_id"], + litellm_params=self.model_call_details["litellm_params"], + optional_params=self.model_call_details["optional_params"], + print_verbose=print_verbose, + call_type=self.call_type + ) + elif callback == "sentry" and add_breadcrumb: + print_verbose("reaches sentry breadcrumbing") + add_breadcrumb( + category="litellm.llm_call", + message=f"Model Call Details pre-call: {self.model_call_details}", + level="info", + ) + elif isinstance(callback, CustomLogger): # custom logger class + callback.log_pre_api_call( + model=self.model, + messages=self.messages, + kwargs=self.model_call_details, + ) + elif callable(callback): # custom logger functions + customLogger.log_input_event( + model=self.model, + messages=self.messages, + kwargs=self.model_call_details, + print_verbose=print_verbose, + callback_func=callback + ) + except Exception as e: + traceback.print_exc() + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while input logging with integrations {traceback.format_exc()}" + ) + print_verbose( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + except: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}" + ) + print_verbose( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + + def post_call(self, original_response, input=None, api_key=None, additional_args={}): + # Log the exact result from the LLM API, for streaming - log the type of response received + litellm.error_logs['POST_CALL'] = locals() + try: + self.model_call_details["input"] = input + self.model_call_details["api_key"] = api_key + self.model_call_details["original_response"] = original_response + self.model_call_details["additional_args"] = additional_args + self.model_call_details["log_event_type"] = "post_api_call" + + # User Logging -> if you pass in a custom logging function + print_verbose(f"RAW RESPONSE:\n{self.model_call_details.get('original_response', self.model_call_details)}\n\n") + print_verbose( + f"Logging Details Post-API Call: logger_fn - {self.logger_fn} | callable(logger_fn) - {callable(self.logger_fn)}" + ) + print_verbose(f"Logging Details Post-API Call: LiteLLM Params: {self.model_call_details}") + if self.logger_fn and callable(self.logger_fn): + try: + self.logger_fn( + self.model_call_details + ) # Expectation: any logger function passed in by the user should accept a dict object + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}" + ) + + # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made + for callback in litellm.input_callback: + try: + if callback == "lite_debugger": + print_verbose("reaches litedebugger for post-call logging!") + print_verbose(f"liteDebuggerClient: {liteDebuggerClient}") + liteDebuggerClient.post_call_log_event( + original_response=original_response, + litellm_call_id=self.litellm_params["litellm_call_id"], + print_verbose=print_verbose, + call_type = self.call_type, + stream = self.stream, + ) + elif callback == "sentry" and add_breadcrumb: + print_verbose("reaches sentry breadcrumbing") + add_breadcrumb( + category="litellm.llm_call", + message=f"Model Call Details post-call: {self.model_call_details}", + level="info", + ) + elif isinstance(callback, CustomLogger): # custom logger class + callback.log_post_api_call( + kwargs=self.model_call_details, + response_obj=None, + start_time=self.start_time, + end_time=None + ) + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while post-call logging with integrations {traceback.format_exc()}" + ) + print_verbose( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + except: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}" + ) + pass + + def _success_handler_helper_fn(self, result=None, start_time=None, end_time=None): + try: + if start_time is None: + start_time = self.start_time + if end_time is None: + end_time = datetime.datetime.now() + self.model_call_details["log_event_type"] = "successful_api_call" + self.model_call_details["end_time"] = end_time + complete_streaming_response = None + + ## BUILD COMPLETE STREAMED RESPONSE + if self.stream: + if result.choices[0].finish_reason is not None: # if it's the last chunk + self.streaming_chunks.append(result) + complete_streaming_response = litellm.stream_chunk_builder(self.streaming_chunks, messages=self.model_call_details.get("messages", None)) + else: + self.streaming_chunks.append(result) + elif isinstance(result, OpenAIObject): + result = result.model_dump() + + if complete_streaming_response: + self.model_call_details["complete_streaming_response"] = complete_streaming_response + + print_verbose(f"success callbacks: {litellm.success_callback}") + + if litellm.max_budget and self.stream: + time_diff = (end_time - start_time).total_seconds() + float_diff = float(time_diff) + litellm._current_cost += litellm.completion_cost(model=self.model, prompt="", completion=result["content"], total_time=float_diff) + + return start_time, end_time, result, complete_streaming_response + except: + pass + + def success_handler(self, result=None, start_time=None, end_time=None, **kwargs): + print_verbose( + f"Logging Details LiteLLM-Success Call" + ) + try: + start_time, end_time, result, complete_streaming_response = self._success_handler_helper_fn(start_time=start_time, end_time=end_time, result=result) + print_verbose(f"success callbacks: {litellm.success_callback}") + + for callback in litellm.success_callback: + try: + if callback == "lite_debugger": + print_verbose("reaches lite_debugger for logging!") + print_verbose(f"liteDebuggerClient: {liteDebuggerClient}") + print_verbose(f"liteDebuggerClient details function {self.call_type} and stream set to {self.stream}") + liteDebuggerClient.log_event( + end_user=kwargs.get("user", "default"), + response_obj=result, + start_time=start_time, + end_time=end_time, + litellm_call_id=self.litellm_call_id, + print_verbose=print_verbose, + call_type = self.call_type, + stream = self.stream, + ) + if callback == "api_manager": + print_verbose("reaches api manager for updating model cost") + litellm.apiManager.update_cost(completion_obj=result, user=self.user) + if callback == "cache": + if litellm.cache != None and self.model_call_details.get('optional_params', {}).get('stream', False) == True: + litellm_call_id = self.litellm_params["litellm_call_id"] + if litellm_call_id in self.litellm_params["stream_response"]: + # append for the given call_id + if self.litellm_params["stream_response"][litellm_call_id]["choices"][0]["message"]["content"] == "default": + self.litellm_params["stream_response"][litellm_call_id]["choices"][0]["message"]["content"] = result["content"] # handle first try + else: + self.litellm_params["stream_response"][litellm_call_id]["choices"][0]["message"]["content"] += result["content"] + else: # init a streaming response for this call id + new_model_response = ModelResponse(choices=[Choices(message=Message(content="default"))]) + self.litellm_params["stream_response"][litellm_call_id] = new_model_response + litellm.cache.add_cache(self.litellm_params["stream_response"][litellm_call_id], **self.model_call_details) + if callback == "promptlayer": + print_verbose("reaches promptlayer for logging!") + promptLayerLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "supabase": + print_verbose("reaches supabase for logging!") + kwargs=self.model_call_details + + # this only logs streaming once, complete_streaming_response exists i.e when stream ends + if self.stream: + if "complete_streaming_response" not in kwargs: + return + else: + print_verbose("reaches supabase for streaming logging!") + result = kwargs["complete_streaming_response"] + + model = kwargs["model"] + messages = kwargs["messages"] + optional_params = kwargs.get("optional_params", {}) + litellm_params = kwargs.get("litellm_params", {}) + supabaseClient.log_event( + model=model, + messages=messages, + end_user=optional_params.get("user", "default"), + response_obj=result, + start_time=start_time, + end_time=end_time, + litellm_call_id=litellm_params.get("litellm_call_id", str(uuid.uuid4())), + print_verbose=print_verbose, + ) + if callback == "wandb": + print_verbose("reaches wandb for logging!") + weightsBiasesLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "langsmith": + print_verbose("reaches langsmtih for logging!") + langsmithLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "llmonitor": + print_verbose("reaches llmonitor for logging!") + model = self.model + + input = self.model_call_details.get("messages", self.model_call_details.get("input", None)) + + # if contains input, it's 'embedding', otherwise 'llm' + type = "embed" if self.call_type == CallTypes.embedding.value else "llm" + + llmonitorLogger.log_event( + type=type, + event="end", + model=model, + input=input, + user_id=self.model_call_details.get("user", "default"), + response_obj=result, + start_time=start_time, + end_time=end_time, + run_id=self.litellm_call_id, + print_verbose=print_verbose, + ) + if callback == "helicone": + print_verbose("reaches helicone for logging!") + model = self.model + messages = kwargs["messages"] + heliconeLogger.log_success( + model=model, + messages=messages, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "langfuse": + print_verbose("reaches langfuse for logging!") + kwargs = {} + for k, v in self.model_call_details.items(): + if k != "original_response": # copy.deepcopy raises errors as this could be a coroutine + kwargs[k] = v + # this only logs streaming once, complete_streaming_response exists i.e when stream ends + if self.stream: + if "complete_streaming_response" not in kwargs: + return + else: + print_verbose("reaches langfuse for streaming logging!") + result = kwargs["complete_streaming_response"] + + langFuseLogger.log_event( + kwargs=kwargs, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "traceloop": + deep_copy = {} + for k, v in self.model_call_details.items(): + if k != "original_response": + deep_copy[k] = v + traceloopLogger.log_event( + kwargs=deep_copy, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if isinstance(callback, CustomLogger): # custom logger class + if self.stream and complete_streaming_response is None: + callback.log_stream_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time + ) + else: + if self.stream and complete_streaming_response: + self.model_call_details["complete_response"] = self.model_call_details.pop("complete_streaming_response", complete_streaming_response) + callback.log_success_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + if callable(callback): # custom logger functions + customLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + callback_func=callback + ) + + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging with integrations {traceback.format_exc()}" + ) + print_verbose( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + except: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}" + ) + pass + + async def async_success_handler(self, result=None, start_time=None, end_time=None, **kwargs): + """ + Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. + """ + start_time, end_time, result, complete_streaming_response = self._success_handler_helper_fn(start_time=start_time, end_time=end_time, result=result) + print_verbose(f"success callbacks: {litellm.success_callback}") + + for callback in litellm._async_success_callback: + try: + if callable(callback): # custom logger functions + await customLogger.async_log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + callback_func=callback + ) + except: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}" + ) + + def failure_handler(self, exception, traceback_exception, start_time=None, end_time=None): + print_verbose( + f"Logging Details LiteLLM-Failure Call" + ) + try: + if start_time is None: + start_time = self.start_time + if end_time is None: + end_time = datetime.datetime.now() + + # on some exceptions, model_call_details is not always initialized, this ensures that we still log those exceptions + if not hasattr(self, "model_call_details"): + self.model_call_details = {} + + self.model_call_details["log_event_type"] = "failed_api_call" + self.model_call_details["exception"] = exception + self.model_call_details["traceback_exception"] = traceback_exception + self.model_call_details["end_time"] = end_time + result = None # result sent to all loggers, init this to None incase it's not created + for callback in litellm.failure_callback: + try: + if callback == "lite_debugger": + print_verbose("reaches lite_debugger for logging!") + print_verbose(f"liteDebuggerClient: {liteDebuggerClient}") + result = { + "model": self.model, + "created": time.time(), + "error": traceback_exception, + "usage": { + "prompt_tokens": prompt_token_calculator( + self.model, messages=self.messages + ), + "completion_tokens": 0, + }, + } + liteDebuggerClient.log_event( + model=self.model, + messages=self.messages, + end_user=self.model_call_details.get("user", "default"), + response_obj=result, + start_time=start_time, + end_time=end_time, + litellm_call_id=self.litellm_call_id, + print_verbose=print_verbose, + call_type = self.call_type, + stream = self.stream, + ) + elif callback == "llmonitor": + print_verbose("reaches llmonitor for logging error!") + + model = self.model + + input = self.model_call_details["input"] + + type = "embed" if self.call_type == CallTypes.embedding.value else "llm" + + llmonitorLogger.log_event( + type=type, + event="error", + user_id=self.model_call_details.get("user", "default"), + model=model, + input=input, + error=traceback_exception, + run_id=self.litellm_call_id, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + elif callback == "sentry": + print_verbose("sending exception to sentry") + if capture_exception: + capture_exception(exception) + else: + print_verbose(f"capture exception not initialized: {capture_exception}") + elif callable(callback): # custom logger functions + customLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + callback_func=callback + ) + elif isinstance(callback, CustomLogger): # custom logger class + callback.log_failure_event( + start_time=start_time, + end_time=end_time, + response_obj=result, + kwargs=self.model_call_details, + ) + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging with integrations {traceback.format_exc()}" + ) + print_verbose( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging {traceback.format_exc()}" + ) + pass + + +def exception_logging( + additional_args={}, + logger_fn=None, + exception=None, +): + try: + model_call_details = {} + if exception: + model_call_details["exception"] = exception + model_call_details["additional_args"] = additional_args + # User Logging -> if you pass in a custom logging function or want to use sentry breadcrumbs + print_verbose( + f"Logging Details: logger_fn - {logger_fn} | callable(logger_fn) - {callable(logger_fn)}" + ) + if logger_fn and callable(logger_fn): + try: + logger_fn( + model_call_details + ) # Expectation: any logger function passed in by the user should accept a dict object + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}" + ) + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}" + ) + pass + + +####### RULES ################### + +class Rules: + """ + Fail calls based on the input or llm api output + + Example usage: + import litellm + def my_custom_rule(input): # receives the model response + if "i don't think i can answer" in input: # trigger fallback if the model refuses to answer + return False + return True + + litellm.post_call_rules = [my_custom_rule] # have these be functions that can be called to fail a call + + response = litellm.completion(model="gpt-3.5-turbo", messages=[{"role": "user", + "content": "Hey, how's it going?"}], fallbacks=["openrouter/mythomax"]) + """ + def __init__(self) -> None: + pass + + def pre_call_rules(self, input: str, model: str): + for rule in litellm.pre_call_rules: + if callable(rule): + decision = rule(input) + if decision is False: + raise litellm.APIResponseValidationError(message="LLM Response failed post-call-rule check", llm_provider="", model=model) # type: ignore + return True + + def post_call_rules(self, input: str, model: str): + for rule in litellm.post_call_rules: + if callable(rule): + decision = rule(input) + if decision is False: + raise litellm.APIResponseValidationError(message="LLM Response failed post-call-rule check", llm_provider="", model=model) # type: ignore + return True + +####### CLIENT ################### +# make it easy to log if completion/embedding runs succeeded or failed + see what happened | Non-Blocking +def client(original_function): + global liteDebuggerClient, get_all_keys + rules_obj = Rules() + def function_setup( + start_time, *args, **kwargs + ): # just run once to check if user wants to send their data anywhere - PostHog/Sentry/Slack/etc. + try: + global callback_list, add_breadcrumb, user_logger_fn, Logging + function_id = kwargs["id"] if "id" in kwargs else None + if litellm.use_client or ("use_client" in kwargs and kwargs["use_client"] == True): + print_verbose(f"litedebugger initialized") + if "lite_debugger" not in litellm.input_callback: + litellm.input_callback.append("lite_debugger") + if "lite_debugger" not in litellm.success_callback: + litellm.success_callback.append("lite_debugger") + if "lite_debugger" not in litellm.failure_callback: + litellm.failure_callback.append("lite_debugger") + if len(litellm.callbacks) > 0: + for callback in litellm.callbacks: + if callback not in litellm.input_callback: + litellm.input_callback.append(callback) + if callback not in litellm.success_callback: + litellm.success_callback.append(callback) + if callback not in litellm.failure_callback: + litellm.failure_callback.append(callback) + if ( + len(litellm.input_callback) > 0 + or len(litellm.success_callback) > 0 + or len(litellm.failure_callback) > 0 + ) and len(callback_list) == 0: + callback_list = list( + set( + litellm.input_callback + + litellm.success_callback + + litellm.failure_callback + ) + ) + set_callbacks( + callback_list=callback_list, + function_id=function_id + ) + ## ASYNC CALLBACKS + if len(litellm.success_callback) > 0: + removed_async_items = [] + for index, callback in enumerate(litellm.success_callback): + if inspect.iscoroutinefunction(callback): + litellm._async_success_callback.append(callback) + removed_async_items.append(index) + + # Pop the async items from success_callback in reverse order to avoid index issues + for index in reversed(removed_async_items): + litellm.success_callback.pop(index) + if add_breadcrumb: + add_breadcrumb( + category="litellm.llm_call", + message=f"Positional Args: {args}, Keyword Args: {kwargs}", + level="info", + ) + if "logger_fn" in kwargs: + user_logger_fn = kwargs["logger_fn"] + # CRASH REPORTING TELEMETRY + crash_reporting(*args, **kwargs) + # INIT LOGGER - for user-specified integrations + model = args[0] if len(args) > 0 else kwargs["model"] + call_type = original_function.__name__ + if call_type == CallTypes.completion.value or call_type == CallTypes.acompletion.value: + if len(args) > 1: + messages = args[1] + elif kwargs.get("messages", None): + messages = kwargs["messages"] + ### PRE-CALL RULES ### + if isinstance(messages, list) and len(messages) > 0 and isinstance(messages[0], dict) and "content" in messages[0]: + rules_obj.pre_call_rules(input="".join(m["content"] for m in messages if isinstance(m["content"], str)), model=model) + elif call_type == CallTypes.embedding.value: + messages = args[1] if len(args) > 1 else kwargs["input"] + stream = True if "stream" in kwargs and kwargs["stream"] == True else False + logging_obj = Logging(model=model, messages=messages, stream=stream, litellm_call_id=kwargs["litellm_call_id"], function_id=function_id, call_type=call_type, start_time=start_time) + return logging_obj + except Exception as e: + import logging + logging.debug(f"[Non-Blocking] {traceback.format_exc()}; args - {args}; kwargs - {kwargs}") + raise e + + def post_call_processing(original_response, model): + try: + call_type = original_function.__name__ + if call_type == CallTypes.completion.value or call_type == CallTypes.acompletion.value: + model_response = original_response['choices'][0]['message']['content'] + ### POST-CALL RULES ### + rules_obj.post_call_rules(input=model_response, model=model) + except Exception as e: + raise e + + def crash_reporting(*args, **kwargs): + if litellm.telemetry: + try: + model = args[0] if len(args) > 0 else kwargs["model"] + exception = kwargs["exception"] if "exception" in kwargs else None + custom_llm_provider = ( + kwargs["custom_llm_provider"] + if "custom_llm_provider" in kwargs + else None + ) + safe_crash_reporting( + model=model, + exception=exception, + custom_llm_provider=custom_llm_provider, + ) # log usage-crash details. Do not log any user details. If you want to turn this off, set `litellm.telemetry=False`. + except: + # [Non-Blocking Error] + pass + + def wrapper(*args, **kwargs): + start_time = datetime.datetime.now() + result = None + logging_obj = kwargs.get("litellm_logging_obj", None) + + # only set litellm_call_id if its not in kwargs + if "litellm_call_id" not in kwargs: + kwargs["litellm_call_id"] = str(uuid.uuid4()) + try: + model = args[0] if len(args) > 0 else kwargs["model"] + except: + raise ValueError("model param not passed in.") + + try: + if logging_obj is None: + logging_obj = function_setup(start_time, *args, **kwargs) + kwargs["litellm_logging_obj"] = logging_obj + + # [OPTIONAL] CHECK BUDGET + if litellm.max_budget: + if litellm._current_cost > litellm.max_budget: + raise BudgetExceededError(current_cost=litellm._current_cost, max_budget=litellm.max_budget) + + # [OPTIONAL] CHECK CACHE + # remove this after deprecating litellm.caching + print_verbose(f"litellm.caching: {litellm.caching}; litellm.caching_with_models: {litellm.caching_with_models}; litellm.cache: {litellm.cache}") + if (litellm.caching or litellm.caching_with_models) and litellm.cache is None: + litellm.cache = Cache() + + print_verbose(f"kwargs[caching]: {kwargs.get('caching', False)}; litellm.cache: {litellm.cache}") + # if caching is false, don't run this + if (kwargs.get("caching", None) is None and litellm.cache is not None) or kwargs.get("caching", False) == True: # allow users to control returning cached responses from the completion function + # checking cache + if (litellm.cache != None or litellm.caching or litellm.caching_with_models): + print_verbose(f"Checking Cache") + cached_result = litellm.cache.get_cache(*args, **kwargs) + if cached_result != None: + print_verbose(f"Cache Hit!") + if "detail" in cached_result: + # implies an error occurred + pass + else: + call_type = original_function.__name__ + print_verbose(f"Cache Response Object routing: call_type - {call_type}; cached_result instace: {type(cached_result)}") + if call_type == CallTypes.completion.value and isinstance(cached_result, dict): + return convert_to_model_response_object(response_object=cached_result, model_response_object=ModelResponse()) + elif call_type == CallTypes.embedding.value and isinstance(cached_result, dict): + return convert_to_model_response_object(response_object=cached_result, response_type="embedding") + else: + return cached_result + # MODEL CALL + result = original_function(*args, **kwargs) + end_time = datetime.datetime.now() + if "stream" in kwargs and kwargs["stream"] == True: + # TODO: Add to cache for streaming + if "complete_response" in kwargs and kwargs["complete_response"] == True: + chunks = [] + for idx, chunk in enumerate(result): + chunks.append(chunk) + return litellm.stream_chunk_builder(chunks, messages=kwargs.get("messages", None)) + else: + return result + elif "acompletion" in kwargs and kwargs["acompletion"] == True: + return result + elif "aembedding" in kwargs and kwargs["aembedding"] == True: + return result + + ### POST-CALL RULES ### + post_call_processing(original_response=result, model=model) + + # [OPTIONAL] ADD TO CACHE + if litellm.caching or litellm.caching_with_models or litellm.cache != None: # user init a cache object + litellm.cache.add_cache(result, *args, **kwargs) + + # LOG SUCCESS - handle streaming success logging in the _next_ object, remove `handle_success` once it's deprecated + threading.Thread(target=logging_obj.success_handler, args=(result, start_time, end_time)).start() + # threading.Thread(target=logging_obj.success_handler, args=(result, start_time, end_time)).start() + my_thread = threading.Thread( + target=handle_success, args=(args, kwargs, result, start_time, end_time) + ) # don't interrupt execution of main thread + my_thread.start() + # RETURN RESULT + result._response_ms = (end_time - start_time).total_seconds() * 1000 # return response latency in ms like openai + return result + except Exception as e: + call_type = original_function.__name__ + if call_type == CallTypes.completion.value: + num_retries = ( + kwargs.get("num_retries", None) + or litellm.num_retries + or None + ) + litellm.num_retries = None # set retries to None to prevent infinite loops + context_window_fallback_dict = kwargs.get("context_window_fallback_dict", {}) + + if num_retries: + if (isinstance(e, openai.APIError) + or isinstance(e, openai.Timeout)): + kwargs["num_retries"] = num_retries + return litellm.completion_with_retries(*args, **kwargs) + elif isinstance(e, litellm.exceptions.ContextWindowExceededError) and context_window_fallback_dict and model in context_window_fallback_dict: + if len(args) > 0: + args[0] = context_window_fallback_dict[model] + else: + kwargs["model"] = context_window_fallback_dict[model] + return original_function(*args, **kwargs) + traceback_exception = traceback.format_exc() + crash_reporting(*args, **kwargs, exception=traceback_exception) + end_time = datetime.datetime.now() + # LOG FAILURE - handle streaming failure logging in the _next_ object, remove `handle_failure` once it's deprecated + if logging_obj: + logging_obj.failure_handler(e, traceback_exception, start_time, end_time) # DO NOT MAKE THREADED - router retry fallback relies on this! + my_thread = threading.Thread( + target=handle_failure, + args=(e, traceback_exception, start_time, end_time, args, kwargs), + ) # don't interrupt execution of main thread + my_thread.start() + if hasattr(e, "message"): + if ( + liteDebuggerClient and liteDebuggerClient.dashboard_url != None + ): # make it easy to get to the debugger logs if you've initialized it + e.message += f"\n Check the log in your dashboard - {liteDebuggerClient.dashboard_url}" + raise e + + async def wrapper_async(*args, **kwargs): + start_time = datetime.datetime.now() + result = None + logging_obj = kwargs.get("litellm_logging_obj", None) + # only set litellm_call_id if its not in kwargs + if "litellm_call_id" not in kwargs: + kwargs["litellm_call_id"] = str(uuid.uuid4()) + try: + model = args[0] if len(args) > 0 else kwargs["model"] + except: + raise ValueError("model param not passed in.") + + try: + if logging_obj is None: + logging_obj = function_setup(start_time, *args, **kwargs) + kwargs["litellm_logging_obj"] = logging_obj + + # [OPTIONAL] CHECK BUDGET + if litellm.max_budget: + if litellm._current_cost > litellm.max_budget: + raise BudgetExceededError(current_cost=litellm._current_cost, max_budget=litellm.max_budget) + + # [OPTIONAL] CHECK CACHE + print_verbose(f"litellm.cache: {litellm.cache}") + print_verbose(f"kwargs[caching]: {kwargs.get('caching', False)}; litellm.cache: {litellm.cache}") + # if caching is false, don't run this + if (kwargs.get("caching", None) is None and litellm.cache is not None) or kwargs.get("caching", False) == True: # allow users to control returning cached responses from the completion function + # checking cache + if (litellm.cache != None): + print_verbose(f"Checking Cache") + cached_result = litellm.cache.get_cache(*args, **kwargs) + if cached_result != None: + print_verbose(f"Cache Hit!") + call_type = original_function.__name__ + if call_type == CallTypes.acompletion.value and isinstance(cached_result, dict): + return convert_to_model_response_object(response_object=cached_result, model_response_object=ModelResponse()) + else: + return cached_result + # MODEL CALL + result = await original_function(*args, **kwargs) + end_time = datetime.datetime.now() + if "stream" in kwargs and kwargs["stream"] == True: + if "complete_response" in kwargs and kwargs["complete_response"] == True: + chunks = [] + for idx, chunk in enumerate(result): + chunks.append(chunk) + return litellm.stream_chunk_builder(chunks, messages=kwargs.get("messages", None)) + else: + return result + + ### POST-CALL RULES ### + post_call_processing(original_response=result, model=model) + + # [OPTIONAL] ADD TO CACHE + if litellm.caching or litellm.caching_with_models or litellm.cache != None: # user init a cache object + litellm.cache.add_cache(result, *args, **kwargs) + # LOG SUCCESS - handle streaming success logging in the _next_ object + asyncio.create_task(logging_obj.async_success_handler(result, start_time, end_time)) + threading.Thread(target=logging_obj.success_handler, args=(result, start_time, end_time)).start() + # RETURN RESULT + if isinstance(result, ModelResponse): + result._response_ms = (end_time - start_time).total_seconds() * 1000 # return response latency in ms like openai + return result + except Exception as e: + call_type = original_function.__name__ + if call_type == CallTypes.acompletion.value: + num_retries = ( + kwargs.get("num_retries", None) + or litellm.num_retries + or None + ) + litellm.num_retries = None # set retries to None to prevent infinite loops + context_window_fallback_dict = kwargs.get("context_window_fallback_dict", {}) + + if num_retries: + kwargs["num_retries"] = num_retries + kwargs["original_function"] = original_function + if (isinstance(e, openai.RateLimitError)): # rate limiting specific error + kwargs["retry_strategy"] = "exponential_backoff_retry" + elif (isinstance(e, openai.APIError)): # generic api error + kwargs["retry_strategy"] = "constant_retry" + return await litellm.acompletion_with_retries(*args, **kwargs) + elif isinstance(e, litellm.exceptions.ContextWindowExceededError) and context_window_fallback_dict and model in context_window_fallback_dict: + if len(args) > 0: + args[0] = context_window_fallback_dict[model] + else: + kwargs["model"] = context_window_fallback_dict[model] + return await original_function(*args, **kwargs) + traceback_exception = traceback.format_exc() + crash_reporting(*args, **kwargs, exception=traceback_exception) + end_time = datetime.datetime.now() + if logging_obj: + logging_obj.failure_handler(e, traceback_exception, start_time, end_time) # DO NOT MAKE THREADED - router retry fallback relies on this! + raise e + + is_coroutine = inspect.iscoroutinefunction(original_function) + + # Return the appropriate wrapper based on the original function type + if is_coroutine: + return wrapper_async + else: + return wrapper + +####### USAGE CALCULATOR ################ + + +# Extract the number of billion parameters from the model name +# only used for together_computer LLMs +def get_model_params_and_category(model_name): + import re + params_match = re.search(r'(\d+b)', model_name) # catch all decimals like 3b, 70b, etc + category = None + if params_match != None: + params_match = params_match.group(1) + params_match = params_match.replace("b", "") + params_billion = float(params_match) + # Determine the category based on the number of parameters + if params_billion <= 3.0: + category = "together-ai-up-to-3b" + elif params_billion <= 7.0: + category = "together-ai-3.1b-7b" + elif params_billion <= 20.0: + category = "together-ai-7.1b-20b" + elif params_billion <= 40.0: + category = "together-ai-20.1b-40b" + elif params_billion <= 70.0: + category = "together-ai-40.1b-70b" + return category + + return None + +def get_replicate_completion_pricing(completion_response=None, total_time=0.0): + # see https://replicate.com/pricing + a100_40gb_price_per_second_public = 0.001150 + # for all litellm currently supported LLMs, almost all requests go to a100_80gb + a100_80gb_price_per_second_public = 0.001400 # assume all calls sent to A100 80GB for now + if total_time == 0.0: + start_time = completion_response['created'] + end_time = completion_response["ended"] + total_time = end_time - start_time + + return a100_80gb_price_per_second_public*total_time + + +def _select_tokenizer(model: str): + # cohere + import pkg_resources + if model in litellm.cohere_models: + tokenizer = Tokenizer.from_pretrained("Cohere/command-nightly") + return {"type": "huggingface_tokenizer", "tokenizer": tokenizer} + # anthropic + elif model in litellm.anthropic_models: + # Read the JSON file + filename = pkg_resources.resource_filename(__name__, 'llms/tokenizers/anthropic_tokenizer.json') + with open(filename, 'r') as f: + json_data = json.load(f) + # Decode the JSON data from utf-8 + json_data_decoded = json.dumps(json_data, ensure_ascii=False) + # Convert to str + json_str = str(json_data_decoded) + # load tokenizer + tokenizer = Tokenizer.from_str(json_str) + return {"type": "huggingface_tokenizer", "tokenizer": tokenizer} + # llama2 + elif "llama-2" in model.lower(): + tokenizer = Tokenizer.from_pretrained("hf-internal-testing/llama-tokenizer") + return {"type": "huggingface_tokenizer", "tokenizer": tokenizer} + # default - tiktoken + else: + return {"type": "openai_tokenizer", "tokenizer": encoding} + +def encode(model: str, text: str): + """ + Encodes the given text using the specified model. + + Args: + model (str): The name of the model to use for tokenization. + text (str): The text to be encoded. + + Returns: + enc: The encoded text. + """ + tokenizer_json = _select_tokenizer(model=model) + enc = tokenizer_json["tokenizer"].encode(text) + return enc + +def decode(model: str, tokens: List[int]): + tokenizer_json = _select_tokenizer(model=model) + dec = tokenizer_json["tokenizer"].decode(tokens) + return dec + +def openai_token_counter(messages: Optional[list]=None, model="gpt-3.5-turbo-0613", text: Optional[str]= None): + """ + Return the number of tokens used by a list of messages. + + Borrowed from https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb. + """ + try: + encoding = tiktoken.encoding_for_model(model) + except KeyError: + print_verbose("Warning: model not found. Using cl100k_base encoding.") + encoding = tiktoken.get_encoding("cl100k_base") + if model == "gpt-3.5-turbo-0301": + tokens_per_message = 4 # every message follows <|start|>{role/name}\n{content}<|end|>\n + tokens_per_name = -1 # if there's a name, the role is omitted + elif model in litellm.open_ai_chat_completion_models: + tokens_per_message = 3 + tokens_per_name = 1 + else: + raise NotImplementedError( + f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""" + ) + num_tokens = 0 + + if text: + num_tokens = len(encoding.encode(text, disallowed_special=())) + elif messages: + for message in messages: + num_tokens += tokens_per_message + for key, value in message.items(): + num_tokens += len(encoding.encode(value, disallowed_special=())) + if key == "name": + num_tokens += tokens_per_name + num_tokens += 3 # every reply is primed with <|start|>assistant<|message|> + return num_tokens + +def token_counter(model="", text=None, messages: Optional[List] = None): + """ + Count the number of tokens in a given text using a specified model. + + Args: + model (str): The name of the model to use for tokenization. Default is an empty string. + text (str): The raw text string to be passed to the model. Default is None. + messages (Optional[List[Dict[str, str]]]): Alternative to passing in text. A list of dictionaries representing messages with "role" and "content" keys. Default is None. + + Returns: + int: The number of tokens in the text. + """ + # use tiktoken, anthropic, cohere or llama2's tokenizer depending on the model + if text == None: + if messages is not None: + print_verbose(f"token_counter messages received: {messages}") + text = "" + for message in messages: + if message.get("content", None): + text += message["content"] + if 'tool_calls' in message: + for tool_call in message['tool_calls']: + if 'function' in tool_call: + function_arguments = tool_call['function']['arguments'] + text += function_arguments + else: + raise ValueError("text and messages cannot both be None") + num_tokens = 0 + if model is not None: + tokenizer_json = _select_tokenizer(model=model) + if tokenizer_json["type"] == "huggingface_tokenizer": + enc = tokenizer_json["tokenizer"].encode(text) + num_tokens = len(enc.ids) + elif tokenizer_json["type"] == "openai_tokenizer": + if model in litellm.open_ai_chat_completion_models: + num_tokens = openai_token_counter(text=text, model=model) + else: + enc = tokenizer_json["tokenizer"].encode(text) + num_tokens = len(enc) + else: + num_tokens = len(encoding.encode(text)) + return num_tokens + + +def cost_per_token(model="", prompt_tokens=0, completion_tokens=0): + """ + Calculates the cost per token for a given model, prompt tokens, and completion tokens. + + Parameters: + model (str): The name of the model to use. Default is "" + prompt_tokens (int): The number of tokens in the prompt. + completion_tokens (int): The number of tokens in the completion. + + Returns: + tuple: A tuple containing the cost in USD dollars for prompt tokens and completion tokens, respectively. + """ + # given + prompt_tokens_cost_usd_dollar = 0 + completion_tokens_cost_usd_dollar = 0 + model_cost_ref = litellm.model_cost + # see this https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models + azure_llms = { + "gpt-35-turbo": "azure/gpt-3.5-turbo", + "gpt-35-turbo-16k": "azure/gpt-3.5-turbo-16k", + "gpt-35-turbo-instruct": "azure/gpt-3.5-turbo-instruct" + } + if model in model_cost_ref: + prompt_tokens_cost_usd_dollar = ( + model_cost_ref[model]["input_cost_per_token"] * prompt_tokens + ) + completion_tokens_cost_usd_dollar = ( + model_cost_ref[model]["output_cost_per_token"] * completion_tokens + ) + return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar + elif "ft:gpt-3.5-turbo" in model: + # fuzzy match ft:gpt-3.5-turbo:abcd-id-cool-litellm + prompt_tokens_cost_usd_dollar = ( + model_cost_ref["ft:gpt-3.5-turbo"]["input_cost_per_token"] * prompt_tokens + ) + completion_tokens_cost_usd_dollar = ( + model_cost_ref["ft:gpt-3.5-turbo"]["output_cost_per_token"] * completion_tokens + ) + return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar + elif model in azure_llms: + model = azure_llms[model] + prompt_tokens_cost_usd_dollar = ( + model_cost_ref[model]["input_cost_per_token"] * prompt_tokens + ) + completion_tokens_cost_usd_dollar = ( + model_cost_ref[model]["output_cost_per_token"] * completion_tokens + ) + return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar + else: + # calculate average input cost, azure/gpt-deployments can potentially go here if users don't specify, gpt-4, gpt-3.5-turbo. LLMs litellm knows + input_cost_sum = 0 + output_cost_sum = 0 + model_cost_ref = litellm.model_cost + for model in model_cost_ref: + input_cost_sum += model_cost_ref[model]["input_cost_per_token"] + output_cost_sum += model_cost_ref[model]["output_cost_per_token"] + avg_input_cost = input_cost_sum / len(model_cost_ref.keys()) + avg_output_cost = output_cost_sum / len(model_cost_ref.keys()) + prompt_tokens_cost_usd_dollar = avg_input_cost * prompt_tokens + completion_tokens_cost_usd_dollar = avg_output_cost * completion_tokens + return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar + + +def completion_cost( + completion_response=None, + model=None, + prompt="", + messages: List = [], + completion="", + total_time=0.0, # used for replicate + ): + """ + Calculate the cost of a given completion call fot GPT-3.5-turbo, llama2, any litellm supported llm. + + Parameters: + completion_response (litellm.ModelResponses): [Required] The response received from a LiteLLM completion request. + + [OPTIONAL PARAMS] + model (str): Optional. The name of the language model used in the completion calls + prompt (str): Optional. The input prompt passed to the llm + completion (str): Optional. The output completion text from the llm + total_time (float): Optional. (Only used for Replicate LLMs) The total time used for the request in seconds + + Returns: + float: The cost in USD dollars for the completion based on the provided parameters. + + Note: + - If completion_response is provided, the function extracts token information and the model name from it. + - If completion_response is not provided, the function calculates token counts based on the model and input text. + - The cost is calculated based on the model, prompt tokens, and completion tokens. + - For certain models containing "togethercomputer" in the name, prices are based on the model size. + - For Replicate models, the cost is calculated based on the total time used for the request. + + Exceptions: + - If an error occurs during execution, the function returns 0.0 without blocking the user's execution path. + """ + try: + if messages != []: + prompt = " ".join([message["content"] for message in messages]) + # Handle Inputs to completion_cost + prompt_tokens = 0 + completion_tokens = 0 + if completion_response is not None: + # get input/output tokens from completion_response + prompt_tokens = completion_response['usage']['prompt_tokens'] + completion_tokens = completion_response['usage']['completion_tokens'] + model = model or completion_response['model'] # check if user passed an override for model, if it's none check completion_response['model'] + else: + prompt_tokens = token_counter(model=model, text=prompt) + completion_tokens = token_counter(model=model, text=completion) + + # Calculate cost based on prompt_tokens, completion_tokens + if "togethercomputer" in model: + # together ai prices based on size of llm + # get_model_params_and_category takes a model name and returns the category of LLM size it is in model_prices_and_context_window.json + model = get_model_params_and_category(model) + # replicate llms are calculate based on time for request running + # see https://replicate.com/pricing + elif ( + model in litellm.replicate_models or + "replicate" in model + ): + return get_replicate_completion_pricing(completion_response, total_time) + prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar = cost_per_token( + model=model, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens + ) + return prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar + except: + return 0.0 # this should not block a users execution path + +####### HELPER FUNCTIONS ################ +def register_model(model_cost: Union[str, dict]): + """ + Register new / Override existing models (and their pricing) to specific providers. + Provide EITHER a model cost dictionary or a url to a hosted json blob + Example usage: + model_cost_dict = { + "gpt-4": { + "max_tokens": 8192, + "input_cost_per_token": 0.00003, + "output_cost_per_token": 0.00006, + "litellm_provider": "openai", + "mode": "chat" + }, + } + """ + loaded_model_cost = {} + if isinstance(model_cost, dict): + loaded_model_cost = model_cost + elif isinstance(model_cost, str): + loaded_model_cost = litellm.get_model_cost_map(url=model_cost) + + for key, value in loaded_model_cost.items(): + ## override / add new keys to the existing model cost dictionary + if key in litellm.model_cost: + for k,v in loaded_model_cost[key].items(): + litellm.model_cost[key][k] = v + # add new model names to provider lists + if value.get('litellm_provider') == 'openai': + if key not in litellm.open_ai_chat_completion_models: + litellm.open_ai_chat_completion_models.append(key) + elif value.get('litellm_provider') == 'text-completion-openai': + if key not in litellm.open_ai_text_completion_models: + litellm.open_ai_text_completion_models.append(key) + elif value.get('litellm_provider') == 'cohere': + if key not in litellm.cohere_models: + litellm.cohere_models.append(key) + elif value.get('litellm_provider') == 'anthropic': + if key not in litellm.anthropic_models: + litellm.anthropic_models.append(key) + elif value.get('litellm_provider') == 'openrouter': + split_string = key.split('/', 1) + if key not in litellm.openrouter_models: + litellm.openrouter_models.append(split_string[1]) + elif value.get('litellm_provider') == 'vertex_ai-text-models': + if key not in litellm.vertex_text_models: + litellm.vertex_text_models.append(key) + elif value.get('litellm_provider') == 'vertex_ai-code-text-models': + if key not in litellm.vertex_code_text_models: + litellm.vertex_code_text_models.append(key) + elif value.get('litellm_provider') == 'vertex_ai-chat-models': + if key not in litellm.vertex_chat_models: + litellm.vertex_chat_models.append(key) + elif value.get('litellm_provider') == 'vertex_ai-code-chat-models': + if key not in litellm.vertex_code_chat_models: + litellm.vertex_code_chat_models.append(key) + elif value.get('litellm_provider') == 'ai21': + if key not in litellm.ai21_models: + litellm.ai21_models.append(key) + elif value.get('litellm_provider') == 'nlp_cloud': + if key not in litellm.nlp_cloud_models: + litellm.nlp_cloud_models.append(key) + elif value.get('litellm_provider') == 'aleph_alpha': + if key not in litellm.aleph_alpha_models: + litellm.aleph_alpha_models.append(key) + elif value.get('litellm_provider') == 'bedrock': + if key not in litellm.bedrock_models: + litellm.bedrock_models.append(key) + return model_cost + +def get_litellm_params( + return_async=False, + api_key=None, + force_timeout=600, + azure=False, + logger_fn=None, + verbose=False, + hugging_face=False, + replicate=False, + together_ai=False, + custom_llm_provider=None, + api_base=None, + litellm_call_id=None, + model_alias_map=None, + completion_call_id=None, + metadata=None +): + litellm_params = { + "return_async": return_async, + "api_key": api_key, + "force_timeout": force_timeout, + "logger_fn": logger_fn, + "verbose": verbose, + "custom_llm_provider": custom_llm_provider, + "api_base": api_base, + "litellm_call_id": litellm_call_id, + "model_alias_map": model_alias_map, + "completion_call_id": completion_call_id, + "metadata": metadata, + "stream_response": {} # litellm_call_id: ModelResponse Dict + } + + return litellm_params + + +def get_optional_params( # use the openai defaults + # 12 optional params + functions=[], + function_call="", + temperature=None, + top_p=None, + n=None, + stream=False, + stop=None, + max_tokens=None, + presence_penalty=None, + frequency_penalty=0, + logit_bias=None, + user=None, + model=None, + custom_llm_provider="", + response_format=None, + seed=None, + tools=None, + tool_choice=None, + max_retries=None, + **kwargs +): + # retrieve all parameters passed to the function + passed_params = locals() + special_params = passed_params.pop("kwargs") + for k, v in special_params.items(): + passed_params[k] = v + default_params = { + "functions":[], + "function_call":"", + "temperature":None, + "top_p":None, + "n":None, + "stream":None, + "stop":None, + "max_tokens":None, + "presence_penalty":None, + "frequency_penalty":None, + "logit_bias": None, + "user":None, + "model":None, + "custom_llm_provider":"", + "response_format": None, + "seed": None, + "tools": None, + "tool_choice": None, + "max_retries": None, + } + # filter out those parameters that were passed with non-default values + non_default_params = {k: v for k, v in passed_params.items() if (k != "model" and k != "custom_llm_provider" and k in default_params and v != default_params[k])} + optional_params = {} + ## raise exception if function calling passed in for a provider that doesn't support it + if "functions" in non_default_params or "function_call" in non_default_params: + if custom_llm_provider != "openai" and custom_llm_provider != "text-completion-openai" and custom_llm_provider != "azure": + if litellm.add_function_to_prompt: # if user opts to add it to prompt instead + optional_params["functions_unsupported_model"] = non_default_params.pop("functions") + else: + raise UnsupportedParamsError(status_code=500, message=f"Function calling is not supported by {custom_llm_provider}. To add it to the prompt, set `litellm.add_function_to_prompt = True`.") + + def _check_valid_arg(supported_params): + print_verbose(f"\nLiteLLM completion() model= {model}; provider = {custom_llm_provider}") + print_verbose(f"\nLiteLLM: Params passed to completion() {passed_params}") + print_verbose(f"\nLiteLLM: Non-Default params passed to completion() {non_default_params}") + unsupported_params = {} + for k in non_default_params.keys(): + if k not in supported_params: + if k == "n" and n == 1: # langchain sends n=1 as a default value + pass + # Always keeps this in elif code blocks + else: + unsupported_params[k] = non_default_params[k] + if unsupported_params and not litellm.drop_params: + raise UnsupportedParamsError(status_code=500, message=f"{custom_llm_provider} does not support parameters: {unsupported_params}. To drop these, set `litellm.drop_params=True`.") + + def _map_and_modify_arg(supported_params: dict, provider: str, model: str): + """ + filter params to fit the required provider format, drop those that don't fit if user sets `litellm.drop_params = True`. + """ + filtered_stop = None + if "stop" in supported_params and litellm.drop_params: + if provider == "bedrock" and "amazon" in model: + filtered_stop = [] + if isinstance(stop, list): + for s in stop: + if re.match(r'^(\|+|User:)$', s): + filtered_stop.append(s) + if filtered_stop is not None: + supported_params["stop"] = filtered_stop + + return supported_params + + ## raise exception if provider doesn't support passed in param + if custom_llm_provider == "anthropic": + ## check if unsupported param passed in + supported_params = ["stream", "stop", "temperature", "top_p", "max_tokens"] + _check_valid_arg(supported_params=supported_params) + # handle anthropic params + if stream: + optional_params["stream"] = stream + if stop is not None: + if type(stop) == str: + stop = [stop] # openai can accept str/list for stop + optional_params["stop_sequences"] = stop + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if max_tokens is not None: + optional_params["max_tokens_to_sample"] = max_tokens + elif custom_llm_provider == "cohere": + ## check if unsupported param passed in + supported_params = ["stream", "temperature", "max_tokens", "logit_bias", "top_p", "frequency_penalty", "presence_penalty", "stop", "n"] + _check_valid_arg(supported_params=supported_params) + # handle cohere params + if stream: + optional_params["stream"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + if n is not None: + optional_params["num_generations"] = n + if logit_bias is not None: + optional_params["logit_bias"] = logit_bias + if top_p is not None: + optional_params["p"] = top_p + if frequency_penalty is not None: + optional_params["frequency_penalty"] = frequency_penalty + if presence_penalty is not None: + optional_params["presence_penalty"] = presence_penalty + if stop is not None: + optional_params["stop_sequences"] = stop + elif custom_llm_provider == "maritalk": + ## check if unsupported param passed in + supported_params = ["stream", "temperature", "max_tokens", "top_p", "presence_penalty", "stop"] + _check_valid_arg(supported_params=supported_params) + # handle cohere params + if stream: + optional_params["stream"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + if logit_bias is not None: + optional_params["logit_bias"] = logit_bias + if top_p is not None: + optional_params["p"] = top_p + if presence_penalty is not None: + optional_params["repetition_penalty"] = presence_penalty + if stop is not None: + optional_params["stopping_tokens"] = stop + elif custom_llm_provider == "replicate": + ## check if unsupported param passed in + supported_params = ["stream", "temperature", "max_tokens", "top_p", "stop", "seed"] + _check_valid_arg(supported_params=supported_params) + + if stream: + optional_params["stream"] = stream + return optional_params + if max_tokens is not None: + if "vicuna" in model or "flan" in model: + optional_params["max_length"] = max_tokens + elif "meta/codellama-13b" in model: + optional_params["max_tokens"] = max_tokens + else: + optional_params["max_new_tokens"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stop is not None: + optional_params["stop_sequences"] = stop + elif custom_llm_provider == "huggingface": + ## check if unsupported param passed in + supported_params = ["stream", "temperature", "max_tokens", "top_p", "stop", "n"] + _check_valid_arg(supported_params=supported_params) + # temperature, top_p, n, stream, stop, max_tokens, n, presence_penalty default to None + if temperature is not None: + if temperature == 0.0 or temperature == 0: + # hugging face exception raised when temp==0 + # Failed: Error occurred: HuggingfaceException - Input validation error: `temperature` must be strictly positive + temperature = 0.01 + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if n is not None: + optional_params["best_of"] = n + optional_params["do_sample"] = True # Need to sample if you want best of for hf inference endpoints + if stream is not None: + optional_params["stream"] = stream + if stop is not None: + optional_params["stop"] = stop + if max_tokens is not None: + # HF TGI raises the following exception when max_new_tokens==0 + # Failed: Error occurred: HuggingfaceException - Input validation error: `max_new_tokens` must be strictly positive + if max_tokens == 0: + max_tokens = 1 + optional_params["max_new_tokens"] = max_tokens + if n is not None: + optional_params["best_of"] = n + if presence_penalty is not None: + optional_params["repetition_penalty"] = presence_penalty + if "echo" in passed_params: + # https://huggingface.co/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.text_generation.decoder_input_details + # Return the decoder input token logprobs and ids. You must set details=True as well for it to be taken into account. Defaults to False + optional_params["decoder_input_details"] = special_params["echo"] + passed_params.pop("echo", None) # since we handle translating echo, we should not send it to TGI request + elif custom_llm_provider == "together_ai": + ## check if unsupported param passed in + supported_params = ["stream", "temperature", "max_tokens", "top_p", "stop", "frequency_penalty"] + _check_valid_arg(supported_params=supported_params) + + if stream: + optional_params["stream_tokens"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + if frequency_penalty is not None: + optional_params["repetition_penalty"] = frequency_penalty # https://docs.together.ai/reference/inference + if stop is not None: + optional_params["stop"] = stop + elif custom_llm_provider == "ai21": + ## check if unsupported param passed in + supported_params = ["stream", "n", "temperature", "max_tokens", "top_p", "stop", "frequency_penalty", "presence_penalty"] + _check_valid_arg(supported_params=supported_params) + + if stream: + optional_params["stream"] = stream + if n is not None: + optional_params["numResults"] = n + if max_tokens is not None: + optional_params["maxTokens"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["topP"] = top_p + if stop is not None: + optional_params["stopSequences"] = stop + if frequency_penalty is not None: + optional_params["frequencyPenalty"] = {"scale": frequency_penalty} + if presence_penalty is not None: + optional_params["presencePenalty"] = {"scale": presence_penalty} + elif custom_llm_provider == "palm": # https://developers.generativeai.google/tutorials/curl_quickstart + ## check if unsupported param passed in + supported_params = ["temperature", "top_p", "stream", "n", "stop", "max_tokens"] + _check_valid_arg(supported_params=supported_params) + + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stream: + optional_params["stream"] = stream + if n is not None: + optional_params["candidate_count"] = n + if stop is not None: + optional_params["stop_sequences"] = stop + if max_tokens is not None: + optional_params["max_output_tokens"] = max_tokens + elif ( + custom_llm_provider == "vertex_ai" + ): + ## check if unsupported param passed in + supported_params = ["temperature", "top_p", "max_tokens", "stream"] + _check_valid_arg(supported_params=supported_params) + + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stream: + optional_params["stream"] = stream + if max_tokens is not None: + optional_params["max_output_tokens"] = max_tokens + elif custom_llm_provider == "sagemaker": + if "llama-2" in model: + # llama-2 models on sagemaker support the following args + """ + max_new_tokens: Model generates text until the output length (excluding the input context length) reaches max_new_tokens. If specified, it must be a positive integer. + temperature: Controls the randomness in the output. Higher temperature results in output sequence with low-probability words and lower temperature results in output sequence with high-probability words. If temperature -> 0, it results in greedy decoding. If specified, it must be a positive float. + top_p: In each step of text generation, sample from the smallest possible set of words with cumulative probability top_p. If specified, it must be a float between 0 and 1. + return_full_text: If True, input text will be part of the output generated text. If specified, it must be boolean. The default value for it is False. + """ + ## check if unsupported param passed in + supported_params = ["temperature", "max_tokens", "stream"] + _check_valid_arg(supported_params=supported_params) + + if max_tokens is not None: + optional_params["max_new_tokens"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stream: + optional_params["stream"] = stream + else: + ## check if unsupported param passed in + supported_params = [] + _check_valid_arg(supported_params=supported_params) + elif custom_llm_provider == "bedrock": + if "ai21" in model: + supported_params = ["max_tokens", "temperature", "top_p", "stream"] + _check_valid_arg(supported_params=supported_params) + # params "maxTokens":200,"temperature":0,"topP":250,"stop_sequences":[], + # https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=j2-ultra + if max_tokens is not None: + optional_params["maxTokens"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["topP"] = top_p + if stream: + optional_params["stream"] = stream + elif "anthropic" in model: + supported_params = ["max_tokens", "temperature", "stop", "top_p", "stream"] + _check_valid_arg(supported_params=supported_params) + # anthropic params on bedrock + # \"max_tokens_to_sample\":300,\"temperature\":0.5,\"top_p\":1,\"stop_sequences\":[\"\\\\n\\\\nHuman:\"]}" + if max_tokens is not None: + optional_params["max_tokens_to_sample"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stop is not None: + optional_params["stop_sequences"] = stop + if stream: + optional_params["stream"] = stream + elif "amazon" in model: # amazon titan llms + supported_params = ["max_tokens", "temperature", "stop", "top_p", "stream"] + _check_valid_arg(supported_params=supported_params) + # see https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-large + if max_tokens is not None: + optional_params["maxTokenCount"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if stop is not None: + filtered_stop = _map_and_modify_arg({"stop": stop}, provider="bedrock", model=model) + optional_params["stopSequences"] = filtered_stop["stop"] + if top_p is not None: + optional_params["topP"] = top_p + if stream: + optional_params["stream"] = stream + elif "meta" in model: # amazon / meta llms + supported_params = ["max_tokens", "temperature", "top_p", "stream"] + _check_valid_arg(supported_params=supported_params) + # see https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-large + if max_tokens is not None: + optional_params["max_gen_len"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stream: + optional_params["stream"] = stream + elif "cohere" in model: # cohere models on bedrock + supported_params = ["stream", "temperature", "max_tokens"] + _check_valid_arg(supported_params=supported_params) + # handle cohere params + if stream: + optional_params["stream"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + elif custom_llm_provider == "aleph_alpha": + supported_params = ["max_tokens", "stream", "top_p", "temperature", "presence_penalty", "frequency_penalty", "n", "stop"] + _check_valid_arg(supported_params=supported_params) + if max_tokens is not None: + optional_params["maximum_tokens"] = max_tokens + if stream: + optional_params["stream"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if presence_penalty is not None: + optional_params["presence_penalty"] = presence_penalty + if frequency_penalty is not None: + optional_params["frequency_penalty"] = frequency_penalty + if n is not None: + optional_params["n"] = n + if stop is not None: + optional_params["stop_sequences"] = stop + elif custom_llm_provider == "ollama": + supported_params = ["max_tokens", "stream", "top_p", "temperature", "frequency_penalty", "stop"] + _check_valid_arg(supported_params=supported_params) + + if max_tokens is not None: + optional_params["num_predict"] = max_tokens + if stream: + optional_params["stream"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if frequency_penalty is not None: + optional_params["repeat_penalty"] = frequency_penalty + if stop is not None: + optional_params["stop_sequences"] = stop + elif custom_llm_provider == "nlp_cloud": + supported_params = ["max_tokens", "stream", "temperature", "top_p", "presence_penalty", "frequency_penalty", "n", "stop"] + _check_valid_arg(supported_params=supported_params) + + if max_tokens is not None: + optional_params["max_length"] = max_tokens + if stream: + optional_params["stream"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if presence_penalty is not None: + optional_params["presence_penalty"] = presence_penalty + if frequency_penalty is not None: + optional_params["frequency_penalty"] = frequency_penalty + if n is not None: + optional_params["num_return_sequences"] = n + if stop is not None: + optional_params["stop_sequences"] = stop + elif custom_llm_provider == "petals": + supported_params = ["max_tokens", "temperature", "top_p", "stream"] + _check_valid_arg(supported_params=supported_params) + # max_new_tokens=1,temperature=0.9, top_p=0.6 + if max_tokens is not None: + optional_params["max_new_tokens"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stream: + optional_params["stream"] = stream + elif custom_llm_provider == "deepinfra": + supported_params = ["temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user"] + _check_valid_arg(supported_params=supported_params) + if temperature is not None: + if temperature == 0 and model == "mistralai/Mistral-7B-Instruct-v0.1": # this model does no support temperature == 0 + temperature = 0.0001 # close to 0 + optional_params["temperature"] = temperature + if top_p: + optional_params["top_p"] = top_p + if n: + optional_params["n"] = n + if stream: + optional_params["stream"] = stream + if stop: + optional_params["stop"] = stop + if max_tokens: + optional_params["max_tokens"] = max_tokens + if presence_penalty: + optional_params["presence_penalty"] = presence_penalty + if frequency_penalty: + optional_params["frequency_penalty"] = frequency_penalty + if logit_bias: + optional_params["logit_bias"] = logit_bias + if user: + optional_params["user"] = user + elif custom_llm_provider == "perplexity": + supported_params = ["temperature", "top_p", "stream", "max_tokens", "presence_penalty", "frequency_penalty"] + _check_valid_arg(supported_params=supported_params) + if temperature is not None: + if temperature == 0 and model == "mistral-7b-instruct": # this model does no support temperature == 0 + temperature = 0.0001 # close to 0 + optional_params["temperature"] = temperature + if top_p: + optional_params["top_p"] = top_p + if stream: + optional_params["stream"] = stream + if max_tokens: + optional_params["max_tokens"] = max_tokens + if presence_penalty: + optional_params["presence_penalty"] = presence_penalty + if frequency_penalty: + optional_params["frequency_penalty"] = frequency_penalty + elif custom_llm_provider == "anyscale": + supported_params = ["temperature", "top_p", "stream", "max_tokens"] + _check_valid_arg(supported_params=supported_params) + optional_params = non_default_params + if temperature is not None: + if temperature == 0 and model == "mistralai/Mistral-7B-Instruct-v0.1": # this model does no support temperature == 0 + temperature = 0.0001 # close to 0 + optional_params["temperature"] = temperature + if top_p: + optional_params["top_p"] = top_p + if stream: + optional_params["stream"] = stream + if max_tokens: + optional_params["max_tokens"] = max_tokens + else: # assume passing in params for openai/azure openai + supported_params = ["functions", "function_call", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "response_format", "seed", "tools", "tool_choice", "max_retries"] + _check_valid_arg(supported_params=supported_params) + if functions is not None: + optional_params["functions"] = functions + if function_call is not None: + optional_params["function_call"] = function_call + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if n is not None: + optional_params["n"] = n + if stream is not None: + optional_params["stream"] = stream + if stop is not None: + optional_params["stop"] = stop + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + if presence_penalty is not None: + optional_params["presence_penalty"] = presence_penalty + if frequency_penalty is not None: + optional_params["frequency_penalty"] = frequency_penalty + if logit_bias is not None: + optional_params["logit_bias"] = logit_bias + if user is not None: + optional_params["user"] = user + if response_format is not None: + optional_params["response_format"] = response_format + if seed is not None: + optional_params["seed"] = seed + if tools is not None: + optional_params["tools"] = tools + if tool_choice is not None: + optional_params["tool_choice"] = tool_choice + if max_retries is not None: + optional_params["max_retries"] = max_retries + optional_params = non_default_params + # if user passed in non-default kwargs for specific providers/models, pass them along + for k in passed_params.keys(): + if k not in default_params.keys(): + optional_params[k] = passed_params[k] + return optional_params + +def get_llm_provider(model: str, custom_llm_provider: Optional[str] = None, api_base: Optional[str] = None, api_key: Optional[str] = None): + try: + dynamic_api_key = None + # check if llm provider provided + + if custom_llm_provider: + return model, custom_llm_provider, dynamic_api_key, api_base + + if api_key and api_key.startswith("os.environ/"): + dynamic_api_key = get_secret(api_key) + # check if llm provider part of model name + if model.split("/",1)[0] in litellm.provider_list and model.split("/",1)[0] not in litellm.model_list: + custom_llm_provider = model.split("/", 1)[0] + model = model.split("/", 1)[1] + if custom_llm_provider == "perplexity": + # perplexity is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.perplexity.ai + api_base = "https://api.perplexity.ai" + dynamic_api_key = get_secret("PERPLEXITYAI_API_KEY") + elif custom_llm_provider == "anyscale": + # anyscale is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.endpoints.anyscale.com/v1 + api_base = "https://api.endpoints.anyscale.com/v1" + dynamic_api_key = get_secret("ANYSCALE_API_KEY") + elif custom_llm_provider == "deepinfra": + # deepinfra is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.endpoints.anyscale.com/v1 + api_base = "https://api.deepinfra.com/v1/openai" + dynamic_api_key = get_secret("DEEPINFRA_API_KEY") + return model, custom_llm_provider, dynamic_api_key, api_base + + # check if api base is a known openai compatible endpoint + if api_base: + for endpoint in litellm.openai_compatible_endpoints: + if endpoint in api_base: + if endpoint == "api.perplexity.ai": + custom_llm_provider = "perplexity" + dynamic_api_key = get_secret("PERPLEXITYAI_API_KEY") + elif endpoint == "api.endpoints.anyscale.com/v1": + custom_llm_provider = "anyscale" + dynamic_api_key = get_secret("ANYSCALE_API_KEY") + elif endpoint == "api.deepinfra.com/v1/openai": + custom_llm_provider = "deepinfra" + dynamic_api_key = get_secret("DEEPINFRA_API_KEY") + return model, custom_llm_provider, dynamic_api_key, api_base + + # check if model in known model provider list -> for huggingface models, raise exception as they don't have a fixed provider (can be togetherai, anyscale, baseten, runpod, et.) + ## openai - chatcompletion + text completion + if model in litellm.open_ai_chat_completion_models or "ft:gpt-3.5-turbo" in model: + custom_llm_provider = "openai" + elif model in litellm.open_ai_text_completion_models: + custom_llm_provider = "text-completion-openai" + ## anthropic + elif model in litellm.anthropic_models: + custom_llm_provider = "anthropic" + ## cohere + elif model in litellm.cohere_models: + custom_llm_provider = "cohere" + ## replicate + elif model in litellm.replicate_models or ":" in model: + model_parts = model.split(":") + if len(model_parts) > 1 and len(model_parts[1])==64: ## checks if model name has a 64 digit code - e.g. "meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3" + custom_llm_provider = "replicate" + elif model in litellm.replicate_models: + custom_llm_provider = "replicate" + ## openrouter + elif model in litellm.openrouter_models: + custom_llm_provider = "openrouter" + ## openrouter + elif model in litellm.maritalk_models: + custom_llm_provider = "maritalk" + ## vertex - text + chat models + elif( + model in litellm.vertex_chat_models or + model in litellm.vertex_code_chat_models or + model in litellm.vertex_text_models or + model in litellm.vertex_code_text_models + ): + custom_llm_provider = "vertex_ai" + ## ai21 + elif model in litellm.ai21_models: + custom_llm_provider = "ai21" + ## aleph_alpha + elif model in litellm.aleph_alpha_models: + custom_llm_provider = "aleph_alpha" + ## baseten + elif model in litellm.baseten_models: + custom_llm_provider = "baseten" + ## nlp_cloud + elif model in litellm.nlp_cloud_models: + custom_llm_provider = "nlp_cloud" + ## petals + elif model in litellm.petals_models: + custom_llm_provider = "petals" + ## bedrock + elif model in litellm.bedrock_models: + custom_llm_provider = "bedrock" + # openai embeddings + elif model in litellm.open_ai_embedding_models: + custom_llm_provider = "openai" + # cohere embeddings + elif model in litellm.cohere_embedding_models: + custom_llm_provider = "cohere" + elif model in litellm.bedrock_embedding_models: + custom_llm_provider = "bedrock" + + if custom_llm_provider is None or custom_llm_provider=="": + print() # noqa + print("\033[1;31mProvider List: https://docs.litellm.ai/docs/providers\033[0m") # noqa + print() # noqa + raise ValueError(f"LLM Provider NOT provided. Pass in the LLM provider you are trying to call. E.g. For 'Huggingface' inference endpoints pass in `completion(model='huggingface/{model}',..)` Learn more: https://docs.litellm.ai/docs/providers") + return model, custom_llm_provider, dynamic_api_key, api_base + except Exception as e: + raise e + + +def get_api_key(llm_provider: str, dynamic_api_key: Optional[str]): + api_key = (dynamic_api_key or litellm.api_key) + # openai + if llm_provider == "openai" or llm_provider == "text-completion-openai": + api_key = ( + api_key or + litellm.openai_key or + get_secret("OPENAI_API_KEY") + ) + # anthropic + elif llm_provider == "anthropic": + api_key = ( + api_key or + litellm.anthropic_key or + get_secret("ANTHROPIC_API_KEY") + ) + # ai21 + elif llm_provider == "ai21": + api_key = ( + api_key or + litellm.ai21_key or + get_secret("AI211_API_KEY") + ) + # aleph_alpha + elif llm_provider == "aleph_alpha": + api_key = ( + api_key or + litellm.aleph_alpha_key or + get_secret("ALEPH_ALPHA_API_KEY") + ) + # baseten + elif llm_provider == "baseten": + api_key = ( + api_key or + litellm.baseten_key or + get_secret("BASETEN_API_KEY") + ) + # cohere + elif llm_provider == "cohere": + api_key = ( + api_key or + litellm.cohere_key or + get_secret("COHERE_API_KEY") + ) + # huggingface + elif llm_provider == "huggingface": + api_key = ( + api_key or + litellm.huggingface_key or + get_secret("HUGGINGFACE_API_KEY") + ) + # nlp_cloud + elif llm_provider == "nlp_cloud": + api_key = ( + api_key or + litellm.nlp_cloud_key or + get_secret("NLP_CLOUD_API_KEY") + ) + # replicate + elif llm_provider == "replicate": + api_key = ( + api_key or + litellm.replicate_key or + get_secret("REPLICATE_API_KEY") + ) + # together_ai + elif llm_provider == "together_ai": + api_key = ( + api_key or + litellm.togetherai_api_key or + get_secret("TOGETHERAI_API_KEY") or + get_secret("TOGETHER_AI_TOKEN") + ) + return api_key + +def get_max_tokens(model: str): + """ + Get the maximum number of tokens allowed for a given model. + + Parameters: + model (str): The name of the model. + + Returns: + int: The maximum number of tokens allowed for the given model. + + Raises: + Exception: If the model is not mapped yet. + + Example: + >>> get_max_tokens("gpt-4") + 8192 + """ + def _get_max_position_embeddings(model_name): + # Construct the URL for the config.json file + config_url = f"https://huggingface.co/{model_name}/raw/main/config.json" + + try: + # Make the HTTP request to get the raw JSON file + response = requests.get(config_url) + response.raise_for_status() # Raise an exception for bad responses (4xx or 5xx) + + # Parse the JSON response + config_json = response.json() + + # Extract and return the max_position_embeddings + max_position_embeddings = config_json.get("max_position_embeddings") + + if max_position_embeddings is not None: + return max_position_embeddings + else: + return None + except requests.exceptions.RequestException as e: + return None + + try: + if model in litellm.model_cost: + return litellm.model_cost[model]["max_tokens"] + model, custom_llm_provider, _, _ = get_llm_provider(model=model) + if custom_llm_provider == "huggingface": + max_tokens = _get_max_position_embeddings(model_name=model) + return max_tokens + else: + raise Exception() + except: + raise Exception("This model isn't mapped yet. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json") + + +def get_model_info(model: str): + """ + Get a dict for the maximum tokens (context window), + input_cost_per_token, output_cost_per_token for a given model. + + Parameters: + model (str): The name of the model. + + Returns: + dict: A dictionary containing the following information: + - max_tokens (int): The maximum number of tokens allowed for the given model. + - input_cost_per_token (float): The cost per token for input. + - output_cost_per_token (float): The cost per token for output. + - litellm_provider (str): The provider of the model (e.g., "openai"). + - mode (str): The mode of the model (e.g., "chat" or "completion"). + + Raises: + Exception: If the model is not mapped yet. + + Example: + >>> get_model_info("gpt-4") + { + "max_tokens": 8192, + "input_cost_per_token": 0.00003, + "output_cost_per_token": 0.00006, + "litellm_provider": "openai", + "mode": "chat" + } + """ + def _get_max_position_embeddings(model_name): + # Construct the URL for the config.json file + config_url = f"https://huggingface.co/{model_name}/raw/main/config.json" + + try: + # Make the HTTP request to get the raw JSON file + response = requests.get(config_url) + response.raise_for_status() # Raise an exception for bad responses (4xx or 5xx) + + # Parse the JSON response + config_json = response.json() + + # Extract and return the max_position_embeddings + max_position_embeddings = config_json.get("max_position_embeddings") + + if max_position_embeddings is not None: + return max_position_embeddings + else: + return None + except requests.exceptions.RequestException as e: + return None + try: + azure_llms = { + "gpt-35-turbo": "azure/gpt-3.5-turbo", + "gpt-35-turbo-16k": "azure/gpt-3.5-turbo-16k", + "gpt-35-turbo-instruct": "azure/gpt-3.5-turbo-instruct" + } + if model in azure_llms: + model = azure_llms[model] + if model in litellm.model_cost: + return litellm.model_cost[model] + model, custom_llm_provider, _, _ = get_llm_provider(model=model) + if custom_llm_provider == "huggingface": + max_tokens = _get_max_position_embeddings(model_name=model) + return { + "max_tokens": max_tokens, + "input_cost_per_token": 0, + "output_cost_per_token": 0, + "litellm_provider": "huggingface", + "mode": "chat" + } + else: + raise Exception() + except: + raise Exception("This model isn't mapped yet. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json") + +def json_schema_type(python_type_name: str): + """Converts standard python types to json schema types + + Parameters + ---------- + python_type_name : str + __name__ of type + + Returns + ------- + str + a standard JSON schema type, "string" if not recognized. + """ + python_to_json_schema_types = { + str.__name__: "string", + int.__name__: "integer", + float.__name__: "number", + bool.__name__: "boolean", + list.__name__: "array", + dict.__name__: "object", + "NoneType": "null", + } + + return python_to_json_schema_types.get(python_type_name, "string") + +def function_to_dict(input_function): # noqa: C901 + """Using type hints and numpy-styled docstring, + produce a dictionnary usable for OpenAI function calling + + Parameters + ---------- + input_function : function + A function with a numpy-style docstring + + Returns + ------- + dictionnary + A dictionnary to add to the list passed to `functions` parameter of `litellm.completion` + """ + # Get function name and docstring + try: + import inspect + from numpydoc.docscrape import NumpyDocString + from ast import literal_eval + except Exception as e: + raise e + + name = input_function.__name__ + docstring = inspect.getdoc(input_function) + numpydoc = NumpyDocString(docstring) + description = "\n".join([s.strip() for s in numpydoc["Summary"]]) + + # Get function parameters and their types from annotations and docstring + parameters = {} + required_params = [] + param_info = inspect.signature(input_function).parameters + + for param_name, param in param_info.items(): + if hasattr(param, "annotation"): + param_type = json_schema_type(param.annotation.__name__) + else: + param_type = None + param_description = None + param_enum = None + + # Try to extract param description from docstring using numpydoc + for param_data in numpydoc["Parameters"]: + if param_data.name == param_name: + if hasattr(param_data, "type"): + # replace type from docstring rather than annotation + param_type = param_data.type + if "optional" in param_type: + param_type = param_type.split(",")[0] + elif "{" in param_type: + # may represent a set of acceptable values + # translating as enum for function calling + try: + param_enum = str(list(literal_eval(param_type))) + param_type = "string" + except Exception: + pass + param_type = json_schema_type(param_type) + param_description = "\n".join([s.strip() for s in param_data.desc]) + + param_dict = { + "type": param_type, + "description": param_description, + "enum": param_enum, + } + + parameters[param_name] = dict( + [(k, v) for k, v in param_dict.items() if isinstance(v, str)] + ) + + # Check if the parameter has no default value (i.e., it's required) + if param.default == param.empty: + required_params.append(param_name) + + # Create the dictionary + result = { + "name": name, + "description": description, + "parameters": { + "type": "object", + "properties": parameters, + }, + } + + # Add "required" key if there are required parameters + if required_params: + result["parameters"]["required"] = required_params + + return result + +def load_test_model( + model: str, + custom_llm_provider: str = "", + api_base: str = "", + prompt: str = "", + num_calls: int = 0, + force_timeout: int = 0, +): + test_prompt = "Hey, how's it going" + test_calls = 100 + if prompt: + test_prompt = prompt + if num_calls: + test_calls = num_calls + messages = [[{"role": "user", "content": test_prompt}] for _ in range(test_calls)] + start_time = time.time() + try: + litellm.batch_completion( + model=model, + messages=messages, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + force_timeout=force_timeout, + ) + end_time = time.time() + response_time = end_time - start_time + return { + "total_response_time": response_time, + "calls_made": 100, + "status": "success", + "exception": None, + } + except Exception as e: + end_time = time.time() + response_time = end_time - start_time + return { + "total_response_time": response_time, + "calls_made": 100, + "status": "failed", + "exception": e, + } + +def validate_environment(model: Optional[str]=None) -> dict: + """ + Checks if the environment variables are valid for the given model. + + Args: + model (Optional[str]): The name of the model. Defaults to None. + + Returns: + dict: A dictionary containing the following keys: + - keys_in_environment (bool): True if all the required keys are present in the environment, False otherwise. + - missing_keys (List[str]): A list of missing keys in the environment. + """ + keys_in_environment = False + missing_keys: List[str] = [] + + if model is None: + return {"keys_in_environment": keys_in_environment, "missing_keys": missing_keys} + ## EXTRACT LLM PROVIDER - if model name provided + try: + custom_llm_provider = get_llm_provider(model=model) + except: + custom_llm_provider = None + # # check if llm provider part of model name + # if model.split("/",1)[0] in litellm.provider_list: + # custom_llm_provider = model.split("/", 1)[0] + # model = model.split("/", 1)[1] + # custom_llm_provider_passed_in = True + + if custom_llm_provider: + if custom_llm_provider == "openai": + if "OPENAI_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("OPENAI_API_KEY") + elif custom_llm_provider == "azure": + if ("AZURE_API_BASE" in os.environ + and "AZURE_API_VERSION" in os.environ + and "AZURE_API_KEY" in os.environ): + keys_in_environment = True + else: + missing_keys.extend(["AZURE_API_BASE", "AZURE_API_VERSION", "AZURE_API_KEY"]) + elif custom_llm_provider == "anthropic": + if "ANTHROPIC_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("ANTHROPIC_API_KEY") + elif custom_llm_provider == "cohere": + if "COHERE_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("COHERE_API_KEY") + elif custom_llm_provider == "replicate": + if "REPLICATE_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("REPLICATE_API_KEY") + elif custom_llm_provider == "openrouter": + if "OPENROUTER_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("OPENROUTER_API_KEY") + elif custom_llm_provider == "vertex_ai": + if ("VERTEXAI_PROJECT" in os.environ + and "VERTEXAI_LOCATION" in os.environ): + keys_in_environment = True + else: + missing_keys.extend(["VERTEXAI_PROJECT", "VERTEXAI_PROJECT"]) + elif custom_llm_provider == "huggingface": + if "HUGGINGFACE_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("HUGGINGFACE_API_KEY") + elif custom_llm_provider == "ai21": + if "AI21_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("AI21_API_KEY") + elif custom_llm_provider == "together_ai": + if "TOGETHERAI_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("TOGETHERAI_API_KEY") + elif custom_llm_provider == "aleph_alpha": + if "ALEPH_ALPHA_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("ALEPH_ALPHA_API_KEY") + elif custom_llm_provider == "baseten": + if "BASETEN_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("BASETEN_API_KEY") + elif custom_llm_provider == "nlp_cloud": + if "NLP_CLOUD_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("NLP_CLOUD_API_KEY") + elif custom_llm_provider == "bedrock": + if "AWS_ACCESS_KEY_ID" in os.environ and "AWS_SECRET_ACCESS_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("AWS_ACCESS_KEY_ID") + missing_keys.append("AWS_SECRET_ACCESS_KEY") + else: + ## openai - chatcompletion + text completion + if model in litellm.open_ai_chat_completion_models or litellm.open_ai_text_completion_models: + if "OPENAI_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("OPENAI_API_KEY") + ## anthropic + elif model in litellm.anthropic_models: + if "ANTHROPIC_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("ANTHROPIC_API_KEY") + ## cohere + elif model in litellm.cohere_models: + if "COHERE_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("COHERE_API_KEY") + ## replicate + elif model in litellm.replicate_models: + if "REPLICATE_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("REPLICATE_API_KEY") + ## openrouter + elif model in litellm.openrouter_models: + if "OPENROUTER_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("OPENROUTER_API_KEY") + ## vertex - text + chat models + elif model in litellm.vertex_chat_models or model in litellm.vertex_text_models: + if ("VERTEXAI_PROJECT" in os.environ + and "VERTEXAI_LOCATION" in os.environ): + keys_in_environment = True + else: + missing_keys.extend(["VERTEXAI_PROJECT", "VERTEXAI_PROJECT"]) + ## huggingface + elif model in litellm.huggingface_models: + if "HUGGINGFACE_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("HUGGINGFACE_API_KEY") + ## ai21 + elif model in litellm.ai21_models: + if "AI21_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("AI21_API_KEY") + ## together_ai + elif model in litellm.together_ai_models: + if "TOGETHERAI_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("TOGETHERAI_API_KEY") + ## aleph_alpha + elif model in litellm.aleph_alpha_models: + if "ALEPH_ALPHA_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("ALEPH_ALPHA_API_KEY") + ## baseten + elif model in litellm.baseten_models: + if "BASETEN_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("BASETEN_API_KEY") + ## nlp_cloud + elif model in litellm.nlp_cloud_models: + if "NLP_CLOUD_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("NLP_CLOUD_API_KEY") + return {"keys_in_environment": keys_in_environment, "missing_keys": missing_keys} + +def set_callbacks(callback_list, function_id=None): + global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, heliconeLogger, aispendLogger, berrispendLogger, supabaseClient, liteDebuggerClient, llmonitorLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, langsmithLogger + try: + for callback in callback_list: + print_verbose(f"callback: {callback}") + if callback == "sentry": + try: + import sentry_sdk + except ImportError: + print_verbose("Package 'sentry_sdk' is missing. Installing it...") + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "sentry_sdk"] + ) + import sentry_sdk + sentry_sdk_instance = sentry_sdk + sentry_trace_rate = ( + os.environ.get("SENTRY_API_TRACE_RATE") + if "SENTRY_API_TRACE_RATE" in os.environ + else "1.0" + ) + sentry_sdk_instance.init( + dsn=os.environ.get("SENTRY_DSN"), + traces_sample_rate=float(sentry_trace_rate), + ) + capture_exception = sentry_sdk_instance.capture_exception + add_breadcrumb = sentry_sdk_instance.add_breadcrumb + elif callback == "posthog": + try: + from posthog import Posthog + except ImportError: + print_verbose("Package 'posthog' is missing. Installing it...") + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "posthog"] + ) + from posthog import Posthog + posthog = Posthog( + project_api_key=os.environ.get("POSTHOG_API_KEY"), + host=os.environ.get("POSTHOG_API_URL"), + ) + elif callback == "slack": + try: + from slack_bolt import App + except ImportError: + print_verbose("Package 'slack_bolt' is missing. Installing it...") + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "slack_bolt"] + ) + from slack_bolt import App + slack_app = App( + token=os.environ.get("SLACK_API_TOKEN"), + signing_secret=os.environ.get("SLACK_API_SECRET"), + ) + alerts_channel = os.environ["SLACK_API_CHANNEL"] + print_verbose(f"Initialized Slack App: {slack_app}") + elif callback == "traceloop": + traceloopLogger = TraceloopLogger() + elif callback == "helicone": + heliconeLogger = HeliconeLogger() + elif callback == "llmonitor": + llmonitorLogger = LLMonitorLogger() + elif callback == "promptlayer": + promptLayerLogger = PromptLayerLogger() + elif callback == "langfuse": + langFuseLogger = LangFuseLogger() + elif callback == "wandb": + weightsBiasesLogger = WeightsBiasesLogger() + elif callback == "langsmith": + langsmithLogger = LangsmithLogger() + elif callback == "aispend": + aispendLogger = AISpendLogger() + elif callback == "berrispend": + berrispendLogger = BerriSpendLogger() + elif callback == "supabase": + print_verbose(f"instantiating supabase") + supabaseClient = Supabase() + elif callback == "lite_debugger": + print_verbose(f"instantiating lite_debugger") + if function_id: + liteDebuggerClient = LiteDebugger(email=function_id) + elif litellm.token: + liteDebuggerClient = LiteDebugger(email=litellm.token) + elif litellm.email: + liteDebuggerClient = LiteDebugger(email=litellm.email) + else: + liteDebuggerClient = LiteDebugger(email=str(uuid.uuid4())) + elif callable(callback): + customLogger = CustomLogger() + except Exception as e: + raise e + +# NOTE: DEPRECATING this in favor of using failure_handler() in Logging: +def handle_failure(exception, traceback_exception, start_time, end_time, args, kwargs): + global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, aispendLogger, berrispendLogger, supabaseClient, liteDebuggerClient, llmonitorLogger + try: + # print_verbose(f"handle_failure args: {args}") + # print_verbose(f"handle_failure kwargs: {kwargs}") + + success_handler = additional_details.pop("success_handler", None) + failure_handler = additional_details.pop("failure_handler", None) + + additional_details["Event_Name"] = additional_details.pop( + "failed_event_name", "litellm.failed_query" + ) + print_verbose(f"self.failure_callback: {litellm.failure_callback}") + for callback in litellm.failure_callback: + try: + if callback == "slack": + slack_msg = "" + if len(kwargs) > 0: + for key in kwargs: + slack_msg += f"{key}: {kwargs[key]}\n" + if len(args) > 0: + for i, arg in enumerate(args): + slack_msg += f"LiteLLM_Args_{str(i)}: {arg}" + for detail in additional_details: + slack_msg += f"{detail}: {additional_details[detail]}\n" + slack_msg += f"Traceback: {traceback_exception}" + slack_app.client.chat_postMessage( + channel=alerts_channel, text=slack_msg + ) + elif callback == "sentry": + capture_exception(exception) + elif callback == "posthog": + print_verbose( + f"inside posthog, additional_details: {len(additional_details.keys())}" + ) + ph_obj = {} + if len(kwargs) > 0: + ph_obj = kwargs + if len(args) > 0: + for i, arg in enumerate(args): + ph_obj["litellm_args_" + str(i)] = arg + for detail in additional_details: + ph_obj[detail] = additional_details[detail] + event_name = additional_details["Event_Name"] + print_verbose(f"ph_obj: {ph_obj}") + print_verbose(f"PostHog Event Name: {event_name}") + if "user_id" in additional_details: + posthog.capture( + additional_details["user_id"], event_name, ph_obj + ) + else: # PostHog calls require a unique id to identify a user - https://posthog.com/docs/libraries/python + unique_id = str(uuid.uuid4()) + posthog.capture(unique_id, event_name) + print_verbose(f"successfully logged to PostHog!") + elif callback == "berrispend": + print_verbose("reaches berrispend for logging!") + model = args[0] if len(args) > 0 else kwargs["model"] + messages = args[1] if len(args) > 1 else kwargs["messages"] + result = { + "model": model, + "created": time.time(), + "error": traceback_exception, + "usage": { + "prompt_tokens": prompt_token_calculator( + model, messages=messages + ), + "completion_tokens": 0, + }, + } + berrispendLogger.log_event( + model=model, + messages=messages, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + elif callback == "aispend": + print_verbose("reaches aispend for logging!") + model = args[0] if len(args) > 0 else kwargs["model"] + messages = args[1] if len(args) > 1 else kwargs["messages"] + result = { + "model": model, + "created": time.time(), + "usage": { + "prompt_tokens": prompt_token_calculator( + model, messages=messages + ), + "completion_tokens": 0, + }, + } + aispendLogger.log_event( + model=model, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + elif callback == "supabase": + print_verbose("reaches supabase for logging!") + print_verbose(f"supabaseClient: {supabaseClient}") + model = args[0] if len(args) > 0 else kwargs["model"] + messages = args[1] if len(args) > 1 else kwargs["messages"] + result = { + "model": model, + "created": time.time(), + "error": traceback_exception, + "usage": { + "prompt_tokens": prompt_token_calculator( + model, messages=messages + ), + "completion_tokens": 0, + }, + } + supabaseClient.log_event( + model=model, + messages=messages, + end_user=kwargs.get("user", "default"), + response_obj=result, + start_time=start_time, + end_time=end_time, + litellm_call_id=kwargs["litellm_call_id"], + print_verbose=print_verbose, + ) + except: + print_verbose( + f"Error Occurred while logging failure: {traceback.format_exc()}" + ) + pass + + if failure_handler and callable(failure_handler): + call_details = { + "exception": exception, + "additional_details": additional_details, + } + failure_handler(call_details) + pass + except Exception as e: + # LOGGING + exception_logging(logger_fn=user_logger_fn, exception=e) + pass + + +def convert_to_model_response_object(response_object: Optional[dict]=None, model_response_object: Optional[Union[ModelResponse, EmbeddingResponse]]=None, response_type: Literal["completion", "embedding"] = "completion"): + try: + if response_type == "completion" and (model_response_object is None or isinstance(model_response_object, ModelResponse)): + if response_object is None or model_response_object is None: + raise Exception("Error in response object format") + choice_list=[] + for idx, choice in enumerate(response_object["choices"]): + message = Message( + content=choice["message"].get("content", None), + role=choice["message"]["role"], + function_call=choice["message"].get("function_call", None), + tool_calls=choice["message"].get("tool_calls", None) + ) + finish_reason = choice.get("finish_reason", None) + if finish_reason == None: + # gpt-4 vision can return 'finish_reason' or 'finish_details' + finish_reason = choice.get("finish_details") + choice = Choices(finish_reason=finish_reason, index=idx, message=message) + choice_list.append(choice) + model_response_object.choices = choice_list + + if "usage" in response_object and response_object["usage"] is not None: + model_response_object.usage.completion_tokens = response_object["usage"].get("completion_tokens", 0) # type: ignore + model_response_object.usage.prompt_tokens = response_object["usage"].get("prompt_tokens", 0) # type: ignore + model_response_object.usage.total_tokens = response_object["usage"].get("total_tokens", 0) # type: ignore + + if "id" in response_object: + model_response_object.id = response_object["id"] + + if "system_fingerprint" in response_object: + model_response_object.system_fingerprint = response_object["system_fingerprint"] + + if "model" in response_object: + model_response_object.model = response_object["model"] + return model_response_object + elif response_type == "embedding" and (model_response_object is None or isinstance(model_response_object, EmbeddingResponse)): + if response_object is None: + raise Exception("Error in response object format") + + if model_response_object is None: + model_response_object = EmbeddingResponse() + + if "model" in response_object: + model_response_object.model = response_object["model"] + + if "object" in response_object: + model_response_object.object = response_object["object"] + + + model_response_object.data = response_object["data"] + + if "usage" in response_object and response_object["usage"] is not None: + model_response_object.usage.completion_tokens = response_object["usage"].get("completion_tokens", 0) # type: ignore + model_response_object.usage.prompt_tokens = response_object["usage"].get("prompt_tokens", 0) # type: ignore + model_response_object.usage.total_tokens = response_object["usage"].get("total_tokens", 0) # type: ignore + + + return model_response_object + except Exception as e: + raise Exception(f"Invalid response object {e}") + + +# NOTE: DEPRECATING this in favor of using success_handler() in Logging: +def handle_success(args, kwargs, result, start_time, end_time): + global heliconeLogger, aispendLogger, supabaseClient, liteDebuggerClient, llmonitorLogger + try: + model = args[0] if len(args) > 0 else kwargs["model"] + input = ( + args[1] + if len(args) > 1 + else kwargs.get("messages", kwargs.get("input", None)) + ) + success_handler = additional_details.pop("success_handler", None) + failure_handler = additional_details.pop("failure_handler", None) + additional_details["Event_Name"] = additional_details.pop( + "successful_event_name", "litellm.succes_query" + ) + for callback in litellm.success_callback: + try: + if callback == "posthog": + ph_obj = {} + for detail in additional_details: + ph_obj[detail] = additional_details[detail] + event_name = additional_details["Event_Name"] + if "user_id" in additional_details: + posthog.capture( + additional_details["user_id"], event_name, ph_obj + ) + else: # PostHog calls require a unique id to identify a user - https://posthog.com/docs/libraries/python + unique_id = str(uuid.uuid4()) + posthog.capture(unique_id, event_name, ph_obj) + pass + elif callback == "slack": + slack_msg = "" + for detail in additional_details: + slack_msg += f"{detail}: {additional_details[detail]}\n" + slack_app.client.chat_postMessage( + channel=alerts_channel, text=slack_msg + ) + elif callback == "aispend": + print_verbose("reaches aispend for logging!") + model = args[0] if len(args) > 0 else kwargs["model"] + aispendLogger.log_event( + model=model, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + except Exception as e: + # LOGGING + exception_logging(logger_fn=user_logger_fn, exception=e) + print_verbose( + f"[Non-Blocking] Success Callback Error - {traceback.format_exc()}" + ) + pass + + if success_handler and callable(success_handler): + success_handler(args, kwargs) + pass + except Exception as e: + # LOGGING + exception_logging(logger_fn=user_logger_fn, exception=e) + print_verbose( + f"[Non-Blocking] Success Callback Error - {traceback.format_exc()}" + ) + pass + + +def acreate(*args, **kwargs): ## Thin client to handle the acreate langchain call + return litellm.acompletion(*args, **kwargs) + + +def prompt_token_calculator(model, messages): + # use tiktoken or anthropic's tokenizer depending on the model + text = " ".join(message["content"] for message in messages) + num_tokens = 0 + if "claude" in model: + try: + import anthropic + except: + Exception("Anthropic import failed please run `pip install anthropic`") + from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT + + anthropic = Anthropic() + num_tokens = anthropic.count_tokens(text) + else: + num_tokens = len(encoding.encode(text)) + return num_tokens + + +def valid_model(model): + try: + # for a given model name, check if the user has the right permissions to access the model + if ( + model in litellm.open_ai_chat_completion_models + or model in litellm.open_ai_text_completion_models + ): + openai.Model.retrieve(model) + else: + messages = [{"role": "user", "content": "Hello World"}] + litellm.completion(model=model, messages=messages) + except: + raise BadRequestError(message="", model=model, llm_provider="") + +def check_valid_key(model: str, api_key: str): + """ + Checks if a given API key is valid for a specific model by making a litellm.completion call with max_tokens=10 + + Args: + model (str): The name of the model to check the API key against. + api_key (str): The API key to be checked. + + Returns: + bool: True if the API key is valid for the model, False otherwise. + """ + messages = [{"role": "user", "content": "Hey, how's it going?"}] + try: + litellm.completion(model=model, messages=messages, api_key=api_key, max_tokens=10) + return True + except AuthenticationError as e: + return False + except Exception as e: + return False + +def _should_retry(status_code: int): + """ + Reimplementation of openai's should retry logic, since that one can't be imported. + https://github.com/openai/openai-python/blob/af67cfab4210d8e497c05390ce14f39105c77519/src/openai/_base_client.py#L639 + """ + # If the server explicitly says whether or not to retry, obey. + # Retry on request timeouts. + if status_code == 408: + return True + + # Retry on lock timeouts. + if status_code == 409: + return True + + # Retry on rate limits. + if status_code == 429: + return True + + # Retry internal errors. + if status_code >= 500: + return True + + return False + +def _calculate_retry_after(remaining_retries: int, max_retries: int, response_headers: Optional[httpx.Headers]=None, min_timeout: int = 0): + """ + Reimplementation of openai's calculate retry after, since that one can't be imported. + https://github.com/openai/openai-python/blob/af67cfab4210d8e497c05390ce14f39105c77519/src/openai/_base_client.py#L631 + """ + try: + import email # openai import + # About the Retry-After header: https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Retry-After + # + # ". See https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Retry-After#syntax for + # details. + if response_headers is not None: + retry_header = response_headers.get("retry-after") + try: + retry_after = int(retry_header) + except Exception: + retry_date_tuple = email.utils.parsedate_tz(retry_header) + if retry_date_tuple is None: + retry_after = -1 + else: + retry_date = email.utils.mktime_tz(retry_date_tuple) + retry_after = int(retry_date - time.time()) + else: + retry_after = -1 + + except Exception: + retry_after = -1 + + # If the API asks us to wait a certain amount of time (and it's a reasonable amount), just do what it says. + if 0 < retry_after <= 60: + return retry_after + + initial_retry_delay = 0.5 + max_retry_delay = 8.0 + nb_retries = max_retries - remaining_retries + + # Apply exponential backoff, but not more than the max. + sleep_seconds = min(initial_retry_delay * pow(2.0, nb_retries), max_retry_delay) + + # Apply some jitter, plus-or-minus half a second. + jitter = 1 - 0.25 * random.random() + timeout = sleep_seconds * jitter + return timeout if timeout >= min_timeout else min_timeout + +# integration helper function +def modify_integration(integration_name, integration_params): + global supabaseClient + if integration_name == "supabase": + if "table_name" in integration_params: + Supabase.supabase_table_name = integration_params["table_name"] + + +# custom prompt helper function +def register_prompt_template(model: str, roles: dict, initial_prompt_value: str = "", final_prompt_value: str = ""): + """ + Register a prompt template to follow your custom format for a given model + + Args: + model (str): The name of the model. + roles (dict): A dictionary mapping roles to their respective prompt values. + initial_prompt_value (str, optional): The initial prompt value. Defaults to "". + final_prompt_value (str, optional): The final prompt value. Defaults to "". + + Returns: + dict: The updated custom prompt dictionary. + Example usage: + ``` + import litellm + litellm.register_prompt_template( + model="llama-2", + initial_prompt_value="You are a good assistant" # [OPTIONAL] + roles={ + "system": { + "pre_message": "[INST] <>\n", # [OPTIONAL] + "post_message": "\n<>\n [/INST]\n" # [OPTIONAL] + }, + "user": { + "pre_message": "[INST] ", # [OPTIONAL] + "post_message": " [/INST]" # [OPTIONAL] + }, + "assistant": { + "pre_message": "\n" # [OPTIONAL] + "post_message": "\n" # [OPTIONAL] + } + } + final_prompt_value="Now answer as best you can:" # [OPTIONAL] + ) + ``` + """ + model = get_llm_provider(model=model)[0] + litellm.custom_prompt_dict[model] = { + "roles": roles, + "initial_prompt_value": initial_prompt_value, + "final_prompt_value": final_prompt_value + } + return litellm.custom_prompt_dict + +####### DEPRECATED ################ + + +def get_all_keys(llm_provider=None): + try: + global last_fetched_at_keys + # if user is using hosted product -> instantiate their env with their hosted api keys - refresh every 5 minutes + print_verbose(f"Reaches get all keys, llm_provider: {llm_provider}") + user_email = ( + os.getenv("LITELLM_EMAIL") + or litellm.email + or litellm.token + or os.getenv("LITELLM_TOKEN") + ) + if user_email: + time_delta = 0 + if last_fetched_at_keys != None: + current_time = time.time() + time_delta = current_time - last_fetched_at_keys + if ( + time_delta > 300 or last_fetched_at_keys == None or llm_provider + ): # if the llm provider is passed in , assume this happening due to an AuthError for that provider + # make the api call + last_fetched_at = time.time() + print_verbose(f"last_fetched_at: {last_fetched_at}") + response = requests.post( + url="http://api.litellm.ai/get_all_keys", + headers={"content-type": "application/json"}, + data=json.dumps({"user_email": user_email}), + ) + print_verbose(f"get model key response: {response.text}") + data = response.json() + # update model list + for key, value in data[ + "model_keys" + ].items(): # follows the LITELLM API KEY format - _API_KEY - e.g. HUGGINGFACE_API_KEY + os.environ[key] = value + # set model alias map + for model_alias, value in data["model_alias_map"].items(): + litellm.model_alias_map[model_alias] = value + return "it worked!" + return None + return None + except: + print_verbose( + f"[Non-Blocking Error] get_all_keys error - {traceback.format_exc()}" + ) + pass + + +def get_model_list(): + global last_fetched_at, print_verbose + try: + # if user is using hosted product -> get their updated model list + user_email = ( + os.getenv("LITELLM_EMAIL") + or litellm.email + or litellm.token + or os.getenv("LITELLM_TOKEN") + ) + if user_email: + # make the api call + last_fetched_at = time.time() + print_verbose(f"last_fetched_at: {last_fetched_at}") + response = requests.post( + url="http://api.litellm.ai/get_model_list", + headers={"content-type": "application/json"}, + data=json.dumps({"user_email": user_email}), + ) + print_verbose(f"get_model_list response: {response.text}") + data = response.json() + # update model list + model_list = data["model_list"] + # # check if all model providers are in environment + # model_providers = data["model_providers"] + # missing_llm_provider = None + # for item in model_providers: + # if f"{item.upper()}_API_KEY" not in os.environ: + # missing_llm_provider = item + # break + # # update environment - if required + # threading.Thread(target=get_all_keys, args=(missing_llm_provider)).start() + return model_list + return [] # return empty list by default + except: + print_verbose( + f"[Non-Blocking Error] get_model_list error - {traceback.format_exc()}" + ) + +####### EXCEPTION MAPPING ################ +def exception_type( + model, + original_exception, + custom_llm_provider, + completion_kwargs={}, + ): + global user_logger_fn, liteDebuggerClient + exception_mapping_worked = False + if litellm.suppress_debug_info is False: + print() # noqa + print("\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m") # noqa + print("LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'.") # noqa + print() # noqa + try: + if model: + error_str = str(original_exception) + if isinstance(original_exception, BaseException): + exception_type = type(original_exception).__name__ + else: + exception_type = "" + + if "Request Timeout Error" in error_str or "Request timed out" in error_str: + exception_mapping_worked = True + raise Timeout( + message=f"APITimeoutError - Request timed out", + model=model, + llm_provider=custom_llm_provider + ) + + if custom_llm_provider == "openai" or custom_llm_provider == "text-completion-openai" or custom_llm_provider == "custom_openai": + if "This model's maximum context length is" in error_str or "Request too large" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"OpenAIException - {original_exception.message}", + llm_provider="openai", + model=model, + response=original_exception.response + ) + elif "invalid_request_error" in error_str and "Incorrect API key provided" not in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"OpenAIException - {original_exception.message}", + llm_provider="openai", + model=model, + response=original_exception.response + ) + elif hasattr(original_exception, "status_code"): + exception_mapping_worked = True + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"OpenAIException - {original_exception.message}", + llm_provider="openai", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"OpenAIException - {original_exception.message}", + model=model, + llm_provider="openai", + ) + if original_exception.status_code == 422: + exception_mapping_worked = True + raise BadRequestError( + message=f"OpenAIException - {original_exception.message}", + model=model, + llm_provider="openai", + response=original_exception.response + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"OpenAIException - {original_exception.message}", + model=model, + llm_provider="openai", + response=original_exception.response + ) + elif original_exception.status_code == 503: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"OpenAIException - {original_exception.message}", + model=model, + llm_provider="openai", + response=original_exception.response + ) + elif original_exception.status_code == 504: # gateway timeout error + exception_mapping_worked = True + raise Timeout( + message=f"OpenAIException - {original_exception.message}", + model=model, + llm_provider="openai", + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"OpenAIException - {original_exception.message}", + llm_provider="openai", + model=model, + request=original_exception.request + ) + else: + # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors + raise APIConnectionError( + __cause__=original_exception.__cause__, + llm_provider=custom_llm_provider, + model=model, + request=original_exception.request + ) + elif custom_llm_provider == "anthropic": # one of the anthropics + if hasattr(original_exception, "message"): + if "prompt is too long" in original_exception.message or "prompt: length" in original_exception.message: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=original_exception.message, + model=model, + llm_provider="anthropic", + response=original_exception.response + ) + if "Invalid API Key" in original_exception.message: + exception_mapping_worked = True + raise AuthenticationError( + message=original_exception.message, + model=model, + llm_provider="anthropic", + response=original_exception.response + ) + if hasattr(original_exception, "status_code"): + print_verbose(f"status_code: {original_exception.status_code}") + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"AnthropicException - {original_exception.message}", + llm_provider="anthropic", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 400 or original_exception.status_code == 413: + exception_mapping_worked = True + raise BadRequestError( + message=f"AnthropicException - {original_exception.message}", + model=model, + llm_provider="anthropic", + response=original_exception.response + ) + elif original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"AnthropicException - {original_exception.message}", + model=model, + llm_provider="anthropic", + request=original_exception.request + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"AnthropicException - {original_exception.message}", + llm_provider="anthropic", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 500: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"AnthropicException - {original_exception.message}", + llm_provider="anthropic", + model=model, + response=original_exception.response + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"AnthropicException - {original_exception.message}", + llm_provider="anthropic", + model=model, + request=original_exception.request + ) + elif custom_llm_provider == "replicate": + if "Incorrect authentication token" in error_str: + exception_mapping_worked = True + raise AuthenticationError( + message=f"ReplicateException - {error_str}", + llm_provider="replicate", + model=model, + response=original_exception.response + ) + elif "input is too long" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"ReplicateException - {error_str}", + model=model, + llm_provider="replicate", + response=original_exception.response + ) + elif exception_type == "ModelError": + exception_mapping_worked = True + raise BadRequestError( + message=f"ReplicateException - {error_str}", + model=model, + llm_provider="replicate", + response=original_exception.response + ) + elif "Request was throttled" in error_str: + exception_mapping_worked = True + raise RateLimitError( + message=f"ReplicateException - {error_str}", + llm_provider="replicate", + model=model, + response=original_exception.response + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 400 or original_exception.status_code == 422 or original_exception.status_code == 413: + exception_mapping_worked = True + raise BadRequestError( + message=f"ReplicateException - {original_exception.message}", + model=model, + llm_provider="replicate", + response=original_exception.response + ) + elif original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"ReplicateException - {original_exception.message}", + model=model, + llm_provider="replicate", + request=original_exception.request + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 500: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=original_exception.response + ) + exception_mapping_worked = True + raise APIError( + status_code=500, + message=f"ReplicateException - {str(original_exception)}", + llm_provider="replicate", + model=model, + request=original_exception.request + ) + elif custom_llm_provider == "bedrock": + if "too many tokens" in error_str or "expected maxLength:" in error_str or "Input is too long" in error_str or "Too many input tokens" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"BedrockException: Context Window Error - {error_str}", + model=model, + llm_provider="bedrock", + response=original_exception.response + ) + if "Malformed input request" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"BedrockException - {error_str}", + model=model, + llm_provider="bedrock", + response=original_exception.response + ) + if "Unable to locate credentials" in error_str or "The security token included in the request is invalid" in error_str: + exception_mapping_worked = True + raise AuthenticationError( + message=f"BedrockException Invalid Authentication - {error_str}", + model=model, + llm_provider="bedrock", + response=original_exception.response + ) + if "throttlingException" in error_str or "ThrottlingException" in error_str: + exception_mapping_worked = True + raise RateLimitError( + message=f"BedrockException: Rate Limit Error - {error_str}", + model=model, + llm_provider="bedrock", + response=original_exception.response + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 500: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=original_exception.response + ) + elif custom_llm_provider == "sagemaker": + if "Unable to locate credentials" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"SagemakerException - {error_str}", + model=model, + llm_provider="sagemaker", + response=original_exception.response + ) + elif custom_llm_provider == "vertex_ai": + if "Vertex AI API has not been used in project" in error_str or "Unable to find your project" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"VertexAIException - {error_str}", + model=model, + llm_provider="vertex_ai", + response=original_exception.response + ) + elif "403" in error_str: + exception_mapping_worked = True + raise AuthenticationError( + message=f"VertexAIException - {error_str}", + model=model, + llm_provider="vertex_ai", + response=original_exception.response + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + exception_mapping_worked = True + raise BadRequestError( + message=f"VertexAIException - {error_str}", + model=model, + llm_provider="vertex_ai", + response=original_exception.response + ) + if original_exception.status_code == 500: + exception_mapping_worked = True + raise APIError( + message=f"VertexAIException - {error_str}", + status_code=500, + model=model, + llm_provider="vertex_ai", + request=original_exception.request + ) + elif custom_llm_provider == "palm": + if "503 Getting metadata" in error_str: + # auth errors look like this + # 503 Getting metadata from plugin failed with error: Reauthentication is needed. Please run `gcloud auth application-default login` to reauthenticate. + exception_mapping_worked = True + raise BadRequestError( + message=f"PalmException - Invalid api key", + model=model, + llm_provider="palm", + response=original_exception.response + ) + if "400 Request payload size exceeds" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"PalmException - {error_str}", + model=model, + llm_provider="palm", + response=original_exception.response + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + exception_mapping_worked = True + raise BadRequestError( + message=f"PalmException - {error_str}", + model=model, + llm_provider="palm", + response=original_exception.response + ) + # Dailed: Error occurred: 400 Request payload size exceeds the limit: 20000 bytes + elif custom_llm_provider == "cohere": # Cohere + if ( + "invalid api token" in error_str + or "No API key provided." in error_str + ): + exception_mapping_worked = True + raise AuthenticationError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=original_exception.response + ) + elif "too many tokens" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"CohereException - {original_exception.message}", + model=model, + llm_provider="cohere", + response=original_exception.response + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 400 or original_exception.status_code == 498: + exception_mapping_worked = True + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 500: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=original_exception.response + ) + elif ( + "CohereConnectionError" in exception_type + ): # cohere seems to fire these errors when we load test it (1k+ messages / min) + exception_mapping_worked = True + raise RateLimitError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=original_exception.response + ) + elif "invalid type:" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=original_exception.response + ) + elif "Unexpected server error" in error_str: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=original_exception.response + ) + else: + if hasattr(original_exception, "status_code"): + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + request=original_exception.request + ) + raise original_exception + elif custom_llm_provider == "huggingface": + if "length limit exceeded" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=error_str, + model=model, + llm_provider="huggingface", + response=original_exception.response + ) + elif "A valid user token is required" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=error_str, + llm_provider="huggingface", + model=model, + response=original_exception.response + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 400: + exception_mapping_worked = True + raise BadRequestError( + message=f"HuggingfaceException - {original_exception.message}", + model=model, + llm_provider="huggingface", + response=original_exception.response + ) + elif original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"HuggingfaceException - {original_exception.message}", + model=model, + llm_provider="huggingface", + request=original_exception.request + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + response=original_exception.response + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + request=original_exception.request + ) + elif custom_llm_provider == "ai21": + if hasattr(original_exception, "message"): + if "Prompt has too many tokens" in original_exception.message: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=original_exception.response + ) + if "Bad or missing API token." in original_exception.message: + exception_mapping_worked = True + raise BadRequestError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=original_exception.response + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + request=original_exception.request + ) + if original_exception.status_code == 422: + exception_mapping_worked = True + raise BadRequestError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=original_exception.response + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + response=original_exception.response + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + request=original_exception.request + ) + elif custom_llm_provider == "nlp_cloud": + if "detail" in error_str: + if "Input text length should not exceed" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + response=original_exception.response + ) + elif "value is not a valid" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + response=original_exception.response + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=500, + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + request=original_exception.request + ) + if hasattr(original_exception, "status_code"): # https://docs.nlpcloud.com/?shell#errors + if original_exception.status_code == 400 or original_exception.status_code == 406 or original_exception.status_code == 413 or original_exception.status_code == 422: + exception_mapping_worked = True + raise BadRequestError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 401 or original_exception.status_code == 403: + exception_mapping_worked = True + raise AuthenticationError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 522 or original_exception.status_code == 524: + exception_mapping_worked = True + raise Timeout( + message=f"NLPCloudException - {original_exception.message}", + model=model, + llm_provider="nlp_cloud", + request=original_exception.request + ) + elif original_exception.status_code == 429 or original_exception.status_code == 402: + exception_mapping_worked = True + raise RateLimitError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 500 or original_exception.status_code == 503: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + request=original_exception.request + ) + elif original_exception.status_code == 504 or original_exception.status_code == 520: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"NLPCloudException - {original_exception.message}", + model=model, + llm_provider="nlp_cloud", + response=original_exception.response + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + request=original_exception.request + ) + elif custom_llm_provider == "together_ai": + import json + try: + error_response = json.loads(error_str) + except: + error_response = {"error": error_str} + if "error" in error_response and "`inputs` tokens + `max_new_tokens` must be <=" in error_response["error"]: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=original_exception.response + ) + elif "error" in error_response and "invalid private key" in error_response["error"]: + exception_mapping_worked = True + raise AuthenticationError( + message=f"TogetherAIException - {error_response['error']}", + llm_provider="together_ai", + model=model, + response=original_exception.response + ) + elif "error" in error_response and "INVALID_ARGUMENT" in error_response["error"]: + exception_mapping_worked = True + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=original_exception.response + ) + + elif "error" in error_response and "API key doesn't match expected format." in error_response["error"]: + exception_mapping_worked = True + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=original_exception.response + ) + elif "error_type" in error_response and error_response["error_type"] == "validation": + exception_mapping_worked = True + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=original_exception.response + ) + elif original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"TogetherAIException - {original_exception.message}", + model=model, + llm_provider="together_ai", + request=original_exception.request + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 524: + exception_mapping_worked = True + raise Timeout( + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + request=original_exception.request + ) + elif custom_llm_provider == "aleph_alpha": + if "This is longer than the model's maximum context length" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=original_exception.response + ) + elif "InvalidToken" in error_str or "No token provided" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=original_exception.response + ) + elif hasattr(original_exception, "status_code"): + print_verbose(f"status code: {original_exception.status_code}") + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model + ) + elif original_exception.status_code == 400: + exception_mapping_worked = True + raise BadRequestError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 500: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=original_exception.response + ) + raise original_exception + raise original_exception + elif custom_llm_provider == "ollama": + if "no attribute 'async_get_ollama_response_stream" in error_str: + exception_mapping_worked = True + raise ImportError("Import error - trying to use async for ollama. import async_generator failed. Try 'pip install async_generator'") + if isinstance(original_exception, dict): + error_str = original_exception.get("error", "") + else: + error_str = str(original_exception) + if "no such file or directory" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"OllamaException: Invalid Model/Model not loaded - {original_exception}", + model=model, + llm_provider="ollama", + response=original_exception.response + ) + elif "Failed to establish a new connection" in error_str: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + response=original_exception.response + ) + elif "Invalid response object from API" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + response=original_exception.response + ) + elif custom_llm_provider == "vllm": + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 0: + exception_mapping_worked = True + raise APIConnectionError( + message=f"VLLMException - {original_exception.message}", + llm_provider="vllm", + model=model, + request=original_exception.request + ) + elif custom_llm_provider == "azure": + if "This model's maximum context length is" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"AzureException - {original_exception.message}", + llm_provider="azure", + model=model, + response=original_exception.response + ) + elif "invalid_request_error" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"AzureException - {original_exception.message}", + llm_provider="azure", + model=model, + response=original_exception.response + ) + elif hasattr(original_exception, "status_code"): + exception_mapping_worked = True + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"AzureException - {original_exception.message}", + llm_provider="azure", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"AzureException - {original_exception.message}", + model=model, + llm_provider="azure", + request=original_exception.request + ) + if original_exception.status_code == 422: + exception_mapping_worked = True + raise BadRequestError( + message=f"AzureException - {original_exception.message}", + model=model, + llm_provider="azure", + response=original_exception.response + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"AzureException - {original_exception.message}", + model=model, + llm_provider="azure", + response=original_exception.response + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"AzureException - {original_exception.message}", + llm_provider="azure", + model=model, + request=original_exception.request + ) + else: + # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors + raise APIConnectionError( + __cause__=original_exception.__cause__, + llm_provider="azure", + model=model, + request=original_exception.request + ) + if "BadRequestError.__init__() missing 1 required positional argument: 'param'" in str(original_exception): # deal with edge-case invalid request error bug in openai-python sdk + exception_mapping_worked = True + raise BadRequestError( + message=f"OpenAIException: This can happen due to missing AZURE_API_VERSION: {str(original_exception)}", + model=model, + llm_provider=custom_llm_provider, + response=original_exception.response + ) + else: # ensure generic errors always return APIConnectionError= + exception_mapping_worked = True + if hasattr(original_exception, "request"): + raise APIConnectionError( + message=f"{str(original_exception)}", + llm_provider=custom_llm_provider, + model=model, + request=original_exception.request + ) + else: + raise APIConnectionError( + message=f"{str(original_exception)}", + llm_provider=custom_llm_provider, + model=model, + request= httpx.Request(method="POST", url="https://api.openai.com/v1/") # stub the request + ) + except Exception as e: + # LOGGING + exception_logging( + logger_fn=user_logger_fn, + additional_args={ + "exception_mapping_worked": exception_mapping_worked, + "original_exception": original_exception, + }, + exception=e, + ) + ## AUTH ERROR + if isinstance(e, AuthenticationError) and ( + litellm.email or "LITELLM_EMAIL" in os.environ + ): + threading.Thread(target=get_all_keys, args=(e.llm_provider,)).start() + # don't let an error with mapping interrupt the user from receiving an error from the llm api calls + if exception_mapping_worked: + raise e + else: + raise original_exception + + +####### CRASH REPORTING ################ +def safe_crash_reporting(model=None, exception=None, custom_llm_provider=None): + data = { + "model": model, + "exception": str(exception), + "custom_llm_provider": custom_llm_provider, + } + executor.submit(litellm_telemetry, data) + # threading.Thread(target=litellm_telemetry, args=(data,), daemon=True).start() + +def get_or_generate_uuid(): + temp_dir = os.path.join(os.path.abspath(os.sep), "tmp") + uuid_file = os.path.join(temp_dir, "litellm_uuid.txt") + try: + # Try to open the file and load the UUID + with open(uuid_file, "r") as file: + uuid_value = file.read() + if uuid_value: + uuid_value = uuid_value.strip() + else: + raise FileNotFoundError + + except FileNotFoundError: + # Generate a new UUID if the file doesn't exist or is empty + try: + new_uuid = uuid.uuid4() + uuid_value = str(new_uuid) + with open(uuid_file, "w") as file: + file.write(uuid_value) + except: # if writing to tmp/litellm_uuid.txt then retry writing to litellm_uuid.txt + try: + new_uuid = uuid.uuid4() + uuid_value = str(new_uuid) + with open("litellm_uuid.txt", "w") as file: + file.write(uuid_value) + except: # if this 3rd attempt fails just pass + # Good first issue for someone to improve this function :) + return + except: + # [Non-Blocking Error] + return + return uuid_value + + +def litellm_telemetry(data): + # Load or generate the UUID + uuid_value = "" + try: + uuid_value = get_or_generate_uuid() + except: + uuid_value = str(uuid.uuid4()) + try: + # Prepare the data to send to litellm logging api + try: + pkg_version = importlib.metadata.version("litellm") + except: + pkg_version = None + if "model" not in data: + data["model"] = None + payload = { + "uuid": uuid_value, + "data": data, + "version:": pkg_version + } + # Make the POST request to litellm logging api + response = requests.post( + "https://litellm-logging.onrender.com/logging", + headers={"Content-Type": "application/json"}, + json=payload, + ) + response.raise_for_status() # Raise an exception for HTTP errors + except: + # [Non-Blocking Error] + return + +######### Secret Manager ############################ +# checks if user has passed in a secret manager client +# if passed in then checks the secret there +def get_secret(secret_name: str): + if secret_name.startswith("os.environ/"): + secret_name = secret_name.replace("os.environ/", "") + if litellm.secret_manager_client is not None: + # TODO: check which secret manager is being used + # currently only supports Infisical + try: + client = litellm.secret_manager_client + if type(client).__module__ + '.' + type(client).__name__ == 'azure.keyvault.secrets._client.SecretClient': # support Azure Secret Client - from azure.keyvault.secrets import SecretClient + secret = retrieved_secret = client.get_secret(secret_name).value + else: # assume the default is infisicial client + secret = client.get_secret(secret_name).secret_value + except: # check if it's in os.environ + secret = os.environ.get(secret_name) + return secret + else: + return os.environ.get(secret_name) + + +######## Streaming Class ############################ +# wraps the completion stream to return the correct format for the model +# replicate/anthropic/cohere +class CustomStreamWrapper: + def __init__(self, completion_stream, model, custom_llm_provider=None, logging_obj=None): + self.model = model + self.custom_llm_provider = custom_llm_provider + self.logging_obj = logging_obj + self.completion_stream = completion_stream + self.sent_first_chunk = False + self.sent_last_chunk = False + self.special_tokens = ["<|assistant|>", "<|system|>", "<|user|>", "", ""] + self.holding_chunk = "" + self.complete_response = "" + if self.logging_obj: + # Log the type of the received item + self.logging_obj.post_call(str(type(completion_stream))) + + def __iter__(self): + return self + + def __aiter__(self): + return self + + def process_chunk(self, chunk: str): + """ + NLP Cloud streaming returns the entire response, for each chunk. Process this, to only return the delta. + """ + try: + chunk = chunk.strip() + self.complete_response = self.complete_response.strip() + + if chunk.startswith(self.complete_response): + # Remove last_sent_chunk only if it appears at the start of the new chunk + chunk = chunk[len(self.complete_response):] + + self.complete_response += chunk + return chunk + except Exception as e: + raise e + + def logging(self, text): + if self.logging_obj: + self.logging_obj.post_call(text) + + def check_special_tokens(self, chunk: str, finish_reason: Optional[str]): + hold = False + if finish_reason: + for token in self.special_tokens: + if token in chunk: + chunk = chunk.replace(token, "") + return hold, chunk + + if self.sent_first_chunk is True: + return hold, chunk + + curr_chunk = self.holding_chunk + chunk + curr_chunk = curr_chunk.strip() + + for token in self.special_tokens: + if len(curr_chunk) < len(token) and curr_chunk in token: + hold = True + elif len(curr_chunk) >= len(token): + if token in curr_chunk: + self.holding_chunk = curr_chunk.replace(token, "") + hold = True + else: + pass + + if hold is False: # reset + self.holding_chunk = "" + return hold, curr_chunk + + + def handle_anthropic_chunk(self, chunk): + str_line = chunk.decode("utf-8") # Convert bytes to string + text = "" + is_finished = False + finish_reason = None + if str_line.startswith("data:"): + data_json = json.loads(str_line[5:]) + text = data_json.get("completion", "") + if data_json.get("stop_reason", None): + is_finished = True + finish_reason = data_json["stop_reason"] + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + elif "error" in str_line: + raise ValueError(f"Unable to parse response. Original response: {str_line}") + else: + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + + def handle_together_ai_chunk(self, chunk): + chunk = chunk.decode("utf-8") + text = "" + is_finished = False + finish_reason = None + if "text" in chunk: + text_index = chunk.find('"text":"') # this checks if text: exists + text_start = text_index + len('"text":"') + text_end = chunk.find('"}', text_start) + if text_index != -1 and text_end != -1: + extracted_text = chunk[text_start:text_end] + text = extracted_text + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + elif "[DONE]" in chunk: + return {"text": text, "is_finished": True, "finish_reason": "stop"} + elif "error" in chunk: + raise ValueError(chunk) + else: + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + + def handle_huggingface_chunk(self, chunk): + try: + if type(chunk) != str: + chunk = chunk.decode("utf-8") # DO NOT REMOVE this: This is required for HF inference API + Streaming + text = "" + is_finished = False + finish_reason = "" + print_verbose(f"chunk: {chunk}") + if chunk.startswith("data:"): + data_json = json.loads(chunk[5:]) + print_verbose(f"data json: {data_json}") + if "token" in data_json and "text" in data_json["token"]: + text = data_json["token"]["text"] + if data_json.get("details", False) and data_json["details"].get("finish_reason", False): + is_finished = True + finish_reason = data_json["details"]["finish_reason"] + elif data_json.get("generated_text", False): # if full generated text exists, then stream is complete + text = "" # don't return the final bos token + is_finished = True + finish_reason = "stop" + + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + elif "error" in chunk: + raise ValueError(chunk) + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + except Exception as e: + traceback.print_exc() + # raise(e) + + def handle_ai21_chunk(self, chunk): # fake streaming + chunk = chunk.decode("utf-8") + data_json = json.loads(chunk) + try: + text = data_json["completions"][0]["data"]["text"] + is_finished = True + finish_reason = "stop" + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + except: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + + def handle_maritalk_chunk(self, chunk): # fake streaming + chunk = chunk.decode("utf-8") + data_json = json.loads(chunk) + try: + text = data_json["answer"] + is_finished = True + finish_reason = "stop" + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + except: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + + def handle_nlp_cloud_chunk(self, chunk): + text = "" + is_finished = False + finish_reason = "" + try: + if "dolphin" in self.model: + chunk = self.process_chunk(chunk=chunk) + else: + data_json = json.loads(chunk) + chunk = data_json["generated_text"] + text = chunk + if "[DONE]" in text: + text = text.replace("[DONE]", "") + is_finished = True + finish_reason = "stop" + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + except Exception as e: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + + def handle_aleph_alpha_chunk(self, chunk): + chunk = chunk.decode("utf-8") + data_json = json.loads(chunk) + try: + text = data_json["completions"][0]["completion"] + is_finished = True + finish_reason = "stop" + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + except: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + + def handle_cohere_chunk(self, chunk): + chunk = chunk.decode("utf-8") + data_json = json.loads(chunk) + try: + text = "" + is_finished = False + finish_reason = "" + if "text" in data_json: + text = data_json["text"] + elif "is_finished" in data_json: + is_finished = data_json["is_finished"] + finish_reason = data_json["finish_reason"] + else: + raise Exception(data_json) + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + except: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + + def handle_azure_chunk(self, chunk): + is_finished = False + finish_reason = "" + text = "" + print_verbose(f"chunk: {chunk}") + if "data: [DONE]" in chunk: + text = "" + is_finished = True + finish_reason = "stop" + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + elif chunk.startswith("data:"): + data_json = json.loads(chunk[5:]) # chunk.startswith("data:"): + try: + if len(data_json["choices"]) > 0: + text = data_json["choices"][0]["delta"].get("content", "") + if data_json["choices"][0].get("finish_reason", None): + is_finished = True + finish_reason = data_json["choices"][0]["finish_reason"] + print_verbose(f"text: {text}; is_finished: {is_finished}; finish_reason: {finish_reason}") + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + except: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + elif "error" in chunk: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + else: + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + + def handle_replicate_chunk(self, chunk): + try: + text = "" + is_finished = False + finish_reason = "" + if "output" in chunk: + text = chunk['output'] + if "status" in chunk: + if chunk["status"] == "succeeded": + is_finished = True + finish_reason = "stop" + elif chunk.get("error", None): + raise Exception(chunk["error"]) + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + except: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + + def handle_openai_chat_completion_chunk(self, chunk): + try: + print_verbose(f"\nRaw OpenAI Chunk\n{chunk}\n") + str_line = chunk + text = "" + is_finished = False + finish_reason = None + original_chunk = None # this is used for function/tool calling + if len(str_line.choices) > 0: + if str_line.choices[0].delta.content is not None: + text = str_line.choices[0].delta.content + else: # function/tool calling chunk - when content is None. in this case we just return the original chunk from openai + original_chunk = str_line + if str_line.choices[0].finish_reason: + is_finished = True + finish_reason = str_line.choices[0].finish_reason + + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + "original_chunk": str_line + } + except Exception as e: + traceback.print_exc() + raise e + + def handle_openai_text_completion_chunk(self, chunk): + try: + str_line = chunk + text = "" + is_finished = False + finish_reason = None + print_verbose(f"str_line: {str_line}") + if "data: [DONE]" in str_line: + text = "" + is_finished = True + finish_reason = "stop" + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + elif str_line.startswith("data:"): + data_json = json.loads(str_line[5:]) + print_verbose(f"delta content: {data_json}") + text = data_json["choices"][0].get("text", "") + if data_json["choices"][0].get("finish_reason", None): + is_finished = True + finish_reason = data_json["choices"][0]["finish_reason"] + print_verbose(f"text: {text}; is_finished: {is_finished}; finish_reason: {finish_reason}") + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + elif "error" in str_line: + raise ValueError(f"Unable to parse response. Original response: {str_line}") + else: + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + + except Exception as e: + traceback.print_exc() + raise e + + def handle_baseten_chunk(self, chunk): + try: + chunk = chunk.decode("utf-8") + if len(chunk) > 0: + if chunk.startswith("data:"): + data_json = json.loads(chunk[5:]) + if "token" in data_json and "text" in data_json["token"]: + return data_json["token"]["text"] + else: + return "" + data_json = json.loads(chunk) + if "model_output" in data_json: + if isinstance(data_json["model_output"], dict) and "data" in data_json["model_output"] and isinstance(data_json["model_output"]["data"], list): + return data_json["model_output"]["data"][0] + elif isinstance(data_json["model_output"], str): + return data_json["model_output"] + elif "completion" in data_json and isinstance(data_json["completion"], str): + return data_json["completion"] + else: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + else: + return "" + else: + return "" + except: + traceback.print_exc() + return "" + + def handle_bedrock_stream(self, chunk): + if hasattr(chunk, "get"): + chunk = chunk.get('chunk') + chunk_data = json.loads(chunk.get('bytes').decode()) + else: + chunk_data = json.loads(chunk.decode()) + if chunk_data: + text = "" + is_finished = False + finish_reason = "" + if "outputText" in chunk_data: + text = chunk_data['outputText'] + # ai21 mapping + if "ai21" in self.model: # fake ai21 streaming + text = chunk_data.get('completions')[0].get('data').get('text') + is_finished = True + finish_reason = "stop" + # anthropic mapping + elif "completion" in chunk_data: + text = chunk_data['completion'] # bedrock.anthropic + stop_reason = chunk_data.get("stop_reason", None) + if stop_reason != None: + is_finished = True + finish_reason = stop_reason + ######## bedrock.cohere mappings ############### + # meta mapping + elif "generation" in chunk_data: + text = chunk_data['generation'] # bedrock.meta + # cohere mapping + elif "text" in chunk_data: + text = chunk_data["text"] # bedrock.cohere + # cohere mapping for finish reason + elif "finish_reason" in chunk_data: + finish_reason = chunk_data["finish_reason"] + is_finished = True + elif chunk_data.get("completionReason", None): + is_finished = True + finish_reason = chunk_data["completionReason"] + elif chunk.get("error", None): + raise Exception(chunk["error"]) + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + return "" + + def chunk_creator(self, chunk): + model_response = ModelResponse(stream=True, model=self.model) + model_response.choices[0].finish_reason = None + response_obj = {} + try: + # return this for all models + completion_obj = {"content": ""} + if self.custom_llm_provider and self.custom_llm_provider == "anthropic": + response_obj = self.handle_anthropic_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj["finish_reason"] + elif self.model == "replicate" or self.custom_llm_provider == "replicate": + response_obj = self.handle_replicate_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj["finish_reason"] + elif ( + self.custom_llm_provider and self.custom_llm_provider == "together_ai"): + response_obj = self.handle_together_ai_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider and self.custom_llm_provider == "huggingface": + response_obj = self.handle_huggingface_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider and self.custom_llm_provider == "baseten": # baseten doesn't provide streaming + completion_obj["content"] = self.handle_baseten_chunk(chunk) + elif self.custom_llm_provider and self.custom_llm_provider == "ai21": #ai21 doesn't provide streaming + response_obj = self.handle_ai21_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider and self.custom_llm_provider == "maritalk": + response_obj = self.handle_maritalk_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider and self.custom_llm_provider == "vllm": + completion_obj["content"] = chunk[0].outputs[0].text + elif self.custom_llm_provider and self.custom_llm_provider == "aleph_alpha": #aleph alpha doesn't provide streaming + response_obj = self.handle_aleph_alpha_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider == "nlp_cloud": + try: + response_obj = self.handle_nlp_cloud_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj["finish_reason"] + except Exception as e: + if self.sent_last_chunk: + raise e + else: + if self.sent_first_chunk is False: + raise Exception("An unknown error occurred with the stream") + model_response.choices[0].finish_reason = "stop" + self.sent_last_chunk = True + elif self.custom_llm_provider and self.custom_llm_provider == "vertex_ai": + try: + + completion_obj["content"] = str(chunk) + except StopIteration as e: + if self.sent_last_chunk: + raise e + else: + model_response.choices[0].finish_reason = "stop" + self.sent_last_chunk = True + elif self.custom_llm_provider == "cohere": + response_obj = self.handle_cohere_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider == "bedrock": + if self.sent_last_chunk: + raise StopIteration + response_obj = self.handle_bedrock_stream(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj["finish_reason"] + self.sent_last_chunk = True + elif self.custom_llm_provider == "sagemaker": + if len(self.completion_stream)==0: + if self.sent_last_chunk: + raise StopIteration + else: + model_response.choices[0].finish_reason = "stop" + self.sent_last_chunk = True + chunk_size = 30 + new_chunk = self.completion_stream[:chunk_size] + completion_obj["content"] = new_chunk + self.completion_stream = self.completion_stream[chunk_size:] + time.sleep(0.05) + elif self.custom_llm_provider == "petals": + if len(self.completion_stream)==0: + if self.sent_last_chunk: + raise StopIteration + else: + model_response.choices[0].finish_reason = "stop" + self.sent_last_chunk = True + chunk_size = 30 + new_chunk = self.completion_stream[:chunk_size] + completion_obj["content"] = new_chunk + self.completion_stream = self.completion_stream[chunk_size:] + time.sleep(0.05) + elif self.custom_llm_provider == "palm": + # fake streaming + response_obj = {} + if len(self.completion_stream)==0: + if self.sent_last_chunk: + raise StopIteration + else: + model_response.choices[0].finish_reason = "stop" + self.sent_last_chunk = True + chunk_size = 30 + new_chunk = self.completion_stream[:chunk_size] + completion_obj["content"] = new_chunk + self.completion_stream = self.completion_stream[chunk_size:] + time.sleep(0.05) + elif self.custom_llm_provider == "ollama": + if "error" in chunk: + exception_type(model=self.model, custom_llm_provider=self.custom_llm_provider, original_exception=chunk["error"]) + completion_obj = chunk + elif self.custom_llm_provider == "text-completion-openai": + response_obj = self.handle_openai_text_completion_chunk(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj["finish_reason"] + else: # openai chat model + response_obj = self.handle_openai_chat_completion_chunk(chunk) + if response_obj == None: + return + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj["finish_reason"] + + model_response.model = self.model + print_verbose(f"model_response: {model_response}; completion_obj: {completion_obj}") + print_verbose(f"model_response finish reason 3: {model_response.choices[0].finish_reason}") + if len(completion_obj["content"]) > 0: # cannot set content of an OpenAI Object to be an empty string + hold, model_response_str = self.check_special_tokens(chunk=completion_obj["content"], finish_reason=model_response.choices[0].finish_reason) # filter out bos/eos tokens from openai-compatible hf endpoints + print_verbose(f"hold - {hold}, model_response_str - {model_response_str}") + if hold is False: + ## check if openai/azure chunk + original_chunk = response_obj.get("original_chunk", None) + if original_chunk: + model_response.id = original_chunk.id + if len(original_chunk.choices) > 0: + try: + delta = dict(original_chunk.choices[0].delta) + model_response.choices[0].delta = Delta(**delta) + except Exception as e: + model_response.choices[0].delta = Delta() + else: + return + model_response.system_fingerprint = original_chunk.system_fingerprint + if self.sent_first_chunk == False: + model_response.choices[0].delta["role"] = "assistant" + self.sent_first_chunk = True + else: + ## else + completion_obj["content"] = model_response_str + if self.sent_first_chunk == False: + completion_obj["role"] = "assistant" + self.sent_first_chunk = True + model_response.choices[0].delta = Delta(**completion_obj) + # LOGGING + threading.Thread(target=self.logging_obj.success_handler, args=(model_response,)).start() + print_verbose(f"model_response: {model_response}") + return model_response + else: + return + elif model_response.choices[0].finish_reason: + model_response.choices[0].finish_reason = map_finish_reason(model_response.choices[0].finish_reason) # ensure consistent output to openai + # LOGGING + threading.Thread(target=self.logging_obj.success_handler, args=(model_response,)).start() + return model_response + elif response_obj is not None and response_obj.get("original_chunk", None) is not None: # function / tool calling branch - only set for openai/azure compatible endpoints + # enter this branch when no content has been passed in response + original_chunk = response_obj.get("original_chunk", None) + model_response.id = original_chunk.id + if len(original_chunk.choices) > 0: + if original_chunk.choices[0].delta.function_call is not None or original_chunk.choices[0].delta.tool_calls is not None: + try: + delta = dict(original_chunk.choices[0].delta) + model_response.choices[0].delta = Delta(**delta) + except Exception as e: + model_response.choices[0].delta = Delta() + else: + return + else: + return + model_response.system_fingerprint = original_chunk.system_fingerprint + if self.sent_first_chunk == False: + model_response.choices[0].delta["role"] = "assistant" + self.sent_first_chunk = True + # LOGGING + threading.Thread(target=self.logging_obj.success_handler, args=(model_response,)).start() # log response + return model_response + else: + return + except StopIteration: + raise StopIteration + except Exception as e: + traceback_exception = traceback.format_exc() + e.message = str(e) + # LOG FAILURE - handle streaming failure logging in the _next_ object, remove `handle_failure` once it's deprecated + threading.Thread(target=self.logging_obj.failure_handler, args=(e, traceback_exception)).start() + raise exception_type(model=self.model, custom_llm_provider=self.custom_llm_provider, original_exception=e) + + ## needs to handle the empty string case (even starting chunk can be an empty string) + def __next__(self): + try: + while True: + if isinstance(self.completion_stream, str) or isinstance(self.completion_stream, bytes): + chunk = self.completion_stream + else: + chunk = next(self.completion_stream) + if chunk is not None and chunk != b'': + response = self.chunk_creator(chunk=chunk) + if response is not None: + return response + except StopIteration: + raise # Re-raise StopIteration + except Exception as e: + # Handle other exceptions if needed + raise e + + + + async def __anext__(self): + try: + if (self.custom_llm_provider == "openai" + or self.custom_llm_provider == "azure" + or self.custom_llm_provider == "custom_openai" + or self.custom_llm_provider == "text-completion-openai" + or self.custom_llm_provider == "huggingface"): + async for chunk in self.completion_stream: + if chunk == "None" or chunk is None: + raise Exception + processed_chunk = self.chunk_creator(chunk=chunk) + if processed_chunk is None: + continue + ## LOGGING + asyncio.create_task(self.logging_obj.async_success_handler(processed_chunk,)) + return processed_chunk + raise StopAsyncIteration + else: # temporary patch for non-aiohttp async calls + return next(self) + except Exception as e: + # Handle any exceptions that might occur during streaming + raise StopAsyncIteration + +class TextCompletionStreamWrapper: + def __init__(self, completion_stream, model): + self.completion_stream = completion_stream + self.model = model + + def __iter__(self): + return self + + def __aiter__(self): + return self + + def __next__(self): + # model_response = ModelResponse(stream=True, model=self.model) + response = TextCompletionResponse() + try: + while True: # loop until a non-empty string is found + # return this for all models + chunk = next(self.completion_stream) + response["id"] = chunk.get("id", None) + response["object"] = "text_completion" + response["created"] = response.get("created", None) + response["model"] = response.get("model", None) + text_choices = TextChoices() + text_choices["text"] = chunk["choices"][0]["delta"]["content"] + text_choices["index"] = response["choices"][0]["index"] + text_choices["finish_reason"] = response["choices"][0]["finish_reason"] + response["choices"] = [text_choices] + return response + except StopIteration: + raise StopIteration + except Exception as e: + print(f"got exception {e}") # noqa + async def __anext__(self): + try: + return next(self) + except StopIteration: + raise StopAsyncIteration + +def mock_completion_streaming_obj(model_response, mock_response, model): + for i in range(0, len(mock_response), 3): + completion_obj = {"role": "assistant", "content": mock_response[i: i+3]} + model_response.choices[0].delta = completion_obj + yield model_response + +########## Reading Config File ############################ +def read_config_args(config_path) -> dict: + try: + import os + + current_path = os.getcwd() + with open(config_path, "r") as config_file: + config = json.load(config_file) + + # read keys/ values from config file and return them + return config + except Exception as e: + raise e + +########## experimental completion variants ############################ + +def completion_with_config(config: Union[dict, str], **kwargs): + """ + Generate a litellm.completion() using a config dict and all supported completion args + + Example config; + config = { + "default_fallback_models": # [Optional] List of model names to try if a call fails + "available_models": # [Optional] List of all possible models you could call + "adapt_to_prompt_size": # [Optional] True/False - if you want to select model based on prompt size (will pick from available_models) + "model": { + "model-name": { + "needs_moderation": # [Optional] True/False - if you want to call openai moderations endpoint before making completion call. Will raise exception, if flagged. + "error_handling": { + "error-type": { # One of the errors listed here - https://docs.litellm.ai/docs/exception_mapping#custom-mapping-list + "fallback_model": "" # str, name of the model it should try instead, when that error occurs + } + } + } + } + } + + Parameters: + config (Union[dict, str]): A configuration for litellm + **kwargs: Additional keyword arguments for litellm.completion + + Returns: + litellm.ModelResponse: A ModelResponse with the generated completion + + """ + if config is not None: + if isinstance(config, str): + config = read_config_args(config) + elif isinstance(config, dict): + config = config + else: + raise Exception("Config path must be a string or a dictionary.") + else: + raise Exception("Config path not passed in.") + + if config is None: + raise Exception("No completion config in the config file") + + models_with_config = config["model"].keys() + model = kwargs["model"] + messages = kwargs["messages"] + + ## completion config + fallback_models = config.get("default_fallback_models", None) + available_models = config.get("available_models", None) + adapt_to_prompt_size = config.get("adapt_to_prompt_size", False) + trim_messages_flag = config.get("trim_messages", False) + prompt_larger_than_model = False + max_model = model + try: + max_tokens = litellm.get_max_tokens(model)["max_tokens"] + except: + max_tokens = 2048 # assume curr model's max window is 2048 tokens + if adapt_to_prompt_size: + ## Pick model based on token window + prompt_tokens = litellm.token_counter(model="gpt-3.5-turbo", text="".join(message["content"] for message in messages)) + try: + curr_max_tokens = litellm.get_max_tokens(model)["max_tokens"] + except: + curr_max_tokens = 2048 + if curr_max_tokens < prompt_tokens: + prompt_larger_than_model = True + for available_model in available_models: + try: + curr_max_tokens = litellm.get_max_tokens(available_model)["max_tokens"] + if curr_max_tokens > max_tokens: + max_tokens = curr_max_tokens + max_model = available_model + if curr_max_tokens > prompt_tokens: + model = available_model + prompt_larger_than_model = False + except: + continue + if prompt_larger_than_model: + messages = trim_messages(messages=messages, model=max_model) + kwargs["messages"] = messages + + kwargs["model"] = model + try: + if model in models_with_config: + ## Moderation check + if config["model"][model].get("needs_moderation"): + input = " ".join(message["content"] for message in messages) + response = litellm.moderation(input=input) + flagged = response["results"][0]["flagged"] + if flagged: + raise Exception("This response was flagged as inappropriate") + + ## Model-specific Error Handling + error_handling = None + if config["model"][model].get("error_handling"): + error_handling = config["model"][model]["error_handling"] + + try: + response = litellm.completion(**kwargs) + return response + except Exception as e: + exception_name = type(e).__name__ + fallback_model = None + if error_handling and exception_name in error_handling: + error_handler = error_handling[exception_name] + # either switch model or api key + fallback_model = error_handler.get("fallback_model", None) + if fallback_model: + kwargs["model"] = fallback_model + return litellm.completion(**kwargs) + raise e + else: + return litellm.completion(**kwargs) + except Exception as e: + if fallback_models: + model = fallback_models.pop(0) + return completion_with_fallbacks(model=model, messages=messages, fallbacks=fallback_models) + raise e + +def completion_with_fallbacks(**kwargs): + nested_kwargs = kwargs.pop("kwargs", {}) + response = None + rate_limited_models = set() + model_expiration_times = {} + start_time = time.time() + original_model = kwargs["model"] + fallbacks = [kwargs["model"]] + nested_kwargs.get("fallbacks", []) + if "fallbacks" in nested_kwargs: + del nested_kwargs["fallbacks"] # remove fallbacks so it's not recursive + litellm_call_id = str(uuid.uuid4()) + + # max time to process a request with fallbacks: default 45s + while response == None and time.time() - start_time < 45: + for model in fallbacks: + # loop thru all models + try: + # check if it's dict or new model string + if isinstance(model, dict): # completion(model="gpt-4", fallbacks=[{"api_key": "", "api_base": ""}, {"api_key": "", "api_base": ""}]) + kwargs["api_key"] = model.get("api_key", None) + kwargs["api_base"] = model.get("api_base", None) + model = model.get("model", original_model) + elif ( + model in rate_limited_models + ): # check if model is currently cooling down + if ( + model_expiration_times.get(model) + and time.time() >= model_expiration_times[model] + ): + rate_limited_models.remove( + model + ) # check if it's been 60s of cool down and remove model + else: + continue # skip model + + # delete model from kwargs if it exists + if kwargs.get("model"): + del kwargs["model"] + + print_verbose(f"trying to make completion call with model: {model}") + kwargs["litellm_call_id"] = litellm_call_id + kwargs = {**kwargs, **nested_kwargs} # combine the openai + litellm params at the same level + response = litellm.completion(**kwargs, model=model) + print_verbose(f"response: {response}") + if response != None: + return response + + except Exception as e: + print_verbose(e) + rate_limited_models.add(model) + model_expiration_times[model] = ( + time.time() + 60 + ) # cool down this selected model + pass + return response + +def process_system_message(system_message, max_tokens, model): + system_message_event = {"role": "system", "content": system_message} + system_message_tokens = get_token_count([system_message_event], model) + + if system_message_tokens > max_tokens: + print_verbose("`tokentrimmer`: Warning, system message exceeds token limit. Trimming...") + # shorten system message to fit within max_tokens + new_system_message = shorten_message_to_fit_limit(system_message_event, max_tokens, model) + system_message_tokens = get_token_count([new_system_message], model) + + return system_message_event, max_tokens - system_message_tokens + +def process_messages(messages, max_tokens, model): + # Process messages from older to more recent + messages = messages[::-1] + final_messages = [] + + for message in messages: + used_tokens = get_token_count(final_messages, model) + available_tokens = max_tokens - used_tokens + if available_tokens <= 3: + break + final_messages = attempt_message_addition(final_messages=final_messages, message=message, available_tokens=available_tokens, max_tokens=max_tokens, model=model) + + return final_messages + +def attempt_message_addition(final_messages, message, available_tokens, max_tokens, model): + temp_messages = [message] + final_messages + temp_message_tokens = get_token_count(messages=temp_messages, model=model) + + if temp_message_tokens <= max_tokens: + return temp_messages + + # if temp_message_tokens > max_tokens, try shortening temp_messages + elif "function_call" not in message: + # fit updated_message to be within temp_message_tokens - max_tokens (aka the amount temp_message_tokens is greate than max_tokens) + updated_message = shorten_message_to_fit_limit(message, available_tokens, model) + if can_add_message(updated_message, final_messages, max_tokens, model): + return [updated_message] + final_messages + + return final_messages + +def can_add_message(message, messages, max_tokens, model): + if get_token_count(messages + [message], model) <= max_tokens: + return True + return False + +def get_token_count(messages, model): + return token_counter(model=model, messages=messages) + + +def shorten_message_to_fit_limit( + message, + tokens_needed, + model): + """ + Shorten a message to fit within a token limit by removing characters from the middle. + """ + + # For OpenAI models, even blank messages cost 7 token, + # and if the buffer is less than 3, the while loop will never end, + # hence the value 10. + if 'gpt' in model and tokens_needed <= 10: + return message + + content = message["content"] + + while True: + total_tokens = get_token_count([message], model) + + if total_tokens <= tokens_needed: + break + + ratio = (tokens_needed) / total_tokens + + new_length = int(len(content) * ratio) -1 + new_length = max(0, new_length) + + half_length = new_length // 2 + left_half = content[:half_length] + right_half = content[-half_length:] + + trimmed_content = left_half + '..' + right_half + message["content"] = trimmed_content + content = trimmed_content + + return message + +# LiteLLM token trimmer +# this code is borrowed from https://github.com/KillianLucas/tokentrim/blob/main/tokentrim/tokentrim.py +# Credits for this code go to Killian Lucas +def trim_messages( + messages, + model: Optional[str] = None, + trim_ratio: float = 0.75, + return_response_tokens: bool = False, + max_tokens = None + ): + """ + Trim a list of messages to fit within a model's token limit. + + Args: + messages: Input messages to be trimmed. Each message is a dictionary with 'role' and 'content'. + model: The LiteLLM model being used (determines the token limit). + trim_ratio: Target ratio of tokens to use after trimming. Default is 0.75, meaning it will trim messages so they use about 75% of the model's token limit. + return_response_tokens: If True, also return the number of tokens left available for the response after trimming. + max_tokens: Instead of specifying a model or trim_ratio, you can specify this directly. + + Returns: + Trimmed messages and optionally the number of tokens available for response. + """ + # Initialize max_tokens + # if users pass in max tokens, trim to this amount + messages = copy.deepcopy(messages) + try: + print_verbose(f"trimming messages") + if max_tokens == None: + # Check if model is valid + if model in litellm.model_cost: + max_tokens_for_model = litellm.model_cost[model]['max_tokens'] + max_tokens = int(max_tokens_for_model * trim_ratio) + else: + # if user did not specify max tokens + # or passed an llm litellm does not know + # do nothing, just return messages + return + + system_message = "" + for message in messages: + if message["role"] == "system": + system_message += '\n' if system_message else '' + system_message += message["content"] + + current_tokens = token_counter(model=model, messages=messages) + print_verbose(f"Current tokens: {current_tokens}, max tokens: {max_tokens}") + + # Do nothing if current tokens under messages + if current_tokens < max_tokens: + return messages + + #### Trimming messages if current_tokens > max_tokens + print_verbose(f"Need to trim input messages: {messages}, current_tokens{current_tokens}, max_tokens: {max_tokens}") + if system_message: + system_message_event, max_tokens = process_system_message(system_message=system_message, max_tokens=max_tokens, model=model) + + if max_tokens == 0: # the system messages are too long + return [system_message_event] + + # Since all system messages are combined and trimmed to fit the max_tokens, + # we remove all system messages from the messages list + messages = [message for message in messages if message["role"] != "system"] + + final_messages = process_messages(messages=messages, max_tokens=max_tokens, model=model) + + # Add system message to the beginning of the final messages + if system_message: + final_messages = [system_message_event] + final_messages + + if return_response_tokens: # if user wants token count with new trimmed messages + response_tokens = max_tokens - get_token_count(final_messages, model) + return final_messages, response_tokens + return final_messages + except Exception as e: # [NON-Blocking, if error occurs just return final_messages + print_verbose(f"Got exception while token trimming{e}") + return messages + +def get_valid_models(): + """ + Returns a list of valid LLMs based on the set environment variables + + Args: + None + + Returns: + A list of valid LLMs + """ + try: + # get keys set in .env + environ_keys = os.environ.keys() + valid_providers = [] + # for all valid providers, make a list of supported llms + valid_models = [] + + for provider in litellm.provider_list: + # edge case litellm has together_ai as a provider, it should be togetherai + provider = provider.replace("_", "") + + # litellm standardizes expected provider keys to + # PROVIDER_API_KEY. Example: OPENAI_API_KEY, COHERE_API_KEY + expected_provider_key = f"{provider.upper()}_API_KEY" + if expected_provider_key in environ_keys: + # key is set + valid_providers.append(provider) + + for provider in valid_providers: + if provider == "azure": + valid_models.append("Azure-LLM") + else: + models_for_provider = litellm.models_by_provider.get(provider, []) + valid_models.extend(models_for_provider) + return valid_models + except: + return [] # NON-Blocking + +# used for litellm.text_completion() to transform HF logprobs to OpenAI.Completion() format +def transform_logprobs(hf_response): + # Initialize an empty list for the transformed logprobs + transformed_logprobs = [] + + # For each Hugging Face response, transform the logprobs + for response in hf_response: + # Extract the relevant information from the response + response_details = response['details'] + top_tokens = response_details.get("top_tokens", {}) + + # Initialize an empty list for the token information + token_info = { + 'tokens': [], + 'token_logprobs': [], + 'text_offset': [], + 'top_logprobs': [], + } + + for i, token in enumerate(response_details['prefill']): + # Extract the text of the token + token_text = token['text'] + + # Extract the logprob of the token + token_logprob = token['logprob'] + + # Add the token information to the 'token_info' list + token_info['tokens'].append(token_text) + token_info['token_logprobs'].append(token_logprob) + + # stub this to work with llm eval harness + top_alt_tokens = { "": -1, "": -2, "": -3 } + token_info['top_logprobs'].append(top_alt_tokens) + + # For each element in the 'tokens' list, extract the relevant information + for i, token in enumerate(response_details['tokens']): + + # Extract the text of the token + token_text = token['text'] + + # Extract the logprob of the token + token_logprob = token['logprob'] + + top_alt_tokens = {} + temp_top_logprobs = [] + if top_tokens != {}: + temp_top_logprobs = top_tokens[i] + + # top_alt_tokens should look like this: { "alternative_1": -1, "alternative_2": -2, "alternative_3": -3 } + for elem in temp_top_logprobs: + text = elem["text"] + logprob = elem["logprob"] + top_alt_tokens[text] = logprob + + # Add the token information to the 'token_info' list + token_info['tokens'].append(token_text) + token_info['token_logprobs'].append(token_logprob) + token_info['top_logprobs'].append(top_alt_tokens) + + # Add the text offset of the token + # This is computed as the sum of the lengths of all previous tokens + token_info['text_offset'].append(sum(len(t['text']) for t in response_details['tokens'][:i])) + + # Add the 'token_info' list to the 'transformed_logprobs' list + transformed_logprobs = token_info + + return transformed_logprobs + +# used in LiteLLM Router +def remove_model_id(original_model_string): + # Find the index of "ModelID" in the string + index_of_model_id = original_model_string.find("-ModelID") + # Remove everything after "-ModelID" if it exists + if index_of_model_id != -1: + return original_model_string[:index_of_model_id] + return original_model_string \ No newline at end of file