core

core/__init__.py

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+
+

core/app/__init__.py

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+
+

core/cli/__init__.py

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+
+

core/llms/__init__.py

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+from .base_llm import BaseLLM
+from .litellm_llm import LLM
+
+__all__ = [
+    "BaseLLM",
+    "LLM",
+]   

core/llms/base_llm.py

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+from abc import ABC, abstractmethod
+
+class BaseLLM(ABC):
+
+    def __init__(self, api_key, model, tools):
+        self.api_key = api_key
+        self.model = model
+        self.tools = tools
+        
+    @abstractmethod
+    def _chat(self, messages, **kargs):
+        pass
+    
+    def chat(self, messages, **kargs):
+        return self._chat(messages, **kargs)    
+    
+    def get_attr(self,attr:str, kwargs:dict):
+        attribute = kwargs.get(attr, None)
+        if attribute is None:
+            if hasattr(self, attr):
+                attribute = getattr(self, attr)
+        return attribute

core/llms/litellm_llm.py

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+from openai.types.chat.chat_completion_chunk import Choice
+from .base_llm import BaseLLM
+from litellm import completion
+from litellm.utils import ModelResponse
+from openai import OpenAI
+
+class LLM(BaseLLM):
+    def __init__(self, api_key, model, tools:list=None):
+        super().__init__(api_key=api_key, model=model, tools=tools)
+
+    
+    def _chat(self, messages, **kwargs):
+        return completion(messages=messages, model=self.model, api_key=self.api_key, **kwargs)
+    
+    def _handle_tool_calls(self, messages:list[ModelResponse], **kwargs):
+        
+        message = messages[-1]
+        
+        if self._tools is None:
+            return messages
+        
+        if message.choices[0].finish_reason in ('stop', 'length'):
+            return messages
+        elif message.choices[0].finish_reason == 'tool_calls':
+            tools_to_call = message.choices[0].message.tool_calls
+            for tool in tools_to_call:
+                print(tool)
+
+            

core/tools/__init__.py

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+
+