Spaces:
Sleeping
Sleeping
File size: 8,029 Bytes
18c0acd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 |
from operator import itemgetter
from typing import Any, List, Type
from langchain.memory import ChatMessageHistory, ConversationBufferWindowMemory
from langchain.output_parsers import PydanticOutputParser
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate
from langchain.schema import AIMessage, HumanMessage
from langchain_community.utilities.dalle_image_generator import DallEAPIWrapper
from langchain_core.output_parsers import StrOutputParser
from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr
from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough
from langchain_openai import OpenAI
from src.models.lc_base_model import ContentGenerator
class QuestionGenerator(ContentGenerator):
class Question(BaseModel):
question: str = Field(alias="Question", description="A question based on the given guidelines")
def __init__(
self,
exam_prompt: str,
level: str,
description: str,
history_chat: List[HumanMessage | AIMessage],
openai_api_key: SecretStr,
chat_temperature: float = 0.3,
) -> None:
"""
Initializes the object with the given exam prompt, level, description, history chat, and OpenAI API key.
Parameters:
exam_prompt (str): The prompt for the exam.
level (str): The level of the exam.
description (str): The description of the exam.
history_chat (List[HumanMessage | AIMessage]): List of chat messages from history.
openai_api_key (SecretStr): The API key for OpenAI.
Returns:
None
"""
super().__init__(openai_api_key=openai_api_key, chat_temperature=chat_temperature)
self.level = level
self.exam_prompt = exam_prompt
self.description = description
self.memory = ConversationBufferWindowMemory(
chat_memory=ChatMessageHistory(messages=history_chat), return_messages=True, k=20
)
self.chain = self._create_chain()
def _get_system_prompt(self) -> ChatPromptTemplate:
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"{format_instructions}"
f"""
You are an excellent english teacher. You teach people to speak English by asking questions
to later evaluate its response.
You will do the following tasks for that purpose:
- Limit your questions to just once per interaction at a time.
- You will generate a question based on the following guidelines in order to
later evaluate the response given by the user.
- Don't repeat ANY previous question from the history.
- Don't ask too abstract or very specific questions.
- here below, some example structure questions are given. This is just a reference of
the kind of questions that is expected you provide. Feel free to ask another questions,
but always in the context of an {self.level} Speaking English exam. Remember to ask
just one question at a time and don't repeat ANY previous questions.
{self.description}
""",
),
MessagesPlaceholder(
variable_name="history",
),
(
"human",
"I'm ready to start, ask me a question. Do NOT repeat ANY previous AI questions from the history.",
),
]
)
return prompt
def _get_output_parser(self, pydantic_schema: Type[BaseModel]) -> PydanticOutputParser[Any]:
return PydanticOutputParser(pydantic_object=pydantic_schema)
def _create_chain(self) -> Runnable[Any, Any]:
response_parser = self._get_output_parser(self.Question)
memory_runable = RunnablePassthrough.assign(
history=RunnableLambda(self.memory.load_memory_variables) | itemgetter("history")
)
parsed_chain = (
memory_runable
| self._get_system_prompt().partial(format_instructions=response_parser.get_format_instructions())
| self.chat_llm
| response_parser
)
unparsed_chain = (
memory_runable
| self._get_system_prompt().partial(format_instructions="")
| self.chat_llm
| StrOutputParser()
)
chain = parsed_chain.with_fallbacks([unparsed_chain])
return chain
def generate(self) -> str:
"""
A function that generates a response by invoking a chain, adding the AI message to chat memory, and returning a question from the response.
"""
response = self.chain.invoke({})
if isinstance(response, BaseModel):
response = response.dict(by_alias=True)
response = response["Question"]
self.memory.chat_memory.add_ai_message(response)
return response
class ImgGenerator(ContentGenerator):
def __init__(
self,
exam_prompt: str,
level: str,
description: str,
openai_api_key: SecretStr,
chat_temperature: float = 0.3,
img_size: str = "256x256",
) -> None:
"""
Initializes the class with the exam prompt, level, description, OpenAI API key, and optional image size.
Parameters:
exam_prompt (str): The prompt for the exam.
level (str): The level of the exam.
description (str): Description of the exam.
openai_api_key (SecretStr): The OpenAI API key.
img_size (str, optional): The size of the image. Default is "256x256".
Returns:
None
"""
super().__init__(openai_api_key, chat_temperature)
self.level = level
self.exam_prompt = exam_prompt
self.description = description
self.dalle = DallEAPIWrapper(size=img_size, api_key=self.openai_api_key.get_secret_value()) # type: ignore[call-arg]
self.chain = self._create_chain()
def _get_system_prompt(self) -> ChatPromptTemplate:
return ChatPromptTemplate.from_messages(
[
(
"system",
f"""You will generate a short image description (one paragraph max)
which will be later used for generating an image taking into
account that this images will be used for evaluating the
user how well can describe this image in the context of
an {self.level} Speaking English exam.
{self.description}
Topics about image descriptions which can appear:
{self.exam_prompt}
""",
),
("human", "{base_input}"),
]
)
def _create_chain(self) -> Runnable[Any, Any]:
img_prompt = PromptTemplate(
input_variables=["image_desc"],
template="""You will now act as a prompt generator. I will describe an image to you, and you will create a prompt
that could be used for image-generation. The style must be realistic:
Description: {image_desc}""",
)
return (
{"image_desc": self._get_system_prompt() | self.chat_llm | StrOutputParser()}
| img_prompt
| OpenAI(temperature=0.7, api_key=self.openai_api_key)
| StrOutputParser()
)
def generate(self) -> str:
"""
Generate function to create and return an image URL based on input parameters.
"""
img_url = self.dalle.run(self.chain.invoke({"base_input": "Generate image description"})[:999])
return img_url
|