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0dd1d12e6a
This pull request adds a new config parameter to the `conversational_qa_chain` function. The config parameter allows for passing metadata, specifically the conversation ID, to the function. This change ensures that the conversation ID is included in the metadata when invoking the `conversational_qa_chain` function.
102 lines
3.2 KiB
Python
102 lines
3.2 KiB
Python
import json
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from typing import AsyncIterable
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from uuid import UUID
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from langchain_community.chat_models import ChatLiteLLM
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from logger import get_logger
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from modules.brain.knowledge_brain_qa import KnowledgeBrainQA
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from modules.chat.dto.chats import ChatQuestion
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from modules.chat.dto.outputs import GetChatHistoryOutput
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from modules.chat.service.chat_service import ChatService
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logger = get_logger(__name__)
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chat_service = ChatService()
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class ProxyBrain(KnowledgeBrainQA):
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"""This is the Proxy brain class.
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Args:
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KnowledgeBrainQA (_type_): A brain that store the knowledge internaly
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"""
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def __init__(
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self,
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**kwargs,
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):
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super().__init__(
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**kwargs,
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)
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def get_chain(self):
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are Quivr. You are an assistant. {custom_personality}",
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),
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MessagesPlaceholder(variable_name="chat_history"),
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("human", "{question}"),
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]
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)
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chain = prompt | ChatLiteLLM(model=self.model, max_tokens=self.max_tokens)
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return chain
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async def generate_stream(
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self, chat_id: UUID, question: ChatQuestion, save_answer: bool = True
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) -> AsyncIterable:
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conversational_qa_chain = self.get_chain()
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transformed_history, streamed_chat_history = (
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self.initialize_streamed_chat_history(chat_id, question)
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)
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response_tokens = []
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config = {"metadata": {"conversation_id": str(chat_id)}}
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async for chunk in conversational_qa_chain.astream(
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{
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"question": question.question,
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"chat_history": transformed_history,
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"custom_personality": (
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self.prompt_to_use.content if self.prompt_to_use else None
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),
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},
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config=config,
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):
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response_tokens.append(chunk.content)
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streamed_chat_history.assistant = chunk.content
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yield f"data: {json.dumps(streamed_chat_history.dict())}"
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self.save_answer(question, response_tokens, streamed_chat_history, save_answer)
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def generate_answer(
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self, chat_id: UUID, question: ChatQuestion, save_answer: bool = True
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) -> GetChatHistoryOutput:
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conversational_qa_chain = self.get_chain()
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transformed_history, streamed_chat_history = (
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self.initialize_streamed_chat_history(chat_id, question)
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)
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config = {"metadata": {"conversation_id": str(chat_id)}}
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model_response = conversational_qa_chain.invoke(
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{
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"question": question.question,
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"chat_history": transformed_history,
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"custom_personality": (
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self.prompt_to_use.content if self.prompt_to_use else None
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),
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},
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config=config,
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)
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answer = model_response.content
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return self.save_non_streaming_answer(
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chat_id=chat_id,
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question=question,
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answer=answer,
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)
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