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72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
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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 modules.brain.knowledge_brain_qa import KnowledgeBrainQA
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from modules.chat.dto.chats import ChatQuestion
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class ClaudeBrain(KnowledgeBrainQA):
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"""This is the Notion brain class. it is a KnowledgeBrainQA has the data is stored locally.
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It is going to call the Data Store internally to get the data.
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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 calculate_pricing(self):
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return 3
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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 Claude powered by 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(
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model="claude-3-haiku-20240307", max_tokens=self.max_tokens
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)
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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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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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):
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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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