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feat: add generate_answer function to support non streamed response for api brain (#1847)
Issue: https://github.com/StanGirard/quivr/issues/1841 Demo: https://github.com/StanGirard/quivr/assets/63923024/56dfa85c-6c2e-4e55-8eda-4723e58ced1d
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@ -1,14 +1,11 @@
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import asyncio
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import json
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from typing import Optional
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from uuid import UUID
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import nest_asyncio
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from fastapi import HTTPException
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from litellm import completion
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from llm.knowledge_brain_qa import KnowledgeBrainQA
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from llm.utils.call_brain_api import call_brain_api
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from llm.utils.get_api_brain_definition_as_json_schema import (
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get_api_brain_definition_as_json_schema,
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)
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from logger import get_logger
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from modules.brain.service.brain_service import BrainService
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from modules.chat.dto.chats import ChatQuestion
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@ -16,6 +13,12 @@ from modules.chat.dto.inputs import CreateChatHistory
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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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from llm.knowledge_brain_qa import KnowledgeBrainQA
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from llm.utils.call_brain_api import call_brain_api
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from llm.utils.get_api_brain_definition_as_json_schema import (
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get_api_brain_definition_as_json_schema,
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)
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brain_service = BrainService()
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chat_service = ChatService()
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@ -56,13 +59,15 @@ class APIBrainQA(
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functions,
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brain_id: UUID,
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recursive_count=0,
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should_log_steps=False,
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):
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if recursive_count > 5:
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yield "🧠<Deciding what to do>🧠"
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yield "The assistant is having issues and took more than 5 calls to the API. Please try again later or an other instruction."
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return
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yield "🧠<Deciding what to do>🧠"
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if should_log_steps:
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yield "🧠<Deciding what to do>🧠"
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response = completion(
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model=self.model,
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temperature=self.temperature,
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@ -98,7 +103,9 @@ class APIBrainQA(
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except Exception:
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arguments = {}
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yield f"🧠<Calling {brain_id} with arguments {arguments}>🧠"
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if should_log_steps:
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yield f"🧠<Calling {brain_id} with arguments {arguments}>🧠"
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try:
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api_call_response = call_brain_api(
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@ -125,6 +132,7 @@ class APIBrainQA(
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functions=functions,
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brain_id=brain_id,
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recursive_count=recursive_count + 1,
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should_log_steps=should_log_steps,
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):
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yield value
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@ -140,7 +148,12 @@ class APIBrainQA(
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yield "**...**"
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break
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async def generate_stream(self, chat_id: UUID, question: ChatQuestion):
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async def generate_stream(
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self,
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chat_id: UUID,
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question: ChatQuestion,
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should_log_steps: Optional[bool] = True,
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):
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if not question.brain_id:
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raise HTTPException(
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status_code=400, detail="No brain id provided in the question"
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@ -198,6 +211,7 @@ class APIBrainQA(
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messages=messages,
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functions=[get_api_brain_definition_as_json_schema(brain)],
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brain_id=question.brain_id,
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should_log_steps=should_log_steps,
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):
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streamed_chat_history.assistant = value
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response_tokens.append(value)
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@ -212,3 +226,27 @@ class APIBrainQA(
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user_message=question.question,
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assistant="".join(response_tokens),
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)
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def generate_answer(self, chat_id: UUID, question: ChatQuestion):
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async def a_generate_answer():
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api_brain_question_answer: GetChatHistoryOutput = None
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async for answer in self.generate_stream(
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chat_id, question, should_log_steps=False
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):
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answer = answer.split("data: ")[1]
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answer_parsed: GetChatHistoryOutput = GetChatHistoryOutput(
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**json.loads(answer)
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)
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if api_brain_question_answer is None:
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api_brain_question_answer = answer_parsed
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else:
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api_brain_question_answer.assistant += answer_parsed.assistant
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return api_brain_question_answer
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nest_asyncio.apply()
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loop = asyncio.get_event_loop()
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result = loop.run_until_complete(a_generate_answer())
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return result
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@ -5,6 +5,7 @@ litellm==1.7.7
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openai==1.1.1
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GitPython==3.1.36
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pdf2image==1.16.3
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nest_asyncio==1.5.6
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pypdf==3.9.0
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supabase==1.1.0
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tiktoken==0.4.0
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