feat: 机器人问答接口:/knowledge/qa/stream

This commit is contained in:
martsforever
2025-11-24 21:47:54 +08:00
parent 2b2eb7e6ab
commit a3e8d739ed
+75 -3
View File
@@ -1,14 +1,15 @@
import asyncio
import json
from http.client import HTTPException
from typing import List
from fastapi import UploadFile, File, Form
from fastapi import UploadFile, File, Form, HTTPException
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.output_parsers import StrOutputParser
from starlette import status
from starlette.requests import Request
from starlette.responses import StreamingResponse
from starlette.responses import StreamingResponse, JSONResponse
from app.general.perform_general_operation import perform_general_operation
from app.utils.db_utils import AsyncSessionDep
from app.utils.knowledge_utils import knowledge_service
from app.utils.llm_utils import create_llm
@@ -45,6 +46,77 @@ def add_knowledge_route(app):
return StreamingResponse(generator_function(), media_type="text/event-stream")
@app.post("/knowledge/qa/stream")
async def knowledge_search(body: dict, session: AsyncSessionDep, request: Request):
# 机器人的id,用来一会查询知识库编码以及判断机器人是否已经禁用
qaId = body.get('input').get('qaId')
# 聊天历史
messages = body.get('input').get('messages')
# 用户问题
question = body.get('input').get('question')
# 查询qa_bot信息
perform_result = await perform_general_operation(
session=session,
module='knowledge_qa_bot',
data={"id": qaId},
debug_data=[],
user=request.state.user,
type='item'
)
if 'error' in perform_result:
return JSONResponse(content={"message": perform_result.get('error')}, status_code=status.HTTP_500_INTERNAL_SERVER_ERROR)
qa_record_bot = perform_result.get('result', None)
# 找不到机器人
if not qa_record_bot:
return JSONResponse(content={"message": f"""无法找到对应问答机器人的编号:{qaId}"""}, status_code=status.HTTP_500_INTERNAL_SERVER_ERROR)
# 机器人已经被禁用
if qa_record_bot.get('disable') == 'Y':
return JSONResponse(content={"message": "该问答机器人已经禁用"}, status_code=status.HTTP_500_INTERNAL_SERVER_ERROR)
# 查询rel_qa_kb信息
perform_result = await perform_general_operation(
session=session,
module='rel_qa_base',
data={"all": True, "filters": [{"id": "01", "field": "qaId", "operator": "=", "value": qaId}]},
debug_data=[],
user=request.state.user,
type='list'
)
if 'error' in perform_result:
return JSONResponse(content={"message": perform_result.get('error')}, status_code=status.HTTP_500_INTERNAL_SERVER_ERROR)
rel_qa_kb_list = perform_result.get('list', [])
kb_codes = [item.get('kbCode') for item in rel_qa_kb_list]
async def generator_function():
param = KnowledgeQueryParam(kb_code=kb_codes, question=question)
search_response = await milvus_service.async_search(param)
chain = create_llm() | StrOutputParser()
# 先把检索结果返回前端
yield f'data: {json.dumps({"type": "retrieve", "data": json.dumps(search_response.model_dump())}, ensure_ascii=False)}\n\n'
external_prompt = qa_record_bot.get('prompt', '')
print("external_prompt", external_prompt, qa_record_bot)
async for chunk in chain.astream([
SystemMessage(content=f"""你需要根据如下内容来回答用户的问题:{search_response.answer},如果内容为'在提供的资料中未找到相关信息',你需要根据你自己的知识来回答用户问题。""" + external_prompt),
*messages,
]):
yield f'data: {json.dumps({"type": "messages", "data": chunk}, ensure_ascii=False)}\n\n'
return StreamingResponse(generator_function(), media_type="text/event-stream")
# @app.post("/knowledge/embed_text")
# async def knowledge_embed_text(text_list: List[str]):
# id_list = await next_id(len(text_list))