feat: add route /knowledge/recall/stream

app/controller/add_knowledge_route.py

@@ -1,12 +1,17 @@
 import asyncio
+import json
 from http.client import HTTPException
 from typing import List
 
 from fastapi import UploadFile, File, Form
+from langchain_core.messages import HumanMessage
+from langgraph.prebuilt import create_react_agent
 from starlette.requests import Request
+from starlette.responses import StreamingResponse
 
 from app.utils.db_utils import AsyncSessionDep
 from app.utils.knowledge_utils import knowledge_service
+from app.utils.llm_utils import create_llm
 from app.utils.milvus_utils import milvus_service, KnowledgeQueryParam
 
 
@@ -15,6 +20,30 @@ def add_knowledge_route(app):
   async def knowledge_search(param: KnowledgeQueryParam):
     return await milvus_service.async_search(param)
 
+  @app.post("/knowledge/recall/stream")
+  async def knowledge_search(param: KnowledgeQueryParam):
+
+    search_response = await milvus_service.async_search(param)
+
+    agent = create_react_agent(create_react_agent(
+      model=create_llm(),
+      prompt=f"""你需要根据如下内容来回答用户的问题：{search_response.answer}"""
+    ))
+
+    async def generator_function():
+      # 先把检索结果返回前端
+      yield f'data: {json.dumps({"type": "retrieve", "data": search_response}, ensure_ascii=False)}\n\n'
+
+      async for chunk in agent.astream(
+        {"messages": [HumanMessage(content=param.question)]},
+        stream_mode="messages"
+      ):
+        # 流式回复
+        msg_chunk = chunk[0]
+        yield f'data: {json.dumps({"type": "messages", "data": msg_chunk.content}, 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))