优化model

chat/api/v1/ai_chat.py

@@ -3,16 +3,15 @@ import asyncio
 
 from fastapi import APIRouter, Depends, Request
 from fastapi.responses import StreamingResponse
+from sqlalchemy.orm import Session
 
 from pydantic import BaseModel
-from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationChain
-# from langchain.chat_models import ChatOpenAI
 from langchain_community.chat_models import ChatOpenAI
 
 from deps.auth import get_current_user
-from services.chat_service import chat_service
-from utils.resp import resp_success
+from services.chat_service import ChatDBService
+from db.session import get_db
 
 router = APIRouter()
 
@@ -30,21 +29,37 @@ def get_deepseek_llm(api_key: str, model: str, openai_api_base: str):
     )
 
 @router.post('/stream')
-async def chat_stream(request: Request):
+async def chat_stream(request: Request, db: Session = Depends(get_db), user=Depends(get_current_user)):
     body = await request.json()
     content = body.get('content')
-    print(content, 'content')
+    conversation_id = body.get('conversation_id')
     model = 'deepseek-chat'
     api_key = os.getenv("DEEPSEEK_API_KEY")
-    openai_api_base="https://api.deepseek.com/v1"
+    openai_api_base = "https://api.deepseek.com/v1"
     llm = get_deepseek_llm(api_key, model, openai_api_base)
 
     if not content or not isinstance(content, str):
         from fastapi.responses import JSONResponse
         return JSONResponse({"error": "content不能为空"}, status_code=400)
 
+    user_id = user["user_id"]
+
+    # 1. 获取或新建对话
+    try:
+        conversation = ChatDBService.get_or_create_conversation(db, conversation_id, user_id, model)
+    except ValueError as e:
+        from fastapi.responses import JSONResponse
+        return JSONResponse({"error": str(e)}, status_code=400)
+    # 2. 插入当前消息
+    ChatDBService.add_message(db, conversation, user_id, content)
+
+    # 3. 查询历史消息，组装上下文
+    history = ChatDBService.get_history(db, conversation.id)
+    history_contents = [msg.content for msg in history]
+    context = '\n'.join(history_contents)
+
     async def event_generator():
-        async for chunk in llm.astream(content):
+        async for chunk in llm.astream(context):
             # 只返回 chunk.content 内容
             if hasattr(chunk, 'content'):
                 yield f"data: {chunk.content}\n\n"