import os import asyncio from fastapi import APIRouter, Depends, Request, Query from fastapi.responses import StreamingResponse from sqlalchemy.orm import Session from typing import List from pydantic import BaseModel, SecretStr from langchain.chains import ConversationChain from api.v1.chat.vo import MessageVO from deps.auth import get_current_user from services.chat_service import ChatDBService from db.session import get_db from models.ai import ChatConversation, ChatMessage from utils.resp import resp_success, Response from langchain_deepseek import ChatDeepSeek router = APIRouter(prefix="/chat", tags=["chat"]) def get_deepseek_llm(api_key: SecretStr, model: str): # deepseek 兼容 OpenAI API,需指定 base_url return ChatDeepSeek( api_key=api_key, model=model, streaming=True, ) @router.post('/stream') async def chat_stream(request: Request, user=Depends(get_current_user), db: Session = Depends(get_db)): body = await request.json() content = body.get('content') conversation_id = body.get('conversation_id') model = 'deepseek-chat' api_key = os.getenv("DEEPSEEK_API_KEY") llm = get_deepseek_llm(SecretStr(api_key), model) 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. 获取对话 try: conversation = ChatDBService.get_conversation(db, conversation_id) conversation = db.merge(conversation) # ✅ 防止 DetachedInstanceError 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) context = [ ("system", "You are a helpful assistant. Answer all questions to the best of your ability in {language}.") ] # 3. 查询历史消息,组装上下文 history = ChatDBService.get_history(db, conversation.id) # === 新增:如果只有一条消息,更新 title === if len(history) == 1: ChatDBService.update_conversation_title(db, conversation.id, content[:255]) for msg in history: # 假设 msg.type 存储的是 'user' 或 'assistant' # role = msg.type if msg.type in ("user", "assistant") else "user" context.append((msg.type, msg.content)) ai_reply = "" async def event_generator(): nonlocal ai_reply async for chunk in llm.astream(context): if hasattr(chunk, 'content'): ai_reply += chunk.content yield f"data: {chunk.content}\n\n" else: ai_reply += chunk yield f"data: {chunk}\n\n" await asyncio.sleep(0.01) # 只保留最新AI回复 if ai_reply: ChatDBService.insert_ai_message(db, conversation, user_id, ai_reply, model) return StreamingResponse(event_generator(), media_type='text/event-stream') @router.post("/conversations") def create_conversation(db: Session = Depends(get_db), user=Depends(get_current_user),): user_id = user["user_id"] model = 'deepseek-chat' conversation = ChatDBService.get_or_create_conversation(db, None, user_id, model, '新对话') return resp_success(data=conversation.id) @router.get('/conversations') async def get_conversations( db: Session = Depends(get_db), user=Depends(get_current_user) ): """获取当前用户的聊天对话列表,last_message为字符串""" user_id = user["user_id"] conversations = db.query(ChatConversation).filter(ChatConversation.user_id == user_id).order_by(ChatConversation.update_time.desc()).all() return resp_success(data=[ { 'id': c.id, 'title': c.title, 'update_time': c.update_time, 'last_message': c.messages[-1].content if c.messages else None, } for c in conversations ]) @router.get('/messages', response_model=Response[List[MessageVO]]) def get_messages( conversation_id: int = Query(...), db: Session = Depends(get_db), user=Depends(get_current_user) ): """获取指定会话的消息列表(当前用户)""" user_id = user["user_id"] query = db.query(ChatMessage).filter(ChatMessage.conversation_id == conversation_id, ChatMessage.user_id == user_id).order_by(ChatMessage.id).all() return resp_success(data=query)