121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
import os
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import asyncio
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from fastapi import APIRouter, Depends, Request, Query
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from fastapi.responses import StreamingResponse
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from sqlalchemy.orm import Session
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from typing import List
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from pydantic import BaseModel, SecretStr
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from langchain.chains import ConversationChain
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from api.v1.vo import MessageVO
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from deps.auth import get_current_user
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from services.chat_service import ChatDBService
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from db.session import get_db
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from models.ai import ChatConversation, ChatMessage
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from utils.resp import resp_success, Response
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from langchain_deepseek import ChatDeepSeek
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router = APIRouter()
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def get_deepseek_llm(api_key: SecretStr, model: str):
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# deepseek 兼容 OpenAI API，需指定 base_url
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return ChatDeepSeek(
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api_key=api_key,
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model=model,
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streaming=True,
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)
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@router.post('/stream')
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async def chat_stream(request: Request, user=Depends(get_current_user), db: Session = Depends(get_db)):
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body = await request.json()
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content = body.get('content')
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conversation_id = body.get('conversation_id')
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model = 'deepseek-chat'
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api_key = os.getenv("DEEPSEEK_API_KEY")
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llm = get_deepseek_llm(SecretStr(api_key), model)
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if not content or not isinstance(content, str):
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from fastapi.responses import JSONResponse
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return JSONResponse({"error": "content不能为空"}, status_code=400)
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user_id = user["user_id"]
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# 1. 获取对话
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try:
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conversation = ChatDBService.get_conversation(db, conversation_id)
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conversation = db.merge(conversation) # ✅ 防止 DetachedInstanceError
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except ValueError as e:
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from fastapi.responses import JSONResponse
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return JSONResponse({"error": str(e)}, status_code=400)
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# 2. 插入当前消息
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ChatDBService.add_message(db, conversation, user_id, content)
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context = [
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("system", "You are a helpful assistant. Answer all questions to the best of your ability in {language}.")
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]
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# 3. 查询历史消息，组装上下文
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history = ChatDBService.get_history(db, conversation.id)
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# === 新增：如果只有一条消息，更新 title ===
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if len(history) == 1:
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ChatDBService.update_conversation_title(db, conversation.id, content[:255])
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for msg in history:
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# 假设 msg.type 存储的是 'user' 或 'assistant'
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# role = msg.type if msg.type in ("user", "assistant") else "user"
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context.append((msg.type, msg.content))
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ai_reply = ""
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async def event_generator():
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nonlocal ai_reply
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async for chunk in llm.astream(context):
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if hasattr(chunk, 'content'):
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ai_reply += chunk.content
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yield f"data: {chunk.content}\n\n"
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else:
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ai_reply += chunk
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yield f"data: {chunk}\n\n"
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await asyncio.sleep(0.01)
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# 只保留最新AI回复
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if ai_reply:
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ChatDBService.insert_ai_message(db, conversation, user_id, ai_reply, model)
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return StreamingResponse(event_generator(), media_type='text/event-stream')
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@router.post("/conversations")
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def create_conversation(db: Session = Depends(get_db), user=Depends(get_current_user),):
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user_id = user["user_id"]
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model = 'deepseek-chat'
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conversation = ChatDBService.get_or_create_conversation(db, None, user_id, model, '新对话')
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return resp_success(data=conversation.id)
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@router.get('/conversations')
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async def get_conversations(
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db: Session = Depends(get_db),
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user=Depends(get_current_user)
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):
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"""获取当前用户的聊天对话列表，last_message为字符串"""
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user_id = user["user_id"]
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conversations = db.query(ChatConversation).filter(ChatConversation.user_id == user_id).order_by(ChatConversation.update_time.desc()).all()
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return resp_success(data=[
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{
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'id': c.id,
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'title': c.title,
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'update_time': c.update_time,
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'last_message': c.messages[-1].content if c.messages else None,
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}
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for c in conversations
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])
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@router.get('/messages', response_model=Response[List[MessageVO]])
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def get_messages(
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conversation_id: int = Query(...),
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db: Session = Depends(get_db),
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user=Depends(get_current_user)
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):
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"""获取指定会话的消息列表（当前用户）"""
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user_id = user["user_id"]
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query = db.query(ChatMessage).filter(ChatMessage.conversation_id == conversation_id,
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ChatMessage.user_id == user_id).order_by(ChatMessage.id).all()
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return resp_success(data=query)
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