357 lines
12 KiB
Python
357 lines
12 KiB
Python
import json
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import os
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from typing import AsyncGenerator, Dict, List, Optional, Union
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import uvicorn
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from fastapi import FastAPI, HTTPException
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from fastapi.responses import StreamingResponse, HTMLResponse, FileResponse
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from fastapi.staticfiles import StaticFiles
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from qwen_agent.llm.schema import ASSISTANT, FUNCTION
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# 自定义版本，不需要text参数，不打印到终端
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def get_content_from_messages(messages: List[dict]) -> str:
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full_text = ''
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content = []
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TOOL_CALL_S = '[TOOL_CALL]'
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TOOL_RESULT_S = '[TOOL_RESPONSE]'
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THOUGHT_S = '[THINK]'
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ANSWER_S = '[ANSWER]'
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for msg in messages:
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if msg['role'] == ASSISTANT:
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if msg.get('reasoning_content'):
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assert isinstance(msg['reasoning_content'], str), 'Now only supports text messages'
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content.append(f'{THOUGHT_S}\n{msg["reasoning_content"]}')
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if msg.get('content'):
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assert isinstance(msg['content'], str), 'Now only supports text messages'
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content.append(f'{ANSWER_S}\n{msg["content"]}')
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if msg.get('function_call'):
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content.append(f'{TOOL_CALL_S} {msg["function_call"]["name"]}\n{msg["function_call"]["arguments"]}')
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elif msg['role'] == FUNCTION:
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content.append(f'{TOOL_RESULT_S} {msg["name"]}\n{msg["content"]}')
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else:
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raise TypeError
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if content:
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full_text = '\n'.join(content)
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return full_text
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from file_loaded_agent_manager import get_global_agent_manager, init_global_agent_manager
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from gbase_agent import update_agent_llm
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from zip_project_handler import zip_handler
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# 全局助手管理器配置
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max_cached_agents = int(os.getenv("MAX_CACHED_AGENTS", "20"))
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# 初始化全局助手管理器
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agent_manager = init_global_agent_manager(max_cached_agents=max_cached_agents)
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app = FastAPI(title="Database Assistant API", version="1.0.0")
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# 添加CORS中间件，支持前端页面
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"], # 在生产环境中应该设置为具体的前端域名
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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class Message(BaseModel):
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role: str
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content: str
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class ChatRequest(BaseModel):
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messages: List[Message]
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model: str = "qwen3-next"
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api_key: Optional[str] = None
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model_server: Optional[str] = None
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zip_url: Optional[str] = None
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generate_cfg: Optional[Dict] = None
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stream: Optional[bool] = False
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extra_prompt: Optional[str] = None
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class ChatResponse(BaseModel):
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choices: List[Dict]
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usage: Optional[Dict] = None
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class ChatStreamResponse(BaseModel):
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choices: List[Dict]
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usage: Optional[Dict] = None
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async def generate_stream_response(agent, messages, request) -> AsyncGenerator[str, None]:
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"""生成流式响应"""
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accumulated_content = ""
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accumulated_args = ""
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chunk_id = 0
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try:
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for response in agent.run(messages=messages):
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previous_content = accumulated_content
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accumulated_content = get_content_from_messages(response)
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# 计算新增的内容
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if accumulated_content.startswith(previous_content):
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new_content = accumulated_content[len(previous_content):]
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else:
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new_content = accumulated_content
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previous_content = ""
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# 只有当有新内容时才发送chunk
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if new_content:
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chunk_id += 1
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# 构造OpenAI格式的流式响应
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chunk_data = {
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"id": f"chatcmpl-{chunk_id}",
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"object": "chat.completion.chunk",
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"created": int(__import__('time').time()),
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"model": request.model,
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"choices": [{
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"index": 0,
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"delta": {
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"content": new_content
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},
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"finish_reason": None
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}]
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}
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yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
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# 发送最终完成标记
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final_chunk = {
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"id": f"chatcmpl-{chunk_id + 1}",
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"object": "chat.completion.chunk",
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"created": int(__import__('time').time()),
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"model": request.model,
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"choices": [{
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"index": 0,
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"delta": {},
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"finish_reason": "stop"
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}]
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}
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yield f"data: {json.dumps(final_chunk, ensure_ascii=False)}\n\n"
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# 发送结束标记
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yield "data: [DONE]\n\n"
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except Exception as e:
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import traceback
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error_details = traceback.format_exc()
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print(f"Error in generate_stream_response: {str(e)}")
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print(f"Full traceback: {error_details}")
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error_data = {
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"error": {
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"message": f"Stream error: {str(e)}",
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"type": "internal_error"
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}
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}
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yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n"
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@app.post("/chat/completions")
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async def chat_completions(request: ChatRequest):
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"""
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Chat completions API similar to OpenAI, supports both streaming and non-streaming
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Args:
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request: ChatRequest containing messages, model, zip_url, etc.
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Returns:
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Union[ChatResponse, StreamingResponse]: Chat completion response or stream
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"""
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try:
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# 从最外层获取zip_url参数
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zip_url = request.zip_url
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if not zip_url:
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raise HTTPException(status_code=400, detail="zip_url is required")
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# 使用ZIP URL获取项目数据
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print(f"从ZIP URL加载项目: {zip_url}")
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project_dir = zip_handler.get_project_from_zip(zip_url)
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if not project_dir:
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raise HTTPException(status_code=400, detail=f"Failed to load project from ZIP URL: {zip_url}")
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# 收集项目目录下所有的 document.txt 文件
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document_files = zip_handler.collect_document_files(project_dir)
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if not document_files:
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print(f"警告: 项目目录 {project_dir} 中未找到任何 document.txt 文件")
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# 从全局管理器获取或创建文件预加载的助手实例
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agent = await agent_manager.get_or_create_agent(
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zip_url=zip_url,
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files=document_files,
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model_name=request.model,
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api_key=request.api_key,
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model_server=request.model_server,
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generate_cfg=request.generate_cfg
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)
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extra_prompt = request.extra_prompt if request.extra_prompt else ""
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# 构建包含项目信息的消息上下文
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messages = [
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# 项目信息系统消息
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{
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"role": "user",
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"content": f"当前项目来自ZIP URL: {zip_url}，项目目录: {project_dir}。已加载 {len(document_files)} 个 document.txt 文件用于检索。\n" + extra_prompt
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},
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# 用户消息批量转换
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*[{"role": msg.role, "content": msg.content} for msg in request.messages]
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]
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# 根据stream参数决定返回流式还是非流式响应
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if request.stream:
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return StreamingResponse(
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generate_stream_response(agent, messages, request),
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media_type="text/event-stream",
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headers={"Cache-Control": "no-cache", "Connection": "keep-alive"}
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)
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else:
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# 非流式响应
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final_responses = agent.run_nonstream(messages)
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if final_responses and len(final_responses) > 0:
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# 取最后一个响应
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final_response = final_responses[-1]
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# 如果返回的是Message对象，需要转换为字典
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if hasattr(final_response, 'model_dump'):
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final_response = final_response.model_dump()
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elif hasattr(final_response, 'dict'):
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final_response = final_response.dict()
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content = final_response.get("content", "")
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# 构造OpenAI格式的响应
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return ChatResponse(
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choices=[{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": content
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},
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"finish_reason": "stop"
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}],
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usage={
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"prompt_tokens": sum(len(msg.content) for msg in request.messages),
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"completion_tokens": len(content),
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"total_tokens": sum(len(msg.content) for msg in request.messages) + len(content)
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}
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)
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else:
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raise HTTPException(status_code=500, detail="No response from agent")
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except Exception as e:
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import traceback
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error_details = traceback.format_exc()
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print(f"Error in chat_completions: {str(e)}")
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print(f"Full traceback: {error_details}")
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raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
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@app.get("/")
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async def root():
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"""Chat page endpoint"""
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return FileResponse("chat.html", media_type="text/html")
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@app.get("/api/health")
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async def health_check():
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"""Health check endpoint"""
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return {"message": "Database Assistant API is running"}
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@app.get("/system/status")
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async def system_status():
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"""获取系统状态信息"""
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# 获取助手缓存统计
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cache_stats = agent_manager.get_cache_stats()
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return {
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"status": "running",
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"storage_type": "File-Loaded Agent Manager",
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"max_cached_agents": max_cached_agents,
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"agent_cache": {
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"total_cached_agents": cache_stats["total_cached_agents"],
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"max_cached_agents": cache_stats["max_cached_agents"],
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"cached_agents": cache_stats["agents"]
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}
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}
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@app.post("/system/cleanup-cache")
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async def cleanup_cache():
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"""清理ZIP文件缓存和助手缓存"""
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try:
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# 清理ZIP文件缓存
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zip_handler.cleanup_cache()
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# 清理助手实例缓存
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cleared_count = agent_manager.clear_cache()
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return {
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"message": "缓存清理成功",
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"cleared_zip_files": True,
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"cleared_agent_instances": cleared_count
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"缓存清理失败: {str(e)}")
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@app.post("/system/cleanup-agent-cache")
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async def cleanup_agent_cache():
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"""仅清理助手实例缓存"""
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try:
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cleared_count = agent_manager.clear_cache()
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return {
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"message": "助手实例缓存清理成功",
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"cleared_agent_instances": cleared_count
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"助手实例缓存清理失败: {str(e)}")
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@app.get("/system/cached-projects")
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async def get_cached_projects():
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"""获取所有缓存的项目信息"""
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try:
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cached_urls = agent_manager.list_cached_zip_urls()
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cache_stats = agent_manager.get_cache_stats()
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return {
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"cached_projects": cached_urls,
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"cache_stats": cache_stats
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"获取缓存项目信息失败: {str(e)}")
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@app.post("/system/remove-project-cache")
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async def remove_project_cache(zip_url: str):
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"""移除特定项目的缓存"""
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try:
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success = agent_manager.remove_cache_by_url(zip_url)
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if success:
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return {"message": f"项目缓存移除成功: {zip_url}"}
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else:
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return {"message": f"未找到项目缓存: {zip_url}", "removed": False}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"移除项目缓存失败: {str(e)}")
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=8000)
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