552 lines
21 KiB
Python
552 lines
21 KiB
Python
import json
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import os
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import tempfile
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import shutil
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from typing import AsyncGenerator, Dict, List, Optional, Union, Any
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from datetime import datetime
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import uvicorn
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from fastapi import FastAPI, HTTPException, Depends, Header
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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 qwen_agent.llm.schema import ASSISTANT, FUNCTION
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from pydantic import BaseModel, Field
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# Import utility modules
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from utils import (
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# Models
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Message, DatasetRequest, ChatRequest, FileProcessRequest,
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FileProcessResponse, ChatResponse,
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# File utilities
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download_file, remove_file_or_directory, get_document_preview,
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load_processed_files_log, save_processed_files_log, get_file_hash,
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# Dataset management
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download_dataset_files, generate_dataset_structure,
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remove_dataset_directory, remove_dataset_directory_by_key,
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# Project management
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generate_project_readme, save_project_readme, get_project_status,
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remove_project, list_projects, get_project_stats,
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# Agent management
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get_global_agent_manager, init_global_agent_manager
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)
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# Import gbase_agent
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from gbase_agent import update_agent_llm
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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# Custom version for qwen-agent messages - keep this function as it's specific to this app
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def get_content_from_messages(messages: List[dict]) -> str:
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"""Extract content from qwen-agent messages with special formatting"""
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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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# Helper functions are now imported from utils module
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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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# 挂载public文件夹为静态文件服务
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app.mount("/public", StaticFiles(directory="public"), name="static")
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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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# Models are now imported from utils module
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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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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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# Models are now imported from utils module
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@app.post("/api/v1/files/process")
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async def process_files(request: FileProcessRequest, authorization: Optional[str] = Header(None)):
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"""
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Process dataset files for a given unique_id.
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Files are organized by key groups, and each group is combined into a single document.txt file.
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Supports zip files which will be extracted and their txt/md contents combined.
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Args:
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request: FileProcessRequest containing unique_id, files (key-grouped dict), system_prompt, and mcp_settings
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authorization: Authorization header containing API key (Bearer <API_KEY>)
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Returns:
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FileProcessResponse: Processing result with file list
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"""
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try:
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unique_id = request.unique_id
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if not unique_id:
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raise HTTPException(status_code=400, detail="unique_id is required")
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# 处理文件：使用按key分组格式
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processed_files_by_key = {}
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if request.files:
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# 使用请求中的文件（按key分组）
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processed_files_by_key = await download_dataset_files(unique_id, request.files)
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total_files = sum(len(files) for files in processed_files_by_key.values())
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print(f"Processed {total_files} dataset files across {len(processed_files_by_key)} keys for unique_id: {unique_id}")
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else:
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print(f"No files provided in request for unique_id: {unique_id}")
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# 使用unique_id获取项目目录
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project_dir = os.path.join("projects", unique_id)
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if not os.path.exists(project_dir):
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raise HTTPException(status_code=400, detail=f"Project directory not found for unique_id: {unique_id}")
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# 收集项目目录下所有的 document.txt 文件
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document_files = []
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for root, dirs, files in os.walk(project_dir):
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for file in files:
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if file == "document.txt":
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document_files.append(os.path.join(root, file))
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# 合并所有处理的文件（包含新按key分组的文件）
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all_files = document_files.copy()
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for key, files in processed_files_by_key.items():
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all_files.extend(files)
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if not all_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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# 保存system_prompt和mcp_settings到项目目录（如果提供）
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if request.system_prompt:
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system_prompt_file = os.path.join(project_dir, "system_prompt.md")
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with open(system_prompt_file, 'w', encoding='utf-8') as f:
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f.write(request.system_prompt)
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print(f"Saved system_prompt for unique_id: {unique_id}")
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if request.mcp_settings:
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mcp_settings_file = os.path.join(project_dir, "mcp_settings.json")
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with open(mcp_settings_file, 'w', encoding='utf-8') as f:
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json.dump(request.mcp_settings, f, ensure_ascii=False, indent=2)
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print(f"Saved mcp_settings for unique_id: {unique_id}")
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# 返回结果包含按key分组的文件信息
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result_files = []
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for key in processed_files_by_key.keys():
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# 添加对应的dataset document.txt路径
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document_path = os.path.join("projects", unique_id, "dataset", key, "document.txt")
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if os.path.exists(document_path):
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result_files.append(document_path)
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# 对于没有在processed_files_by_key中但存在的document.txt文件，也添加到结果中
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existing_document_paths = set(result_files) # 避免重复
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for doc_file in document_files:
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if doc_file not in existing_document_paths:
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result_files.append(doc_file)
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return FileProcessResponse(
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success=True,
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message=f"Successfully processed {len(result_files)} document files across {len(processed_files_by_key)} keys",
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unique_id=unique_id,
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processed_files=result_files
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)
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except HTTPException:
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raise
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except Exception as e:
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print(f"Error processing files: {str(e)}")
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raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
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@app.post("/api/v1/chat/completions")
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async def chat_completions(request: ChatRequest, authorization: Optional[str] = Header(None)):
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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, dataset with unique_id, system_prompt, mcp_settings, and files
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authorization: Authorization header containing API key (Bearer <API_KEY>)
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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Authorization header中提取API key
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api_key = None
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if authorization:
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# 移除 "Bearer " 前缀
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if authorization.startswith("Bearer "):
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api_key = authorization[7:]
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else:
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api_key = authorization
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# 获取unique_id
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unique_id = request.unique_id
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if not unique_id:
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raise HTTPException(status_code=400, detail="unique_id is required")
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# 使用unique_id获取项目目录
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project_dir = os.path.join("projects", unique_id)
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if not os.path.exists(project_dir):
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raise HTTPException(status_code=400, detail=f"Project directory not found for unique_id: {unique_id}")
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# 收集额外参数作为 generate_cfg
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exclude_fields = {'messages', 'model', 'model_server', 'unique_id', 'stream'}
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generate_cfg = {k: v for k, v in request.model_dump().items() if k not in exclude_fields}
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# 从全局管理器获取或创建助手实例（配置读取逻辑已在agent_manager内部处理）
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agent = await agent_manager.get_or_create_agent(
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unique_id=unique_id,
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project_dir=project_dir,
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model_name=request.model,
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api_key=api_key,
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model_server=request.model_server,
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generate_cfg=generate_cfg
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)
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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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for msg in request.messages:
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if msg.role == "assistant":
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# 对assistant消息进行[ANSWER]分割处理，只保留最后一段
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content_parts = msg.content.split("[ANSWER]")
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if content_parts:
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# 取最后一段非空文本
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last_part = content_parts[-1].strip()
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messages.append({"role": msg.role, "content": last_part})
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else:
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messages.append({"role": msg.role, "content": msg.content})
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else:
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messages.append({"role": msg.role, "content": msg.content})
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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("/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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"""清理助手缓存"""
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try:
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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_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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cache_stats = agent_manager.get_cache_stats()
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return {
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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(unique_id: 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_unique_id(unique_id)
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if success:
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return {"message": f"项目缓存移除成功: {unique_id}"}
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else:
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return {"message": f"未找到项目缓存: {unique_id}", "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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@app.get("/api/v1/files/{unique_id}/status")
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async def get_files_processing_status(unique_id: str):
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"""获取项目的文件处理状态"""
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try:
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# Load processed files log
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processed_log = load_processed_files_log(unique_id)
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# Get project directory info
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project_dir = os.path.join("projects", unique_id)
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project_exists = os.path.exists(project_dir)
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# Collect document.txt files
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document_files = []
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if project_exists:
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for root, dirs, files in os.walk(project_dir):
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for file in files:
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if file == "document.txt":
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document_files.append(os.path.join(root, file))
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return {
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"unique_id": unique_id,
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"project_exists": project_exists,
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"processed_files_count": len(processed_log),
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"processed_files": processed_log,
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"document_files_count": len(document_files),
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"document_files": document_files,
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"log_file_exists": os.path.exists(os.path.join("projects", unique_id, "processed_files.json"))
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}
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except Exception as e:
|
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raise HTTPException(status_code=500, detail=f"获取文件处理状态失败: {str(e)}")
|
||
|
||
|
||
@app.post("/api/v1/files/{unique_id}/reset")
|
||
async def reset_files_processing(unique_id: str):
|
||
"""重置项目的文件处理状态，删除处理日志和所有文件"""
|
||
try:
|
||
project_dir = os.path.join("projects", unique_id)
|
||
log_file = os.path.join("projects", unique_id, "processed_files.json")
|
||
|
||
# Load processed log to know what files to remove
|
||
processed_log = load_processed_files_log(unique_id)
|
||
|
||
removed_files = []
|
||
# Remove all processed files and their dataset directories
|
||
for file_hash, file_info in processed_log.items():
|
||
# Remove local file in files directory
|
||
if 'local_path' in file_info:
|
||
if remove_file_or_directory(file_info['local_path']):
|
||
removed_files.append(file_info['local_path'])
|
||
|
||
# Handle new key-based structure first
|
||
if 'key' in file_info:
|
||
# Remove dataset directory by key
|
||
key = file_info['key']
|
||
if remove_dataset_directory_by_key(unique_id, key):
|
||
removed_files.append(f"dataset/{key}")
|
||
elif 'filename' in file_info:
|
||
# Fallback to old filename-based structure
|
||
filename_without_ext = os.path.splitext(file_info['filename'])[0]
|
||
dataset_dir = os.path.join("projects", unique_id, "dataset", filename_without_ext)
|
||
if remove_file_or_directory(dataset_dir):
|
||
removed_files.append(dataset_dir)
|
||
|
||
# Also remove any specific dataset path if exists (fallback)
|
||
if 'dataset_path' in file_info:
|
||
if remove_file_or_directory(file_info['dataset_path']):
|
||
removed_files.append(file_info['dataset_path'])
|
||
|
||
# Remove the log file
|
||
if remove_file_or_directory(log_file):
|
||
removed_files.append(log_file)
|
||
|
||
# Remove the entire files directory
|
||
files_dir = os.path.join(project_dir, "files")
|
||
if remove_file_or_directory(files_dir):
|
||
removed_files.append(files_dir)
|
||
|
||
# Also remove the entire dataset directory (clean up any remaining files)
|
||
dataset_dir = os.path.join(project_dir, "dataset")
|
||
if remove_file_or_directory(dataset_dir):
|
||
removed_files.append(dataset_dir)
|
||
|
||
# Remove README.md if exists
|
||
readme_file = os.path.join(project_dir, "README.md")
|
||
if remove_file_or_directory(readme_file):
|
||
removed_files.append(readme_file)
|
||
|
||
return {
|
||
"message": f"文件处理状态重置成功: {unique_id}",
|
||
"removed_files_count": len(removed_files),
|
||
"removed_files": removed_files
|
||
}
|
||
except Exception as e:
|
||
raise HTTPException(status_code=500, detail=f"重置文件处理状态失败: {str(e)}")
|
||
|
||
|
||
|
||
|
||
if __name__ == "__main__":
|
||
uvicorn.run(app, host="0.0.0.0", port=8001)
|