2a02a4030a
- 添加 WeasyPrint 依赖以支持 PDF 导出功能 - 新增 docker-entrypoint.sh 统一管理容器启动流程 - 添加容器健康检查机制（/health 端点） - 配置容器自动重启策略（unless-stopped） - 优化日志输出，仅使用 stdout 适配容器环境 - 改进 update-and-run.sh 添加健康状态检查 - 统一脚本中的 sudo 使用规范
280 lines
9.2 KiB
Python
280 lines
9.2 KiB
Python
from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from contextlib import asynccontextmanager
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import os
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import sys
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from pathlib import Path
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from dotenv import load_dotenv
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import logging
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from typing import List, Optional
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# 配置日志系统 - 在最开始配置,确保所有模块都能使用
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# 仅输出到 stdout，容器环境下通过 docker logs 收集日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.StreamHandler(sys.stdout)
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]
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)
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# Ensure 'src' directory is in python path for 'storage' module imports
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# This is required because fetchers use 'from storage.file_io import ...'
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ROOT_DIR = Path(__file__).resolve().parent.parent.parent
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SRC_DIR = ROOT_DIR / "src"
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if str(SRC_DIR) not in sys.path:
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sys.path.insert(0, str(SRC_DIR))
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# 导入新路由
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from app.api import data_routes, analysis_routes, chat_routes
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load_dotenv()
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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# 新架构使用 AsyncSession，不需要显式初始化
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yield
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app = FastAPI(
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title="FA3 Stock Analysis API",
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version="2.0.0",
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description="架构重构后的股票分析 API",
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lifespan=lifespan
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)
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# Configure CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"], # For development convenience
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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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# 挂载新的 API 路由
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app.include_router(data_routes.router, prefix="/api")
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app.include_router(analysis_routes.router, prefix="/api")
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app.include_router(chat_routes.router, prefix="/api")
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@app.get("/")
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def read_root():
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return {
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"status": "ok",
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"message": "FA3 Stock Analysis API v2.0",
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"architecture": "refactored",
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"endpoints": {
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"data": "/api/data/*",
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"analysis": "/api/analysis/*",
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"config": "/api/config"
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},
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"docs": "/docs"
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}
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@app.get("/health")
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def health_check():
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return {
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"status": "healthy",
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"database": "fa3",
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"version": "2.0.0"
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}
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# 配置端点
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from fastapi import Request, Depends
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from app.database import get_db
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from app.models import Setting
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select
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from pydantic import BaseModel
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class ConfigUpdateRequest(BaseModel):
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key: str
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value: str
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@app.get("/api/config")
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async def get_config_compat(db: AsyncSession = Depends(get_db)):
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"""获取配置（从 PostgreSQL）"""
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try:
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result = await db.execute(select(Setting))
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settings = result.scalars().all()
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config_map = {}
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if settings:
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config_map = {setting.key: setting.value for setting in settings}
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# Compatibility: Map common UPPERCASE keys to lowercase for frontend
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if "AI_MODEL" in config_map and "ai_model" not in config_map:
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config_map["ai_model"] = config_map["AI_MODEL"]
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if "AVAILABLE_MODELS" in config_map and "available_models" not in config_map:
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config_map["available_models"] = config_map["AVAILABLE_MODELS"]
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if "AI_DISCUSSION_ROLES" in config_map and "ai_discussion_roles" not in config_map:
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config_map["ai_discussion_roles"] = config_map["AI_DISCUSSION_ROLES"]
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if "AI_QUESTION_LIBRARY" in config_map and "ai_question_library" not in config_map:
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config_map["ai_question_library"] = config_map["AI_QUESTION_LIBRARY"]
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# Ensure available_models is present
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if "available_models" not in config_map:
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# Default models list as requested
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import json
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default_models = [
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"gemini-2.0-flash",
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"gemini-2.5-flash",
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"gemini-3-flash-preview",
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"gemini-3-pro-preview"
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]
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config_map["available_models"] = json.dumps(default_models)
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# Ensure defaults for other keys exist if not in DB
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defaults = {
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"ai_model": "gemini-2.5-flash",
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"data_source_cn": "Tushare",
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"data_source_hk": "iFinD",
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"data_source_us": "iFinD",
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"data_source_jp": "iFinD",
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"data_source_vn": "iFinD"
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}
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for k, v in defaults.items():
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if k not in config_map:
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config_map[k] = v
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return config_map
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except Exception as e:
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# 如果表不存在，返回默认配置
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print(f"Config read error (using defaults): {e}")
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import json
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return {
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"ai_model": "gemini-2.5-flash",
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"available_models": json.dumps([
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"gemini-2.0-flash",
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"gemini-2.5-flash",
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"gemini-3-flash-preview",
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"gemini-3-pro-preview"
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]),
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"data_source_cn": "Tushare",
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"data_source_hk": "iFinD",
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"data_source_us": "iFinD",
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"data_source_jp": "iFinD",
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"data_source_vn": "iFinD"
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}
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@app.post("/api/config")
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async def update_config_compat(
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request: ConfigUpdateRequest,
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db: AsyncSession = Depends(get_db)
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):
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"""更新配置（到 PostgreSQL）"""
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try:
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result = await db.execute(
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select(Setting).where(Setting.key == request.key)
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)
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setting = result.scalar_one_or_none()
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if setting:
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setting.value = request.value
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else:
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setting = Setting(key=request.key, value=request.value)
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db.add(setting)
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await db.commit()
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return {"status": "ok", "key": request.key, "value": request.value}
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except Exception as e:
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# 如果数据库操作失败，至少返回成功状态（配置会保存在前端）
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print(f"Config update error: {e}")
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return {"status": "ok", "key": request.key, "value": request.value, "note": "saved in memory only"}
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# 搜索端点
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from app.services.analysis_service import get_genai_client
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import json
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import logging
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import time
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logger = logging.getLogger(__name__)
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class StockSearchRequest(BaseModel):
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query: str
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model: str = "gemini-2.0-flash" # 支持前端传入模型参数
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class StockSearchResponse(BaseModel):
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market: str
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symbol: str
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company_name: str
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@app.post("/api/search", response_model=list[StockSearchResponse])
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async def search_stock(request: StockSearchRequest):
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"""使用 AI 搜索股票"""
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logger.info(f"🔍 [搜索] 开始搜索股票: {request.query}")
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start_time = time.time()
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try:
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from google.genai import types
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client = get_genai_client()
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prompt = f"""请利用Google搜索查找 "{request.query}" 对应的上市股票信息。
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返回最匹配的股票，优先返回准确匹配的公司。
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返回格式必须是 JSON 数组，每个元素包含：
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- market: 市场代码（CH/HK/US/JP/VN），例如：腾讯是 HK，茅台是 CH，英伟达是 US。
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- symbol: 股票代码（如果是CH，通常是6位数字；HK是5位；US是字母）。
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- company_name: 公司简称。
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示例：
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[{{"market": "HK", "symbol": "00700", "company_name": "腾讯控股"}}]
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请直接返回 JSON，不要添加任何其他文字。最多返回5个结果。"""
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# 启用 Google Search Grounding
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grounding_tool = types.Tool(google_search=types.GoogleSearch())
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# 使用请求中的模型，默认为 gemini-2.5-flash
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model_name = request.model or "gemini-2.5-flash"
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logger.info(f"🤖 [搜索-LLM] 调用 {model_name} 进行股票搜索")
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llm_start = time.time()
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response = client.models.generate_content(
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model=model_name,
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contents=prompt,
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config=types.GenerateContentConfig(
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tools=[grounding_tool],
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temperature=0.1
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)
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)
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llm_elapsed = time.time() - llm_start
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usage = response.usage_metadata
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prompt_tokens = usage.prompt_token_count if usage else 0
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completion_tokens = usage.candidates_token_count if usage else 0
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total_tokens = prompt_tokens + completion_tokens
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logger.info(f"✅ [搜索-LLM] 模型响应完成, 耗时: {llm_elapsed:.2f}秒, Tokens: prompt={prompt_tokens}, completion={completion_tokens}, total={total_tokens}")
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# 解析响应
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text = response.text.strip()
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# 移除可能的 markdown 代码块标记
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if text.startswith("```json"):
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text = text[7:]
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if text.startswith("```"):
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text = text[3:]
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if text.endswith("```"):
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text = text[:-3]
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text = text.strip()
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results = json.loads(text)
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total_elapsed = time.time() - start_time
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logger.info(f"✅ [搜索] 搜索完成, 找到 {len(results)} 个结果, 总耗时: {total_elapsed:.2f}秒")
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return results
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except Exception as e:
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elapsed = time.time() - start_time
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logger.error(f"❌ [搜索] 搜索失败: {e}, 耗时: {elapsed:.2f}秒")
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print(f"Search error: {e}")
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# ----------------------------------------------------------------------
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# Chat Endpoint - Configured via app.api.chat_routes
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# ----------------------------------------------------------------------
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