1、修改api文件位置
2、意图识别继承langfuse
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import os
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from fastapi import FastAPI, HTTPException, Request
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from fastapi.responses import JSONResponse
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel, Field, ConfigDict
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from typing import Dict, List, Any, Optional
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import asyncio
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from dotenv import load_dotenv
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import json
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import time
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import datetime
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import logging
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# 加载环境变量
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load_dotenv()
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import sys
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sys.path.append(os.getcwd())
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from rag2_0.intent_recognition import AsyncIntentRecognizer
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# 确保日志目录存在
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os.makedirs('data/logs', exist_ok=True)
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(process)d - %(thread)d - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.StreamHandler(),
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logging.FileHandler(f'data/logs/intent_recognition_{datetime.datetime.now().strftime("%Y%m%d")}.log', encoding='utf-8')
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]
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)
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logging.getLogger('httpx').setLevel(logging.WARNING)
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logging.getLogger('openai').setLevel(logging.WARNING)
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logger = logging.getLogger(__name__)
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# 定义请求模型
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class IntentRecognizeRequest(BaseModel):
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query: str
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enable_query_expansion: bool = True
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conversation_context: Dict = None
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chat_history: Optional[List] = None
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previous_slots: str | Dict = None
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# 定义槽位填充响应模型
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class SlotFillingResponse(BaseModel):
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is_complete: bool = False
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missing_slots: Dict[str, Any] = Field(default_factory=dict)
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filled_data: Dict[str, Any] = Field(default_factory=dict)
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class QueryExpandResponse(BaseModel):
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# 必须包含的all字段
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all: List[str] = Field(default_factory=list)
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model_config = ConfigDict(extra='allow')
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# 定义响应模型
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class IntentRecognizeResponse(BaseModel):
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source_query: str
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source_query_keys: List[str]
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vertical_classification: str
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sub_classification: str
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rewrite_query: str
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keywords: List[Dict[str, str]] = Field(default_factory=list)
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has_slot_filling: bool = False
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slot_filling: SlotFillingResponse = Field(default_factory=SlotFillingResponse)
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query_expand: QueryExpandResponse = Field(default_factory=QueryExpandResponse)
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dinge_qingdan_info: Dict[str, Any] = Field(default_factory=dict)
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# 创建FastAPI应用
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app = FastAPI(
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title="意图识别服务",
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description="基于LLM的意图识别和问题改写服务",
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version="2.0"
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)
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# 添加CORS中间件
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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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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# 全局变量存储AsyncIntentRecognizer实例
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_instance = None
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# 应用启动事件
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@app.on_event("startup")
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async def startup_event():
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global _instance
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_instance = await AsyncIntentRecognizer.create()
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logger.info("AsyncIntentRecognizer初始化完成")
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@app.post("/intent_recognize1")
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async def intent_recognize(request: Request):
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data = await request.json()
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print(data)
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return {"message": "success"}
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@app.post("/intent_recognize", response_model=IntentRecognizeResponse, summary="意图识别", description="识别用户查询的意图并进行问题改写")
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async def intent_recognize(request: IntentRecognizeRequest):
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try:
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if not request.query:
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raise HTTPException(status_code=400, detail="缺少query参数")
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enable_query_expansion = request.enable_query_expansion
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start_time = time.time()
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current_softname = request.conversation_context.get("current_softname", "")
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result = await _instance.process_query_async(
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query=request.query,
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conversation_context=request.conversation_context,
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chat_history=request.chat_history,
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previous_slots=request.previous_slots,
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use_jieba=True,
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enable_query_expansion=enable_query_expansion,
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cur_soft_name=current_softname
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)
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dinge_qingdan_info = result["dinge_qingdan_info"]
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end_time = time.time()
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current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S %z")
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logger.info(f"意图识别耗时: {end_time - start_time:.2f}秒")
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# 提取分类信息
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classification = result["classification"]
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# 提取关键词信息
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keywords = result["keywords"]
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keywords_list = []
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if keywords and keywords.get("terms"):
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for term in keywords["terms"]:
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keywords_list.append({
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"名称": term["name"]
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})
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# 提取槽位填充信息
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slot_filling = result.get("slot_filling", {})
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# 构建响应
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response = IntentRecognizeResponse(
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source_query=request.query,
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source_query_keys=result["query_keys"],
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vertical_classification=classification["vertical_classification"],
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sub_classification=classification["sub_classification"],
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rewrite_query=result["rewrite"]["rewrite"],
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keywords=keywords_list,
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has_slot_filling=len(slot_filling) != 0,
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slot_filling=SlotFillingResponse(
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is_complete=slot_filling.get("is_complete", False),
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missing_slots=slot_filling.get("missing_slots", {}),
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filled_data=slot_filling.get("filled_data", {})
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),
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query_expand=QueryExpandResponse(**result["query_expand"]),
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dinge_qingdan_info=dinge_qingdan_info
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)
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return response
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except HTTPException as e:
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raise e
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except Exception as e:
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logger.error(f"意图识别出错: {str(e)}", exc_info=True)
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raise HTTPException(status_code=500, detail=str(e))
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# 添加健康检查端点
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@app.get("/health", summary="健康检查")
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async def health_check():
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return {"status": "ok"}
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if __name__ == "__main__":
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# 使用uvicorn启动服务
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import uvicorn
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uvicorn.run(
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"rag2_0.dify.intent_recognition_api:app",
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host="0.0.0.0",
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port=9001,
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reload=True, # 开发环境启用热重载
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workers=1 # 生产环境可以增加worker数量
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)
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# 生产环境可以使用以下命令启动:
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# uvicorn rag2_0.dify.intent_recognition_api:app --host 0.0.0.0 --port 8001 --workers 10
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