优化意图识别API，移除同步意图识别器，改为使用异步意图识别器，更新相关逻辑以支持异步处理，增强错误处理和日志记录，同时更新请求和响应模型以适应新的API结构。

2025-07-07 17:51:10 +08:00
parent b9bff7f512
commit 1f3e97d081
7 changed files with 146 additions and 910 deletions
@@ -186,7 +186,6 @@ class DifyComparisonTester:
 要求
 1、分析待评估的回答与标准答案的匹配程度(包括内容、步骤、主体等)
 2、如果待评估的回答与标准答案在核心内容和关键信息(步骤)上一致，即使表达方式不同，也应判定为"正确"。
-3、只要大体描述一致，即使缺失了一些步骤，也应判定为"正确"。
 3、如果待评估的回答存在明显的错误信息，应判定为"错误"。
 4、请严格按json格式输出：
 {{
@@ -1,14 +1,14 @@
-from gevent import monkey
-monkey.patch_all()
-
-from flask import Flask, request, Response
 import os
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+from typing import Dict, List, Any, Optional
+import asyncio
+
 from dotenv import load_dotenv
 import json
 import time
-from gevent.lock import RLock
-
-
 import datetime
 import logging
 # 加载环境变量
@@ -16,7 +16,7 @@ load_dotenv()

 import sys
 sys.path.append(os.getcwd())
-from rag2_0.intent_recognition import IntentRecognizer
+from rag2_0.intent_recognition import AsyncIntentRecognizer


 logging.basicConfig(
@@ -31,32 +31,87 @@ logging.getLogger('openai').setLevel(logging.WARNING)

 logger = logging.getLogger(__name__)

-app = Flask(__name__)

-api_key = os.getenv("OPENAI_API_KEY")
-base_url = os.getenv("OPENAI_API_BASE")
-model_name = os.getenv("LLM_MODEL_NAME", "gpt-3.5-turbo")
-_instance = IntentRecognizer(api_key=api_key, base_url=base_url, model_name=model_name)

-@app.route('/intent_recognize', methods=['POST'])
-def intent_recognize():
+
+# 定义请求模型
+class IntentRecognizeRequest(BaseModel):
+    query: str
+    conversation_context: str = ""
+    chat_history: Optional[List] = None
+    previous_slots: str | Dict = None
+
+# 定义槽位填充响应模型
+class SlotFillingResponse(BaseModel):
+    is_complete: bool = False
+    missing_slots: Dict[str, Any] = Field(default_factory=dict)
+    filled_data: Dict[str, Any] = Field(default_factory=dict)
+
+class QueryExpandResponse(BaseModel):
+    all: List[str] = Field(default_factory=list)
+    step_back: Dict[str, Any] = Field(default_factory=Dict)
+    follow_up: Dict[str, Any] = Field(default_factory=Dict)
+    hyde: Dict[str, Any] = Field(default_factory=Dict)
+    multi_questions: Dict[str, Any] = Field(default_factory=Dict)
+
+# 定义响应模型
+class IntentRecognizeResponse(BaseModel):
+    source_query: str
+    source_query_keys: List[str]
+    vertical_classification: str
+    sub_classification: str
+    rewrite_query: str
+    keywords: List[Dict[str, str]] = Field(default_factory=list)
+    has_slot_filling: bool = False
+    slot_filling: SlotFillingResponse = Field(default_factory=SlotFillingResponse)
+    query_expand: QueryExpandResponse = Field(default_factory=QueryExpandResponse)
+
+# 创建FastAPI应用
+app = FastAPI(
+    title="意图识别服务",
+    description="基于LLM的意图识别和问题改写服务",
+    version="2.0"
+)
+
+# 添加CORS中间件
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 全局变量存储AsyncIntentRecognizer实例
+_instance = None
+
+# 应用启动事件
+@app.on_event("startup")
+async def startup_event():
+    global _instance
+    # 初始化AsyncIntentRecognizer实例
+    api_key = os.getenv("OPENAI_API_KEY")
+    base_url = os.getenv("OPENAI_API_BASE")
+    model_name = os.getenv("LLM_MODEL_NAME", "gpt-3.5-turbo")
+    _instance = await AsyncIntentRecognizer.create(api_key=api_key, base_url=base_url, model_name=model_name)
+    logger.info("AsyncIntentRecognizer初始化完成")
+
+@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")
+async def intent_recognize(request: IntentRecognizeRequest):
    try:
-        data = request.get_json(force=True)
-        query = data.get('query')
-        conversation_context = data.get('conversation_context', "")
-        chat_history = data.get('chat_history', None)
-        previous_slots = data.get('previous_slots', None)
-        if not query:
-            return Response(json.dumps({"error": "缺少query参数"}, ensure_ascii=False), content_type='application/json; charset=utf-8', status=400)
+        if not request.query:
+            raise HTTPException(status_code=400, detail="缺少query参数")
        
        start_time = time.time()
        
-        result = _instance.process_query(query=query, 
-                                          conversation_context=conversation_context, 
-                                          chat_history=chat_history, 
-                                          previous_slots=previous_slots,
-                                          use_jieba=False,
-                                          enable_query_expansion=True)
+        result = await _instance.process_query_async(
+            query=request.query, 
+            conversation_context=request.conversation_context, 
+            chat_history=request.chat_history, 
+            previous_slots=request.previous_slots,
+            use_jieba=False,
+            enable_query_expansion=True
+        )
        
        end_time = time.time()
        current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S %z")
@@ -67,41 +122,61 @@ def intent_recognize():
        
        # 提取关键词信息
        keywords = result["keywords"]
-        keywords_str = ""
+        keywords_list = []
        if keywords and keywords.get("terms"):
-            term_details = []
            for term in keywords["terms"]:
-                term_info = {
-                    "名称": term["name"],
-                }
-                term_details.append(term_info)
-            keywords_str = term_details
+                keywords_list.append({
+                    "名称": term["name"]
+                })
        
        # 提取槽位填充信息
        slot_filling = result.get("slot_filling", {})
        
-        response_result = {
-            "source_query": query,
-            "source_query_keys": result["query_keys"],
-            "vertical_classification": classification["vertical_classification"],
-            "sub_classification": classification["sub_classification"],
-            "rewrite_query": result["rewrite"]["rewrite"],
-            "keywords": keywords_str,
-            "has_slot_filling": len(slot_filling)!=0,
-            "slot_filling": {
-                "is_complete": slot_filling.get("is_complete", False),
-                "missing_slots": slot_filling.get("missing_slots", {}),
-                "filled_data": slot_filling.get("filled_data", {})
-            },
-            "query_expand": result["query_expand"]
-        }
-        return Response(json.dumps(response_result, ensure_ascii=False), content_type='application/json; charset=utf-8')
+        # 构建响应
+        response = IntentRecognizeResponse(
+            source_query=request.query,
+            source_query_keys=result["query_keys"],
+            vertical_classification=classification["vertical_classification"],
+            sub_classification=classification["sub_classification"],
+            rewrite_query=result["rewrite"]["rewrite"],
+            keywords=keywords_list,
+            has_slot_filling=len(slot_filling) != 0,
+            slot_filling=SlotFillingResponse(
+                is_complete=slot_filling.get("is_complete", False),
+                missing_slots=slot_filling.get("missing_slots", {}),
+                filled_data=slot_filling.get("filled_data", {})
+            ),
+            query_expand=QueryExpandResponse(
+                all=result["query_expand"]["all"],
+                step_back=result["query_expand"]["step_back"],
+                follow_up=result["query_expand"]["follow_up"],
+                hyde=result["query_expand"]["hyde"],
+                multi_questions=result["query_expand"]["multi_questions"]
+            )
+        )
+        
+        return response
+        
+    except HTTPException as e:
+        raise e
    except Exception as e:
-        logger.error(f"意图识别出错: {str(e)}",exc_info=True)
-        return Response(json.dumps({"error": str(e)}, ensure_ascii=False), content_type='application/json; charset=utf-8', status=500)
+        logger.error(f"意图识别出错: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+# 添加健康检查端点
+@app.get("/health", summary="健康检查")
+async def health_check():
+    return {"status": "ok"}

 if __name__ == "__main__":
-    # 开发环境使用Flask内置服务器
-    # 生产环境使用gunicorn支持高并发 uv run gunicorn -w 10 -k gevent -b 0.0.0.0:8001 rag2_0.dify.intent_recognition_api:app
-    # uv run gunicorn -w 10 -k gevent --preload -b 0.0.0.0:8001 rag2_0.dify.intent_recognition_api:app
-    app.run(host="0.0.0.0", port=8001, threaded=True) 
+    # 使用uvicorn启动服务
+    import uvicorn
+    uvicorn.run(
+        "rag2_0.dify.intent_recognition_api:app", 
+        host="0.0.0.0", 
+        port=8001, 
+        reload=False,  # 开发环境启用热重载
+        workers=1     # 生产环境可以增加worker数量
+    )
+    # 生产环境可以使用以下命令启动:
+    # uvicorn rag2_0.dify.intent_recognition_api:app --host 0.0.0.0 --port 8001 --workers 10