ouyangyouzhang
d155565ef6
2、更新poetry.lock以反映Poetry版本的变化，添加jieba依赖， 3、重构意图识别逻辑以支持多轮对话，优化槽位填充和意图分类功能，增强代码可读性和维护性。
97 lines
3.9 KiB
Python
97 lines
3.9 KiB
Python
from flask import Flask, request, Response
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import os
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from dotenv import load_dotenv
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from rag2_0.intent_recognition import IntentRecognizer
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import json
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import time
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import threading
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import datetime
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# 加载环境变量
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load_dotenv()
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app = Flask(__name__)
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# 创建线程锁，用于保护共享资源
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recognizer_lock = threading.Lock()
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# 使用单例模式创建意图识别器
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class RecognizerSingleton:
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_instance = None
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_lock = threading.Lock()
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@classmethod
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def get_instance(cls):
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if cls._instance is None:
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with cls._lock:
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if cls._instance is None:
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api_key = os.getenv("OPENAI_API_KEY")
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base_url = os.getenv("OPENAI_API_BASE")
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model_name = os.getenv("LLM_MODEL_NAME", "gpt-3.5-turbo")
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cls._instance = IntentRecognizer(api_key=api_key, base_url=base_url, model_name=model_name)
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return cls._instance
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@app.route('/intent_recognize', methods=['POST'])
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def intent_recognize():
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try:
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data = request.get_json(force=True)
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query = data.get('query')
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conversation_context = data.get('conversation_context', "")
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chat_history = data.get('chat_history', None)
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previous_slots = data.get('previous_slots', None)
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if not query:
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return Response(json.dumps({"error": "缺少query参数"}, ensure_ascii=False), content_type='application/json; charset=utf-8', status=400)
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start_time = time.time()
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# 获取单例实例并使用线程锁保护关键操作
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recognizer = RecognizerSingleton.get_instance()
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result = recognizer.process_query(query, conversation_context, chat_history, previous_slots)
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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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print(f"[{current_time}] [{os.getpid()}] [INFO] 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_str = ""
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if keywords and keywords.get("terms"):
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term_details = []
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for term in keywords["terms"]:
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term_info = {
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"名称": term["name"],
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# "同义词": ";".join(term["synonymous"]) if term["synonymous"] else "",
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# "描述": term["description"]
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}
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term_details.append(term_info)
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keywords_str = term_details
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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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response_result = {
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"source_query": 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_str,
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"has_slot_filling": len(slot_filling)!=0,
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"slot_filling": {
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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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}
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return Response(json.dumps(response_result, ensure_ascii=False), content_type='application/json; charset=utf-8')
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except Exception as e:
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print(f"意图识别出错: {str(e)}")
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return Response(json.dumps({"error": str(e)}, ensure_ascii=False), content_type='application/json; charset=utf-8', status=500)
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if __name__ == "__main__":
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# 开发环境使用Flask内置服务器
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# 生产环境使用gunicorn支持高并发 poetry run gunicorn -w 10 -k gevent -b 0.0.0.0:8001 rag2_0.dify.intent_recognition_api:app
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app.run(host="0.0.0.0", port=8001, threaded=True) |