多轮问询优化

api/mian.py

@@ -201,45 +201,74 @@ def agent():
             # 再进行槽位抽取
             entities = slot_recognizer.recognize(query)
 
-            print(f"意图识别后的label:{predicted_label}, id:{predicted_id},槽位抽取后的实体：{entities},message:{messages}")
+            print(f"第一轮意图识别后的label:{predicted_label}, id:{predicted_id},槽位抽取后的实体：{entities},message:{messages}")
-
-            #必须槽位缺失检查
-            status, sk = check_lost(predicted_id, entities)
-            if status == CheckResult.NEEDS_MORE_ROUNDS:
-                return jsonify({"code": 10001, "msg": "成功",
-                                "answer": { "miss": sk},
-                                })
-
-            #工程名和项目名标准化
-            print(f"start to check_project_standard_slot")
-            result, information = check_project_standard_slot(predicted_id, entities)
-            print(f"end  check_project_standard_slot,{result},{information}")
-            if result == CheckResult.NEEDS_MORE_ROUNDS:
-                return jsonify({
-                    "code": 10001, "msg": "成功",
-                    "answer": {"miss": information},
-                })
-
-            return jsonify({
-                "code": 200,"msg": "成功",
-                "answer": {"int": predicted_id, "label": predicted_label, "probability": predicted_probability, "slot": entities },
-            })
 
         # 如果是后续轮次（多轮对话），这里只做示例，可能需要根据具体需求进行处理
         else:
             query = messages[0].content  # 使用 Message 对象的 .content 属性
+            # 先进行意图识别
+            predicted_label, predicted_probability, predicted_id = intent_recognizer.predict(query)
+            entities = multi_slot_recognizer(predicted_id, messages)
+
+            print(f"多轮意图识别后的label:{predicted_label}, id:{predicted_id},槽位抽取后的实体：{entities},message:{messages}")
+
+        #必须槽位缺失检查
+        status, sk = check_lost(predicted_id, entities)
+        if status == CheckResult.NEEDS_MORE_ROUNDS:
+            return jsonify({"code": 10001, "msg": "成功",
+                            "answer": { "miss": sk},
+                            })
+
+        #工程名和项目名标准化
+        print(f"start to check_project_standard_slot")
+        result, information = check_project_standard_slot(predicted_id, entities)
+        print(f"end  check_project_standard_slot,{result},{information}")
+        if result == CheckResult.NEEDS_MORE_ROUNDS:
             return jsonify({
-                "user_id": user_id,
+                "code": 10001, "msg": "成功",
-                "query": query,
+                "answer": {"miss": information},
-                "message_count": len(messages)
             })
 
+        return jsonify({
+            "code": 200,"msg": "成功",
+            "answer": {"int": predicted_id, "label": predicted_label, "probability": predicted_probability, "slot": entities },
+        })
+
     except ValidationError as e:
         return jsonify({"error": e.errors()}), 400  # 捕捉 Pydantic 错误并返回
     except Exception as e:
         return jsonify({"error": str(e)}), 500  # 捕捉其他错误并返回
 
 
+def multi_slot_recognizer(intention_id, messages):
+    from openai import OpenAI
+    final_slot = {}
+    api_base_url = "http://36.33.26.201:27861/v1"
+    api_key = 'EMPTY'
+    model_name = 'qwen2.5-instruct'
+    client = OpenAI(base_url = api_base_url, api_key = api_key)
+
+    prompt = f''' 
+    根据用户的输入{messages}，抽取出用户想了解的问题，要求：保持客观真实，简单明了，不要多余解释和阐述
+    '''
+
+    message = [{"role": "system", "content": prompt}]
+    message.extend(messages)
+    # print(message)
+    response = client.chat.completions.create(
+        messages=message,
+        model=model_name,
+        max_tokens=1000,
+        temperature=0.001,
+        stream=False
+    )
+    res = response.choices[0].message.content
+
+    print(f"多轮意图后用户想要的问题是{res}")
+    entries = slot_recognizer.recognize(res)
+
+    return entries
+
 def check_lost(int_res, slot):
     #labels: ["天气查询","互联网查询","页面切换","日计划数量查询","周计划数量查询","日计划作业内容","周计划作业内容","施工人数","作业考勤人数","知识问答"]
     mapping = {