fix: agent recognize data type of age and height for people wrong

src/agents/extract_people_agent.py

@@ -1,4 +1,5 @@
 
+import datetime
 import json
 import logging
 from langchain.prompts import ChatPromptTemplate
@@ -12,6 +13,7 @@ class ExtractPeopleAgent(BaseAgent):
         self.prompt = ChatPromptTemplate.from_messages([
             (
                 "system",
+                f"现在是{datetime.datetime.now().strftime('%Y-%m-%d')}，"
                 "你是一个专业的婚姻、交友助手，善于从一段文字描述中，精确获取用户的以下信息：\n"
                 "姓名 name\n"
                 "性别 gender\n"
@@ -20,6 +22,8 @@ class ExtractPeopleAgent(BaseAgent):
                 "婚姻状况 marital_status\n"
                 "择偶要求 match_requirement\n"
                 "以上信息需要严格按照 JSON 格式输出 字段名与条目中英文保持一致。\n"
+                "其中，'年龄 age' 和 '身高(cm) height' 必须是一个整数，不能是一个字符串；\n"
+                "并且，'性别 gender' 根据识别结果，必须从 男,女,未知 三选一填写。\n"
                 "除了上述基本信息，还有一个字段\n"
                 "个人介绍 introduction\n"
                 "其余的信息需要按照字典的方式进行提炼和总结，都放在个人介绍字段中\n"
@@ -34,8 +38,15 @@ class ExtractPeopleAgent(BaseAgent):
         response = self.llm.invoke(prompt)
         logging.info(f"llm response: {response.content}")
         try:
-            return People.from_dict(json.loads(response.content))
+            people = People.from_dict(json.loads(response.content))
+            err = people.validate()
+            if not err.success:
+                raise ValueError(f"Failed to validate people info: {err.info}")
+            return people
         except json.JSONDecodeError:
             logging.error(f"Failed to parse JSON from LLM response: {response.content}")
             return None
+        except ValueError as e:
+            logging.error(f"Failed to validate people info: {e}")
+            return None
     pass

src/models/people.py

@@ -2,9 +2,11 @@
 # created by mmmy on 2025-09-30
 
 import json
+import logging
 from typing import Dict
 from sqlalchemy import Column, Integer, String, Text, DateTime, func
 from utils.rldb import RLDBBaseModel
+from utils.error import ErrorCode, error
 
 class PeopleRLDBModel(RLDBBaseModel):
     __tablename__ = 'peoples'
@@ -129,4 +131,20 @@ class People:
             introduction=json.dumps(self.introduction, ensure_ascii=False),
             comments=json.dumps(self.comments, ensure_ascii=False),
             cover=self.cover,
-        )
+        )
+    
+    def validate(self) -> error:
+        err = error(ErrorCode.SUCCESS, "")
+        if not self.name:
+            logging.error("Name is required")
+            err = error(ErrorCode.MODEL_ERROR, "Name is required")
+        if not self.gender in ['男', '女', '未知']:
+            logging.error("Gender must be '男', '女', or '未知'")
+            err = error(ErrorCode.MODEL_ERROR, "Gender must be '男', '女', or '未知'")
+        if not isinstance(self.age, int) or self.age <= 0:
+            logging.error("Age must be an integer and greater than 0")
+            err = error(ErrorCode.MODEL_ERROR, "Age must be an integer and greater than 0")
+        if not isinstance(self.height, int) or self.height <= 0:
+            logging.error("Height must be an integer and greater than 0")
+            err = error(ErrorCode.MODEL_ERROR, "Height must be an integer and greater than 0")
+        return err

src/utils/error.py

@@ -1,14 +1,20 @@
 
+from enum import Enum
+import logging
 from typing import Protocol
 
+class ErrorCode(Enum):
+    SUCCESS = 0
+    MODEL_ERROR = 1000
 
 class error(Protocol):
     _error_code: int = 0
     _error_info: str = ""
 
-    def __init__(self, error_code: int, error_info: str):
+    def __init__(self, error_code: ErrorCode, error_info: str):
-        self._error_code = error_code
+        self._error_code = int(error_code.value)
         self._error_info = error_info
+        logging.info(f"errorcode: {type(self._error_code)}")
     def __str__(self) -> str:
         return f"{self.__class__.__name__}({self._error_code}, {self._error_info})"