feat: basic function for management of people

- recognize people info by input text or image
- create a people and save into relational database and vector database and object storage
- delete a people by people id
- get peoples with pagination and fitlers by gender, age and height
- get peoples with topN and searching by nature language

src/ai/agent.py

@@ -0,0 +1,55 @@
+import json
+import logging
+from langchain_openai import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+
+from models.people import People
+
+class BaseAgent:
+    def __init__(self):
+        self.llm = ChatOpenAI(
+            openai_api_key="56d82040-85c7-4701-8f87-734985e27909",
+            openai_api_base="https://ark.cn-beijing.volces.com/api/v3",
+            model_name="ep-20250722161445-n9lfq"
+        )
+    pass
+
+class ExtractPeopleAgent(BaseAgent):
+    def __init__(self):
+        super().__init__()
+        self.prompt = ChatPromptTemplate.from_messages([
+            (
+                "system",
+                "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"
+                "年龄 age\n"
+                "身高(cm) height\n"
+                # "体重(kg) weight\n"
+                "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"
+                "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"
+                "个人介绍的字典的 key 需要使用提炼好的中文。\n"
+            ),
+            ("human", "{input}")
+        ])
+    
+    def extract_people_info(self, text: str) -> People:
+        """从文本中提取个人信息"""
+        prompt = self.prompt.format_prompt(input=text)
+        response = self.llm.invoke(prompt)
+        logging.info(f"llm response: {response.content}")
+        try:
+            return People.from_dict(json.loads(response.content))
+        except json.JSONDecodeError:
+            logging.error(f"Failed to parse JSON from LLM response: {response.content}")
+            return None
+    pass
+
+class SummaryPeopleAgent(BaseAgent):
+    def __init__(self):
+        super().__init__()
+    pass