mamamiyear
52d1bc5cf4
- 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
55 lines
2.1 KiB
Python
55 lines
2.1 KiB
Python
import json
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import logging
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from langchain_openai import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate
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from models.people import People
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class BaseAgent:
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def __init__(self):
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self.llm = ChatOpenAI(
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openai_api_key="56d82040-85c7-4701-8f87-734985e27909",
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openai_api_base="https://ark.cn-beijing.volces.com/api/v3",
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model_name="ep-20250722161445-n9lfq"
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)
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pass
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class ExtractPeopleAgent(BaseAgent):
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def __init__(self):
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super().__init__()
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self.prompt = ChatPromptTemplate.from_messages([
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(
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"system",
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"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"
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"姓名 name\n"
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"性别 gender\n"
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"年龄 age\n"
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"身高(cm) height\n"
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# "体重(kg) weight\n"
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"婚姻状况 marital_status\n"
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"择偶要求 match_requirement\n"
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"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"
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"除了上述基本信息，还有一个字段\n"
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"个人介绍 introduction\n"
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"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"
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"个人介绍的字典的 key 需要使用提炼好的中文。\n"
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),
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("human", "{input}")
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])
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def extract_people_info(self, text: str) -> People:
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"""从文本中提取个人信息"""
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prompt = self.prompt.format_prompt(input=text)
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response = self.llm.invoke(prompt)
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logging.info(f"llm response: {response.content}")
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try:
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return People.from_dict(json.loads(response.content))
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except json.JSONDecodeError:
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logging.error(f"Failed to parse JSON from LLM response: {response.content}")
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return None
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pass
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class SummaryPeopleAgent(BaseAgent):
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def __init__(self):
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super().__init__()
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pass |