pip install langchain openai python-dotenv

OPENAI_API_KEY=your-api-key-here

from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from dotenv import load_dotenv
import os

# 加载环境变量
load_dotenv()

# 初始化聊天模型
chat = ChatOpenAI(temperature=0, model="gpt-3.5-turbo")

# 定义提示模板
prompt_template = ChatPromptTemplate.from_messages([
    ("system", "你是一个天气助手，根据用户提供的城市返回天气信息。"),
    ("human", "告诉我{city}当前的天气情况")
])

# 创建基础链
basic_chain = prompt_template | chat

from langchain.output_parsers import StructuredOutputParser, ResponseSchema
from langchain.schema import OutputParserException

# 定义响应模式
response_schemas = [
    ResponseSchema(name="city", description="查询的城市名称"),
    ResponseSchema(name="temperature", description="当前温度，单位摄氏度"),
    ResponseSchema(name="condition", description="天气状况描述"),
    ResponseSchema(name="confidence", description="回答的置信度，0-100")
]

output_parser = StructuredOutputParser.from_response_schemas(response_schemas)

# 更新提示模板以包含格式指令
format_instructions = output_parser.get_format_instructions()
prompt_template = ChatPromptTemplate.from_messages([
    ("system", f"你是一个天气助手，根据用户提供的城市返回天气信息。\n{format_instructions}"),
    ("human", "告诉我{city}当前的天气情况")
])

# 创建带解析器的链
parsing_chain = prompt_template | chat | output_parser

try:
    result = parsing_chain.invoke({"city": "北京"})
    print(result)
except OutputParserException as e:
    print(f"解析失败: {e}")

from typing import Dict, Any

def validate_weather_data(data: Dict[str, Any]) -> bool:
    """自定义天气数据验证函数"""

    # 检查必要字段是否存在
    required_fields = ['city', 'temperature', 'condition', 'confidence']
    if not all(field in data for field in required_fields):
        return False

    # 检查温度是否为数字且在合理范围内(-50到50)
    try:
        temp = float(data['temperature'])
        if not (-50 <= temp <= 50):
            return False
    except ValueError:
        return False

    # 检查置信度是否为整数且在0-100之间
    try:
        confidence = int(data['confidence'])
        if not (0 <= confidence <= 100):
            return False
    except ValueError:
        return False

    # 检查城市名称是否非空且长度合理(2-50字符)
    city = data['city'].strip()
    if not (2 <= len(city) <= 50):
        return False

    return True

# 使用验证函数处理结果
result = parsing_chain.invoke({"city": "北京"})
if validate_weather_data(result):
    print("数据验证通过:", result)
else:
    print("数据验证失败:", result)

from langchain.schema import StrOutputParser

verification_prompt = """
请分析以下天气信息是否合理。考虑以下因素：
1. 温度值是否在合理范围内(-50到50摄氏度)
2. 天气描述是否符合常识(如不会同时出现"晴天"和"暴雨")
3. {city}在这个季节通常的天气模式

原始信息: {original_info}

请用JSON格式返回你的分析结果，包含以下字段:
- "is_reasonable": (true/false)
- "issues": [列出发现的问题]
- "confidence": (0-100表示你对判断的信心)
"""

verification_chain = (
    {"original_info": lambda x: str(x), "city": lambda x: x["city"]}
    | ChatPromptTemplate.from_template(verification_prompt)
    | chat 
    | StrOutputParser()
)

def full_validation(city: str):
    """完整的验证流程"""

    # Step1:获取原始响应并解析为结构化数据


if validate_weather_data(result):
            print("初步数据格式验证通过")

            # Step3: LLM自洽性检查


print("\n完整示例运行结束")

def full_validation(city: str):
"""完整的验证流程"""

# Step1:获取原始响应并解析为结构化数据

try:
result=parsing_chain.invoke({"city": city})
print("原始响应:", result)

# Step2:自定义格式和逻辑验证

if validate_weather_data(result):  
print("初步数据格式验证通过")

# Step3: LLM自洽性检查

verification_result=verification_chain.invoke({
"original_info": result,
"city": city  
})

print("\n自洽性检查结果:")
print(verification_result)

return {
"original_data": result,
"validation_result": verification_result,
"is_valid": validate_weather_data(result)  
}

except OutputParserException as e:
print(f"\n错误：无法解析响应 - {e}")
return None  

except Exception as e:
print(f"\n发生意外错误 - {e}")
return None  

#运行完整示例  

if __name__=="__main__":
validation_result=full_validation("北京")  
if validation_result:

print("\n最终验证摘要:")
print(f"-城市:{validation_result['original_data']['city']}")
print(f"-温度:{validation_result['original_data']['temperature']}°C")  
print(f"-状态:{validation_result['original_data']['condition']}")
print(f"-置信度:{validation_result['original_data']['confidence']}%")  

print("\n完整示例运行结束")