339 lines
12 KiB
Python
339 lines
12 KiB
Python
from __future__ import annotations
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## 单独运行的时候需要添加
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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import re
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import warnings
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from typing import Dict
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForChainRun,
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CallbackManagerForChainRun,
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)
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain.pydantic_v1 import Extra, root_validator
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from langchain.schema import BasePromptTemplate
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from langchain.schema.language_model import BaseLanguageModel
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import requests
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from typing import List, Any, Optional
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from datetime import datetime
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from langchain.prompts import PromptTemplate
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from server.agent import model_container
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from pydantic import BaseModel, Field
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## 使用和风天气API查询天气
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KEY = "1234567890wangweiwei"
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# key长这样，这里提供了示例的key，这个key没法使用，你需要自己去注册和风天气的账号，然后在这里填入你的key
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_PROMPT_TEMPLATE = """
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用户会提出一个关于天气的问题，你的目标是拆分出用户问题中的区，市 并按照我提供的工具回答。
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例如 用户提出的问题是: 上海浦东未来1小时天气情况？
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则 提取的市和区是: 上海 浦东
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如果用户提出的问题是: 上海未来1小时天气情况？
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则 提取的市和区是: 上海 None
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请注意以下内容:
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1. 如果你没有找到区的内容,则一定要使用 None 替代，否则程序无法运行
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2. 如果用户没有指定市 则直接返回缺少信息
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问题: ${{用户的问题}}
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你的回答格式应该按照下面的内容，请注意，格式内的```text 等标记都必须输出，这是我用来提取答案的标记。
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```text
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${{拆分的市和区，中间用空格隔开}}
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```
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... weathercheck(市 区)...
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```output
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${{提取后的答案}}
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```
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答案: ${{答案}}
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这是一个例子：
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问题: 上海浦东未来1小时天气情况？
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```text
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上海 浦东
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```
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...weathercheck(上海 浦东)...
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```output
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预报时间: 1小时后
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具体时间: 今天 18:00
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温度: 24°C
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天气: 多云
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风向: 西南风
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风速: 7级
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湿度: 88%
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降水概率: 16%
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Answer: 上海浦东一小时后的天气是多云。
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现在，这是我的问题：
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问题: {question}
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"""
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PROMPT = PromptTemplate(
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input_variables=["question"],
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template=_PROMPT_TEMPLATE,
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)
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def get_city_info(location, adm, key):
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base_url = 'https://geoapi.qweather.com/v2/city/lookup?'
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params = {'location': location, 'adm': adm, 'key': key}
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response = requests.get(base_url, params=params)
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data = response.json()
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return data
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def format_weather_data(data, place):
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hourly_forecast = data['hourly']
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formatted_data = f"\n 这是查询到的关于{place}未来24小时的天气信息: \n"
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for forecast in hourly_forecast:
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# 将预报时间转换为datetime对象
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forecast_time = datetime.strptime(forecast['fxTime'], '%Y-%m-%dT%H:%M%z')
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# 获取预报时间的时区
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forecast_tz = forecast_time.tzinfo
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# 获取当前时间（使用预报时间的时区）
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now = datetime.now(forecast_tz)
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# 计算预报日期与当前日期的差值
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days_diff = (forecast_time.date() - now.date()).days
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if days_diff == 0:
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forecast_date_str = '今天'
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elif days_diff == 1:
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forecast_date_str = '明天'
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elif days_diff == 2:
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forecast_date_str = '后天'
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else:
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forecast_date_str = str(days_diff) + '天后'
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forecast_time_str = forecast_date_str + ' ' + forecast_time.strftime('%H:%M')
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# 计算预报时间与当前时间的差值
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time_diff = forecast_time - now
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# 将差值转换为小时
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hours_diff = time_diff.total_seconds() // 3600
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if hours_diff < 1:
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hours_diff_str = '1小时后'
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elif hours_diff >= 24:
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# 如果超过24小时，转换为天数
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days_diff = hours_diff // 24
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hours_diff_str = str(int(days_diff)) + '天'
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else:
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hours_diff_str = str(int(hours_diff)) + '小时'
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# 将预报时间和当前时间的差值添加到输出中
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formatted_data += '预报时间: ' + forecast_time_str + ' 距离现在有: ' + hours_diff_str + '\n'
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formatted_data += '温度: ' + forecast['temp'] + '°C\n'
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formatted_data += '天气: ' + forecast['text'] + '\n'
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formatted_data += '风向: ' + forecast['windDir'] + '\n'
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formatted_data += '风速: ' + forecast['windSpeed'] + '级\n'
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formatted_data += '湿度: ' + forecast['humidity'] + '%\n'
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formatted_data += '降水概率: ' + forecast['pop'] + '%\n'
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# formatted_data += '降水量: ' + forecast['precip'] + 'mm\n'
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formatted_data += '\n'
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return formatted_data
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def get_weather(key, location_id, place):
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url = "https://devapi.qweather.com/v7/weather/24h?"
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params = {
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'location': location_id,
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'key': key,
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}
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response = requests.get(url, params=params)
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data = response.json()
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return format_weather_data(data, place)
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def split_query(query):
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parts = query.split()
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adm = parts[0]
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if len(parts) == 1:
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return adm, adm
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location = parts[1] if parts[1] != 'None' else adm
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return location, adm
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def weather(query):
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location, adm = split_query(query)
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key = KEY
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if key == "":
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return "请先在代码中填入和风天气API Key"
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try:
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city_info = get_city_info(location=location, adm=adm, key=key)
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location_id = city_info['location'][0]['id']
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place = adm + "市" + location + "区"
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weather_data = get_weather(key=key, location_id=location_id, place=place)
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return weather_data + "以上是查询到的天气信息，请你查收\n"
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except KeyError:
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try:
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city_info = get_city_info(location=adm, adm=adm, key=key)
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location_id = city_info['location'][0]['id']
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place = adm + "市"
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weather_data = get_weather(key=key, location_id=location_id, place=place)
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return weather_data + "重要提醒：用户提供的市和区中，区的信息不存在，或者出现错别字，因此该信息是关于市的天气，请你查收\n"
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except KeyError:
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return "输入的地区不存在，无法提供天气预报"
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class LLMWeatherChain(Chain):
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llm_chain: LLMChain
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llm: Optional[BaseLanguageModel] = None
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"""[Deprecated] LLM wrapper to use."""
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prompt: BasePromptTemplate = PROMPT
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"""[Deprecated] Prompt to use to translate to python if necessary."""
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input_key: str = "question" #: :meta private:
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output_key: str = "answer" #: :meta private:
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@root_validator(pre=True)
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def raise_deprecation(cls, values: Dict) -> Dict:
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if "llm" in values:
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warnings.warn(
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"Directly instantiating an LLMWeatherChain with an llm is deprecated. "
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"Please instantiate with llm_chain argument or using the from_llm "
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"class method."
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)
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if "llm_chain" not in values and values["llm"] is not None:
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prompt = values.get("prompt", PROMPT)
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values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
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return values
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@property
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def input_keys(self) -> List[str]:
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"""Expect input key.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Expect output key.
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:meta private:
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"""
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return [self.output_key]
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def _evaluate_expression(self, expression: str) -> str:
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try:
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output = weather(expression)
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except Exception as e:
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output = "输入的信息有误，请再次尝试"
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return output
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def _process_llm_result(
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self, llm_output: str, run_manager: CallbackManagerForChainRun
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) -> Dict[str, str]:
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run_manager.on_text(llm_output, color="green", verbose=self.verbose)
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llm_output = llm_output.strip()
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text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
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if text_match:
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expression = text_match.group(1)
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output = self._evaluate_expression(expression)
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run_manager.on_text("\nAnswer: ", verbose=self.verbose)
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run_manager.on_text(output, color="yellow", verbose=self.verbose)
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answer = "Answer: " + output
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elif llm_output.startswith("Answer:"):
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answer = llm_output
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elif "Answer:" in llm_output:
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answer = "Answer: " + llm_output.split("Answer:")[-1]
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else:
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return {self.output_key: f"输入的格式不对: {llm_output},应该输入 (市 区)的组合"}
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return {self.output_key: answer}
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async def _aprocess_llm_result(
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self,
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llm_output: str,
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run_manager: AsyncCallbackManagerForChainRun,
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) -> Dict[str, str]:
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await run_manager.on_text(llm_output, color="green", verbose=self.verbose)
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llm_output = llm_output.strip()
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text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
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if text_match:
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expression = text_match.group(1)
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output = self._evaluate_expression(expression)
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await run_manager.on_text("\nAnswer: ", verbose=self.verbose)
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await run_manager.on_text(output, color="yellow", verbose=self.verbose)
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answer = "Answer: " + output
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elif llm_output.startswith("Answer:"):
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answer = llm_output
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elif "Answer:" in llm_output:
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answer = "Answer: " + llm_output.split("Answer:")[-1]
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else:
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raise ValueError(f"unknown format from LLM: {llm_output}")
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return {self.output_key: answer}
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def _call(
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self,
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inputs: Dict[str, str],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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_run_manager.on_text(inputs[self.input_key])
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llm_output = self.llm_chain.predict(
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question=inputs[self.input_key],
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stop=["```output"],
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callbacks=_run_manager.get_child(),
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)
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return self._process_llm_result(llm_output, _run_manager)
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async def _acall(
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self,
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inputs: Dict[str, str],
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
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await _run_manager.on_text(inputs[self.input_key])
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llm_output = await self.llm_chain.apredict(
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question=inputs[self.input_key],
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stop=["```output"],
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callbacks=_run_manager.get_child(),
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)
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return await self._aprocess_llm_result(llm_output, _run_manager)
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@property
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def _chain_type(self) -> str:
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return "llm_weather_chain"
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@classmethod
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def from_llm(
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cls,
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llm: BaseLanguageModel,
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prompt: BasePromptTemplate = PROMPT,
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**kwargs: Any,
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) -> LLMWeatherChain:
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llm_chain = LLMChain(llm=llm, prompt=prompt)
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return cls(llm_chain=llm_chain, **kwargs)
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def weathercheck(query: str):
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model = model_container.MODEL
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llm_weather = LLMWeatherChain.from_llm(model, verbose=True, prompt=PROMPT)
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ans = llm_weather.run(query)
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return ans
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class WhetherSchema(BaseModel):
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location: str = Field(description="应该是一个地区的名称，用空格隔开，例如：上海 浦东，如果没有区的信息，可以只输入上海")
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if __name__ == '__main__':
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result = weathercheck("苏州姑苏区今晚热不热？")
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