356 lines
11 KiB
Python
356 lines
11 KiB
Python
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## 使用和风天气API查询天气
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from __future__ import annotations
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import sys
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import os
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from server.chat.utils import get_ChatOpenAI
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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 configs.model_config import LLM_MODEL, TEMPERATURE
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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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from datetime import datetime
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def format_weather_data(data):
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hourly_forecast = data['hourly']
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formatted_data = ''
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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)) + '天后'
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else:
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hours_diff_str = str(int(hours_diff)) + '小时后'
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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: ' + hours_diff_str + '\n'
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formatted_data += '具体时间: ' + forecast_time_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\n'
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return formatted_data
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def get_weather(key, location_id, time: str = "24"):
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if time:
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url = "https://devapi.qweather.com/v7/weather/" + time + "h?"
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else:
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time = "3" # 免费订阅只能查看3天的天气
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url = "https://devapi.qweather.com/v7/weather/" + time + "d?"
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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)
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def split_query(query):
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parts = query.split()
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location = parts[0] if parts[0] != 'None' else parts[1]
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adm = parts[1]
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time = parts[2]
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return location, adm, time
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def weather(query):
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location, adm, time = split_query(query)
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if time != "None" and int(time) > 24:
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return "只能查看24小时内的天气，无法回答"
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if time == "None":
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time = "24" # 免费的版本只能24小时内的天气
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key = "" # 和风天气API 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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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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weather_data = get_weather(key=key, location_id=location_id, time=time)
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return weather_data
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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
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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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# raise ValueError(f"错误: {expression}，输入的信息不对")
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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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raise ValueError(f"unknown format from LLM: {llm_output}")
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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,
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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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from langchain import PromptTemplate
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_PROMPT_TEMPLATE = """用户将会向您咨询天气问题，您不需要自己回答天气问题，而是将用户提问的信息提取出来区，市和时间三个元素后使用我为你编写好的工具进行查询并返回结果，格式为 区+市+时间 每个元素用空格隔开。如果缺少信息，则用 None 代替。
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问题: ${{用户的问题}}
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```text
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${{拆分的区，市和时间}}
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```
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... weather(query)...
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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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这是两个例子：
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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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浦东 上海 1
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```
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...weather(浦东 上海 1)...
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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:
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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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问题: 北京市朝阳区未来24小时天气如何？
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```text
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朝阳 北京 24
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```
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...weather(朝阳 北京 24)...
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```output
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预报时间: 23小时后
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具体时间: 明天 17:00
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温度: 26°C
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天气: 霾
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风向: 西南风
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风速: 11级
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湿度: 65%
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降水概率: 20%
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Answer:
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预报时间: 23小时后
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具体时间: 明天 17:00
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温度: 26°C
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天气: 霾
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风向: 西南风
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风速: 11级
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湿度: 65%
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降水概率: 20%
|
|||
|
|
|
|||
|
|
现在，这是我的问题：
|
|||
|
|
问题: {question}
|
|||
|
|
"""
|
|||
|
|
PROMPT = PromptTemplate(
|
|||
|
|
input_variables=["question"],
|
|||
|
|
template=_PROMPT_TEMPLATE,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def weathercheck(query: str):
|
|||
|
|
model = get_ChatOpenAI(
|
|||
|
|
streaming=False,
|
|||
|
|
model_name=LLM_MODEL,
|
|||
|
|
temperature=TEMPERATURE,
|
|||
|
|
)
|
|||
|
|
llm_weather = LLMWeatherChain.from_llm(model, verbose=True, prompt=PROMPT)
|
|||
|
|
ans = llm_weather.run(query)
|
|||
|
|
return ans
|
|||
|
|
|