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import pandas as pd
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from langchain.chat_models import ChatOpenAI
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from langchain.agents import initialize_agent, AgentType, AgentOutputParser
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from typing import List, Union
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from langchain.schema import AgentAction, AgentFinish, OutputParserException
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from langchain.tools.base import StructuredTool
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from typing import Optional
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import requests
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import telebot
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OPENAI_API = "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"
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BOT_KEY = '6415742729:AAHVyDkHHF57ZsVd9gJjVtXjKE2M9CydzPk'
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WELCOME_MSG = """"
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Привет! ✨ Мы K-Lab, Команда, занимающаяся разработкой роботов 🤖 и машинным обучением и мы рады видеть тебя в нашей системе управления метеоданными!
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Спроси что-нибудь у нашего бота 🙂
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"""
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# Weather AGW
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AGW_PORT = 8045
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AGW_HOST = 'localhost'
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AGW_URL = f"http://{AGW_HOST}:{AGW_PORT}/"
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bot = telebot.TeleBot(BOT_KEY)
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def fetch_get_sensors(params={}):
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try:
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response = requests.post(AGW_URL + 'api/v1/sensors/get-with-params', json=params)
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response.raise_for_status()
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data = response.json()
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return data
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except requests.exceptions.RequestException as e:
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print('Error fetching data:', e)
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return None
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def fetch_get_agregator(params={}):
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try:
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response = requests.post(AGW_URL + 'api/v1/agregator/get-with-params', json=params)
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response.raise_for_status()
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data = response.json()
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return data
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except requests.exceptions.RequestException as e:
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print('Error fetching data:', e)
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return None
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def fetch_get_weather_data(params={}):
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try:
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response = requests.get(AGW_URL + 'api/v1/measures/get-for-ai')
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response.raise_for_status()
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data = response.json()
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return data
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except requests.exceptions.RequestException as e:
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print('Error fetching data:', e)
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return None
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def get_sensors_insight() -> str:
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"""Tool that using for get meteo weather sensors"""
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data = fetch_get_sensors()
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return data
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def get_agregators_insight() -> str:
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"""Tool that using for get meteo weather sensors"""
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data = fetch_get_agregator()
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return data
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def get_weather_data_history_insight() -> str:
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"""Tool that using for get meteo weather history from sensors using in the meteo system"""
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data = fetch_get_weather_data()
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return data
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sensors_insights_tool = StructuredTool.from_function(get_sensors_insight)
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agregators_insights_tool = StructuredTool.from_function(get_agregators_insight)
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weather_history_insights_tool = StructuredTool.from_function(get_weather_data_history_insight)
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chat = ChatOpenAI(model_name="gpt-3.5-turbo-16k", temperature=0.2, openai_api_key=OPENAI_API)
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tools = [sensors_insights_tool, agregators_insights_tool, weather_history_insights_tool]
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class CustomOutputParser(AgentOutputParser):
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def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
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# Check if agent should finish
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if "Final Answer:" in llm_output:
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final_answer = llm_output.split("Final Answer:")[-1].strip()
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print("final is - " + final_answer)
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return AgentFinish(
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return_values={"output": final_answer},
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log=llm_output,
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)
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# Parse out the action and action input
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regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
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match = re.search(regex, llm_output, re.DOTALL)
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if not match:
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raise ValueError(f"Could not parse LLM output: `{llm_output}`")
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action = match.group(1).strip()
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action_input = match.group(2)
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# Return the action and action input
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return AgentAction(
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tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output
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)
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output_parser = CustomOutputParser()
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agent_chain = initialize_agent(
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tools,
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chat,
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max_iterations=3,
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agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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verbose=True,
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output_parser=output_parser
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)
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# print(get_weather_data_history_insight())
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@bot.message_handler(commands=['start', 'hello'])
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def send_welcome(message):
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bot.reply_to(message, WELCOME_MSG)
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@bot.message_handler(func=lambda msg: True)
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def echo_all(message):
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user_id = message.from_user.id
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print(message.text)
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bot.reply_to(message, "AI думает... 🤔")
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result = agent_chain(message.text)
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if (result):
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final_answer = result['output']
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print(final_answer)
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bot.reply_to(message, str(final_answer))
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bot.infinity_polling()
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