बाहरी API को उपकरणों के रूप में एकीकृत करना

from langchain.tools import Tool

def get_current_time(city: str) -> str:
    """Gets the current time for a specified city."""
    # This would typically make an actual API call
    if city.lower() == "london":
        return "10:30 AM GMT"
    elif city.lower() == "new york":
        return "05:30 AM EST"
    else:
        return "Time not available for that city."

# This function will be wrapped into a Tool later.

import os
from langchain.tools import Tool
from langchain_openai import OpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain import hub

# Mock function for getting time (simulates API)
def get_current_time(city: str) -> str:
    """Gets the current time for a specified city."""
    if city.lower() == "london":
        return "10:30 AM GMT"
    elif city.lower() == "new york":
        return "05:30 AM EST"
    else:
        return "Time not available for that city."

# Create the LangChain Tool
time_tool = Tool(
    name="get_time",
    func=get_current_time,
    description="Useful for getting the current time for a given city. Input should be a city name (e.g., 'London')."
)

# List of tools available to the agent
tools = [time_tool]

# Initialize the LLM (ensure OPENAI_API_KEY is set)
llm = OpenAI(temperature=0)

# Pull the ReAct prompt template from LangChain Hub
prompt = hub.pull("hwchase17/react")

# Create the agent
agent = create_react_agent(llm, tools, prompt)

# Create the AgentExecutor to run the agent
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Invoke the agent with a query
response = agent_executor.invoke({"input": "What time is it in London?"})
print(response["output"])

import requests

def fetch_data_from_api(query: str) -> str:
    """Fetches data from a hypothetical external API."""
    try:
        # Example: calling a public API (e.g., a mock weather API)
        # Replace with your actual API endpoint and parameters
        api_url = f"https://api.example.com/data?q={query}" # Placeholder URL
        response = requests.get(api_url)
        response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
        data = response.json()
        return str(data) # Return relevant part of the data
    except requests.exceptions.RequestException as e:
        return f"Error fetching data: {e}"

# This function would then be wrapped in a LangChain Tool.

import requests
from langchain.tools import Tool

# Mock weather API function
def get_weather_for_city(city: str) -> str:
    """
    Fetches the current weather for a specified city.
    Uses a mock API for demonstration.
    """
    # In a real app, replace with an actual weather API call
    mock_weather_data = {
        "london": {"temperature": "15C", "conditions": "Cloudy"},
        "new york": {"temperature": "22C", "conditions": "Sunny"},
        "paris": {"temperature": "18C", "conditions": "Partly Cloudy"}
    }
    weather = mock_weather_data.get(city.lower())
    if weather:
        return f"The current weather in {city} is {weather['temperature']} and {weather['conditions']}."
    else:
        return f"Weather data not available for {city}."

# Create the LangChain Tool for weather
weather_tool = Tool(
    name="get_weather",
    func=get_weather_for_city,
    description="Useful for getting the current weather conditions (temperature, conditions) for a specific city. Input should be a city name (e.g., 'London')."
)

# This tool can now be added to an agent's tool list.

import os
from langchain.tools import Tool
from langchain_openai import OpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain import hub

# Mock weather API function (from previous scene)
def get_weather_for_city(city: str) -> str:
    """
    Fetches the current weather for a specified city.
    Uses a mock API for demonstration.
    """
    mock_weather_data = {
        "london": {"temperature": "15C", "conditions": "Cloudy"},
        "new york": {"temperature": "22C", "conditions": "Sunny"},
        "paris": {"temperature": "18C", "conditions": "Partly Cloudy"}
    }
    weather = mock_weather_data.get(city.lower())
    if weather:
        return f"The current weather in {city} is {weather['temperature']} and {weather['conditions']}."
    else:
        return f"Weather data not available for {city}."

# Create the LangChain Tool for weather
weather_tool = Tool(
    name="get_weather",
    func=get_weather_for_city,
    description="Useful for getting the current weather conditions (temperature, conditions) for a specific city. Input should be a city name (e.g., 'London')."
)

# List of tools available to the agent
tools = [weather_tool]

# Initialize the LLM (ensure OPENAI_API_KEY is set)
llm = OpenAI(temperature=0)

# Pull the ReAct prompt template from LangChain Hub
prompt = hub.pull("hwchase17/react")

# Create the agent
agent = create_react_agent(llm, tools, prompt)

# Create the AgentExecutor to run the agent
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Invoke the agent with a query
response = agent_executor.invoke({"input": "What's the weather like in Paris?"})
print(response["output"])