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# Importing necessary libraries and modules
from langchain_core.tools.base import BaseTool
from dotenv import load_dotenv
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_community.tools import TavilySearchResults, DuckDuckGoSearchResults
from langchain_tavily import TavilySearch
import os
from pydantic import PrivateAttr
from langchain_community.document_loaders import WebBaseLoader
import json
import requests
load_dotenv(".env", override=True) # Loading environment variables
# Defining the WebSearchTool class which extends BaseTool
class WebSearchTool(BaseTool):
name: str = "web_search_tool"
description: str = "Perform a web search and extract concise factual answers. The query should be concise, below 400 characters. Use for online facts not in GAIA/Wikipedia—e.g. sports stats, Olympic participation, published papers, museum specimen locations, competition winners, and other up-to-date info."
_search: TavilySearch = PrivateAttr()
def __init__(self):
# Initializing the WebSearchTool
super().__init__()
self._search = TavilySearch(max_results=3, topic="general") # Setting up the TavilySearch with specific parameters
def _run(self, query: str) -> dict:
# Method to run the web search tool with the given query
search_results = [] # Initializing the list for search results
search_results.append(self._search.run(query)) # Performing the search and adding the results to the list
return search_results # Returning the search results
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