A Coding Implementation to Build an Advanced Web Intelligence Agent with Tavily and Gemini AI

import os
import json
import asyncio
from typing import List, Dict, Any
from dataclasses import dataclass
from rich.console import Console
from rich.progress import track
from rich.panel import Panel
from rich.markdown import Markdown

from langchain_tavily import TavilyExtract
from langchain.chat_models import init_chat_model
from langgraph.prebuilt import create_react_agent

@dataclass
class WebIntelligence:
    """Web Intelligence Configuration"""
    tavily_key: str = os.getenv("TAVILY_API_KEY", "")
    google_key: str = os.getenv("GOOGLE_API_KEY", "")
    extract_depth: str = "advanced"
    max_urls: int = 10

@dataclass
class WebIntelligence:
    """Web Intelligence Configuration"""
    tavily_key: str = os.getenv("TAVILY_API_KEY", "")
    google_key: str = os.getenv("GOOGLE_API_KEY", "")
    extract_depth: str = "advanced"
    max_urls: int = 10
The WebIntelligence dataclass serves as a structured configuration container, holding API keys for Tavily and Google Gemini, and setting extraction parameters like extract_depth and the maximum number of URLs (max_urls). It simplifies the management and access of crucial settings, ensuring seamless integration and customization of web content extraction tasks within the intelligence agent.

class SmartWebAgent:
    """Intelligent Web Content Extraction & Analysis Agent"""
   
    def __init__(self, config: WebIntelligence):
        self.config = config
        self.console = Console()
        self._setup_environment()
        self._initialize_tools()
   
    def _setup_environment(self):
        """Setup API keys with interactive prompts"""
        if not self.config.tavily_key:
            self.config.tavily_key = input("
                            
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