🔎 AI-Powered Search & Chat Agent

This project implements an AI-powered search and chat agent using LangChain, LangGraph, and Tavily Search API. The agent can retrieve real-time search results and engage in interactive conversations with users, providing a seamless and dynamic experience.

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🚀 Features

• 🔍 Search Engine Integration: Uses Tavily API to fetch real-time web search results.
• 💬 Conversational AI: Chat with an LLM-powered agent that remembers the conversation.
• ⏳ Streaming Responses: Real-time token streaming for a more engaging chat experience.
• 🧠 Memory Handling: Retains conversation history for contextual understanding.
• 📊 LangSmith Tracing: Debugging and tracing using LangSmith (optional).
• 🔧 Tool Integration: Extensible architecture for adding more tools.

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🛠️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/your-username/your-repository.git
cd your-repository

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Set Up Environment Variables

Create a .env file and add your API keys:

LANGSMITH_TRACING=true  # Set to false if debugging is not needed
LANGSMITH_API_KEY=your-langsmith-api-key
TAVILY_API_KEY=your-tavily-api-key
OPENAI_API_KEY=your-openai-api-key

4️⃣ Run the Chatbot

python SearchEngineAgent.py

⚡ Usage

	1.	Start the script and enter your search query.
	2.	The agent fetches search results and returns real-time data.
	3.	Engage in a conversation with the LLM-powered assistant.
	4.	Chat history is stored, and you can ask about previous searches.
	5.	Type "exit" to terminate the session.

📂 Project Structure

📂 project-folder
│-- SearchEngineAgent.py  # Main script
│-- .env                  # Environment variables (ignored in Git)
│-- requirements.txt       # Python dependencies
│-- README.md              # This file

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🛠️ Built With

• LangChain - Framework for building LLM applications.
• LangGraph - Agent execution framework.
• Tavily Search - Real-time search API.
• OpenAI API - LLM-powered chat agent.
• Python - Main programming language.

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🔮 Future Improvements

• Enhanced Search Capabilities (support for multiple sources).
• Integration with More LLMs (GPT-4, Claude, etc.).
• Custom User Profiles & Memory Handling.
• Web-Based Frontend Interface.