LangChain Search Engine Chat

A powerful search engine chat application built with LangChain, Streamlit, and Groq. This application combines multiple search tools (Arxiv, Wikipedia, and DuckDuckGo) to provide comprehensive answers to your questions.

🌟 Features

• Real-time chat interface using Streamlit
• Integration with multiple search tools:
  • Arxiv for academic papers
  • Wikipedia for general knowledge
  • DuckDuckGo for web search
• Powered by Groq's Llama3-8b-8192 model
• Streaming responses with real-time agent thoughts
• Secure API key management
• Beautiful and intuitive UI

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/Search_Engine_LangChain.git
cd Search_Engine_LangChain

2. Install the required packages:

pip install -r requirements.txt

3. Create a .env file in the root directory and add your API keys:

GROQ_API_KEY="your_groq_api_key"
LANGCHAIN_API_KEY="your_langchain_api_key"
LANGCHAIN_PROJECT="your_project_name"

Running the Application

1. Start the Streamlit app:

streamlit run app.py

2. Open your web browser and navigate to the URL shown in the terminal (typically http://localhost:8501)

💡 Usage

1. Enter your question in the chat input field
2. The application will:
   • Search across multiple sources (Arxiv, Wikipedia, DuckDuckGo)
   • Process the information using the Groq LLM
   • Display the results in real-time
   • Show the agent's thought process

🛠️ Built With

• Streamlit - For the web interface
• LangChain - For building the search agent
• Groq - For the LLM backend
• Arxiv - For academic paper search
• Wikipedia - For general knowledge
• DuckDuckGo - For web search

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

👤 Author

Vedanth Nayak

• GitHub: @vedanthnyk25

🙏 Acknowledgments

• Thanks to the LangChain team for their excellent framework
• Groq for providing the LLM capabilities
• All the open-source tools and libraries used in this project