RAG-Chatbot-using-TavilySearch

A Retrieval-Augmented Generation (RAG) chatbot built using TavilySearch and LangChain, designed to respond to user queries with precise, well-structured answers in Markdown format. The chatbot leverages the TavilySearch tool to retrieve relevant web-based information and presents it via a Streamlit application.

Features

• Integration with TavilySearch: Uses TavilySearchResults for retrieving web-based data to enhance responses.
• Markdown Formatting: Outputs responses in clean Markdown format with headings, lists, tables, and code blocks for better readability.
• Streamlit Interface: Provides a user-friendly web interface for asking queries and viewing responses.
• Tool-Assisted Responses: Employs LangChain’s tool-calling agents for dynamic query resolution.

Technologies Used

• Python
• Streamlit: For creating the web-based user interface.
• LangChain: Framework for building tool-augmented chatbots.
• TavilySearchResults: Tool for retrieving web search results.
• ChatGroq: AI model for generating conversational responses.
• dotenv: For managing environment variables.

Installation

Follow these steps to set up the project locally:

1. Clone the repository:

   git clone https://github.com/Osama-Abo-Bakr/RAG-Chatbot-using-TavilySearch.git
   cd RAG-Chatbot-using-TavilySearch

2. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install the required dependencies:

   pip install -r requirements.txt

4. Create a .env file and set up your environment variables. For example:

   OPENAI_API_KEY=your_openai_api_key
   TAVILY_API_KEY=your_tavily_api_key
   

Usage

1. Run the application:

   streamlit run app.py

2. Open your web browser and navigate to http://localhost:8501.

3. Enter your query in the chat input box and receive well-structured responses.

Code Overview

response_to_json(user_query)

This function:

• Integrates the TavilySearchResults tool for web searches.
• Uses LangChain to create a tool-calling agent with a custom prompt.
• Executes the agent to generate responses based on user input.

main()

This function:

• Loads environment variables.
• Configures the Streamlit interface.
• Handles user queries and displays both user input and the chatbot’s responses.

Example Query

1. Input: What are the benefits of machine learning?

2. Output:

   # Benefits of Machine Learning
   
   - Automates repetitive tasks.
   - Enhances decision-making processes.
   - Improves efficiency in various industries.
   

Contributing

Contributions are welcome! Please fork the repository and create a pull request for any improvements or bug fixes.

License

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

Contact

For questions or support, feel free to reach out:

• GitHub: Osama-Abo-Bakr

---

Enjoy building and exploring with the RAG-Chatbot!