The RAG-ChatGPT project demonstrates the implementation of Retrieval-Augmented Generation (RAG) using ChatGPT. This approach enhances the capabilities of language models by integrating external knowledge sources, enabling more accurate and contextually relevant responses.

• Retrieval-Augmented Generation (RAG): Combines retrieval mechanisms with generative models to provide informed responses.
• Integration with ChatGPT: Leverages the power of ChatGPT for natural language understanding and generation.
• Streamlit Interface: Offers an interactive web interface for user interaction.

Follow these steps to set up and run the project locally.

• Python 3.12.2: Ensure that Python is installed on your system.
• Required Libraries: Install the necessary Python packages.

1. Clone the Repository:

   git clone https://github.com/ravipriy/RAG-Chatgpt.git
   cd RAG-Chatgpt

2. Install Dependencies:

   Use the following command to install the required packages:

   pip install -r requirements.txt

3. Extract Chroma Database:

   • Locate the chroma_db.rar file in the repository.
   • Extract its contents into the same directory to set up the Chroma database.

   Note: The initial extraction may take some time.

1. Start the Streamlit Application:

   Execute the following command to launch the application:

   streamlit run app.py

2. Access the Interface:

   Open your web browser and navigate to http://localhost:8501 to interact with the application.

• User Queries: Input your questions or prompts into the interface.
• RAG Responses: The system retrieves relevant information and generates informed responses using ChatGPT.

Contributions are welcome! If you have suggestions or improvements, please fork the repository, create a new branch, and submit a pull request. Ensure that your contributions align with the project's objectives and maintain code quality.

Happy Coding!