GEMMA 7B with GROQ - Streamlit PDF to Vector Database API

Welcome to the GEMMA 7B with GROQ repository! This project showcases the integration of the GEMMA 7B language model with GROQ to process a fixed PDF document, convert its content into a vector database, and provide an interactive Q&A interface using Streamlit. Users can ask questions and receive answers, along with the relevant data, all through a user-friendly web application.

Overview

This repository provides a streamlined solution for converting a PDF document into a searchable vector database using the GROQ language and GEMMA 7B model. The application is built with Streamlit to enable an interactive question-and-answer interface. Users can explore the content of the PDF, ask questions, and view relevant data used to generate responses.

Features

• PDF Processing: Extract text from a fixed PDF document (default is a ViT research paper).
• Vectorization: Convert extracted text into vector representations using GEMMA 7B.
• Interactive Q&A: Ask questions and receive answers, along with the relevant data, through a Streamlit web app.
• Data Folder: Easily change the PDF document by placing a new file in the data folder.
• Environment Configuration: Manage API keys using a .env file.

Getting Started

Prerequisites

• Python 3.8 or higher
• Streamlit
• GROQ library
• GEMMA 7B model
• Required Python packages (listed in requirements.txt)
• GROQ and Google API keys

Installation

1. Clone the repository:

   git clone https://github.com/NevroHelios/gemma7b-groq-streamlit.git
   cd gemma7b-groq-streamlit

2. Install dependencies:

   pip install -r requirements.txt

3. Download and set up the GEMMA 7B model.

4. Obtain your GROQ and Google API keys and add them to a .env file in the root directory:

   GROQ_API_KEY=your_groq_api_key
   GOOGLE_API_KEY=your_google_api_key
   

Usage

1. Prepare the PDF:

   • Place your PDF file in the data directory (default file is a ViT research paper).

2. Run the Streamlit App:

   • Start the Streamlit app:

     streamlit run app.py

3. Interact with the App:

   • Open your browser and navigate to the provided local URL (e.g., http://localhost:8501).
   • Ask questions about the PDF content and view relevant data.

Directory Structure

• data/: Contains the PDF file to be processed. Default file is a ViT research paper.
• app.py: Main Streamlit application file.
• requirements.txt: List of required Python packages.
• .env: File to store API keys securely.

Contributing

We welcome contributions to enhance the functionality and performance of this project. Please fork the repository and submit a pull request with your changes. Ensure that your code follows the established style guidelines and passes all tests.

License

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

Acknowledgements

Special thanks to the developers and contributors of GEMMA 7B, GROQ, and Streamlit for their powerful tools and support.

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Feel free to explore, use, and contribute to this repository. If you have any questions or need assistance, please open an issue.

Happy coding!