imanoop7 / rag_with_colbert

RAG with ColBERT

Welcome to the RAG with ColBERT repository, where we combine the power of Retriever-Augmented Generation (RAG) with the efficiency of ColBERT for advanced search capabilities in large text collections.

Overview

ColBERT is a state-of-the-art retrieval model that leverages BERT's deep understanding of language to provide fast and accurate search results. By integrating ColBERT with RAG, we aim to enhance the retrieval process, enabling the generation of more relevant and context-aware responses.

Requirements

• Python 3.8+
• PyTorch 1.8+
• Transformers 4.5+
• Gemini Model

ColBERT

The ColBERT model is known for its ability to perform scalable BERT-based search over large text collections in just tens of milliseconds, making it an ideal choice for real-time applications.

Notebook

RAG_with_colbert.ipynb is a Jupyter notebook that provides a step-by-step guide to setting up and using the RAG with ColBERT model. It includes instructions for data preprocessing, model configuration, and usage examples.

Setup

To get started with RAG with ColBERT, follow these steps:

1. Clone the repository:

   git clone https://github.com/imanoop7/RAG_with_Colbert.git

2. Install the required dependencies:

   pip install -r requirements.txt

3. Launch the notebook

Usage

Once you have set up the model, you can use it to perform efficient and accurate searches across your text datasets, leveraging the combined strengths of RAG and ColBERT.

Contributing

We welcome contributions to improve the RAG with ColBERT integration. Feel free to fork the repository, make your changes, and submit a pull request.

License

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

Acknowledgments

• The ColBERT team for their innovative approach to scalable search.