Easily use and train state of the art late-interaction retrieval methods (ColBERT) in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.

Build a Streamlit Chatbot using Langchain, ColBERT, Ragatouille, and ChromaDB

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repo for using LLMs as a librarian to classify documents and extract type-specific metadata from them before filing them into an OpenSearch database and a vector store run by RAGatouille/Byaldi using a Flask server I wrote.

A repository dedicated for learning about RAG (Retrieval Augmented Generation)

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A set of tools to compare different RAG technologies

ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. This can be used as a potential alternative to Dense Embeddings in Retrieval Augmented Generation. In this video we explore using ColBERTv2 with RAGatouille and compare it with OpenAI Embedding models

Submission to the SingularityNET's Photrek Risk Assessment Hackathon

Messing around with RAGatouille library

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A RAG pipeline using ColBERT via RAGatouille

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Index-free document search and reranking with ColBERT late-interaction scoring via RAGatouille

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Code for ArXiv CS RAG Huggingface Space: Generate scientific paper abstract embeddings using ColBERTv2 and ask questions