GitHub - bclavie/RAGatouille: 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.

Ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the LLM’s context. There are existing tools and frameworks that help you build these pipelines but evaluating it and quantifying your pipeline performance can be hard. This is wh... See more

ColBERT is a

fast

and

accurate

retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds.

Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage.

As Figure 1 illustrates, ColBERT relies on fine-grained contextual late interaction : it encod... See more

Text embeddings are a critical piece of many pipelines, from search, to RAG, to vector databases and more. Most embedding models are BERT/Transformer-based and typically have short context lengths (e.g., 512). That’s only about two pages of text, but documents can be very long – books, legal cases, TV screenplays, code repositories, etc can be tens... See more