RAGatouille

Colbert is great! - Check out https://github.com/AnswerDotAI/RAGatouille by the excellent https://x.com/bclavie Relatedly ColPali (https://arxiv.org/abs/2407.01449) is gaining a ton of steam in the IR community. In general we are skeptical of silver bullets in retrieval.

Hey HN! If you’re at all interested in LLMs/LLM-apps, you’ve probably heard of RAG: Retrieval-Assisted Generation, i.e. retrieving relevant documents to give to your LLM as context to answer user queries. Today, I’m releasing RAGatouille v0.0.1, whose aim is to make it as easy as can be to improve your RAG pipelines by leveraging state-of-the-art Information Retrieval research. As of right now, there’s quite a big gap between common everyday practice and the IR literature, and a lot of the gap is because there just aren’t good ways to quickly try out and leverage SotA IR techniques. RAGatouille aims to contribute to that problem! We do have a bit of a roadmap to support more IR papers, like UDAPDR [1], but for now, we focus on integrating ColBERT[2]/ColBERTv2[3], super strong retrieval methods, who are particularly good at generalising to