RAGatouille, a simple lib to use&train top retrieval models in RAG apps
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, a simple lib to use&train top retrieval models in RAG apps launched on January 4, 2024 with 15 votes, #1,068 of 4,437 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
RAGatouille is a Python library that simplifies implementing advanced retrieval models in RAG applications. It focuses on making state-of-the-art information retrieval techniques accessible by integrating methods like ColBERT, which excel at retrieving relevant documents for language model context. The library targets developers building LLM applications who want to improve their RAG pipelines without the complexity of directly applying academic research. It bridges the gap between common retrieval practices and cutting-edge IR techniques, with plans to expand support for additional methods.
written from the facts on this page · September 2026
From the sources
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. - AnswerDotAI/RAGatouille
In the maker’s words, at launch
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…
Does a similar job
all alternatives →
Demystifying Advanced RAG Pipelines2023 · github.com · ▲131I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…
Ragas – Open-source library for evaluating RAG pipelines2024 · github.com · ▲121Ragas is an open-source library for evaluating and testing RAG and other LLM applications. Github: https://docs.ragas.io/en/stable/, docs: https://docs.ragas.io/. Ragas provides you with different sets of metrics and methods like synthetic test data generation to help you evaluate your RAG applications. Ragas started off by scratching our own itch for evaluating our RAG chatbots last year. Problems Ragas can solve - How do you choose the best components for your RAG, such as the retriever, reranker, and LLM? - How do you formulate a test dataset…
Turn any website into a knowledge base for LLMs2024 · embedding.io · ▲305I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
RAGLite – A Python package for the unhobbling of RAG2024 · github.com · ▲19RAGLite is a Python package for building Retrieval-Augmented Generation (RAG) applications. RAG applications can be magical when they work well, but anyone who has built one knows how much the output quality depends on the quality of retrieval and augmentation. With RAGLite, we set out to unhobble RAG by mapping out all of its subproblems and implementing the best solutions to those subproblems. For example, RAGLite solves the chunking problem by partitioning documents in provably optimal level 4 semantic chunks. Another unique contribution is its optimal closed-form linear query adapter…
Tilores Identity RAG2024 · tilores.io · ▲323Customer data search, unification and retrieval for LLMs
More ai this month
the category →
Paintings by AI models: programs run through a simulation of oil paint.
AI · 22d ago · stillwet.art
FastRouter.ai▲364Route requests to the right LLM for cost, latency & quality
AI · 23d ago · fastrouter.ai
Source: Show HN post ↗ · launch text quoted in part
Launched alongside, January 2024
the whole month →
Amie▲1,334Turn meeting notes into automated workflows. Handle meetings, summaries, todos and emails (soon) with your AI personal assistant.
Work · 2024 · amie.so
- IM
Sit in Shade - Discover the optimal seat to stay in the shade during your journey. Sit In Shade provides real-time sun exposure data and route visualization.
Life & fun · 2024 · sitinshade.com
