# AnswerDotAI/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.

Repository: https://github.com/AnswerDotAI/RAGatouille
Canonical: https://ross.abutalabs.com/products/ragatouille
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2025-05-17T12:45:38+00:00

## Health v2 (maintenance only)
Score: 26/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 22, release rhythm 8, longevity 69
- inputs: {"age_days": 978, "days_push": 473, "days_rel": 568, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3953, forks 274 (observed 2026-08-28T04:08:30.460412+00:00)

## What it is
RAGatouille is a Python library that makes it easy to use and train state-of-the-art late-interaction retrieval models like ColBERT in any RAG pipeline. It wraps research-grade retrieval methods in a simple, modular API so developers can improve retrieval quality without deep IR expertise.

## Use cases
- use ColBERT for retrieval in a RAG pipeline
- train a custom late-interaction retriever on my own documents
- improve retrieval quality over dense embeddings like ada-002
- rerank search results with ColBERT
- build a multilingual retriever with limited training data
- index documents for semantic search with late-interaction models

## When to choose
- you want state-of-the-art retrieval quality in a RAG application
- dense embeddings underperform on your domain and you need better generalization
- you want to fine-tune a retriever with small amounts of data, including non-English
- you prefer a simple pip-installable Python API over running ColBERT tooling manually

## When to avoid
- you need a production-scale vector database with distributed indexing and filtering
- your stack is not Python
- you only need basic keyword search or simple dense embeddings
- you need a fully managed hosted retrieval service

## Facets
- artifact type: library
- maturity: active
- function: rag, search-engine, machine-learning, llm-inference, llm-training
- domain: large-language-models, machine-learning
- platform: python
- tags: colbert, late-interaction, retrieval, embeddings, reranking, information-retrieval, retrieval-augmented-generation, search, natural-language-processing

## Member repositories
- AnswerDotAI/RAGatouille (main) score 26

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:30.460412+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:24:30.675931+00:00, confidence not recorded.
  - readme: https://github.com/AnswerDotAI/RAGatouille (fetched 2026-08-28T04:08:30.460412+00:00, sha ac88a5491ccc)
  - registry_pypi: https://pypi.org/pypi/ragatouille/json (fetched 2026-08-29T09:18:03.912893+00:00, sha 83154aee7a32)
- Data as of 2026-08-30T08:39:29.467469+00:00.