# Danielskry/Awesome-RAG

😎 Awesome list of Retrieval-Augmented Generation (RAG) applications in Generative AI.

Repository: https://github.com/Danielskry/Awesome-RAG
Canonical: https://ross.abutalabs.com/products/awesome-rag
License: CC0-1.0
License Family: permissive
Topics: artificial-intelligence, generative-ai, large-language-models, retrieval-augmented-generation, rag
Last push: 2026-07-09T14:58:23+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 91, release rhythm 35, longevity 62
- inputs: {"age_days": 873, "days_push": 55, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1349, forks 196 (observed 2026-08-28T04:04:27.902838+00:00)

## What it is
A curated awesome list cataloging tools, frameworks, techniques, and learning materials for building Retrieval-Augmented Generation (RAG) systems. It organizes the RAG ecosystem into sections covering architecture patterns, frameworks, evaluation metrics, databases, and production best practices.

## Use cases
- find frameworks for building a RAG pipeline
- learn retrieval-augmented generation from scratch
- compare vector databases for grounding LLM answers
- evaluate RAG system quality with metrics
- discover advanced RAG techniques like hybrid search or reranking
- prepare a RAG system for production deployment

## When to choose
- you need a curated map of the RAG ecosystem before picking tools
- you are learning RAG concepts and want authoritative links and tutorials
- you want references for RAG evaluation, architecture patterns, and best practices

## When to avoid
- you need runnable RAG software rather than a link collection
- you want a maintained library or framework with code to integrate
- you need deep technical detail on a single RAG component

## Facets
- artifact type: learning-resource
- maturity: active
- function: rag, documentation, developer-tools
- domain: large-language-models, artificial-intelligence, awesome-lists, tutorials
- platform: -
- tags: awesome-list, curated-resources, generative-ai, llm, resource-map, retrieval-augmented-generation, web-server

## Member repositories
- Danielskry/Awesome-RAG (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:27.902838+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 2026-08-30T04:42:19.603544+00:00, confidence not recorded.
  - readme: https://github.com/Danielskry/Awesome-RAG (fetched 2026-08-28T04:04:27.902838+00:00, sha 103b4e12d519)
- Data as of 2026-08-30T08:39:29.467469+00:00.