# athina-ai/rag-cookbooks

This repository contains various advanced techniques for Retrieval-Augmented Generation (RAG) systems.

Repository: https://github.com/athina-ai/rag-cookbooks
Canonical: https://ross.abutalabs.com/products/rag-cookbooks
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: ai, chromadb, cookbooks, faiss, langchain, llm, llms, openai, pinecone, python, qdrant, rag, tutorials, weaviate
Last push: 2025-02-17T10:05:33+00:00

## Health v2 (maintenance only)
Score: 25/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 7, release rhythm 35, longevity 46
- inputs: {"age_days": 644, "days_push": 562, "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 2568, forks 325 (observed 2026-08-28T04:07:01.236150+00:00)

## What it is
A collection of Jupyter notebook cookbooks demonstrating advanced and agentic Retrieval-Augmented Generation (RAG) techniques, progressing from naive RAG to sophisticated methods with references to research papers. It provides ready-to-use implementations and evaluation guidance for developers and researchers building RAG systems.

## Use cases
- learn advanced RAG techniques
- implement agentic RAG pipelines
- evaluate RAG system accuracy
- compare vector databases like pinecone and weaviate for RAG
- reduce LLM hallucinations with retrieval-augmented generation
- find RAG research paper implementations
- build a RAG chatbot over private documents

## When to choose
- you want runnable notebook examples of advanced and agentic RAG patterns
- you need evaluation guidance alongside RAG implementations
- you want a progression from naive RAG to state-of-the-art techniques with paper references

## When to avoid
- you need a production-ready RAG framework rather than educational notebooks
- you want a non-Python stack
- you need maintained library APIs instead of example code

## Facets
- artifact type: learning-resource
- maturity: active
- function: rag, llm-inference, agent-framework, machine-learning
- domain: large-language-models, artificial-intelligence, tutorials
- platform: python, cross-platform
- tags: cookbooks, jupyter-notebooks, vector-databases, langchain, agentic-rag, embeddings, retrieval-augmented-generation

## Member repositories
- athina-ai/rag-cookbooks (main) score 25

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:01.236150+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 2026-08-30T02:23:21.317246+00:00, confidence not recorded.
  - readme: https://github.com/athina-ai/rag-cookbooks (fetched 2026-08-28T04:07:01.236150+00:00, sha f58c1927df91)
- Data as of 2026-08-30T08:39:29.467469+00:00.