# FullStackRetrieval-com/RetrievalTutorials

Repository: https://github.com/FullStackRetrieval-com/RetrievalTutorials
Canonical: https://ross.abutalabs.com/products/retrievaltutorials
Homepage: https://retrieval-tutorials.vercel.app
Language: Jupyter Notebook
License Family: other
Last push: 2024-06-18T18:24:45+00:00

## Health v2 (maintenance only)
Score: 26/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 70
- inputs: {"age_days": 988, "days_push": 806, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1482, forks 236 (observed 2026-08-28T04:04:51.088586+00:00)

## What it is
A collection of Jupyter Notebook tutorials and guides teaching retrieval and Retrieval Augmented Generation (RAG) for LLM applications, from FullStackRetrieval.com. It covers the full retrieval landscape with code, videos, and evaluations, mostly in Python using OpenAI and popular libraries like LangChain and LlamaIndex.

## Use cases
- learn how to build a RAG pipeline from scratch
- understand chunking strategies for documents before embedding
- improve retrieval quality for my LLM chatbot
- compare LangChain vs LlamaIndex for retrieval
- evaluate whether retrieved context actually helps my model
- get an overview of the advanced retrieval landscape
- tutorials on embeddings and vector search for beginners

## When to choose
- you are learning RAG and retrieval concepts from the ground up
- you want framework-agnostic explanations with runnable Python notebooks
- you need a mental model of the retrieval process before picking tools

## When to avoid
- you need a production-ready RAG framework or library rather than educational material
- you want a maintained tool with a license and active releases
- you need non-Python examples or fully open-source model coverage

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: rag, nlp, machine-learning, llm-inference, data-science
- domain: large-language-models, artificial-intelligence, tutorials
- platform: python, cross-platform
- tags: retrieval, rag-tutorials, jupyter-notebooks, education, llm-context, embeddings, chunking, openai, retrieval-augmented-generation, natural-language-processing

## Member repositories
- FullStackRetrieval-com/RetrievalTutorials (main) score 26

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:51.088586+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 2026-08-30T04:34:04.752834+00:00, confidence not recorded.
  - readme: https://github.com/FullStackRetrieval-com/RetrievalTutorials (fetched 2026-08-28T04:04:51.088586+00:00, sha 8a85bcea135e)
  - homepage: https://retrieval-tutorials.vercel.app (fetched 2026-08-29T11:40:32.626616+00:00, sha 966809881453)
- Data as of 2026-08-30T08:39:29.467469+00:00.