# bragai/bRAG-langchain

Everything you need to know to build your own RAG application

Repository: https://github.com/bragai/bRAG-langchain
Canonical: https://ross.abutalabs.com/products/brag-langchain
Homepage: https://bragai.dev
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: agentic-rag, chatbot, llm, machine-learning, python, rag, ai, retrieval-augmented-generation
Last push: 2026-08-03T00:19:12+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 95, release rhythm 35, longevity 46
- inputs: {"age_days": 655, "days_push": 31, "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 4149, forks 502 (observed 2026-08-28T04:08:36.447739+00:00)

## What it is
A collection of Jupyter notebooks teaching how to build Retrieval-Augmented Generation (RAG) applications, from basic pipelines to advanced techniques like multi-querying, routing, and custom RAG builds. It serves as a hands-on tutorial resource using LangChain, OpenAI embeddings, and vector stores such as ChromaDB and Pinecone.

## Use cases
- learn how to build a rag application
- tutorial for retrieval augmented generation with langchain
- build a rag chatbot from scratch
- understand multi-query rag pipelines
- learn semantic and logical routing for rag
- set up vector store retrieval with chromadb or pinecone

## When to choose
- you want a step-by-step, notebook-based introduction to RAG
- you are learning LangChain and vector store integration
- you need boilerplate starter code for a customizable RAG chatbot
- you want to explore advanced retrieval techniques like multi-querying and query routing

## When to avoid
- you need a production-ready RAG framework rather than educational notebooks
- you want a maintained library with a stable API to depend on
- you need a non-Python or no-API-key solution
- you require a permissive license for commercial reuse

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

## Member repositories
- bragai/bRAG-langchain (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:36.447739+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 2026-08-29T18:22:54.651784+00:00, confidence not recorded.
  - readme: https://github.com/bragai/bRAG-langchain (fetched 2026-08-28T04:08:36.447739+00:00, sha 21b352a596b6)
  - homepage: https://bragai.dev (fetched 2026-08-29T09:13:51.787383+00:00, sha fdab3859c97f)
- Data as of 2026-08-30T08:39:29.467469+00:00.