langchain-ai/rag-from-scratch

langchain-ai/rag-from-scratch has 9,621 GitHub stars and 2,257 forks. GitHot measured +8 stars in 24 hours. Last checked 2026-11-07 08:04 UTC. Last push 2025-06-26.

How these counts are measured

LLMs are trained on a large but fixed corpus of data, limiting their ability to reason about private or recent information. Fine-tuning is one way to mitigate this, but is often not well-suited for factual recall and can be costly. Retrieval augmented generation (RAG) has emerged as a popular and powerful mechanism to expand an LLM's knowledge base, using documents retrieved from an external data source to ground the LLM generation via in-context learning. These notebooks accompany a video playlist that builds up an understanding of RAG from scratch, starting with the basics of indexing, retrieval, and generation.

9,621 stars 2,257 forks +8 in 24h

Measured star growth

in 24h: +8
in 7 days: Collecting history
in 15 days: Collecting history
in 1 month: Collecting history

Net star changes over overlapping periods, not daily totals. Missing measurements are left blank. Stars measure attention, not software quality.

About

LLMs are trained on a large but fixed corpus of data, limiting their ability to reason about private or recent information. Fine-tuning is one way to mitigate this, but is often not well-suited for factual recall and can be costly. Retrieval augmented generation (RAG) has emerged as a popular and powerful mechanism to expand an LLM's knowledge base, using documents retrieved from an external data source to ground the LLM generation via in-context learning. These notebooks accompany a video playlist that builds up an understanding of RAG from scratch, starting with the basics of indexing, retrieval, and generation.

Developer Tools

Repository

Language: Jupyter Notebook

Before you use it

Check the repository’s installation instructions, requirements and open issues before using it.

No license is listed by GitHub. Check usage permissions with the author.

Activity

Created: 2024-01-31
Last push: 2025-06-26
Stars / 24h: 8
Stars / 7 days: Collecting history
Last checked: 2026-11-07 08:04 UTC

README.md

RAG From Scratch

LLMs are trained on a large but fixed corpus of data, limiting their ability to reason about private or recent information. Fine-tuning is one way to mitigate this, but is often not well-suited for factual recall and can be costly. Retrieval augmented generation (RAG) has emerged as a popular and powerful mechanism to expand an LLM's knowledge base, using documents retrieved from an external data source to ground the LLM generation via in-context learning. These notebooks accompany a video playlist that builds up an understanding of RAG from scratch, starting with the basics of indexing, retrieval, and generation. rag_detail_v2

Explore related projects

codecrafters-io/build-your-own-x

Master programming by recreating your favorite technologies from scratch.

public-apis/public-apis

A collective list of free APIs

morluto/rea

Reverse engineer anything with agents, from app behavior down to native binaries.

openclaw/openclaw

The AI that really does things. Any OS. Any Platform. The lobster way. 🦞