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RAG & Knowledge Systems

Rag From Scratch

Rag From Scratch is an open-source JavaScript repository that teaches developers to build retrieval-augmented generation pipelines step by step, using code examples and explanations of embeddings, vector search, retrieval and context-augmented generation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Rag From Scratch — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when learning RAG implementation concepts or when seeking a small, understandable starting point for a local knowledge-retrieval prototype.

Who it is for

  • Developers learning how RAG systems work at the component level.
  • Builders exploring a local document-question-answering prototype.

Documented uses

  • Understand how document loading, splitting and chunking prepare knowledge for retrieval.
  • Generate embeddings and store them for vector search.
  • Retrieve and refine relevant context before sending it to a local language model.
  • Compare basic, hybrid and multi-query retrieval approaches.
Before you choose

Requirements & limits

The repository is public and identifies its license as MIT.

  • Requires Node.js 18 or later and a local LLM for the documented workflow.
  • Embeddings and vector math use npm packages; Kuzu is listed as optional.
  • The examples emphasize understanding pipeline components and local code rather than using a large RAG framework.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install dependencies and start the introductory example

Prepare the repository and launch the documented introductory RAG example.

Before you start

  • Node.js 18 or later
  • A local LLM, for example node-llama-cpp
  • npm packages for embeddings and vector math

Steps

  1. Install the repository dependencies with npm install.

  2. Run the introductory example with node 00_how_rag_works/example.js.

Expected outcome

The documented introductory example is started after dependencies are installed.

Guide limitations

  • The README does not specify model setup details or the expected output of the example.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Rag From ScratchUnderstand how document loading, splitting and chunking prepare knowledge for retrieval.CLI, Self-hostedThe repository is public and identifies its license as MIT.
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
8 October 2026
Original website title
pguso/rag-from-scratch · Official homepage title · retained in its source language
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