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RAG & Knowledge Systems

Rag Fundamentals

Rag Fundamentals is a public GitHub collection of Jupyter notebooks that teaches retrieval-augmented generation through practical implementation examples. It covers foundational pipelines and document or web retrieval alongside advanced retrievers, hybrid search, rank fusion, reranking, multimodal and agentic RAG, and evaluation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Rag Fundamentals — 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 you want hands-on notebook examples to learn RAG or investigate a specific retrieval technique before shaping a system of your own.

Who it is for

  • Learners studying retrieval-augmented generation implementation
  • Developers comparing retrieval, fusion and reranking approaches
  • Teams exploring notebook examples for document Q&A, knowledge systems, research or multimodal retrieval

Documented uses

  • Understand the stages of a RAG pipeline, from chunking and embeddings through retrieval, reranking and language-model generation
  • Study different retrieval strategies for documents, web content and complex queries
  • Compare examples of keyword and semantic hybrid search, rank fusion and reranking
  • Explore notebook examples for multimodal, graph-based and agentic RAG
Before you choose

Requirements & limits

The project is presented as a public GitHub repository. Its README documents cloning the repository and installing dependencies with pip.

  • It is a notebook-based learning and implementation resource, not a hosted RAG service.
  • The documented setup uses a requirements file; the notebooks may also involve services or models specific to their examples.
  • The collection spans multiple techniques, but the README does not provide comparative benchmark results.

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

Set up the repository and start with Basic RAG

Prepare the notebook collection and follow its stated progression from foundational to advanced topics.

Before you start

  • A way to clone the GitHub repository
  • Python and pip for the documented requirements installation

Steps

  1. Clone the repository.

  2. Install dependencies with the command below.

    Documented command
    pip install -r requirements.txt

  3. Start with the Basic RAG notebooks.

  4. Continue to retriever techniques, reranking and hybrid approaches, then explore multimodal and agentic RAG and evaluation.

Expected outcome

A prepared local repository and a documented route through its RAG learning materials.

Guide limitations

  • These instructions cover setup and topic progression, not execution of a specific notebook or deployment of a RAG application.
  • Individual notebooks may rely on services or models beyond the general setup instructions.
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 FundamentalsUnderstand the stages of a RAG pipeline, from chunking and embeddings through retrieval, reranking and language-model generationWebThe project is presented as a public GitHub repository. Its README documents cloning the repository and installing dependencies with pip.
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
18 October 2026
Original website title
Abeshith/RAG-FundaMentals · Official homepage title · retained in its source language
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