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RAG & Knowledge Systems

Retrieval Augmented Generation Intro Project

A public educational repository of Python Jupyter Notebook demonstrations for retrieval-augmented generation using LlamaIndex, with examples for basic RAG, sentence-window retrieval and auto-merging retrieval.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Retrieval Augmented Generation Intro Project — 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 a notebook-based introduction to RAG and its included retrieval strategies, with an environment file and instructions for connecting to OpenAI models.

Who it is for

  • Learners exploring retrieval-augmented generation with Python notebooks.
  • Developers seeking examples to modify for retrieval over their own documents.

Documented uses

  • Study a basic RAG pipeline through a notebook example.
  • Explore sentence-window and auto-merging retrieval approaches.
  • Adapt the included document example or use personal documents for experiments.
Before you choose

Requirements & limits

  • The project is organized around Python Jupyter notebooks and includes an NLP.yml environment definition.
  • Access to OpenAI models requires adding an API key to common/openAI.env.
  • The repository includes an introductory PDF and an example text file; users can replace the example with their own documents.

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

Prepare the notebooks for local RAG experiments

Set up the project’s documented environment and credentials before exploring its notebook demos.

Before you start

  • A local machine with Python and Jupyter Notebook.
  • The project repository, including the NLP.yml environment file.
  • An OpenAI API key to access OpenAI models.

Steps

  1. Clone or download the repository to your local machine.

  2. Import python_env/NLP.yml to create the project environment.

  3. Enter your OpenAI API key in common/openAI.env.

  4. Read the introductory PDF, then open one of the three notebooks in the code folder.

Expected outcome

The project files and documented credentials are prepared for exploring the basic RAG, sentence-window retrieval, and auto-merging notebooks.

Guide limitations

  • The README describes setup and notebook exploration but does not document specific execution outputs or a deployment process.
  • OpenAI model access requires an API key.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
18 October 2026
Original website title
HenryHengLUO/Retrieval-Augmented-Generation-Intro-Project · Official homepage title · retained in its source language
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