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RAG & Knowledge Systems

Simple Rag Langchain

Simple Rag Langchain is a GitHub repository of seven Jupyter notebooks that teach learners to build retrieval-augmented generation applications with LangChain, from fundamentals and document preparation to an end-to-end RAG pipeline.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Simple Rag Langchain — 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 or teaching how a LangChain RAG workflow is assembled, from document ingestion and chunking to vector storage, retrieval and a complete pipeline.

Who it is for

  • Beginners learning LangChain and RAG with basic Python knowledge.
  • Learners with LLM experience who want to focus on embeddings, vector stores and retrieval methods.
  • Instructors or mixed-level classes using progressive notebook-based material.

Documented uses

  • Learn LangChain fundamentals and LCEL through notebook examples.
  • Load PDF, CSV, JSON and HTML documents and explore batch loading.
  • Compare text-splitting approaches and chunk sizes.
  • Study OpenAI and Gemini embeddings and vector-store options.
  • Build a RAG pipeline and explore similarity search, MMR and custom retrievers.
Before you choose

Requirements & limits

The repository's README documents a local Python and Jupyter setup and requires an OpenAI API key; a Google API key is optional for the Gemini notebook.

  • The course is notebook-based and requires a local Python environment rather than providing a hosted learning interface.
  • The documented setup requires an OpenAI API key; Gemini examples require a Google API key.
  • Python 3.9–3.11 are specified, with a caution about possible compatibility issues on Python 3.12 and later.
  • The notebooks cover InMemory, FAISS and Chroma stores, plus similarity, MMR and custom retrieval.

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 notebook course

Prepare the documented local environment and open the first notebook.

Before you start

  • Python 3.9, 3.10 or 3.11
  • Basic Python knowledge
  • An OpenAI API key
  • Google API key only if using Notebook 04's Gemini example

Steps

  1. Clone or download the repository and enter its project directory.

  2. Install the required packages with pip install -r requirements.txt.

  3. Copy .env.example to .env and add the OpenAI key; add a Google key if using the Gemini example.

  4. Start Jupyter with jupyter notebook, open 01_Introduction_and_Fundamentals.ipynb and follow the notebooks in order.

Expected outcome

A local Jupyter environment is prepared to work through the course, beginning with LangChain fundamentals.

Guide limitations

  • The README cautions that Python 3.12 and later may have compatibility issues.
  • An OpenAI API key is required; the Google key is optional and identified for Notebook 04.
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
sourangshupal/simple-rag-langchain · Official homepage title · retained in its source language
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