---
title: RAG
created_date: 2025-07-23
updated_date: 2025-07-23
aliases:
tags:
---
# RAG
## LangChain Tutorial
[Build a Retrieval Augmented Generation (RAG) App: Part 1 | 🦜️🔗 LangChain](https://python.langchain.com/docs/tutorials/rag/)

### Indexing
The indexing is a pipeline for ingesting data and index it. This usually happens offline.
1. We need to *load* the data. This can come from a variety of different sources
2. We need to *split* the data into chunks because its easier for indexing and for passing it into a model.
3. We need to *store* and index the splits such that we can search over them later. Examples are VectorStore and Embeddings model.