LangChain RAG Tutorial
by LangChain
Build a working question-answering system over your own documents, step by step.
Overview
This is the canonical 'build a RAG app' walkthrough from the LangChain docs. It moves through the full pipeline — loading documents, splitting them, embedding and storing them in a vector store, retrieving relevant chunks, and generating a grounded answer — with runnable code at each step. A second part adds conversational memory. It's the most direct path from zero to a functioning Q&A-over-your-docs system.
At a Glance
- Topic
- RAG
- Level
- Beginner
- Format
- Tutorial
- Cost
- Free
- Duration
- ~1-2 hours
- Provider
- LangChain
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓The end-to-end RAG pipeline: load, split, embed, retrieve, generate
- ✓Using a vector store for retrieval
- ✓Grounding answers in retrieved context
- ✓Adding conversational memory to RAG
Highlights
- •Official, runnable, and beginner-friendly
- •Complete pipeline in one sitting
Who It’s For
Best For
- ✓Developers building their first RAG app
Prerequisites
- •Basic Python
FAQ
What is LangChain RAG Tutorial?
The official LangChain tutorial for building a retrieval-augmented generation app end to end, from loading and splitting to retrieval and generation.
Is LangChain RAG Tutorial free?
LangChain RAG Tutorial is free to access.
What level is LangChain RAG Tutorial for?
LangChain RAG Tutorial is aimed at a beginner audience. Recommended background: Basic Python.
How long does LangChain RAG Tutorial take?
Expect roughly ~1-2 hours. Most learners work through it at their own pace.
What will I learn from LangChain RAG Tutorial?
You'll learn: The end-to-end RAG pipeline: load, split, embed, retrieve, generate; Using a vector store for retrieval; Grounding answers in retrieved context; Adding conversational memory to RAG.