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Retrieval Augmented Generation (RAG) with LangChain

Learn cutting-edge methods for integrating external data with LLMs using Retrieval Augmented Generation (RAG) with LangChain.

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4 hours12 videos38 exercises

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Course Description

Build RAG Systems with LangChain

Retrieval Augmented Generation (RAG) is a technique used to overcome one of the main limitations of large language models (LLMs): their limited knowledge. RAG systems integrate external data from a variety of sources into LLMs. This process of connecting multiple different systems is usually tedious, but LangChain makes this a breeze!

Learn State-of-the-Art Splitting and Retrieval Methods

Level-up your RAG architecture! You'll learn how to load and split code files, including Python and Markdown files to ensure that splits are "aware" of code syntax. You'll split your documents using tokens instead of characters to ensure that your retrieved documents stay within your model's context window. Discover how semantic splitting can help retain context by detecting when the subject in the text shifts and splitting at these points. Finally, learn to evaluate your RAG architecture robustly with LangSmith and Ragas.

Discover the Graph RAG Architecture

Flip your RAG architecture on its head and discover how graph-based, rather than vector-based RAG systems can improve your system's understanding of the entities and relationships in your documents. You'll learn how to convert unstructured text data into graphs using LLMs to do the translation! Then, you'll store these graph documents in a Neo4j graph database and integrate it into a wider RAG system to complete the application.
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  1. 1

    Building RAG Applications with LangChain

    Free

    Discover how to integrate external data sources into chat models with LangChain. Learn how to load, split, embed, store, and retrieve data for use in LLM applications.

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    Loading Documents for RAG with LangChain
    50 xp
    Loading PDF files for RAG
    100 xp
    Loading HTML files for RAG
    100 xp
    Text splitting, embeddings, and vector storage
    50 xp
    Getting started with text splitting
    100 xp
    Recursively splitting documents
    100 xp
    Embedding and storing documents
    100 xp
    Building an LCEL retrieval chain
    50 xp
    Creating the retrieval prompt
    100 xp
    Building the retrieval chain
    100 xp
  2. 2

    Improving the RAG Architecture

    Discover state-of-the-art techniques for loading, splitting, and retrieving documents, including loading Python files, splitting semantically, and using MRR and self-query retrieval methods. Learn to evaluate your RAG architecture using robust metrics and frameworks.

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Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

datasets

RAG Academic Paper PDFDataCamp Blog HTMLLangChain README MarkdownChatbot Python FileRAG Workflow Python File

collaborators

Collaborator's avatar
James Chapman
Meri Nova HeadshotMeri Nova

Machine Learning Engineer

I am a self-taught ML engineer and the Technical Founder of Break Into Data, a platform dedicated to helping individuals break into data science and machine learning.
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