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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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.For Business
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Building RAG Applications with LangChain
FreeDiscover 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.
Loading Documents for RAG with LangChain50 xpLoading PDF files for RAG100 xpLoading HTML files for RAG100 xpText splitting, embeddings, and vector storage50 xpGetting started with text splitting100 xpRecursively splitting documents100 xpEmbedding and storing documents100 xpBuilding an LCEL retrieval chain50 xpCreating the retrieval prompt100 xpBuilding the retrieval chain100 xp - 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.
Loading and splitting code files50 xpLoading code files100 xpSplitting Python files100 xpAdvanced splitting methods50 xpSplitting by tokens100 xpSplitting semantically100 xpOptimizing document retrieval50 xpSparse vs. dense retrieval100 xpUnderstanding BM25100 xpSparse retrieval with BM25100 xpIntroduction to RAG evaluation50 xpRagas context precision evaluation100 xpRagas faithfulness evaluation100 xpString evaluation100 xp - 3
Introduction to Graph RAG
Discover how graph databases and retrieval can overcome some of the limitations of traditional vector-based storage and retrieval.
From vectors to graphs50 xpCreating graph documents100 xpGetting to know graphs50 xpStoring and querying documents50 xpBuilding-up your graph database100 xpQuerying your graph database100 xpCreating the Graph RAG chain50 xpA journey through the Graph RAG system100 xpChaining, Graph RAG style!100 xpImproving graph retrieval50 xpGraph RAG with filtering100 xpValidating Cypher queries100 xpCreating a Cypher few-shot prompt100 xpCongratulations!50 xp
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RAG Academic Paper PDFDataCamp Blog HTMLLangChain README MarkdownChatbot Python FileRAG Workflow Python Filecollaborators
prerequisites
Developing LLM Applications with LangChain
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