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-50%Retrieval Augmented Generation (RAG) with LangChain
4.8(1,890)18k studentsINTERMEDIATEEnglish
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Course Overview
The Retrieval Augmented Generation (RAG) with LangChain course teaches advanced techniques for enhancing Large Language Models with external data sources. You will learn syntax-aware document splitting, token-based chunking, semantic splitting, RAG evaluation with LangSmith and Ragas, and Graph RAG architectures using Neo4j graph databases.
Who Is This Course For?
Built for intermediate AI engineers, Python developers, and data scientists looking to design and optimize production-ready RAG applications using LangChain.
Key Takeaways
- Implement syntax-aware code splitting for Python and Markdown files and token-based document chunking.
- Use semantic splitting techniques to retain document context based on topic transitions.
- Evaluate RAG pipeline performance robustly using LangSmith and Ragas frameworks.
- Construct Graph RAG architectures by translating text into graph documents stored in Neo4j databases.
About this course
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.Advertisement
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