course
Retrieval Augmented Generation (RAG) with LangChain
MellanliggandeFärdighetsnivå
Uppdaterad 2024-12PythonArtificial Intelligence3 timmar12 videos38 exercises3,150 XP16,214Uttalande om prestation
Skapa ditt gratiskonto
eller
Genom att fortsätta accepterar du våra Användarvillkor, vår Integritetspolicy och att dina uppgifter lagras i USA.Älskad av elever på tusentals företag
Utbilda 2 eller fler personer?
Testa DataCamp for BusinessKursbeskrivning
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.Förkunskapskrav
Developing LLM Applications with LangChain1
Building RAG Applications with LangChain
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.
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.
3
Introduction to Graph RAG
Discover how graph databases and retrieval can overcome some of the limitations of traditional vector-based storage and retrieval.
Retrieval Augmented Generation (RAG) with LangChain
Kursen är
Få ett prestationsutlåtande
Lägg till denna inloggningsuppgifter i din LinkedIn-profil, ditt CV eller ditt CVDela det på sociala medier och i ditt prestationssamtalRegistrera Dig Nu
Gå med över 19 miljoner elever och börja Retrieval Augmented Generation (RAG) with LangChain idag!
Skapa ditt gratiskonto
eller
Genom att fortsätta accepterar du våra Användarvillkor, vår Integritetspolicy och att dina uppgifter lagras i USA.Utveckla dina datakunskaper med DataCamp för mobilen
Gör framsteg när du är på språng med våra mobila kurser och dagliga 5-minuters kodningsutmaningar.