Retrieval Augmented Generation (RAG) with LangChain

Overview

Expand your understanding of integrating external data with Large Language Models (LLMs) through Retrieval Augmented Generation (RAG) using LangChain. Although LLMs are now embedded in various technologies, they are limited by their training datasets.

RAG addresses this challenge by combining LLMs with real-time external data.

In this course, delve into advanced methods for loading, processing, and retrieving external data tailored for LLMs, using the latest models such as GPT-4o-Mini. You'll work with vector databases and harness the power of the LangChain framework to construct efficient RAG applications.

Conclude your learning journey with an exploration of Graph RAG, offering a unique approach to data retrieval through the use of graph databases, ensuring enhanced reliability and accuracy in your applications.

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