This repository contains the notebooks used during the "Learn RAG from Scratch" course on freeCodeCamp, taught by a LangChain software engineer. In this course, you'll learn how to build a Retrieval Augmented Generation (RAG) system, integrating your custom data with the power of Large Language Models (LLMs).
The course covers the following key concepts and techniques:
- Query Construction: How to formulate questions or information requests that align with your data and model.
- Query Translation: Techniques for translating user queries into forms that are more understandable to the retrieval system.
- Routing: Methods for directing queries to the correct data source or sub-system.
- Indexing: Building efficient and searchable indexes of your data for fast retrieval.
- Retrieval: How to retrieve the most relevant pieces of data based on user queries.
- Generation: Using Large Language Models to generate human-like responses based on retrieved data.
The course concepts are summarized in the "summar.png" mindmap included in this repository. It visually organizes the steps involved in building a RAG system.
