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🧠 Guide to Building RAG (Retrieval-Augmented Generation) Applications
| Date | Stars |
|---|---|
| 2026-07-31 | 317 |
| 2026-08-06 | 323 |
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# RAG All-in-one
## Hello there! 👋
I'm Long Le, a Machine Learning Engineer passionate about building AI systems. This repository is my collection of RAG (Retrieval-Augmented Generation) resources to help you build powerful AI applications.
Feel free to connect with me on social media to discuss AI, machine learning, or this project:
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## Introduction
RAG All-in-one is a guide to building Retrieval-Augmented Generation (RAG) applications. It offers a comprehensive collection of tools, libraries, and frameworks for RAG systems, organized by key components of the RAG architecture. This resource serves as a centralized directory to help you discover the most relevant technologies for each part of your RAG pipeline.
### RAG Architecture Diagram
## RAG Components
| Component | Description |
|-----------|-------------|
| [📚 Courses and Learning Materials](#courses-and-learning-materials) | Comprehensive courses and learning resources for mastering RAG systems |
| [📄 Document Ingestor](#document-ingestor) | Tools for ingesting and processing raw documents. Document loaders, parsers, and preprocessing tools |
| [✂️ Chunking Techniques](#chunking-techniques) | Methods and tools for breaking down documents into manageable pieces for processing and retrieval |
| [🔍 Retrieval](#retrieval) | Advanced techniques and methods for retrieving relevant information in RAG systems using LlamaIndex |
| [🔄 Query Transform](#query-transform) | Advanced techniques for improving query quality and retrieval effectiveness in RAG systems |
| [🤖 Agent Framework](#agent-framework) | End-to-end frameworks for building RAG applications. Unified solutions for RAG implementation |
| [📀 Database](#database) | Databases optimized for storing and searching vector embeddings. Vector storage, similarity search, and indexing |
| [💻 LLM](#llm) | Large Language Models for generating responses. LLM providers and frameworks |
| [📝 Embedding](#embedding) | Models and services for creating text embeddings. Embedding models and APIs |
| [🔧 Fine-tuning](#fine-tuning) | Tools and techniques for customizing LLMs to specific domains or tasks |
| [🖥️ LLM Observability](#llm-observability) | Tools for monitoring and analyzing LLM performance. Logging, tracing, and analytics |
| [📕 Prompt Techniques](#prompt-techniques) | Methods for effective prompt engineering. Prompt templates and frameworks |
| [🤔 Evaluation](#evaluation) | Tools for assessing RAG system performance. Metrics and evaluation frameworks |
| [📺 User Interface](#user-interface) | Tools for building interactive AI interfaces. UI frameworks for RAG applications |
| [🚀 Complete RAG Applications](#complete-rag-applications) | Ready-to-use, comprehensive RAG systems that integrate various components of the RAG stack |
## Courses and Learning Materials
Comprehensive courses and learning resources for mastering RAG systems.
| Course Name | Platform | Description | Link | Level |
|-------------|----------|-------------|------|-------|
| Building and Evaluating Advanced RAG Applications |  | Advanced retrieval methods, sentence-window retrieval, auto-merging retrieval, and evaluation metrics | [Link](https://www.deeplearning.ai/short-courses/building-evaluating-advanced-rag/) | Beginner |
| Learn RAG with LLMWare |  |Excerpt of 34,596 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e79835090079e185, topic:rag, desc:retrieval-augmented generation, desc:retrieval augmented