RAG All-in-one is a comprehensive guide and curated directory for building Retrieval-Augmented Generation (RAG) applications. It targets ML engineers and researchers by organizing a vast array of tools, libraries, frameworks, and learning resources across all key components of the RAG architecture, simplifying the discovery and selection process for RAG pipeline development.

How It Works

The project functions as a centralized, categorized index of RAG technologies. It systematically breaks down the RAG pipeline into distinct components—from document ingestion and chunking to retrieval, query transformation, agent frameworks, databases, LLMs, embeddings, fine-tuning, observability, prompt engineering, evaluation, and user interfaces. For each component, it lists relevant libraries, tools, and learning materials, often with links to their respective GitHub repositories or documentation.

Quick Start & Requirements

This repository is a guide, not a runnable application. It does not provide a single installation command or quick-start script. Building a RAG application would require users to select and integrate various tools and libraries listed within, such as LangChain, LlamaIndex, FAISS, Milvus, OpenAI API, Hugging Face models, and others, along with their respective dependencies (e.g., Python, specific libraries, potentially GPUs for LLM operations).

Highlighted Details

• Comprehensive RAG Component Catalog: Detailed listings for Courses, Document Ingestors, Chunking Techniques, Retrieval methods, Query Transformation, Agent Frameworks, Vector Databases, LLMs, Embedding Models, Fine-tuning tools, LLM Observability platforms, Prompt Techniques, Evaluation frameworks, and User Interface libraries.
• Extensive Tooling: Features a wide range of popular and specialized libraries like LangChain, LlamaIndex, Haystack, FAISS, Milvus, Qdrant, Chroma, OpenAI API, Hugging Face models, Sentence Transformers, and many more.
• Advanced RAG Techniques: Highlights specific methods for retrieval (e.g., Fusion Retrieval, Intelligent Reranking, Multi-modal Retrieval), query transformation (e.g., Hypothetical Document Embeddings - HyDE), and agent frameworks.
• Learning Resources: Includes curated lists of courses, books, and guides for mastering RAG concepts and implementation.

Maintenance & Community

The repository is authored by Long Le, a Machine Learning Engineer. No specific community channels (e.g., Discord, Slack) or details on other contributors, sponsorships, or partnerships are provided within the README.

Licensing & Compatibility

No specific open-source license is mentioned in the provided README content. Users should verify licensing for individual tools and libraries listed.

Limitations & Caveats

As a curated directory, this repository does not offer a ready-to-deploy RAG system. Users are responsible for selecting, integrating, and configuring the various components. The README does not detail specific performance benchmarks for the listed tools or provide guidance on their comparative strengths and weaknesses beyond their categorization.