lehoanglong95/rag-all-in-one

🧠 Guide to Building RAG (Retrieval-Augmented Generation) Applications

/ 100

Emerging

This is a comprehensive directory that helps AI system builders gather the right tools and knowledge for creating powerful AI applications that can answer questions using specific documents. It takes various components like document loaders, chunking methods, and databases, and guides you through assembling them to produce AI applications that leverage your own information. Machine Learning Engineers, AI Developers, and anyone building custom AI-powered question-answering systems would find this useful.

256 stars. No commits in the last 6 months.

Use this if you are developing AI applications that need to generate accurate and context-rich responses based on specific internal documents or knowledge bases.

Not ideal if you are looking for a ready-to-use, off-the-shelf AI application without any development or technical assembly.

AI development machine learning engineering information retrieval natural language processing enterprise search

No License Stale 6m No Package No Dependents

Maintenance 2 / 25

Adoption 10 / 25

Maturity 8 / 25

Community 19 / 25

How are scores calculated?

Stars

256

Forks

Language

—

License

—

Compare

rag-all-in-one and rag-zero-to-hero-guide

Higher-rated alternatives

Renumics/renumics-rag

Visualization for a Retrieval-Augmented Generation (RAG) Assistant 🤖❤️📚

VectorInstitute/retrieval-augmented-generation

Reference Implementations for the RAG bootcamp

naver/bergen

Benchmarking library for RAG

KalyanKS-NLP/rag-zero-to-hero-guide

Comprehensive guide to learn RAG from basics to advanced.

alan-turing-institute/t0-1

Application of Retrieval-Augmented Reasoning on a domain-specific body of knowledge

Explore RAG Tools

All categories Trending RAG directory Insights