Danielskry/Awesome-RAG

Compiles tools, frameworks, and architecture patterns for building RAG systems—covering everything from naive retrieval pipelines to advanced approaches like agentic RAG, graph-based retrieval, and multimodal generation. Organizes resources across Python ecosystems (LangChain, LlamaIndex, Haystack), vector databases, evaluation metrics, and production deployment strategies. Serves as a structured knowledge map connecting RAG theory, implementation tutorials, and best practices for reducing hallucinations through grounded context retrieval.

1,071 stars. Actively maintained with 9 commits in the last 30 days.