Open Source RAG Projects

Retrieval-augmented generation (RAG) combines information retrieval with language models. A system searches a collection of trusted sources for relevant material, then provides that context to a model as it generates a response. This helps ground answers in specific documents, support knowledge-intensive question answering, and keep information current without relying only on a model’s training data. RAG can also make it easier to trace responses back to source material, though retrieval quality and source coverage affect results.

Open source tools in this area include document parsers, indexing and embedding pipelines, search backends, orchestration frameworks, evaluation tools, and interfaces for querying knowledge bases. When choosing one, consider its license, maintenance activity, supported data formats and models, deployment requirements, integrations, and how well it handles permissions and citations. These tools are useful to developers, researchers, and organizations building searchable assistants or document-based applications.