OpenRAG: An Open-Source Stack for RAG
Retrieval-augmented generation is not a feature; it is a knowledge architecture problem.
Signal summary
OpenRAG is useful as a practical signal because it points to the infrastructure behind source-grounded AI: ingestion, chunking, indexing, retrieval, prompting, evaluation, and application wiring. It helps make RAG feel less like a product checkbox and more like a stack of decisions.
Why it matters
The quality of a source-grounded AI system depends on what it knows, how that knowledge is represented, what it is allowed to retrieve, and how users can inspect the evidence. Weak RAG produces confident noise. Strong RAG behaves more like a governed bridge between source material and generated output.
LGI reading
HKE's Vault Bridge is a more ambitious version of the same problem. It is not enough to retrieve semantically similar chunks. The system needs provenance, source boundaries, review status, cross-links, freshness, and a way to promote useful connections without silently rewriting the record.
Use this if you are thinking about
knowledge bases, RAG systems, internal search, source-grounded assistants, retrieval evaluation, and the infrastructure layer behind AI-enabled knowledge work.
Related LGI concept
Vault Bridge
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