Free local RAG tool could help cut repeated AI search costs

Cortex RAG is an open-source tool that combines several search-improvement methods instead of relying only on basic vector search. It adds document background to each text chunk before indexing, rewrites a question in several ways, and merges the best results. It also checks relationships between entities, so it can find links that simple similarity search may miss.

A large language model grades each retrieved chunk to reduce weak or misleading material before an answer is formed. A reranking step puts the most relevant results first, and HyDE creates a possible answer to help search when the original question is too short or vague. A semantic cache makes repeated questions fast by reusing prior results.

Chat memory supports back-and-forth conversations. It runs on a laptop without cloud services or API keys, and is aimed at internal search, compliance automation, and knowledge systems.