JetBrains explains storage and search for its Air Context code RAG system
AIJetBrains explains how Air Context stores millions of code vectors with approximate nearest-neighbor indexes and retrieves them in milliseconds. The post, the second in a series, compares pre-filtering, filtered ANN and post-filtering for enforcing repository, branch and permission constraints, and describes incremental index updates as commits land.
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JetBrains AI Blog · Eduard Gurskiy · blog.jetbrains.comPublished · added here
Original title: Building a RAG Pipeline for Semantic Code Search: A Developer Diary and Field Notes