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A production RAG pipeline gives coding agents citable evidence from repositories

blog.jetbrains.com 18 min read Editor score 7/10

JetBrains describes building and shipping a code-search RAG pipeline intended to give LLM agents precise, citable repository evidence, with field notes beginning on parsing, chunking and vectorization. The production lessons are relevant to teams grounding coding agents in large codebases.

What
Air Context is a production RAG pipeline intended to give coding agents precise, citable evidence from real repositories.
How
Its first stages parse and chunk raw source files, then vectorize those units to support semantic search and retrieval.
Why it matters
Semantic code search can help agents find relevant code in large repositories without relying on exact keywords.
parse chunks index retrieve raw source files parsing andchunking vectorization semantic search LLM agents
Repository source files pass through parsing and vectorization before semantic search serves agents. Sketched from the article's own text.

Read on blog.jetbrains.com → Hacker News · 9 commentsLobsters · 1 comments

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