Building a RAG Pipeline for Semantic Code Search
JetBrains developers have built Air Context, a production-grade RAG pipeline designed to enable semantic code search for AI agents. The system addresses the limitations of traditional keyword-based tools by indexing source code to capture meaning rather than exact text matches. This article details the initial stages of the pipeline, specifically focusing on the challenges of parsing, chunking, and vectorizing large codebases.
- ▪Air Context is a RAG pipeline developed by JetBrains to provide LLM agents with precise, citable evidence from real repositories.
- ▪Traditional search tools like grep are insufficient for AI agents because they require exact keyword matches rather than semantic understanding.
- ▪The development process highlights the critical importance of proper parsing and chunking to handle large-scale codebases effectively.
- ▪Embedding entire files is counterproductive for agentic navigation, while overly granular line-by-line embedding presents its own distinct challenges.
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| Original publisher | The JetBrains Blog |
| Canonical URL | https://blog.jetbrains.com/ai/2026/09/building-a-rag-pipeline-for-semantic-code-search-a-developer-diary-and-field-notes/ |
| Publication time | Sun, 04 Oct 2026 17:51:48 +0000 |
| Retrieval time | 2026-10-04T18:14:55.034Z |
| Last seen | 2026-10-04T18:14:55.034Z |
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| Cluster | AcaeN4UdSEbk · 1 stories |
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JetBrains AI Supercharge your tools with AI-powered features inside many JetBrains products Follow Follow: RSS RSS Explore More All News How-To's AI in IDEs Research Agentic AI AI Building a RAG Pipeline for Semantic Code Search: A Developer Diary and Field Notes Adam Malek Ashot Kazaryan Part 1: Parsing, chunking, and vectorization Some time ago, we set out to build the best semantic code search platform we could: a RAG pipeline that gives LLM agents precise, citable evidence from real repositories instead of whatever grep happens to surface. The eventual solution was Air Context. We got it working, we got it into production, and we collected a lot of scar tissue along the way. In this series of posts, we’ll share the parts we wish someone had told us on day one.
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