Building a RAG Pipeline for Semantic Code Search
4 hours ago
- JetBrains developed Air Context, a RAG pipeline for semantic code search to help LLM agents find relevant code by meaning rather than keywords.
- Structure-aware chunking uses language-specific parsers (e.g., for Java, Python, Kotlin) to divide code into semantically meaningful units, avoiding naive fixed-size splitting.
- Chunk quality is evaluated using an LLM-as-a-judge strategy and end-to-end retrieval tests to ensure proper boundaries.
- Vectorization transforms code chunks into fixed-length vectors; binary quantization (1-bit per dimension) is used to reduce storage while preserving search quality.
- Binary vectors are compared using Hamming distance, which is faster and more storage-efficient than cosine similarity, but limits absolute relevance thresholding.
- Embedding includes file path metadata (truncated to keep both ends) to improve retrieval accuracy, especially in monorepos with deep directory structures.
- Source code privacy is ensured by not storing code content, running open-weight embedding models on own infrastructure, and avoiding third-party cloud services.
- The series continues with topics like storage, query latency, evaluation, and agent integration in future posts.