Cursor uses a RAG pipeline to index codebases for AI-powered code assistance. The process involves chunking code into semantically meaningful units using abstract syntax trees, generating embeddings for semantic search, obfuscating file paths for privacy, and storing embeddings in Turbopuffer vector database. Source code remains local while only embeddings and metadata are stored remotely. The system uses Merkle trees to efficiently detect changes and keep the index synchronized every five minutes, updating only modified files.
Table of contents
(1) Exploring the Codebase RAG Pipeline(2) Keeping Codebase Index Up to Date(3) Wrapping It Up171 Impressions