Developers using modern IDEs notice that Cursor’s suggestions feel eerily precise. The secret lies in its retrieval‑augmented generation (RAG) pipeline, which treats a repository as a searchable knowledge base rather than raw text. By coupling semantic search with a continuously refreshed index, coding agents can retrieve exactly the code fragments they need.

Cursor first splits source files into meaningful code chunks—functions, classes, or logical blocks—using the open‑source Chonkie library. Under the hood, tree‑sitter builds an abstract syntax tree (AST), allowing the chunker to group AST nodes until a token limit is reached. Each chunk then receives a vector embedding plus metadata such as file path and line range.

To protect proprietary code, Cursor masks file paths on the client before transmission, and respects a user‑defined .cursorignore list. Embeddings and metadata land in Turbopuffer, a serverless vector database that blends semantic and full‑text search. Because only vectors travel to the cloud, the original source stays on the developer’s machine, easing privacy concerns.