Securely indexing large codebases

Semantic search significantly enhances agent performance by improving response accuracy, code retention, and request satisfaction. Cursor, a tool for semantic search, builds a searchable index of codebases using a Merkle tree to efficiently detect file changes, reducing the need to reprocess entire repositories. This method speeds up indexing by reusing existing indexes from teammates rather than rebuilding them from scratch, leading to faster query times, especially for large repositories. By employing cryptographic hashes and similarity hashes (simhashes), Cursor ensures that only authorized code is accessed, allowing new users to quickly perform semantic searches using a copied index while maintaining data privacy and integrity. This approach drastically reduces the time-to-first-query, improving onboarding speed and efficiency for users working with large codebases.