ruvector

Vector similarity search in Node.js is executed by inserting embeddings and querying the nearest neighbors using an HNSW index. This Skill provides a Rust NAPI with a WASM fallback to manage embeddings and retrieve similar vectors quickly.

HNSW index management involves building and optimizing Hierarchical Navigable Small Worlds indexes to accelerate vector similarity search. You need it when querying large datasets for nearest neighbors, such as building recommendation systems based on feature embeddings.

Yes, the vector database works without native Rust compilation by utilizing a WASM fallback. This ensures embedding insertion, deletion, and similarity search operations remain accessible across Node.js environments where native NAPI compilation is unavailable.

You insert and manage embeddings using the provided programmatic API or CLI-based database management commands. This facilitates inserting, deleting, and counting vectors with associated metadata for data infrastructure needs.

The best way to benchmark vector similarity search performance is using the built-in benchmarking tools included in this Skill. This evaluates the HNSW index retrieval speed and embedding insertion throughput in your Node.js environment.

Yes, you can run the vector database in server mode to enable programmatic API access. This allows remote management of HNSW index operations and embedding queries for distributed data infrastructure requirements.