ruvector-core-pkg

An HNSW vector database uses the Hierarchical Navigable Small World algorithm to index high-dimensional vectors, enabling rapid nearest-neighbor search. It achieves sub-millisecond query times and supports metadata filtering for fast, relevant result retrieval.

You can add vector indexing to a Node.js application by integrating an embedded vector database with N-API bindings. This allows you to perform rapid insertions and conduct nearest-neighbor searches directly within your JavaScript runtime.

Yes, you can use Rust to build a high-performance vector search engine by utilizing N-API bindings. This approach bridges Rust's native execution speed with Node.js, enabling over 50,000 vector insertions per second for heavy workloads.

Yes, the vector database supports metadata filtering during search. You can filter nearest-neighbor search results based on associated metadata, allowing for precise and targeted querying within your indexed vector dataset.

The best way to handle large-scale vector insertions in Node.js is using an embedded HNSW vector database. It leverages Rust N-API bindings to process over 50,000 inserts per second, maintaining high throughput for large datasets.

Use an embedded vector store when you need sub-millisecond nearest-neighbor search directly inside your application runtime. It is ideal for building recommendation engines or searchable knowledge bases without the overhead of external database servers.