What is zvec.org?

Zvec is an in-process vector database that provides millisecond semantic search at billion-vector scale. It runs directly inside applications with no external services required, enabling local deployment and simplified integration. Zvec supports dense and sparse vectors, multi-vector queries, filtered vector search, and GROUP BY–style grouped search for refined, context-aware retrieval.

A compact Python API exposes create, insert, and query operations for embedding storage and similarity search. Benchmarks on the Cohere 10M dataset report ~1 hour index build time and over 8,500 queries per second. Common use cases include retrieval-augmented generation (RAG), semantic image search, and natural-language code search.

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zvec.org's key features

Low-latency vector similarity search
In-process local deployment (runs directly in-app, no external services)
Support for dense and sparse vectors and multi-vector queries
Filtered vector search combining semantic search with attribute filters
Simple, intuitive Python API for collection creation, insertion, and querying

zvec.org use cases

Create a responsive, search-as-you-type semantic product search inside your web or mobile app using Zvec's in-process vector index to deliver millisecond results at billion-vector scale without external services, leveraging multi-vector queries and attribute filters for precise faceted e-commerce search
Develop an instant knowledge-base and document retrieval system for customer support using Zvec's compact Python API to store embeddings and run filtered, grouped semantic searches that return relevant passages within the application
Build privacy-preserving, on-device recommendation and personalization engines by running local dense/sparse and multi-vector searches with Zvec to match user behavior and content embeddings at low latency while avoiding network calls