zvec is an embedded vector database engine and indexing library designed for high-dimensional similarity search. It functions as a hybrid search engine and a retrieval-augmented generation knowledge base, allowing for the storage and retrieval of dense and sparse vectors.
The system is distinguished by its hybrid retrieval pipeline, which fuses vector similarity, full-text keyword matching, and scalar metadata filtering into single query operations. It supports a plugin-based model integration system for registering custom embedding models and rerankers, as well as language bindings for native application integration.
The project provides comprehensive data management through isolated local collection persistence, write-ahead logging, and dynamic schema mapping. Its search capabilities cover approximate nearest neighbor search at billion-scale, multimodal semantic search, and result reranking, while optimizing performance via memory-mapped I/O and vector index compression.
The engine facilitates AI agent integration by exposing database interfaces and reusable operation skill sets to connect agents to structured data stores.
