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zvec

Alibaba's open-source in-process vector database: billion-scale similarity search embedded in your app, with DiskANN on-disk indexing, native full-text search and hybrid retrieval.

14,948 931 C++ Apache-2.0updated today
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Curator's take

The 'SQLite of vector search' position, executed with Alibaba-scale engineering: runs inside your process (no server to operate), DiskANN keeps memory flat at large scale, and v0.5 added native FTS + hybrid retrieval so one MultiQuery spans dense, sparse, filters and text — no external search engine. Battle-tested internally before open-sourcing. NOT for multi-service architectures needing a shared, network-accessible store with auth and replication — in-process is the whole point and the whole limitation; C++ core with Python/Go/Rust SDKs, so debugging beneath the binding is not for everyone.

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alibaba%2Fzvec | Trendshift

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Zvec is an open-source, in-process vector database — lightweight, lightning-fast, and designed to embed directly into applications. Battle-tested within Alibaba Group, it delivers production-grade, low-latency and scalable similarity search with minimal setup.

[!Important] 🚀 v0.5.0 (June 12, 2026)

  • Full-Text Search (FTS): Native full-text search — attach an FTS index to any string field and query it with natural-language or structured expressions, no external search engine required.
  • Hybrid Retrieval: Combine full-text and vector search in a single MultiQuery across dense vectors, sparse vectors, scalar filters, and text.
  • DiskANN Index: New on-disk index that keeps the bulk of the index on disk, drastically cutting memory usage for large-scale datasets.
  • Ecosystem & Platforms: New official Go / Rust SDKs, the Zvec Studio visual tool, and RISC-V support.

👉 Read the Release Notes | View Roadmap 📍

💫 Features

  • Blazing Fast: Searches billions of vectors in milliseconds.
  • Simple, Just Works: Install and start searching in seconds. Pure local, no servers, no config, no fuss.
  • Dense + Sparse Vectors: Support dense and sparse embeddings, multi-vector queries, and a rich selection of vector index types that scale from memory to disk.
  • Full-Text Search (FTS): Native keyword-based full-text search — query string fields with natural-language or structured expressions.
  • Hybrid Search: Fuse vector similarity, full-text search, and structured filters in a single query for precise

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