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zvec

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Zvec is Alibaba's open-source, in-process vector database. Like SQLite for embeddings, it embeds directly into your app with no server to deploy, delivering millisecond similarity search, full-text search, and hybrid queries across Python, Node.js, Go, Rust, and Flutter.

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v0.8.0 · 39.4 MB

An embeddable, SQLite-style vector database for millisecond local similarity search.

Core Features

  • Millisecond vector search scaling to billions of entries
  • Fully embedded and local—no server, no configuration
  • Dense, sparse, and multi-vector query support
  • Native full-text search with structured expressions
  • Hybrid queries fusing vectors, keywords, and filters

What It Can't Do

  • •zvec is an embedded library, not a standalone server—it cannot serve remote clients over the network like Milvus or Qdrant. Writes are single-process exclusive; multiple processes may read concurrently but cannot write together. Data lives in local files, so backups and disaster recovery are your responsibility. Only 64-bit Python 3.10–3.14 is supported. Uniform quantization boosts speed but may reduce recall, so enable it carefully for precision-sensitive workloads.

Use Cases

  • Local knowledge retrieval for RAG apps and AI agents
  • Offline semantic search on mobile or edge devices
  • Similarity search for documents, code, or images
  • Real-time similar-item recall in recommender systems

Zvec is an open-source, in-process vector database developed and battle-tested at Alibaba Group, designed to embed directly into applications with minimal setup. Written in C++ with official SDKs for Python, Node.js, Go, Rust, and Dart/Flutter, it searches billions of vectors in milliseconds and runs on Linux, macOS, Windows, Android, and iOS. Unlike server-based vector databases such as Milvus or Qdrant, zvec needs no separate service—think SQLite rather than PostgreSQL—so it fits notebooks, CLI tools, edge devices, and mobile apps. Compared with index-only libraries like FAISS, zvec adds production essentials out of the box: write-ahead logging for crash-safe durability, document CRUD, dense and sparse vectors, native full-text search, and hybrid queries that fuse vector similarity, keywords, and structured filters in one call. Multi-process concurrent reads are supported, while writes are single-process exclusive. It is a strong fit for RAG pipelines, semantic search, recommendation, and local-first AI agents that need low-latency retrieval without operating a database cluster.

Tags

vector-databasesimilarity-searchembeddingshybrid-searchragin-process

Getting Started

1

Download installer

Click the button above to download the installer for your system

2

Install the software

Double-click the downloaded installer and follow the prompts

3

Make sure you have 64-bit Python 3.10–3.14, then run pip install zvec

4

Define a CollectionSchema in your code with the collection name, vector field, and dimensions

5

Create and open a collection with create_and_open, then insert documents with vectors

Install Guide
  1. Make sure you have 64-bit Python 3.10–3.14, then run pip install zvec
  2. Define a CollectionSchema in your code with the collection name, vector field, and dimensions
  3. Create and open a collection with create_and_open, then insert documents with vectors
File Integrity

SHA-256 checksum verified

Checksum extracted from GitHub official Release page

SHA256 Checksum

98779b81a0c2c3845336b8f5da8203908116bbf55275805ae9124779e786dc20

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Open Source Transparency

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Environment Guide

Uninstall Info

Run pip uninstall zvec to remove the Python package (or npm uninstall @zvec/zvec for Node.js). Then delete the local data directory (e.g., ./zvec_example) to remove all stored data—no background services or leftovers remain.

No Extra Dependencies

Ready to use after download. No additional runtime required.

Project Info
LicenseApache-2.0
Last Updated2026-10-22 18:17:25
GitHub RepositoryOfficial Website

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