// rag · alibaba/zvec

zvec

Lightweight, lightning-fast in-process vector database from Alibaba that embeds in your app.

★ 16.1k stars⑂ 1.0k forksApache-2.0updated 2026-09
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

zvec is an open-source rag & knowledge project by alibaba (alibaba/zvec on GitHub). It has 16,079 GitHub stars and 1,008 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 29 September 2026.

Repository
alibaba/zvec
GitHub stars
16,079
Forks
1,008
License
Apache-2.0
Last updated on GitHub
29 September 2026
Listing
Full install guide
Official site
Official site
// how it works

How it works

  1. Decide where search should live

    Choose zvec when you want vector search inside your process - no separate database server to deploy, configure or keep running.

  2. Install the SDK for your language

    Add the Python, Node.js, Go, Rust or Dart package to your project in one command.

  3. Define a collection and insert vectors

    Create a schema for your collection, insert documents with their embeddings, and let zvec build the index.

  4. Query with hybrid search

    Run vector similarity, full-text keyword or combined hybrid queries, and tune index and quantization options as data grows.

// features

Why people love it

In-process embedding

Zvec runs inside your application with no server, no config and no fuss - install it and start searching in seconds.

Dense and sparse vectors

Supports dense and sparse embeddings, multi-vector queries, and a rich selection of vector index types that scale from memory to disk.

Full-text search

Native keyword-based full-text search lets you query string fields with natural-language or structured expressions.

Hybrid search

Combines vector similarity and full-text results in one system for retrieval that catches both meaning and exact terms.

Multiple language SDKs

Official packages for Python (pip), Node.js (npm), Go, Rust and Dart/Flutter, all backed by the same C++ core.

Quantization and disk indexes

Index options including IVF-RaBitQ and PQ-INT8 quantization, plus DiskANN support, keep large collections fast and compact.

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// run it

Run it yourself

  1. Clone the repository

    Clone the zvec source if you want to build from source or explore the C++ core and examples.

    git clone https://github.com/alibaba/zvec.git
    cd zvec
  2. Install the Python SDK

    Most users simply install the prebuilt package - 64-bit Python 3.10 to 3.14 - rather than building from source.

    pip install zvec
  3. Run the one-minute example

    Create a collection, insert vectors and query them using the quickstart example from the README and docs.

    python -c "import zvec; print(zvec.__version__)"  # then follow the one-minute example - see README for the exact code
// use cases

What you can build

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// faq

Questions, answered

Who maintains zvec?

Zvec is an Alibaba open-source project, battle-tested within Alibaba Group, with community contributors.

Is zvec free to use?

Yes. It is Apache-2.0 licensed, so you can use it in commercial products.

Does zvec need a paid API key?

No. It runs locally in your process; you only need your own embedding model or API to turn text into vectors.

How does zvec compare with FAISS or Milvus?

FAISS is a library for vector search only, and Milvus is a server database; zvec is an embeddable database that adds full-text and hybrid search, schemas and multiple language SDKs on top of fast vector indexes.

How do I get started?

Install it with pip install zvec (or the npm, Go, Rust or Dart package), then follow the one-minute example in the README to create a collection and query it.

// keep exploring

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// alternatives

Alternatives in RAG & Knowledge

The most-starred other RAG & Knowledge projects in the LibreAI catalogue — the realistic shortlist if zvec is not the right fit.

Get zvec

Free and open — grab the source, star the repo, and start creating.