Repository: alibaba/zvec

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README.md Copy Chapter

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English | <a href="./README_CN.md">中文</a>
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<a href="https://zvec.org/en/docs/quickstart/">🚀 <strong>Quickstart</strong> </a> |
<a href="https://zvec.org/en/">🏠 <strong>Home</strong> </a> |
<a href="https://zvec.org/en/docs/">📚 <strong>Docs</strong> </a> |
<a href="https://zvec.org/en/docs/benchmarks/">📊 <strong>Benchmarks</strong> </a> |
<a href="https://deepwiki.com/alibaba/zvec">🔎 <strong>DeepWiki</strong> </a> |
<a href="https://discord.gg/rKddFBBu9z">🎮 <strong>Discord</strong> </a> |
<a href="https://x.com/ZvecAI">🐦 <strong>X (Twitter)</strong> </a>
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Zvec is an open-source, in-process vector database — lightweight, lightning-fast, and designed to embed directly into applications. Built on Proxima (Alibaba's battle-tested vector search engine), it delivers production-grade, low-latency, scalable similarity search with minimal setup.

- Relaxed collection path restrictions and improved Windows path handling.

> 🚀 v0.3.0 (April 3, 2026)

> - New Platforms: Initial Windows (MSVC) and Android support. Published official Windows Python and Node.js packages.

- Efficiency: RabitQ quantization and CPU Auto-Dispatch for optimized SIMD execution.

- Ecosystem: C-API for custom language bindings and MCP / Skill integration for AI Agents.

> 👉 Read the Release Notes | View Roadmap 📍

💫 Features Copy Chapter

- Blazing Fast: Searches billions of vectors in milliseconds.
- Simple, Just Works: Install and start searching in seconds. No servers, no config, no fuss.
- Dense + Sparse Vectors: Work with both dense and sparse embeddings, with native support for multi-vector queries in a single call.
- Hybrid Search: Combine semantic similarity with structured filters for precise results.
- Runs Anywhere: As an in-process library, Zvec runs wherever your code runs — notebooks, servers, CLI tools, or even edge devices.

📦 Installation Copy Chapter

Python Python')" title="Copy section prompt for LLMs"> Copy Section

Requirements: Python 3.10 - 3.14

pip install zvec

Node.js Node.js')" title="Copy section prompt for LLMs"> Copy Section

npm install @zvec/zvec

✅ Supported Platforms Copy Section

- Linux (x86_64, ARM64)
- macOS (ARM64)
- Windows (x86_64)

🛠️ Building from Source Copy Section

If you prefer to build Zvec from source, please check the Building from Source guide.

⚡ One-Minute Example Copy Chapter

import zvec
Define collection schema

schema = zvec.CollectionSchema(
    name="example",
    vectors=zvec.VectorSchema("embedding", zvec.DataType.VECTOR_FP32, 4),
)
Create collection

collection = zvec.create_and_open(path="./zvec_example", schema=schema)
Insert documents

collection.insert([
    zvec.Doc(id="doc_1", vectors={"embedding": [0.1, 0.2, 0.3, 0.4]}),
    zvec.Doc(id="doc_2", vectors={"embedding": [0.2, 0.3, 0.4, 0.1]}),
])
Search by vector similarity

results = collection.query(
    zvec.VectorQuery("embedding", vector=[0.4, 0.3, 0.3, 0.1]),
    topk=10
)
Results: list of {'id': str, 'score': float, ...}, sorted by relevance

print(results)

📈 Performance at Scale Copy Chapter

Zvec delivers exceptional speed and efficiency, making it ideal for demanding production workloads.

<img src="https://zvec.oss-cn-hongkong.aliyuncs.com/qps_10M.svg" width="800" alt="Zvec Performance Benchmarks" />

For detailed benchmark methodology, configurations, and complete results, please see our Benchmarks documentation.

🤝 Join Our Community Copy Chapter

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❤️ Contributing Copy Chapter

We welcome and appreciate contributions from the community! Whether you're fixing a bug, adding a feature, or improving documentation, your help makes Zvec better for everyone.

Check out our Contributing Guide to get started!