A lightweight, lightning-fast, in-process vector database

🚀 Quickstart | 🏠 Home | 📚 Docs | 📊 Benchmarks | 🎮 Discord | 🐦 X (Twitter)

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.

• 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.

Requirements: Python 3.10 - 3.12

pip install zvec

npm install @zvec/zvec

• Linux (x86_64, ARM64)
• macOS (ARM64)

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

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)

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

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

Stay updated and get support — scan or click:

💬 DingTalk

📱 WeChat

🎮 Discord Join Server

🐦 X (Twitter) Follow @zvec_ai

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!