Zvec

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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.

💫 Features

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

Python

Requirements: Python 3.10 - 3.12

pip install zvec

Node.js

npm install @zvec/zvec

✅ Supported Platforms

Linux (x86_64, ARM64)
macOS (ARM64)

🛠️ Building from Source

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

⚡ One-Minute Example

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

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

Zvec Performance Benchmarks

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

Tags: storage vector ai in-memory

Last modified 10 March 2026