The official Node.js binding for Zvec, an open-source, in-process vector database that is lightweight, lightning-fast, and designed to embed directly into applications. Battle-tested within Alibaba Group, it delivers production-grade, low-latency and scalable similarity search with minimal setup.

💫 Features

• Blazing Fast: Searches billions of vectors in milliseconds.
• Simple, Just Works: Install with npm and start searching in seconds. Pure local, no servers, no config, no fuss.
• Dense + Sparse Vectors: Support dense and sparse embeddings, multi-vector queries, and a rich selection of vector index types that scale from memory to disk.
• Full-Text Search (FTS): Native keyword-based full-text search — query string fields with natural-language or structured expressions.
• Hybrid Search: Fuse vector similarity, full-text search, and structured filters in a single query for precise results.
• Durable Storage: Write-ahead logging (WAL) guarantees persistence — data is never lost, even on process crash or power failure.
• Concurrent Access: Multiple processes can read the same collection simultaneously; writes are single-process exclusive.
• Runs Anywhere: As an in-process library, Zvec runs wherever your code runs — notebooks, servers, CLI tools, or even edge devices.

📦 Installation

npm install @zvec/zvec

✅ Supported Platforms

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

🛠️ Building from Source

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

⚡ One-Minute Example

import { ZVecCreateAndOpen, ZVecCollectionSchema, ZVecDataType } from "@zvec/zvec";

// Define collection schema
const schema = new ZVecCollectionSchema({
  name: "example",
  vectors: { name: "embedding", dataType: ZVecDataType.VECTOR_FP32, dimension: 4 },
});

// Create collection
const collection = ZVecCreateAndOpen("./zvec_example", schema);

// Insert documents
collection.insertSync([
  { id: "doc_1", vectors: { embedding: [0.1, 0.2, 0.3, 0.4] } },
  { id: "doc_2", vectors: { embedding: [0.2, 0.3, 0.4, 0.1] } },
]);

// Search by vector similarity
const results = collection.querySync({
  fieldName: "embedding",
  vector: [0.4, 0.3, 0.3, 0.1],
  topk: 10,
});

// Results: array of { id, score, vectors, fields }, sorted by relevance
console.log(results);

📈 Performance at Scale

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

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

❤️ Contributing

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.