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Zvec is an open-source, in-process vector database — 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.

[!Important] 🚀 v0.8.0

• Broader platform support: Added Intel Mac and Windows ARM64 builds, wheels, and SDKs; extended DiskANN to Windows, Android, and iOS, and RaBitQ to Windows x86_64.
• Chinese FTS ready to use: Embedded the default Jieba dictionary and HMM model, so Chinese tokenization needs no separate dictionary files or path configuration.
• Lower index build costs: Reduced peak memory during IVF / DiskANN builds and merges, and accelerated HNSW RaBitQ construction through vector reuse and batched lookups. Build and nested training tasks now enforce configured thread limits more strictly.
• Faster distance computation: Extensively refactored quantizers and distance calculations to benefit multiple quantization types and metrics, with major improvements to INT4 kernels and Linux ARM64 performance.
• Faster search: Added faster Uniform quantizers and optimized Vamana graph layout and prefetching. Uniform quantization avoids per-point score reconstruction, though a shared min/max range can increase quantization error and reduce recall.
• Bug fixes and improvements: Added more flexible UTF-8 document IDs up to 1,024 bytes; fixed concurrent reads during segment switches, index rebuilding with deleted rows, and correctness issues in LIKE and floating-point range filters.

👉 Read the Release Notes

💫 Features

• Blazing Fast: Searches billions of vectors in milliseconds.
• Simple, Just Works: Install 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

Zvec offers official SDKs across multiple languages:

Python requires 64-bit Python 3.10–3.14; Java requires Java 8+.

Searching code or documents? Try zvec-grep (zg) — a local-first search CLI that unifies ripgrep, BM25, and vector search, built for humans and AI agents.

Prefer a visual tool? Try Zvec Studio to browse data and debug queries — no code required.

✅ Cross-Platform Support

Linux · macOS · Windows · Android · iOS

Zvec supports all major platforms — from servers and desktops to mobile devices.
For more details, please see the platform support documentation.

🛠️ Building from Source

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

⚡ One-Minute Python 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.Query(field_name="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.

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

🤝 Join Our Community

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❤️ 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!