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url: https://github.com/alibaba/zvec date_fetched: 2026-07-05 backfilled: true

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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

🚀 v0.5.0 (June 12, 2026)

• Full-Text Search (FTS): Native full-text search — attach an FTS index to any string field and query it with natural-language or structured expressions, no external search engine required.
• Hybrid Retrieval: Combine full-text and vector search in a single- MultiQueryacross dense vectors, sparse vectors, scalar filters, and text.
• DiskANN Index: New on-disk index that keeps the bulk of the index on disk, drastically cutting memory usage for large-scale datasets.

• Ecosystem & Platforms: New official Go / Rust SDKs, the Zvec Studio visual tool, and RISC-V support.

• 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.

Zvec offers official SDKs across multiple languages:

• Python:- pip install zvec(requires Python 3.10–3.14)
• Node.js:- npm install @zvec/zvec
• Go: High-performance Go bindings.
• Rust: High-performance Rust bindings.
• Dart/Flutter:- flutter pub add zvec

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

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

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.

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!