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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
🚀 v0.6.0 (July 20, 2026)
- Group-By Search: Retrieve top-K results per group instead of globally (group-by deduplication) across Flat, HNSW, HNSW-RaBitQ, and sparse indexes.
- Random Rotation Quantization: Optional random rotation for INT8/INT4 quantization distributes variance evenly across dimensions, significantly boosting recall.
- Enhanced Full-Text Search: Upgraded FTS pipeline with a Unicode UAX #29 standard tokenizer, UTF-8 / ASCII folding, and a Snowball-based stemmer supporting 34+ languages.
- Faster & More Robust: Block-max skip speeds up FTS conjunction queries by 22–38%, plus a new DiskANN C API and numerous stability fixes.
💫 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:
pip install zvec(requires 64-bit Python 3.10–3.14) - Node.js:
npm install @zvec/zvec - Go: High-performance Go bindings.
- Rust:
cargo add zvec-rust - Dart/Flutter:
flutter pub add zvec
Prefer a visual tool? Try Zvec Studio to browse data and debug queries — no code required.
✅ 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 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.
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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!
