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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.
- 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.
Requirements: Python 3.10 - 3.12
- Linux (x86_64, ARM64)
- macOS (ARM64)
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.
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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!