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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. ## 💫 Features - **Blazing Fast**: Searches billions of vectors in milliseconds. - **Simple, Just Works**: Install with `pip install zvec` 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. ## 📦 Installation Install Zvec from PyPI with a single command: ```bash pip install zvec ``` **Requirements**: - Python 3.10 - 3.12 - **Supported platforms**: - Linux (x86_64) - macOS (ARM64) If you prefer to build Zvec from source, please check the [Building from Source](https://zvec.org/en/docs/build/) guide. ## ⚡ One-Minute Example ```python 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) ``` ## 📈 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](https://zvec.org/en/docs/benchmarks/).
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