GitHub – alibaba/zvec: A lightweight, lightning-fast, in-process vector database Skip to content You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert alibaba / zvec Public Notifications You must be signed in to change notification settings Fork 139 Star 2.8k A lightweight, lightning-fast, in-process vector database zvec.org/en/ License Apache-2.0 license 2.8k stars 139 forks Branches Tags Activity Star Notifications You must be signed in to change notification settings alibaba/zvec main Branches Tags Go to file Code Open more actions menu Folders and files Name Name Last commit message Last commit date Latest commit History 61 Commits 61 Commits .github .github cmake cmake examples/ c++ examples/ c++ python python scripts scripts src src tests tests thirdparty thirdparty tools tools .clang-format .clang-format .gitignore .gitignore .gitmodules .gitmodules .pre-commit-config.yaml .pre-commit-config.yaml CMakeLists.txt CMakeLists.txt CODE_OF_CONDUCT.md CODE_OF_CONDUCT.md CONTRIBUTING.md CONTRIBUTING.md LICENSE LICENSE README.md README.md pyproject.toml pyproject.toml View all files Repository files navigation 🚀 Quickstart | 🏠 Home | 📚 Docs | 📊 Benchmarks | 🎮 Discord | 🐦 X (Twitter) 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 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 Python Requirements : Python 3.10 – 3.12 pip install zvec Node.js npm install @zvec/zvec ✅ Supported Platforms Linux (x86_64, ARM64) macOS (ARM64) 🛠️ 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 simila

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