zvec-ai @ZvecAI
⚡ Lightning-fast, lightweight embedded vector DB. 🚀 High performance made simple. - Github: https://t.co/aoQStiZ5G8 - Discord: https://t.co/E4j6cRwGQT zvec.org Joined February 2026-
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🎉 Zvec 0.5.0 Release is Live! This release focuses on retrieval, indexing, ecosystem, and platform & hardware. Give it a try and let us know what you think! ✨ ✨ What's new: • 🔍 Retrieval: Native full-text search works out of the box; a single query fuses full-text search, semantic vectors, and conditional filters, with multi-way result merging handled automatically; the MultiQuery interface is implemented natively in C/C++, so every language SDK can integrate hybrid retrieval with ease • 💾 On-Disk Indexing: New DiskANN on-disk index significantly reduces memory overhead in large-scale scenarios; it complements HNSW/IVF/Flat to cover the full range of retrieval needs, from pure in-memory to on-disk • 🌐 Ecosystem: Brand-new official Go SDK and Rust SDK for smoother multi-language access; plus the new visual tool Zvec Studio for zero-code data browsing and query debugging • 🖥️ Platform & Hardware: Added RISC-V support, further expanding hardware compatibility 📚 Learn more: • 📄 Release Notes: zvec.org/en/blog/2026-0… • 🧭 Roadmap: github.com/alibaba/zvec/i… • 🐛 Report bugs / requests: github.com/alibaba/zvec/i… • 💬 Discuss & share: github.com/alibaba/zvec/d…
A nice write-up on what local-first vector search can actually look like on desktop apps. This post uses Zvec + Obsidian as an example to show: 1. local semantic search 2. image + text retrieval 3. related context surfacing while writing 4. fully on-device retrieval pipelines Feels like we're moving from “vector databases in the cloud” toward “vector search as a native capability inside apps”. Worth a read: zvec.org/en/blog/2026-0…
🎉 Zvec v0.4.0 is Live! This release focuses on mobile coverage, search capabilities, and stability. Give it a try and let us know what you think! ✨ What's new: • 📱 Mobile Coverage: Official Dart/Flutter SDK for Android (arm64-v8a) and iOS (arm64) • 🔍 Retrieval Enhancement: Enlarged topK limit to support large-scale candidate recall for downstream re-ranking pipelines • 🎯 Quantization Accuracy: Fixed SQ8 quantizer recall drop caused by missing int8 rounding in metadata computation • 🖥️ Cross-platform Stability: Fixed Windows drive-root path handling and improved OS error reporting; relaxed collection path validation For more details, please read the release blog: zvec.org/en/blog/2026-0… We also shared a deep dive into the engineering behind our lightweight core: zvec.org/en/blog/2026-0…
Two ways to use Zvec with your AI Coding Agent: 1️⃣ MCP Server — Your agent operates the vector DB directly via natural language. No code needed. zvec.org/en/docs/ai/mcp/ 2️⃣ Agent Skill — Your agent writes Zvec code (Python/Node.js) for you. Just describe your use case (RAG, e-commerce search, etc.) and get working code with schema design. zvec.org/en/docs/ai/ski… More native AI support is on the way! 🚀
🎉 Zvec v0.3.0 is Live! This release brings full multi-platform support for Windows, macOS, and Linux, along with official SDKs for Python, Node.js, and C. We've integrated the RabitQ quantization algorithm and added batch optimization for IP/L2 distance calculations on x86. We have also added MCP and Skill framework support for AI Agents. ⚡Performance and simplicity are always our top priorities. For more details, please read the blog post: zvec.org/en/blog/2026-0…
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