概要
Zvec は、プロセス内ベクトルデータベースであり、高性能な類似性検索に特化して設計されています。密ベクトルと疎ベクトル、全文検索、ハイブリッド検索をサポートし、複数のインデックスタイプを提供します。先行書き込みログによる永続ストレージを実現し、Python、Node.js、Go、Rust、Dart などの公式 SDK を提供しています。
README プレビュー
\n English | 中文\n\n\n\n \n \n \n \n\n\n\n \n \n \n \n \n \n\n\n\n \n\n\n\n 🚀 Quickstart |\n 🏠 Home |\n 📚 Docs |\n 📊 Benchmarks |\n 🔎 DeepWiki |\n 🎮 Discord |\n 🐦 X (Twitter) \n\n\n**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.\n\n> [!Important]\n> 🚀 **v0.5.0 (June 12, 2026)**\n>\n> - **Full-Text Search (FTS)**: Native full-text search — attach an FTS index to any string field and query it with natural-language or structured expressions, no external search engine required.\n> - **Hybrid Retrieval**: Combine full-text and vector search in a single `MultiQuery` across dense vectors, sparse vectors, scalar filters, and text.\n> - **DiskANN Index**: New on-disk index that keeps the bulk of the index on disk, drastically cutting memory usage for large-scale datasets.\n> - **Ecosystem & Platforms**: New official [Go](https://github.com/zvec-ai/zvec-go) / [Rust](https://github.com/zvec-ai/zvec-rust) SDKs, the [Zvec Studio](https://github.com/zvec-ai/zvec-studio) visual tool, and RISC-V support.\n>\n> 👉 [Read the Release Notes](https://github.com/alibaba/zvec/releases/tag/v0.5.0) | [View Roadmap 📍](https://github.com/alibaba/zvec/issues/309)\n\n## 💫 Features\n\n- **Blazing Fast**: Searches billions of vectors in milliseconds.\n- **Simple, Just Works**: [Install](#-installation) and start searching in seconds. Pure local, no servers, no config, no fuss.\n- **Dense + Sparse Vectors**: Support dense and sparse embeddings, multi-vector queries, and a rich selection of [vector index types](https://zvec.org/en/docs/db/concepts/vector-index/#vector-index-types) that scale from memory to disk.\n- **Full-Text Search (FTS)**: Native keyword-based full-text search — query string fields with natural-language or structured expressions