RAG・知識ベース
turbovec
A blazing-fast vector index in Rust, built on TurboQuant - with Python bindings
★ 17.4k スターMIT
ガイドを見る →zvecは、alibabaが公開しているオープンソースのRAG・知識ベースプロジェクトです（GitHub: alibaba/zvec）。GitHubスターは16,079、フォークは1,008で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年9月29日です。
Lightweight, lightning-fast in-process vector database from Alibaba that embeds in your app.
Choose zvec when you want vector search inside your process - no separate database server to deploy, configure or keep running.
Add the Python, Node.js, Go, Rust or Dart package to your project in one command.
Create a schema for your collection, insert documents with their embeddings, and let zvec build the index.
Run vector similarity, full-text keyword or combined hybrid queries, and tune index and quantization options as data grows.
Zvec runs inside your application with no server, no config and no fuss - install it and start searching in seconds.
Supports dense and sparse embeddings, multi-vector queries, and a rich selection of vector index types that scale from memory to disk.
Native keyword-based full-text search lets you query string fields with natural-language or structured expressions.
Combines vector similarity and full-text results in one system for retrieval that catches both meaning and exact terms.
Official packages for Python (pip), Node.js (npm), Go, Rust and Dart/Flutter, all backed by the same C++ core.
Index options including IVF-RaBitQ and PQ-INT8 quantization, plus DiskANN support, keep large collections fast and compact.
Clone the zvec source if you want to build from source or explore the C++ core and examples.
git clone https://github.com/alibaba/zvec.git
cd zvecMost users simply install the prebuilt package - 64-bit Python 3.10 to 3.14 - rather than building from source.
pip install zvecCreate a collection, insert vectors and query them using the quickstart example from the README and docs.
python -c "import zvec; print(zvec.__version__)" # then follow the one-minute example - see README for the exact codeZvec is an Alibaba open-source project, battle-tested within Alibaba Group, with community contributors.
Yes. It is Apache-2.0 licensed, so you can use it in commercial products.
No. It runs locally in your process; you only need your own embedding model or API to turn text into vectors.
FAISS is a library for vector search only, and Milvus is a server database; zvec is an embeddable database that adds full-text and hybrid search, schemas and multiple language SDKs on top of fast vector indexes.
Install it with pip install zvec (or the npm, Go, Rust or Dart package), then follow the one-minute example in the README to create a collection and query it.
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