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https://github.com/alibaba/zvec

A lightweight, lightning-fast, in-process vector database
https://github.com/alibaba/zvec

agent-skills db embedded faiss fts fulltext-search hnsw llm-memory local rag search-engine semantic-search similarity-search vector-database vector-db

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A lightweight, lightning-fast, in-process vector database

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**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.



> [!Important]

> 🚀 **v0.7.0 (August 24, 2026)**

>

> - **[zvec-grep](https://github.com/zvec-ai/zvec-grep) (`zg`)**: Local-first workspace search that unifies ripgrep, BM25, and vector search behind one CLI — built for humans and AI agents.

> - **[ReMe](https://github.com/agentscope-ai/ReMe) integration**: zvec is now a file store backend in ReMe, the memory management kit for agents, providing in-process HNSW ANN search.

> - **DiskANN productionization**: Adds **Linux ARM64 / macOS ARM64** support and an **io_uring** async I/O backend, with automatic fallback to the best available I/O option — no user intervention needed.

> - **Index optimization**: New **IVF-RaBitQ** index and **PQ-INT8** quantizer; RaBitQ supports runtime **AVX2 / AVX512** dispatch, so the same binary automatically picks the best path on each CPU.

> - **Deployment experience improved**: Prebuilt dynamic libraries slimmed significantly (macOS arm64 C API library 37→22 MB, **-40%**); new **musl libc / Alpine Linux** support; prebuilt SDK binaries for Linux (glibc/musl), macOS, Windows, Android, and iOS published with every release.

> - **DocIterator**: New iterator for streaming full-collection document traversal across C++, C, and Python.

> - **Full-text search**: New **N-gram tokenizer**, better suited for phrase, code, and short-text search.

>

> 👉 [Read the Release Notes](https://github.com/alibaba/zvec/releases/tag/v0.7.0) | [View Roadmap 📍](https://github.com/alibaba/zvec/issues/309)



## 💫 Features



- **Blazing Fast**: Searches billions of vectors in milliseconds.

- **Simple, Just Works**: [Install](#-installation) and start searching in seconds. Pure local, no servers, no config, no fuss.

- **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.

- **Full-Text Search (FTS)**: Native keyword-based full-text search — query string fields with natural-language or structured expressions.

- **Hybrid Search**: Fuse vector similarity, full-text search, and structured filters in a single query for precise results.

- **Durable Storage**: Write-ahead logging (WAL) guarantees persistence — data is never lost, even on process crash or power failure.

- **Concurrent Access**: Multiple processes can read the same collection simultaneously; writes are single-process exclusive.

- **Runs Anywhere**: As an in-process library, Zvec runs wherever your code runs — notebooks, servers, CLI tools, or even edge devices.



## 📦 Installation



Zvec offers official SDKs across multiple languages:



- **[Python](https://pypi.org/project/zvec/)**: `pip install zvec` (requires 64-bit Python 3.10–3.14)

- **[Node.js](https://www.npmjs.com/package/@zvec/zvec)**: `npm install @zvec/zvec`

- **[Go](https://github.com/zvec-ai/zvec-go)**: High-performance Go bindings.

- **[Rust](https://crates.io/crates/zvec-rust)**: `cargo add zvec-rust`

- **[Dart/Flutter](https://pub.dev/packages/zvec)**: `flutter pub add zvec`



Searching code or documents? Try **[zvec-grep](https://github.com/zvec-ai/zvec-grep)** (`zg`) — a local-first search CLI that unifies ripgrep, BM25, and vector search, built for humans and AI agents.



Prefer a visual tool? Try **[Zvec Studio](https://github.com/zvec-ai/zvec-studio)** to browse data and debug queries — no code required.



### ✅ Supported Platforms



- Linux (x86_64, ARM64; glibc & musl)

- macOS (ARM64, x86_64)

- Windows (x86_64)



### 🛠️ Building from Source



If you prefer to build Zvec from source, please check the [Building from Source](https://zvec.org/en/docs/db/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.Query(field_name="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.



Zvec Performance Benchmarks



For detailed benchmark methodology, configurations, and complete results, please see our [Benchmarks documentation](https://zvec.org/en/docs/db/benchmarks/).



## 🤝 Join Our Community









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## ❤️ Contributing



We welcome and appreciate contributions from the community! Whether you're fixing a bug, adding a feature, or improving documentation, your help makes Zvec better for everyone.



Check out our [Contributing Guide](./CONTRIBUTING.md) to get started!