zvec/README.md

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  <a href="https://zvec.org/en/docs/quickstart/">🚀 <strong>Quickstart</strong> </a> |
  <a href="https://zvec.org/en/">🏠 <strong>Home</strong> </a> |
  <a href="https://zvec.org/en/docs/">📚 <strong>Docs</strong> </a> |
  <a href="https://zvec.org/en/docs/benchmarks/">📊 <strong>Benchmarks</strong> </a> |
  <a href="https://discord.gg/rKddFBBu9z">🎮 <strong>Discord</strong> </a> |
  <a href="https://x.com/zvec_ai">🐦 <strong>X (Twitter)</strong> </a>
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**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 with `pip install zvec` 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

Install Zvec from PyPI with a single command:

```bash
pip install zvec
```

**Requirements**:

- Python 3.10 - 3.12
- **Supported platforms**:
  - Linux (x86_64/ARM64)
  - macOS (ARM64)

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

<img src="https://zvec.oss-cn-hongkong.aliyuncs.com/qps_10M.svg" width="800" alt="Zvec Performance Benchmarks" />

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