# Zvec
**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.
[📚 Quick Start](https://zvec.org/en/docs/quickstart/) | 🌐 [Website](https://zvec.org/en/) | [📖 Documentation](https://zvec.org/en/docs/) | [📊 Benchmarks](https://zvec.org/en/docs/benchmarks/) | [💜 Discord](https://discord.gg/ZSbbfAKk)
## 💫 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)
- macOS (ARM64/x86_64)
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.
For detailed benchmark methodology, configurations, and complete results, please see our [Benchmarks documentation](https://zvec.org/en/docs/benchmarks/).
## ❤️ 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!
For detailed benchmark methodology, configurations, and complete results, please see our [Benchmarks documentation](https://zvec.org/en/docs/benchmarks/).
## ❤️ 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!