zvec— agent skill

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<p align="center"> <a href="https://zvec.org/en/docs/db/quickstart/">🚀 <strong>Quickstart</strong> </a> | <a href="https://zvec.org/en/">🏠 <strong>Home</strong> </a> | <a href="https://zvec.org/en/docs/db/">📚 <strong>Docs</strong> </a> | <a href="https://zvec.org/en/docs/db/benchmarks/">📊 <strong>Benchmarks</strong> </a> | <a href="https://deepwiki.com/alibaba/zvec">🔎 <strong>DeepWiki</strong> </a> | <a href="https://discord.gg/rKddFBBu9z">🎮 <strong>Discord</strong> </a> | <a href="https://x.com/ZvecAI">🐦 <strong>X (Twitter)</strong> </a> </p>

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] 🚀 v0.5.0 (June 12, 2026)

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- 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. - Hybrid Retrieval: Combine full-text and vector search in a single MultiQuery across dense vectors, sparse vectors, scalar filters, and text. - DiskANN Index: New on-disk index that keeps the bulk of the index on disk, drastically cutting memory usage for large-scale datasets. - Ecosystem & Platforms: New official Go / Rust SDKs, the Zvec Studio visual tool, and RISC-V support.

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👉 Read the Release Notes | View Roadmap 📍

💫 Features

• Blazing Fast: Searches billions of vectors in milliseconds.
• Simple, Just Works: Install 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 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 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://github.com/zvec-ai/zvec-rust): High-performance Rust bindings.
• [Dart/Flutter](https://pub.dev/packages/zvec): flutter pub add zvec

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)
• macOS (ARM64)
• Windows (x86_64)

🛠️ Building from Source

If you prefer to build Zvec from source, please check the Building from Source guide.

⚡ One-Minute Example

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.

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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 to get started!