Zvec Agent Skills

Official AI Agent skills for building with Zvec vector database.

Zvec Assistant is an AI Agent Skill that provides developers with complete technical guidance for the Zvec vector database. It supports Python and Node.js development environments, covering comprehensive documentation from basic concepts to advanced usage.

What is Zvec?

Zvec is an open-source, high-performance, lightweight in-process vector database built on Alibaba’s Proxima engine. No server or external infrastructure required, ready to use out of the box.

Key Features:

• ⚡ Sub-millisecond search latency, supporting billions of vectors
• 🧩 Single package installation, zero configuration, ready to use
• ✨ Supports dense vectors + sparse vectors, native multi-vector queries
• 🎯 Hybrid search: semantic similarity + structured filtering
• 📦 Pure in-process library, runs anywhere

Use Cases

• RAG Systems: Document retrieval capabilities for LLM applications
• Semantic Search: Intelligent search based on vector similarity
• Recommendation Systems: Similar product/content recommendations
• Multimodal Search: Joint image + text search
• Keyword + Semantic Hybrid Search: BM25 + vector embeddings

Installation

Install as AI Agent Skill

npx skills add github:zvec-ai/zvec-agent-skills --skill "zvec"

Install Zvec Package

Choose installation based on your development language:

Python:

pip install zvec

Node.js:

npm install @zvec/zvec

Quick Start

Python

import zvec

# Create Collection
schema = zvec.CollectionSchema(
    name="my_collection",
    fields=[
        zvec.FieldSchema(name="title", data_type=zvec.DataType.STRING),
    ],
    vectors=[
        zvec.VectorSchema(
            name="embedding",
            data_type=zvec.DataType.VECTOR_FP32,
            dimension=768,
            index_param=zvec.HnswIndexParam(
                metric_type=zvec.MetricType.COSINE
            ),
        ),
    ],
)

collection = zvec.create_and_open("./my_data", schema)

# Insert document
collection.upsert(zvec.Doc(
    id="doc_1",
    vectors={"embedding": [0.1] * 768},
    fields={"title": "Hello World"},
))

# Search
results = collection.query(
    vectors=zvec.VectorQuery(
        field_name="embedding",
        vector=[0.1] * 768,
    ),
    topk=10,
)

Node.js