官方AI代理技能，用于使用Zvec矢量数据库构建。

Zvec 助手是一项 AI 代理技能，为开发者提供 Zvec 向量数据库的完整技术指导。它支持 Python 和 Node.js 开发环境，涵盖从基础概念到高级使用的全面文档。

什么是“子”？

Zvec 是一个开源、高性能、轻量化的进程向量数据库，基于阿里巴巴的 Proxima 引擎构建。无需服务器或外部基础设施，开箱即用。

主要功能：

• ⚡ 亚毫秒级搜索延迟，支持数十亿向量
• 🧩 单一封装安装，零配置，现成即用
• ✨ 支持密集向量 + 稀疏向量，原生多向量查询
• 🎯 混合搜索：语义相似性 + 结构化过滤
• 📦 纯处理中库，可随地运行

使用场景

• RAG 系统：LLM 应用的文档检索功能
• 语义搜索：基于向量相似性的智能搜索
• 推荐系统：类似的产品/内容推荐
• 多模态搜索：联合图像+文本搜索

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