zvec
zvec-ai/zvec-agent-skills · skills.sh
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About
Skill publicada por zvec-ai/zvec-agent-skills no skills.sh. Instale com: npx skills add zvec-ai/zvec-agent-skills@zvec
Details
- Kind
- Agent skills
- Publisher
- zvec-ai
- Origin
- skillssh
- Category
- ferramentas
- Stars
- 21
- Forks
- 3
- Last push
- 2026-04-09T07:50:05Z
- Repository state
- ativo
- Language
- TypeScript
- License
- Apache-2.0
- Added
- 2026-10-07 05:32:57
- Updated
- 2026-10-07 05:32:57
- Origin id
zvec-ai/zvec-agent-skills/zvec
README
# Zvec Agent Skills
[](https://opensource.org/licenses/Apache-2.0)
[](https://www.python.org/)
[](https://nodejs.org/)
[](https://github.com/zvec-ai/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](https://zvec.org/) 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
```bash
npx skills add github:zvec-ai/zvec-agent-skills --skill "zvec"
```
### Install Zvec Package
Choose installation based on your development language:
**Python:**
```bash
pip install zvec
```
**Node.js:**
```bash
npm install @zvec/zvec
```
## Quick Start
### Python
```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
```typescript
import { ZVecCreateAndOpen, ZVecCollectionSchema, ZVecFieldSchema, ZVecVectorSchema, ZVecDataType, ZVecHnswIndexParams, ZVecMetricType } from "@zvec/zvec";
const schema = new ZVecCollectionSchema({
name: "my_collection",
fields: [new ZVecFieldSchema({ name: "title", dataType: ZVecDataType.STRING })],
vectors: [new ZVecVectorSchema({
name: "embedding",
dataType: ZVecDataType.VECTOR_FP32,
dimension: 768,
indexParams: new ZVecHnswIndexParams({ metricType: ZVecMetricType.COSINE }),
})],
});
const collection = ZVecCreateAndOpen("./my_data", schema);
```
## Project Structure
```
zvec-agent-skills/
├── skills/ # Built skill output directory
│ └── zvec/
│ ├── SKILL.md # Skill main document
│ ├── quick-start/ # Quick start
│ │ ├── python.md
│ │ └── typescript.md
│ ├── collection-management/# Collection management
│ ├── data-operations/ # Data operations
│ ├── vector-search/ # Vector search
│ ├── rag-system/ # RAG system
│ ├── hybrid-search/ # Hybrid search
│ ├── multimodal-search/ # Multimodal search
│ ├── data-model.md # Data model
│ ├── api-cheatsheet.md # API cheatsheet
│ └── troubleshooting.md # Troubleshooting
├── src/ # Source code directory
│ └── zvec/
│ ├── SKILL.md # Skill main document source
│ ├── templates/ # Markdown templates
│ ├── code/ # Code examples
│ │ ├── python/
│ │ └── typescript/
│ └── general/ # General documents
├── scripts/ # Build scripts
│ └── build-skills.ts # Skill build script
├── package.json # Project configuration
├── CLAUDE.md # Developer guide
└── README.md # Project readme
```
## Build
This project uses a build system to convert source code into distributable skills.
```bash
# Install dependencies
bun install
# Build skills
bun run build
```
The built output is located in the `skills/zvec/` directory.
## Usage Scenarios
### RAG Document Retrieval
Build intelligent document retrieval systems for LLM applications:
```
I want to build a RAG system with Python and Zvec for technical support knowledge base.
Documents are in Markdown format and need semantic search support.
```
### E-commerce Product Search
Implement semantic product search with filtering capabilities:
```
I need to implement e-commerce product search in a Node.js project:
- Support semantic search by product name and description
- Filter by price, category, and brand
- Support multimodal (image + text) search
```
### Log Troubleshooting Knowledge Base
Store and search system log failure cases:
```
I want to use Zvec to store system log failure cases and support semantic search for similar issues.
Using Python with about 100k data entries.
```
## Available Topics
### Python
- [Quick Start](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/quick-start/python.md) - Quick start with Zvec Python API
- [Collection Management](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/collection-management/python.md) - Create, open, and manage Collections
- [Data Operations](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/data-operations/python.md) - Insert, update, and delete documents
- [Vector Search](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/vector-search/python.md) - Single-vector, multi-vector, and hybrid search
- [RAG System](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/rag-system/python.md) - Build document retrieval system
- [Hybrid Search](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/hybrid-search/python.md) - Vector similarity + scalar filtering
- [Multimodal Search](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/multimodal-search/python.md) - Image + text joint search
### Node.js
- [Quick Start](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/quick-start/typescript.md) - Quick start with Zvec Node.js API
- [Collection Management](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/collection-management/typescript.md) - Create, open, and manage Collections
- [Data Operations](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/data-operations/typescript.md) - Insert, update, and delete documents
- [Vector Search](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/vector-search/typescript.md) - Single-vector, multi-vector, and hybrid search
- [RAG System](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/rag-system/typescript.md) - Build document retrieval system
- [Hybrid Search](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/hybrid-search/typescript.md) - Vector similarity + scalar filtering
- [Multimodal Search](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/multimodal-search/typescript.md) - Image + text joint search
### General
- [Data Model](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/data-model.md) - Zvec data model overview
- [API Cheatsheet](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/api-cheatsheet.md) - Python & Node.js API quick reference
- [Troubleshooting](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/skills/zvec/troubleshooting.md) - Common issues and solutions
## Usage Examples
### Hybrid Search (Vector + Filter)
```python
# Search similar products while filtering by price and stock
results = collection.query(
vectors=zvec.VectorQuery(
field_name="description_vec",
vector=query_vector,
),
filter="price >= 100 AND price <= 500 AND in_stock == true",
topk=10,
)
```
### Multi-Vector Search
```python
# Search using both image and text vectors
results = collection.query(
vectors=[
zvec.VectorQuery(field_name="image_vec", vector=image_query),
zvec.VectorQuery(field_name="text_vec", vector=text_query),
],
reranker=zvec.WeightedReRanker(
topn=10,
metric=zvec.MetricType.COSINE,
weights={"image_vec": 0.7, "text_vec": 0.3},
),
)
```
## Best Practices
For detailed usage guidelines in Claude Code and Claude Desktop, see [CLAUDE.md](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/CLAUDE.md).
### Quick Tips
- **Specify your development language** when asking questions (Python or Node.js)
- **Describe your use case** for more targeted recommendations
- **Ask for architectural advice** when designing your schema and indexing strategy
## Contributing
Contributions are welcome! Please check [CONTRIBUTING.md](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/CONTRIBUTING.md) to learn how to participate.
## License
This project is licensed under the [Apache License 2.0](https://github.com/zvec-ai/zvec-agent-skills/blob/HEAD/LICENSE).
## Related Links
- [GitHub Repository](https://github.com/zvec-ai/agent-skills)
- [Zvec Official Documentation](https://zvec.org/en/docs/)
- [Zvec Python API](https://zvec.org/api-reference/python/)
- [Zvec Node.js API](https://zvec.org/api-reference/nodejs/)
- [Zvec GitHub](https://github.com/alibaba/zvec)