A comprehensive skill for generating, managing, and searching high-dimensional vector embeddings for semantic search and AI applications.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install embeddings
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install embeddings using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Embeddings?
The Embeddings skill provides a robust framework for handling vector representations of text, images, and code. As part of the Openclaw Skills library, it empowers developers to build sophisticated semantic search engines, recommendation systems, and retrieval-augmented generation (RAG) workflows by bridging the gap between raw data and machine-understandable vectors.
By leveraging this skill, users can navigate the complexities of provider selection, chunking strategies, and vector database optimization. It ensures that your AI agents can efficiently process large datasets, maintain context through intelligent document splitting, and execute high-performance similarity searches across various infrastructure backends.
Embeddings Use Cases
- Building semantic search engines for large document repositories
- Implementing Retrieval-Augmented Generation (RAG) for LLM applications
- Creating cross-modal search systems for text-to-image or image-to-image queries
- Optimizing technical documentation search with specialized code embedding models
- Developing recommendation engines based on content similarity
How Embeddings Works
- Select an embedding provider based on quality, cost, and data privacy requirements.
- Prepare source content by applying chunking strategies with appropriate overlap and token limits.
- Generate vectors using batch API calls or local models to maximize throughput.
- Normalize and store embeddings in a vector database like Pinecone, Weaviate, or pgvector.
- Perform similarity searches using query embeddings that match the original model's dimensions and normalization.
Embeddings Setup
To begin using this skill within the Openclaw Skills ecosystem, configure your environment with the necessary API keys and install your preferred vector client:
# Install common embedding and vector DB clients
pip install openai cohere pinecone-client qdrant-client
# Set environment variables for your chosen providers
export OPENAI_API_KEY='your-key-here'
export COHERE_API_KEY='your-key-here'
Refer to the internal documentation for specific provider comparisons and storage patterns.
Embeddings Data Schema & Taxonomy
The skill organizes data around the relationship between source chunks and high-dimensional vectors. Use the following schema for storage consistency:
| Field | Description | Type |
|---|---|---|
| id | Unique identifier for the chunk | String |
| text | The raw text or source content | String |
| vector | The generated embedding array | Float Array |
| metadata | Contextual tags (source, category, filters) | JSON |
| hash | Hash of source text for caching | String |
Embeddings Advanced Features
- Multi-provider switching for hybrid or cost-optimized workflows
- Semantic boundary detection for more accurate document chunking
- Integration with specialized models like Voyage for code-specific embeddings
- Hybrid search capabilities combining vector similarity with keyword filtering
- Automatic caching mechanisms to reduce API costs and prevent redundant generation
SKILL.md
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