A specialized utility for generating high-dimensional text embeddings using AIMLAPI models like text-embedding-3-large.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install aiml-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 aiml-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 AIMLAPI Embeddings?
This skill provides a streamlined interface for interacting with AIMLAPI to convert text into numerical vectors. These embeddings are essential for developers building modern AI applications that require semantic understanding, similarity matching, or sophisticated data organization. By utilizing Openclaw Skills, developers can quickly integrate advanced embedding capabilities into their workflows without complex boilerplate, allowing for rapid experimentation with different vector dimensions and models.
AIMLAPI Embeddings Use Cases
- Implementing semantic search within internal documentation or large databases.
- Clustering massive datasets to identify patterns and relationships between text samples.
- Building recommendation systems based on content similarity rather than just keyword matching.
- Preparing data for vector databases used in RAG (Retrieval-Augmented Generation) pipelines.
How AIMLAPI Embeddings Works
- The user provides a text input and specifies an AIMLAPI embedding model such as text-embedding-3-large.
- The script authenticates with the AIMLAPI service using the pre-configured API key.
- The raw text is transmitted to the API, which processes it into a high-dimensional vector based on the specified dimensionality.
- The resulting vector representation is returned and saved to a designated output directory for use in downstream AI tasks.
AIMLAPI Embeddings Setup
To use this within the Openclaw Skills ecosystem, set your API key and run the generation script:
export AIMLAPI_API_KEY="sk-aimlapi-..."
python3 scripts/gen_embeddings.py --input "Your text here" --model text-embedding-3-large --dimensions 1024 --out-dir ./out/embeddings
AIMLAPI Embeddings Data Schema & Taxonomy
| Argument | Description |
|---|---|
--input |
The source text string to be converted into a vector. |
--model |
The specific AIMLAPI model used (e.g., text-embedding-3-large). |
--dimensions |
The output vector length (e.g., 1024 or 1536). |
--out-dir |
The destination directory for saving generated embedding files. |
AIMLAPI Embeddings Advanced Features
- Customizable output dimensionality to balance storage performance and search accuracy.
- Support for multiple state-of-the-art embedding models through a single, unified interface.
- Automated output directory management to keep large-scale embedding tasks organized.
- Seamless integration with other Openclaw Skills for end-to-end data processing workflows.
SKILL.md
Loading
Related Openclaw Skills
Moltbook
Moltbook provides the social infrastructure for AI agents to post, comment, upvote, and collaborate within a verified community ecosystem.
Memory Search
A high-performance semantic search tool that allows AI agents to recall personal context, project details, and past interactions.
Portfolio Tracker
A privacy-first, local investment portfolio tracker for managing stocks, crypto, and blockchain assets via Openclaw Skills.
bagsworld
bagsworld is a pixel art world where AI agents manifest as visual creatures or buildings to participate in a shared on-chain economy.
AIMLAPI LLM + Reasoning
Execute high-performance AIMLAPI chat completions and complex reasoning workflows with built-in retries and structured output support.
AIMLAPI Music Generation
Generate high-fidelity music, songs, and soundtracks using top-tier AI models through the Openclaw Skills framework.
Featured*
