Generate text embeddings via AIMLAPI. Use for semantic search, clustering, or high-dimensional text representations with text-embedding-3-large and other models.
Scanned 9/7/2026
Install to Claude Code
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---
name: aimlapi-embeddings
description: Generate text embeddings via AIMLAPI. Use for semantic search, clustering, or high-dimensional text representations with text-embedding-3-large and other models.
env:
- AIMLAPI_API_KEY
primaryEnv: AIMLAPI_API_KEY
---
# AIMLAPI Embeddings
## Overview
Converts text into high-dimensional numerical representations (vectors) using AIMLAPI's embedding models like `text-embedding-3-large`.
## Quick start
```bash
export AIMLAPI_API_KEY="sk-aimlapi-..."
python3 {baseDir}/scripts/gen_embeddings.py --input "Laura is a DJ."
```
## Tasks
### Generate embeddings
Use `scripts/gen_embeddings.py` to get vector representations of text.
```bash
python3 {baseDir}/scripts/gen_embeddings.py \
--input "Knowledge is power." \
--model text-embedding-3-large \
--dimensions 1024 \
--out-dir ./out/embeddings
```
## References
- `references/endpoints.md`: API schema and parameter details.
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Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.