By name: embed description: Receives a text question and returns its embedding vector using gemini-embedding-001 via Vertex AI. Use this when you need a semantic embedding for a question — for downstream vector search, similarity comparison, or passing to another tool.
Embed Skill
Embeds a text question using gemini-embedding-001 and returns the resulting vector as a JSON array.
Usage
python "${CLAUDE_PLUGIN_ROOT:?set by the plugin system; if empty, invoke this via the Skill tool}"/skills/embed/scripts/embed.py --question "<your question>"
Output is a JSON array of 3072 floats printed to stdout:
[0.012, -0.034, 0.091, ...]
Steps
- Run the script with the user's question as
--question. - Capture stdout — that is the embedding vector.
- Return or pass the vector as needed.
Prerequisites
Google Cloud SDK with active credentials:
gcloud auth application-default login
gcloud config set project <project-id>
Troubleshooting
| Symptom | Fix |
|---|---|
GCP authentication required |
Run gcloud auth application-default login |
No GCP project configured |
Run gcloud config set project <project-id> |
API error 401 |
Re-authenticate with gcloud auth application-default login |