embed
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.
Datos de origen
- Repositorio
- mozilla/rag-skills
- Última actividad en el origen
- 25 de junio de 2026 a las 13:30
- Idioma detectado de SKILL.md
- inglés
- Estrellas
- 2
- Forks
- 2
Opciones de instalación
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Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.
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SKILL.md
Instrucciones de origen · Vista previa de solo lectura- 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` (Vertex AI, project `mozdata`) and returns the resulting vector as a JSON array.
## Usage
```bash
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:
```json
[0.012, -0.034, 0.091, ...]
```
## Steps
1. Run the script with the user's question as `--question`.
2. Capture stdout — that is the embedding vector.
3. Return or pass the vector as needed (e.g. to the `vector-search` skill).
## Authentication
This skill connects to Vertex AI using a service account — the `cloud-platform` scope is enforced on the impersonated token in code. Just log in:
```bash
gcloud auth application-default login
```
Your authenticated Google Cloud credentials are only used to create a new and temporary access token on behalf of the service account defined in `SERVICE_ACCOUNT`, which requires the `roles/iam.serviceAccountTokenCreator` role on that service account. Your login no longer needs `--scopes`.
## Troubleshooting
| Symptom | Fix |
|---------|-----|
| `GCP authentication required` / `API error 401` | Re-run `gcloud auth application-default login`; confirm you have `roles/iam.serviceAccountTokenCreator` on the service account in `SERVICE_ACCOUNT` |
| `Missing dependency` | `pip install google-auth requests` |
| `Unexpected response from Vertex AI` | Confirm Vertex AI is enabled in project `mozdata` and the embedding model is available |
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