Gemini Embed Content: Google Gemini action

Inputs

The fields this action accepts.

Every field can be mapped from an upstream trigger, AI step, table row, or hard-coded literal.

Field	Type	Required	Description
Embedding Model model	options	Required	Embedding Model. Options: Gemini Embedding 001 (3K dim), Text Embedding 004
Text text	string	Required	Text (required)

Sample request

{
  "model": "{{trigger.model}}",
  "text": "{{trigger.text}}"
}

Returns

{
  "embedding": {
    "values": [
      0.012,
      -0.045
    ]
  }
}

Use these fields in downstream nodes for routing, logging, or error handling.

FAQ

Questions about Gemini Embed Content.

What does the Gemini Embed Content action do in Google Gemini?

Generates embeddings using text-embedding-004 or gemini-embedding-001. For RAG-pipeline vector generation in Google AI ecosystem workflows.

What inputs does Gemini Embed Content require?

Required: Embedding Model, Text. Every input accepts a static value or a variable from any upstream node in your workflow.

Can I use dynamic inputs from earlier workflow nodes?

Yes. Any field on this action can pull values from upstream nodes, whether that's a form response, a trigger payload, an AI output, or a lookup result.

What happens if Google Gemini returns an error?

The workflow pauses on the failed node, the error message is captured in the run log, and you can retry the run with one click. Auto-retry policies are configurable per workflow with exponential backoff up to 5 attempts.

Does Gemini Embed Content support batch operations?

Yes. Run Gemini Embed Content inside a Loop node to process arrays. Tiny Command handles Google Gemini's rate limits automatically so you don't have to throttle manually.

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