Create Embedding: OpenAI 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
Text input	string	Required	The text to embed
Model model	options	Required	Model. Options: text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002

Sample request

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

Returns

{
  "data": [
    {
      "index": 0,
      "object": "embedding",
      "embedding": [
        0.0023,
        -0.009
      ]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "total_tokens": 5,
    "prompt_tokens": 5
  },
  "object": "list"
}

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

FAQ

Questions about Create Embedding.

What does the Create Embedding action do in OpenAI?

Generates vector embeddings for text using text-embedding-3-small or text-embedding-3-large. The de-facto embeddings default for RAG and semantic search.

What inputs does Create Embedding require?

Required: Text, Model. 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 OpenAI 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 Create Embedding support batch operations?

Yes. Run Create Embedding inside a Loop node to process arrays. Tiny Command handles OpenAI's rate limits automatically so you don't have to throttle manually.

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