ActionAzure OpenAIUpdated May 2026

How do I generate embeddings via Azure OpenAI?

Short answer: Drop the "Azure OpenAI → Create Embedding" action anywhere in your workflow, map the inputs from upstream nodes, and publish.

Use this action All 2 Azure OpenAI actions

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 text	string	Required	Text (required)

Sample request

{
  "text": "{{trigger.text}}"
}

Returns

{
  "data": [
    {
      "index": 0,
      "embedding": [
        0.012
      ]
    }
  ],
  "usage": {
    "total_tokens": 5,
    "prompt_tokens": 5
  }
}

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

Triggered by

Apps that pair well as the trigger for Create Embedding.

Any of these apps can fire this action as part of a workflow.

Google Sheets → Azure OpenAI

2 Google Sheets triggers

HubSpot → Azure OpenAI

18 HubSpot triggers

FAQ

Questions about Create Embedding.

What does the Create Embedding action do in Azure OpenAI?

Generates embeddings using a deployed embedding model (text-embedding-3-large, text-embedding-3-small, etc.). Dimensions match the OpenAI direct equivalents. For vector workflows on Azure-resident data, this avoids the OpenAI-direct data-flow path.

What inputs does Create Embedding require?

Required: 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 Azure 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 Azure OpenAI's rate limits automatically so you don't have to throttle manually.

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