ActionAzure OpenAIUpdated September 2026
How do I generate embeddings via Azure OpenAI?
Short answer: You can create embedding in Azure OpenAI by hand from its own interface, but it won’t repeat itself. On TinyCommand, add the Azure OpenAI Create Embedding action to a workflow, map its 1 input from any upstream app, and it runs automatically every time the trigger fires. No code, and a free tier to start.
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.
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 run history shows the failed step with its input and the error message Azure OpenAI returned, so you can fix the input and run the workflow again. Automatic retries are built into HTTP steps only, where you can also set your own retry count and delay.
Does Create Embedding support batch operations?
Run Create Embedding inside a loop to process each item in an array. TinyCommand does not throttle calls for you, so for large arrays add a Delay step if you need to stay within Azure OpenAI's rate limits.
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