How to mistral embedding in Mistral AI

Mistral Embedding in Mistral AI — start free

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	Text. e.g. "Text to embed"
Model model	options	Optional	Model. Options: Mistral Embed

Sample request

{
  "input": "Text to embed",
  "model": "{{trigger.model}}"
}

Returns

{
  "data": [
    {
      "index": 0,
      "embedding": [
        0.1,
        -0.2,
        0.3
      ]
    }
  ],
  "model": "mistral-embed",
  "usage": {
    "total_tokens": 10,
    "prompt_tokens": 10
  }
}

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

FAQ

Questions about Mistral Embedding.

What does the Mistral Embedding action do in Mistral AI?

Generates embeddings with mistral-embed. For RAG-pipeline vector generation when you want EU-hosted embedding inference.

What inputs does Mistral 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 Mistral AI 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 Mistral Embedding support batch operations?

Yes. Run Mistral Embedding inside a Loop node to process arrays. TinyCommand handles Mistral AI's rate limits automatically so you don't have to throttle manually.

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