Upload File
mistral.upload_file
ActionAI & modelsconnection required
Calls the Mistral AI API with the connection's credentials.
Upload a file to the Mistral workspace for fine-tuning, batch inference or OCR, and return the file id to reference later.
Connection
- Required
- Yes — the node will not save without one
- Auth type
- API key
- Providers
- Mistral AI
- Secret shape
- {"api_key": "…"} — a Mistral API key
- Config it needs
- base_url, default_model
- Resolved by
- config.connection_id, else node.connection_id
Configuration
5 fields, 2 required. A dotted path is a nested object and name[] an array of objects — the editor renders the same tree.
| Field | Type | Required | Default | Constraints | Example |
|---|---|---|---|---|---|
filename Name to store the file under, e.g. training.jsonl. | string | Yes | — | — | training.jsonl |
content The file body: text as-is, or a base64 blob with content_encoding=base64. | string | Yes | — | — | — |
content_encoding Set to base64 when content is a base64 blob from an upstream file node. | string | No | text | one of "text", "base64" | — |
purpose What the file is for; Mistral validates the body against this. | string | No | fine-tune | one of "fine-tune", "batch", "ocr" | — |
content_type MIME type to send. Guessed from the filename when left blank. | string | No | — | — | — |
Example configuration
Every value below is the contract’s own example or default — nothing invented.
{
"content_encoding": "text",
"filename": "training.jsonl",
"purpose": "fine-tune"
}Output
One shape.
result
| Field | Type | Example |
|---|---|---|
id The file id to reference in later calls. | string | — |
filename The name the file was stored under. | string | — |
bytes Stored size in bytes. | number | — |
purpose What the file may be used for. | string | — |
created_at Upload time as a Unix timestamp. | number | — |
mimetype The MIME type Mistral recorded for the file. | string | — |
{
"bytes": 0,
"created_at": 0,
"filename": "",
"id": "",
"mimetype": "",
"purpose": ""
}See Upload File run in a workflow.
In a technical demo we add the Mistral AI connection, drop the node on the canvas and let Workflow Health pre-flight it before the first real run.