ActionHugging FaceUpdated September 2026

How do I generate embeddings on Hugging Face?

Short answer: You can hf feature extraction (embeddings) in Hugging Face by hand from its own interface, but it won’t repeat itself. On TinyCommand, add the Hugging Face HF Feature Extraction (Embeddings) action to a workflow, map its 2 inputs from any upstream app, and it runs automatically every time the trigger fires. No code, and a free tier to start.

HF Feature Extraction (Embeddings) in Hugging Face — start free All 4 Hugging Face actions

HF Feature Extraction (Embeddings) in Hugging Face — 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
Model model	string	Required	HF embedding model id
Text text	string	Required	Text (required)

Sample request

{
  "model": "e.g. BAAI/bge-large-en-v1.5",
  "text": "{{trigger.text}}"
}

Returns

[
  0.0123,
  -0.0456,
  0.0789
]

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

Triggered by

Apps that pair well as the trigger for HF Feature Extraction (Embeddings).

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

Google Sheets → Hugging Face

2 Google Sheets triggers

HubSpot → Hugging Face

18 HubSpot triggers

FAQ

Questions about HF Feature Extraction (Embeddings).

What does the HF Feature Extraction (Embeddings) action do in Hugging Face?

Generates sentence embeddings from any sentence-transformers or BGE-class model on the HF Hub. For RAG-pipeline vector generation when you want a specific HF-hosted embedding model.

What inputs does HF Feature Extraction (Embeddings) require?

Required: Model, 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 Hugging Face returns an error?

The run history shows the failed step with its input and the error message Hugging Face 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 HF Feature Extraction (Embeddings) support batch operations?

Run HF Feature Extraction (Embeddings) 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 Hugging Face's rate limits.

More actions

Other Hugging Face actions.

Action

Hugging Face Chat Completion

Runs chat completion against any text-generation model on Hugging Face Hub that supports the TGI server. OpenAI-compatible message-array shape. Model selection via model ID (e.g., meta-llama/Llama-3.3-70B-Instruct).

Action

HF Text Classification

Runs any classifier model from HF Hub on text input — sentiment, toxicity, NER, intent. Pick the right classifier for your use case from the broad Hub catalog.

Action

HF Text to Image

Generates images from text using any HF-hosted image model (SDXL, SD3.5, Flux variants, community fine-tunes). For accessing niche or specific image models from the broad HF catalog.

HF Feature Extraction (Embeddings) in Hugging Face — start free