Prepare campaign feedback for semantic grouping
Make feedback easier to compare across campaigns by converting each incoming note into a reusable embedding.
Trigger
Webhook received
Notis starts this workflow when an external tool or custom backend sends an HTTP request.
Action
Generate Text Embeddings
Tool to convert text into vector embeddings for feature extraction, semantic search, and similarity tasks. Use when you need numerical representations of text for ML applications.
Why this helps
Similar feedback can be hard to recognize when it arrives in different words across launches.
- Prepare feedback text for semantic similarity workflows.
- Help downstream tools group related feedback themes.
- Reduce the effort of rediscovering similar comments.
Setup
Build it in a few focused steps.
- 1Connect Toneden and Hugging Face to Notis once through the portal.
- 2Create an automation in the portal or ask Notis to embed incoming campaign feedback.
- 3In one instruction, ask Notis to send the feedback text in each incoming request to a Hugging Face embedding model and return the embedding result with its campaign context.
- 4Choose an incoming webhook trigger and a channel for run reports.
- 5Test with one real feedback note and verify that your downstream process can use the response.
Questions about this workflow
Does this group feedback automatically?
No. It generates embeddings that can support a separate similarity search or grouping process.
What text should be embedded?
Specify the relevant feedback text and any context you want included in the prompt.
When this happens · Trigger
Do this · Action
Supported Triggers and Actions
Notis builds workflows that link Toneden to Hugging Face. A trigger fires from one place; an action lands in another.
Toneden triggers
Hugging Face actions
Recurring trigger
Notis starts this workflow on a schedule, such as daily, weekly, or during business hours.
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When this happens · Trigger
Do this · Action
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