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Embedding Models

An embedding model gives your search a sense of meaning: without one, search matches words; with one, it matches meaning.

  • Each record becomes a list of numbers — a numeric fingerprint of its meaning — so similar meanings sit near each other.
  • “Running shoes” then brings up “trainers”, and “laptop for travel” finds a “lightweight ultrabook” — matches with no words in common.
  • You bring the model, with your own API key.
1

What an embedding model does

An embedding model gives your search meaning. It turns each record into a list of numbers that captures what it is about, so a search for “running shoes” can also find “trainers”. Without one, search matches words; with one, it matches meaning. They live under Models → Embedding Models.

The empty Embedding Models tab
2

Add one, bring your own key

Click Add Embedding Model and pick a provider — OpenAI, Gemini, Voyage or Microsoft Foundry. You bring your own API key, so embedding runs on your account: your models, your limits, your bill.

Choosing an embedding provider
3

Pick a model, attach it to a list

Choose the model and paste your key — checked with a live call before it is saved. Attach the model when you create a list, or add it to a group later: records re-embed in the background with no search downtime while the change settles in.

The create-embedding-model dialog

Auto-plays · use Back / Next to step through at your own pace.

Add an embedding model
  1. Go to Models → Embedding Models and click Add Embedding Model.
  2. Pick a provider: OpenAI, Gemini, Voyage or Microsoft Foundry. Embedding runs on your own key: your models, your limits, your bill.
  3. Choose the model and paste your provider API key. The key is checked with a live call before it is saved.
  4. Click Add. The model can now be attached to lists and groups.
The Add Open AI Model dialog with a model name and API key
Attach it: what meaning-based search unlocks

Attach a model when you create a list, or add one to a group later. Once records have fingerprints:

  • Meaning-based matching: “warm winter coat” also finds a “padded parka”.
  • Related results: “more like this”, from the records closest in meaning.
  • Image and multimodal search: with a model that reads images too, search by picture as well as words.
Changing the model happens in the background
  • Every record needs a new fingerprint — minutes on a large list — so it runs as a background job on a list and a group alike. Change List Model saves and closes straight away.
  • Search keeps working throughout: records not yet re-fingerprinted are still found on their text.
  • On a group the switch is all-or-nothing — it keeps searching with the old model until every list has caught up. No half-way state.
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