Load your catalogue

Recommendations are just similarity: "more like this" means "records whose meaning sits closest to this one". So the setup is the same as any semantic list — records plus an embedding model.

Create a list with a model

Create a list — books, say — and attach an embedding model. The model reads each record's descriptive text into meaning, so two books about the same thing land near each other. That closeness is what "you may also like" reads off; without a model there's nothing to measure similarity against.

Load the records

Import your catalogue. The field that carries the description is the one that matters — it's what the model turns into similarity:

POST https://api.searchstack.dev/search-result/Demo/books/with-fields
X-API-Key: {your key}
Content-Type: application/json

[
  { "name": "The Glass Hours", "description": "a slow-burn mystery set in a coastal town…" },
  { "name": "North Light",     "description": "two strangers, one lighthouse, a long winter…" }
]

Checkpoint. Your catalogue is loaded and embedded, so every record now has neighbours. Next, turn those neighbours into a "more like this" list — and let clicks make it smarter.

Go deeper: Embedding models and similarity in the reference.