That darn conceptual search sure is hard :) The technical approach to achieving this involves a sentence embedding that then uses vector search to match documen

That darn conceptual search sure is hard :) The technical approach to achieving this involves a sentence embedding that then uses vector search to match documents based on a distance metric like cosine similarity. If you encode a description of a shirt in an embedding trained on all shopping item descriptions, it should match up with the search query. The trick is in getting a sentence embedding from a short query to match a longer description in a document description - long summaries of text in embeddings tends to average too much and cloud meaning. The other problem is including the vector search feature without screwing up other searches.