Semantic search

Search that matches the meaning of a shopper's query against products in your catalog, not just keyword overlap. A search for 'something warm for snowy hikes' returns insulated jackets even if those exact words aren't in the product titles.

In depth

Semantic search uses a language model to convert a shopper's question into a numerical representation (an embedding) that captures meaning, then compares it to the same kind of representation for every product in your catalog. The closest matches in meaning surface, regardless of whether the shopper used the same words as the product description. This solves the classic keyword-search failure mode where a query like 'gift for a 10-year-old who loves dinosaurs' returns nothing useful because the catalog doesn't literally contain those words. Emporiqa combines semantic search with traditional keyword search (BM25) in a hybrid setup, so popular exact-match queries still score well alongside fuzzy intent queries.

See also

Related glossary entries

Visual search
A feature where shoppers upload a product photo and the chatbot returns the matching or closest item from the store's catalog. Works on any …
RAG (Retrieval-Augmented Generation)
An AI pattern where the chatbot first retrieves the most relevant facts from your store (products, policies, FAQs) and then generates the answer grounded …
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