Semantic search for product discovery
Illustrative retail scenario: semantic search can improve product discovery.
Illustrative scenario
Illustrative retail scenario: a catalog contains product descriptions and images, while keyword search misses synonyms, natural-language queries, or typos.
Semantic search can map product content and user queries into the same embedding space so related products can be retrieved even when wording differs.
A retrieval workflow can combine semantic matching with metadata filters to preserve merchandising logic.
How QDivZero can fit
QDivZero Flexible Vector Database can store vectors generated from catalog content and return top-k semantic matches.
Recommendations API can rerank results using browsing history and affinity signals. Semantic search can improve product discovery and reduce zero-result queries; outcomes depend on catalog quality, model choice, and query traffic.
How QDivZero fits in
Product vectors indexed
Catalog images and descriptions are processed through Compute to generate embeddings stored in Flexible Vector Database.
Semantic query matching
Customer searches are converted to vectors in real time and similar products are returned by semantic distance.
Personalized reranking
Recommendations API reranks results using browsing history and affinity signals.
Illustrative workflow
Index catalog images and descriptions through the QDivZero ingestion workflow
Map category taxonomy to vector metadata filters
A/B test semantic search against a keyword baseline
Connect Recommendations API to an existing profile system for reranking
Monitor search quality and iterate on the embedding model choice