What is Vector Search?

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Vector search retrieves items by comparing numerical representations of meaning, called embeddings, rather than exact text matches. In ecommerce, it can power semantic product search, similarity matching, recommendations, and AI-assisted discovery.

The vectors are usually produced by an embedding model that converts text, images, or other data into numerical representations. At query time, the search engine converts the user’s request into the same vector space and retrieves the nearest items according to a similarity metric. In ecommerce, vectors can represent product titles, descriptions, attributes, images, support content, or even customer intent.

Vector search is commonly used for:

• Natural-language product search where wording differs from catalog terminology.

• Visual similarity and “find products like this” experiences.

• Retrieval for AI assistants and RAG systems that need relevant product or policy context.

• Recommendation or discovery features that compare semantic similarity across a large catalog.

The limitation is that similarity is not the same as correctness. A vector engine may return a conceptually similar product that is out of stock, outside budget, or incompatible with the buyer’s requirement. Production commerce search therefore combines vectors with metadata filters, business rules, and often a second ranking stage. Good embeddings help users find candidates; authoritative commerce data decides whether those candidates are actually valid.

Example: A retailer can embed product descriptions and images so a query or uploaded reference image retrieves visually or semantically similar items before normal business filters and ranking are applied.