Quick definition
Semantic search is an information retrieval method that finds relevant results based on the meaning of a query, not on literal word matching. It converts both the query and the searchable content into embeddings—numerical vectors—and retrieves the items whose meaning is closest, even if they share no exact vocabulary.
What does it mean?
Traditional keyword search (often called lexical search) works by comparing literal text, with variations like manual synonyms or stemming (reducing words to their root). Its limits show up quickly: if a shopper searches for "coat for snow" and the product is labeled "thermal jacket for cold weather," there is no word match, even though the meaning is practically the same.
Semantic search solves this by operating on meaning instead of text. Both the query and each product or document are converted into an embedding by a specialized model, and the items whose vector is closest to the query's vector are retrieved, using mathematical similarity measures. The result tolerates synonyms, typos, regional slang, and completely different phrasings of the same intent.
It's worth distinguishing it from hybrid search, which combines semantic search with traditional lexical and structured filters (exact price, category, availability). In digital commerce practice, most modern systems don't use pure semantic search but a combination: meaning to find relevance, exact filters for business constraints.
Why it matters
Keyword search systematically fails against the way people actually describe what they're looking for: with ambiguous, incomplete, colloquial language, or simply vocabulary different from the catalog's. Semantic search solves that problem at the root, allowing natural-language queries—"something comfortable for standing at work all day"—to return relevant results without the shopper having to guess the exact words the catalog uses internally.
This is critical in large catalogs, in catalogs with inconsistent vocabulary across vendors, or in B2B commerce, where buyers describe functional needs ("resistant to extreme temperatures") rather than exact product names.
How it works
The process first requires generating embeddings for the entire catalog or document base, storing them in a vector database optimized for similarity search at scale. When a query arrives, it is also converted into an embedding using the same model, and a nearest-neighbor search is run against the database, returning the items with the highest semantic similarity.
In production implementations, this result is usually combined with additional business rules: weighting by inventory availability, applying price filters, or blending the semantic result with traditional lexical search signals to capture both meaning and relevant exact matches (brand names, product codes).
Applied example in AI Commerce
A shopper at a home goods ecommerce store searches for "something to make my kitchen smell nice without using aerosols." A keyword search engine finds no direct matches. A semantic search system interprets the full intent—air freshener, aerosol-free, kitchen context—and returns essential oil diffusers, scented candles, and activated charcoal sachets: products no keyword filter would have connected to that specific query.
Related concepts
Semantic search depends directly on Embeddings to represent meaning, and on a Vector Database to retrieve results efficiently at scale. It is the retrieval mechanism RAG uses before an LLM generates a response. It also applies in AI Personalization, comparing a customer's profile against the available catalog.
Common mistakes
Assuming semantic search completely replaces the need for exact filters: in commerce, constraints like price, size, or exact availability still require structured logic, not just meaning similarity. Confusing it with generic "AI-powered search," without distinguishing whether it actually uses embeddings or just manual synonym rules. Finally, underestimating how much quality depends on the embedding model used: one trained on general language can underperform on the technical or specialized vocabulary of a specific catalog.
The Edgebound Labs perspective
In the lab we don't measure semantic search by how well it answers carefully chosen example queries, but by its behavior against the real, messy queries shoppers actually type. A system that looks flawless in a demo with five prepared queries can fail against the real vocabulary of a two-hundred-thousand-product catalog—verifying it with real data, not ideal cases, is part of the method.
Frequently asked questions about semantic search
Does semantic search replace keyword search?
Not always. Many systems combine both (hybrid search) to capture both meaning and relevant exact matches.
What do I need to implement semantic search?
An embedding model, a vector database to store the embeddings, and a structured catalog or document base as the source.
Does it work with catalogs in multiple languages?
It depends on the embedding model used; some models are multilingual and place equivalent meanings close together across different languages.
Is semantic search slower than keyword search?
With a well-indexed vector database, response times are comparable to traditional search, even on large catalogs.
Can semantic search fail?
Yes, especially if the embedding model isn't well tuned to the domain's vocabulary or if the catalog has poor or incomplete descriptions.
Can it be combined with price or availability filters?
Yes, that's standard practice: semantic relevance is applied together with exact structured filters on business attributes.
Keep exploring the glossary
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