Semantic Search definition
Semantic search is a search technique that finds results based on the meaning of a query rather than exact keyword matches. It converts queries and documents into vector embeddings and retrieves the items whose meaning is closest, so a search for cancel my plan can find a help article titled how to end your subscription.
How semantic search works
An embedding model turns each document, or each chunk of a long document, into a vector: a list of hundreds or thousands of numbers that places it in a space where similar meanings sit close together. These vectors are stored in a vector database or a search engine with vector support. At query time, the query is embedded with the same model and the system retrieves the nearest vectors using a measure such as cosine similarity.
To stay fast over millions of items, engines use approximate nearest neighbor indexes such as HNSW, trading a tiny amount of accuracy for large speed gains. The result is a ranked list of documents that match the query's intent, even when they share no words with it. Our entry on embeddings explains how those vectors are produced.
Semantic search vs keyword search
Keyword search, typically using the BM25 ranking function in engines such as Elasticsearch, OpenSearch or Postgres full-text search, matches the exact words in a query. It is fast, transparent and excellent for names, codes, part numbers and precise phrases, but it misses synonyms and paraphrases: laptop does not match notebook computer unless someone configured that synonym.
Semantic search handles paraphrases, synonyms and natural-language questions, and works across languages with multilingual models. It is weaker on exact identifiers, rare jargon and very short queries, and its results are harder to explain. Because each approach covers the other's weaknesses, many production systems combine them in hybrid search.
In practice the choice is rarely either-or. Even a semantic system benefits from keyword fallbacks for codes and names, and even a keyword engine benefits from embeddings for queries that would otherwise return zero results, a common source of lost sales and frustrated users.
Where semantic search is used
Semantic search now sits behind many everyday experiences, often invisibly, wherever people describe what they want in their own words instead of the exact terms a catalog or document uses internally. Common applications, several of which can share one embedding index, include:
- Help centers and support bots that understand how customers actually describe problems
- Ecommerce search for descriptive queries such as warm waterproof jacket for hiking
- Enterprise knowledge search across policies, wikis and tickets
- Retrieval for RAG systems that ground LLM answers in company documents
- Recommendations and duplicate detection based on content similarity
Building semantic search that works
Quality depends on details. Split long documents into sensible chunks with titles and context, choose an embedding model suited to your language and domain, store metadata such as category or date for filtering, and rerank the top results for precision. Most importantly, build a test set of real queries with known good results and measure recall before and after every change.
Nexzem builds semantic and hybrid search for product catalogs, support content and internal knowledge bases, usually starting from the client's existing database or search engine rather than adding a separate vector store, unless the scale or feature set genuinely requires one.