What is semantic search? Examples and APIs

Semantic search retrieves information based on the meaning, context, and intent of a query rather than requiring exact word matches, typically by converting text into numerical embeddings and comparing them through vector similarity measures such as cosine similarity. It complements keyword or lexical search, which excels at precise identifiers, product codes, error messages, and citations but can miss synonyms and paraphrases; consequently, many systems use hybrid retrieval combining both approaches. The discussion distinguishes vector search, which finds mathematically similar vectors across data types, from semantic search, which more broadly seeks to interpret user intent and may also use reranking or knowledge graphs. Common applications include e-commerce discovery, enterprise knowledge search, retrieval-augmented generation, and web research for AI agents. Available tools range from public-web semantic search APIs such as Exa to managed search services, enterprise platforms including Elasticsearch and OpenSearch, and vector databases such as Pinecone, with the appropriate choice depending on whether users need web data, supplied private data, or greater infrastructure control.

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