Traditional search matches exact keywords: search for "refund policy" and you only get results containing those literal words. Semantic search instead converts both the query and the searchable content into embeddings, then ranks results by how close their meaning is to the query's meaning, surfacing a document titled "return and reimbursement guidelines" even though it shares almost no words with the search term.

This is the mechanism that makes AI agent retrieval actually useful in practice. A support agent, a knowledge base, or an internal document search built on semantic search finds what's relevant to a question's intent, not just what happens to contain the same phrasing the user typed.

Frequently Asked Questions

How is semantic search different from keyword search?

Keyword search matches literal words and phrases. Semantic search matches meaning, using embeddings to find content that's conceptually related to a query even when the vocabulary is completely different.

What makes semantic search possible technically?

Embeddings and a vector database. The query gets converted into an embedding, and the search finds the stored embeddings closest to it in that numerical space, then returns whatever content those matches represent.

Does semantic search replace keyword search entirely?

Not always, many production systems combine both, using keyword matching to catch exact terms (an order number, a product SKU) that semantic search alone might not prioritize highly enough, alongside semantic matching for conceptual queries.

Why does semantic search matter for an AI agent's knowledge base?

Because real users rarely phrase a question the exact way a policy document is written. Semantic search closes that gap, so an agent can find the right answer even when the customer's wording and the source document's wording don't overlap.

Can semantic search return the wrong result if it's too loosely matched?

Yes, this is a real failure mode, a query can retrieve a document that's topically related but not actually the right answer. Well-tuned systems set a relevance threshold and fall back to escalation or a broader search rather than confidently returning a loosely related match.

How does semantic search relate to retrieval-augmented generation (RAG)?

RAG uses semantic search as its retrieval step, finding the most relevant documents by meaning before handing them to the model to generate a grounded response, semantic search is the specific mechanism RAG relies on to find that context.