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What is Semantic Search?

Semantic Search is a search method that retrieves results based on the meaning and intent behind a query, rather than requiring exact keyword matches. It typically works by converting both the query and the searchable content into numeric vector embeddings, then finding the content whose embeddings are mathematically closest to the query's.

TL;DR

Semantic Search finds results based on what a query means, not just which exact words it contains, so a search for sales declined can still surface a document that says revenue dropped.

Formula

Semantic Search is not calculated with a single formula, but its underlying matching typically relies on cosine similarity between the query's embedding vector and each candidate document's embedding vector, ranking results by similarity score.

Why It Matters

Semantic Search matters because traditional keyword search fails whenever a user's words don't exactly match the words in the source content, even when the meaning is identical, which happens constantly in real usage. It's the mechanism that makes retrieval-augmented generation possible at scale, since an AI system needs to find relevant source material before it can ground an answer in it. Without semantic matching, a retrieval system would only surface documents containing the literal query terms, missing paraphrased or differently worded but highly relevant content. For any AI feature that answers questions from a company's own data, semantic search quality directly caps the quality of the answers built on top of it, since a system can't ground an answer in a document it never retrieved.

Example

A user searches a company's internal knowledge base for why is churn increasing. A keyword search would only return documents literally containing the word churn. A semantic search also surfaces a document titled subscription cancellation trends, because the underlying embeddings recognize the two phrases as closely related in meaning, even though they share no exact words.

Frequently Asked Questions

  • Keyword search matches literal words and phrases. Semantic search matches based on meaning, using vector embeddings, so it can find relevant content even when the query and the source text use completely different wording.

  • Embeddings convert both the query and the searchable content into numeric vectors that capture meaning, allowing the system to mathematically compare how close in meaning two pieces of text are, rather than comparing their literal characters.

  • No. Semantic search is the retrieval step, finding relevant source material. Retrieval-augmented generation is the broader process that uses that retrieved material to ground an AI-generated answer.

  • Yes, it can retrieve content that's semantically similar but not actually the most relevant or accurate source, which is why practitioners often pair it with a separate relevance or faithfulness check rather than trusting retrieval results outright.

  • In practice, yes, at any meaningful scale. A vector database is purpose-built to store embeddings and retrieve the closest matches quickly, which is what makes real-time semantic search feasible across large datasets.

Related Terms

EmbeddingVector DatabaseRetrieval AccuracyNatural Language QueryRetrieval-Augmented Generation (RAG)
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