What is semantic search?

Semantic search retrieves results based on the meaning behind a query rather than requiring exact keyword matches, so it understands intent and context.

How does semantic search work?

It typically converts text into embeddings and uses vector search to find content whose meaning is closest to the query, often blended with traditional keyword matching.

Is semantic search the same as vector search?

Not exactly. Vector search is the underlying technique often used to implement semantic search, but semantic search is the broader goal of retrieving by meaning.

Why do search engines combine semantic and keyword search?

Hybrid search covers both natural-language understanding and exact matches for names, codes, or rare terms that embeddings alone can miss.

Where is semantic search commonly used?

It powers enterprise search tools, e-commerce search bars, customer support systems, and the retrieval layer behind most RAG-based AI assistants.

Does semantic search require a large language model?

It requires an embedding model to convert text into vectors, which is often smaller and cheaper to run than a full generative large language model.

How does semantic search relate to a knowledge graph?

A [knowledge graph](/glossary/knowledge-graph) provides structured, verified relationships between entities, and some systems combine it with semantic search to improve accuracy and grounding of results.

AI Fundamentals

Semantic Search

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Semantic search is an information retrieval approach that ranks results by contextual meaning and user intent rather than by exact keyword matches.

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Definition

Semantic search is an information retrieval approach that ranks results by contextual meaning and user intent rather than by exact keyword matches.

Overview

Traditional search engines rely heavily on matching the literal words in a query against an index. Semantic search instead uses natural language understanding — typically powered by embedding models — to interpret what a query actually means, so that a search for "affordable laptop for coding" can surface results about budget-friendly programming machines even if those exact words never appear together in the source text. Under the hood, semantic search usually converts both the query and the searchable content into vectors and performs vector search to find the closest matches. Many production systems combine this with classic keyword-based (lexical) search in a hybrid approach, since exact matches still matter for names, codes, and rare terms that embeddings can under-represent. Semantic search has become foundational to modern AI products, from enterprise knowledge-base assistants to e-commerce search bars, and it is the retrieval layer underneath most retrieval-augmented generation systems used by chatbots built on large language models.

Key Concepts

Ranks results by meaning and intent, not just literal keyword overlap
Typically implemented using embedding models and vector search
Handles synonyms, paraphrasing, and related concepts naturally
Often combined with lexical search in a hybrid retrieval setup
Improves recall for natural-language and conversational queries
Powers modern enterprise search and AI assistant retrieval layers

Use Cases

Enterprise knowledge-base and internal documentation search

E-commerce product search and discovery

Customer support ticket and FAQ matching

Legal and medical document retrieval

Chatbot and virtual assistant question answering

Research paper and content discovery platforms

Frequently Asked Questions

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