Semantic Search

Data
About 1 min read

A search technology that understands the meaning and intent of a query to find highly relevant results, rather than relying on exact keyword matches.

Also known as
Semantic SearchSemantic Search

Detailed explanation

Semantic search is a technology that returns highly relevant results by understanding meaning and intent, rather than matching search terms and documents word-for-word. Its core is vector similarity search, which converts text into numerical vectors (embeddings) containing meaning, and then finds document vectors close to the query vector. It retrieves results with the same meaning even if synonyms or expressions differ, and is typically implemented in combination with vector databases. It elevates search quality in corporate document search, e-commerce product discovery, customer support, and reference document retrieval for RAG-based chatbots.

Why it matters in tool selection

Because semantic search groups and finds the same intent even when expressions differ—such as 'change password' and 'account security settings'—it catches results that keyword search would miss. When choosing a tool, you should check which embedding model is used, whether it accurately reflects Korean semantics, whether it supports hybrid search combined with keyword search, and whether processing costs fit your data scale.

What to look for when choosing a tool

  • Does it support embedding models that accurately reflect Korean semantics?
  • Does it offer a hybrid approach that uses both keyword search and semantic search?
  • Are the search speed and costs manageable as the number of documents grows?
  • Can it return the reference paragraphs of the results for verification?

Real-world examples

When searching 'How do I apply for leave?' on an internal wiki, keyword search only finds documents containing the word 'leave,' but semantic search also retrieves documents like 'Annual Leave Guidelines.' The RAG chatbot then generates a response based on the retrieved documents to provide internal guidance with clear sources.

Related terms

EmbeddingVector DatabaseRAGNLP