Glossary · AI & Agents
Semantic Search
Overview
How semantic search works
Semantic search converts text into vector embeddings, numerical representations that capture meaning, then stores them in a vector database. When someone searches, their query is embedded the same way and compared to stored vectors using similarity math like cosine distance. The closest vectors are returned as the most relevant results, so matches are based on concepts rather than shared words.
Semantic vs keyword search
Traditional keyword search matches literal terms, so a search for 'car' can miss a page that only says 'automobile.' Semantic search understands that the two are related and returns both. It handles synonyms, natural-language questions, and misspellings far better, which is why modern site search, support, and AI systems rely on it. Many production systems combine both approaches, called hybrid search.
Where businesses use semantic search
Semantic search powers internal knowledge bases, product discovery, help centers, and the retrieval step inside RAG systems that feed accurate context to an AI assistant. Our team applies it in RAG Development and Knowledge Assistant (RAG) projects, indexing a company's documents so employees and customers get precise answers in natural language instead of scrolling through keyword results.
Where we use it
Related Zen in Tech services
How our team puts Semantic Search to work in real projects.
FAQ
Semantic Search — common questions
What is the difference between semantic search and keyword search?
Keyword search matches exact words, while semantic search matches meaning using vector embeddings. Semantic search returns relevant results even when the query uses different wording, synonyms, or natural-language phrasing that keyword search would miss.
Does semantic search need a vector database?
Most implementations use a vector database or a vector-enabled store like pgvector to hold embeddings and run fast similarity searches at scale. For small datasets you can search vectors in memory, but a dedicated store is standard for production.
Is semantic search the same as RAG?
No. Semantic search is the retrieval technique that finds relevant documents by meaning. RAG uses that retrieval step and then feeds the results to a large language model to generate an answer, so semantic search is a component of RAG.
Keep exploring
Related terms
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