Semantic Search
Semantic search is an information retrieval approach that ranks results by contextual meaning and user intent rather than by exact keyword matches.
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
Frequently Asked Questions
From the Blog
Semantic Search Explained: Beyond Keywords
Semantic search finds results by meaning rather than exact words, using vector embeddings so a query and a relevant document match even with no shared terms.
Read More AI & TechnologyWhat Is Semantic Search and How Does It Work?
Semantic search finds results by meaning, not keywords, using embeddings to represent text as vectors and matching queries to the closest ones in vector space.
Read More AI & TechnologyHow to Choose an Embedding Model for Search
Choosing an embedding model for search means balancing retrieval quality, dimension size, cost, and language coverage against your data. Here's how to decide.
Read More AI & TechnologyWhat Is Cosine Similarity in AI Search
Cosine similarity measures how alike two vectors are by the angle between them, ignoring length. It's the core scoring method behind semantic and vector search.
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