Semantic Search
Search that understands the meaning and intent behind a query rather than just matching keywords. It uses embeddings to find results that are conceptually related even if they use different words.
Why It Matters
Semantic search dramatically improves search quality — users find what they actually need, not just documents that happen to contain their exact search terms.
Example
Searching 'How to fix a slow computer' and finding results about 'PC performance optimization' and 'speeding up Windows' even though those pages never mention 'fix' or 'slow'.
Think of it like...
Like asking a knowledgeable librarian for help versus searching a card catalog — the librarian understands what you really need, not just the words you used.
Related Terms
Embedding
A numerical representation of data (text, images, etc.) as a vector of numbers in a high-dimensional space. Similar items are placed closer together in this space, enabling machines to understand semantic relationships.
Vector Database
A specialized database designed to store, index, and search high-dimensional vector embeddings efficiently. It enables fast similarity searches across millions or billions of vectors.
Cosine Similarity
A metric that measures the similarity between two vectors by calculating the cosine of the angle between them. Values range from -1 (opposite) to 1 (identical), with 0 meaning unrelated.
Retrieval-Augmented Generation
A technique that enhances LLM outputs by first retrieving relevant information from external knowledge sources and then using that information as context for generation. RAG combines the power of search with the fluency of language models.