Semantic Search
Search by meaning, not just keywords. Semantic search understands that 'performance optimization' and 'making things faster' mean the same thing, finding relevant content even when the words do not match.
How it works
Content is converted into high-dimensional vectors that capture meaning using state-of-the-art embedding models. When you search, your query is embedded in the same space and the nearest vectors are returned. This means content about 'reducing latency' matches a search for 'making the API faster' because the meanings are close in vector space, even though no words overlap.
Semantic search results showing conceptually similar items
Why it matters
You rarely remember the exact words used in a document written months ago. Semantic search bridges the vocabulary gap between how you think about a topic now and how you described it then. It turns your knowledge base from a filing cabinet that requires the right label into an assistant that understands what you mean.
Query and matching result with different but semantically equivalent wording