Semantic Search
Semantic search finds content by meaning instead of exact words: the query and the documents are converted into embeddings, and the closest matches are returned even when no keyword overlaps.
No jargon, no math
In plain words, what is it?
Classic search compares strings: it only finds the words you type. Semantic search compares meanings. Here is the flow: you index the corpus once, converting every document into an embedding stored in a vector database. At each question, the query is turned into the same format and measured against all the vectors; the closest ones come back on top, with a similarity score. Matches below a threshold are cut, so the system can say “I found nothing” instead of passing off a near miss as the answer. In production it is often combined with a keyword pass: the hybrid covers jargon and abbreviations that embeddings alone can misread. The limit: if the right document is not in the corpus, no search magic brings it back.
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Embedding. Your query is turned into a vector of numbers.
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Index check. If the corpus is not embedded yet, that happens first, once.
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Comparison. The query vector is measured against every document vector.
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Ranking. Matches come back ordered by similarity score.
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Threshold. Weak matches are cut, so nothing is forced.
Terms that live nearby.
Embedding
An embedding is a list of numbers that captures the meaning of a piece of text, image or audio, so that content with similar meaning gets similar numbers and the computer can measure closeness with plain arithmetic.
ReadVector Database
A vector database is a database built to store numeric fingerprints of content (embeddings) and find the closest ones to any query in milliseconds, powering semantic search, recommendations and AI retrieval.
ReadReranking
Reranking is a second, slower pass over search results: a cross-encoder model reads each candidate together with the query and rescores it for true relevance, so the top results are the actually-right ones, not just the roughly-similar ones.
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