A support portal should find articles that mean the same thing even when users phrase questions differently. What converts text into a form that enables this semantic search?
A.Keyword tokenization
Tokenization splits text — it doesn't capture meaning.
B.Text embeddings stored in a vector database
Correct. Embeddings encode semantics; vector search finds similar meaning.
C.Regular-expression matching
Regex matches literal patterns only.
D.TF-IDF indexing
TF-IDF is keyword statistics — fails on paraphrases that share no words.
Why this answer
Embeddings map text into high-dimensional vectors where semantic similarity ≈ vector proximity. Storing document embeddings in a vector store enables meaning-based search that survives different phrasing.
Correct answer: B · Last reviewed 2026-10-26
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