3/5 Enter semantic search! It uses text embeddings to turn words into vectors. It then uses similarity to find the most similar vector among responses to the query. The outcome? A more accurate, relevant response!
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3/5 Enter semantic search! It uses text embeddings to turn words into vectors. It then uses similarity to find the most similar vector among responses to the query. The outcome? A more accurate, relevant response!