Search engines like Google provide valuable information by organizing data based on relevance and quality, making vast knowledge accessible with limited queries.
Traditional lexical-based search engines might not accurately interpret user intentions, leading to the emergence of Semantic Search that considers the meaning of words and sentences.
Semantic search utilizes NLP and vector databases to improve search capabilities by focusing on the meaning of text, leading to more accurate results based on query meaning.
This article explores the concept of Semantic Search and its correlation with vector databases, aiming to enhance understanding and utilization of these advanced search technologies.
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