Vector Databases and Semantic Search Fundamentals

Traditional search engines fail on queries with synonyms or different phrasings; semantic search solves this by converting text into high-dimensional vectors

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Embedding models represent words and sentences as vectors, placing semantically similar concepts near each other regardless of exact wording

This approach improves result accuracy by understanding semantic intent rather than simple keyword matching

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#vector_databases #semantic_search #embeddings #nlp #search_engines

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