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Semantic Search

A search approach that uses AI to understand the meaning and intent behind queries rather than just matching keywords, delivering more relevant results by comprehending context and semantics.

Semantic search uses embeddings to represent queries and documents as numerical vectors, matching them by meaning rather than exact words. Google introduced BERT into search in 2019, affecting 10% of all English-language queries. The approach powers modern enterprise search, RAG systems, and e-commerce product discovery. Vector databases like Pinecone, Weaviate, and Milvus have raised over $300 million collectively to support the growing demand for semantic search infrastructure.

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