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Embeddings and Vector Search: How Semantic Retrieval Works and Where It Fails
Embeddings convert text into vectors that place similar meanings close together, and vector search retrieves the nearest stored passages to a query. This guide explains how they work, why they miss exact terms and negation, how hybrid search and reranking fix that, how to choose where to store vectors, and how to measure retrieval quality.
CONCEPT / HOW LLMS WORK, FOR PMS
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