Vector Search (Semantic Search)

Also known as semantic search, similarity search

Vector search finds the nearest matches to a query by comparing embeddings in vector space, rather than matching exact keywords the way traditional full-text search does. It's what lets a RAG pipeline surface a passage about "cancel my plan" when the user typed "how do I stop being billed." In production it's usually paired with metadata filters and a reranker, since raw nearest-neighbor results alone are rarely precise enough to hand straight to the model.

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