O que é busca vetorial?
Em termos técnicos, A similarity-search method that represents queries and stored items as numeric embeddings, then retrieves the nearest vectors according to a distance or similarity metric.
Por que isso é importante?
Isso é importante porque Vector search can find conceptually related text, images, audio, products, or records even when they share few exact keywords. It is a common retrieval layer for semantic search, recommendations, and RAG.
Como funciona
O sistema funciona assim: An embedding model converts each item and query into vectors. A vector index uses exact or approximate nearest-neighbor algorithms to return the closest candidates, often followed by filtering or reranking.
Exemplo técnico
Exemplo: A support question about cancelling a subscription retrieves documentation chunks about account termination even though the query and source use different words.
Notas de implementação
Notas técnicas: Match the embedding model and distance metric, measure recall and latency, choose chunking carefully, preserve metadata filters, handle model migrations, and consider hybrid search when exact terms matter.
