What is Vector Search?

Summary

Vector search retrieves results by comparing embeddings that represent meaning across text, images, audio, and other content rather than matching exact words. A model creates embeddings, an index stores them for fast similarity search, and a query embedding is matched against the index using nearest-neighbor methods. Exhaustive k-nearest neighbor search can become too slow at millions of items, so production systems commonly use approximate nearest neighbor search, trading some precision for speed. The guide positions vector search behind semantic search, RAG, recommendations, and multimodal or cross-language retrieval, while hybrid search combines dense and sparse vectors, keyword results, metadata filtering, and reranking to improve reliability. Quality depends on embeddings, filters, index freshness, and infrastructure, with vector search requiring more memory and compute; Databricks AI Search is presented as a managed service supporting these capabilities and Unity Catalog governance.

Context

Traditional keyword search matches exact words and remains effective for structured queries such as order IDs, product codes, and known document titles, but it can miss relevant content expressed with different wording or formats. Vector search addresses meaning-based retrieval for AI assistants, enterprise search, recommendations, and retrieval-augmented generation.

Approach / What changed

The process creates embeddings for content and queries, stores them in an index, and retrieves nearby embeddings using nearest-neighbor search. Production systems generally use approximate nearest neighbor indexes for speed. Hybrid search combines dense semantic vectors with sparse keyword-oriented vectors, then can apply metadata filtering and reranking.

Takeaways

• Approximate nearest neighbor search avoids comparing a query with every indexed item, making retrieval practical at large scale while introducing a small precision trade-off.
• Hybrid search combines semantic breadth with exact-match precision, helping retrieve both conceptually related content and identifiers such as product codes, error codes, or proper names.
• Retrieval quality depends on embedding-model fit, metadata filters, and index freshness; vector search also requires more memory and compute than keyword search.

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