What is Vector Search?

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Summary

Vector search retrieves information based on meaning and context rather than exact keyword matches, using embeddings to identify similar text, images, audio and other content.

It solves the limitations of keyword-only search, helping systems recognize synonyms, search across languages and formats, and retrieve relevant information for use cases such as RAG, enterprise search, recommendations and anomaly detection.

Production systems often combine vector and keyword search for stronger results, while managed services such as Databricks AI Search add reranking, metadata filtering, automated index updates and governance to improve relevance and simplify operations.

Vector search is a search technique that finds results based on meaning, not just matching keywords. Where traditional search matches exact words, vector search compares embeddings. These numeric representations capture the meaning of text, images, audio and other content. Results are ranked by how closely their embeddings match those of the query, not by shared words. This makes vector search a core retrieval layer behind modern AI assistants, semantic search systems and retrieval-augmented generation (RAG) . This guide covers how vector search works, how it compares to keyword and semantic search, common examples and use cases and how to evaluate it in practice. How does vector search work? Vector search works in three stages: creating embeddings, building an index and matching a query against that index. Create embeddings A model converts each item into an embedding, a numeric representation that captures its meaning. Documents, product descriptions, images and audio clips can all be represented this way. Items with similar meanings tend to have similar embeddings Build an index Those embeddings are stored in a structure designed for fast similarity search. The index makes it possible to search across millions of items efficiently. Match the query When a query arrives, it is converted into an embedding using the same model. The system then finds the stored embeddings closest to the query and returns the associated results.

Finding those closest matches is called nearest neighbor search. The simplest approach, k-nearest neighbor (k-NN) search, compares the query against every item in the index and returns the k closest matches. While accurate, it becomes too slow as datasets grow into the millions. Most production systems use approximate nearest neighbor (ANN) search instead. ANN uses specialized indexes to identify likely matches without comparing every item. It trades a small amount of precision for dramatically faster performance, making vector search practical at scale. Vector search in practice A simple search illustrates how vector search differs from keyword search. Search for "dog." A keyword search returns results containing that exact word. A vector search can also return results for "puppy," "canine" and "golden retriever." Those terms are conceptually related to "dog," even though they use different words. The search engine is looking for the concept, not the exact word. Vector search also works across formats. A text query like "red sneakers" can return product images that match the description, even though the image contains no text. Keyword search cannot make that connection because it relies on matching words. Vector search retrieves content based on semantic similarity, regardless of format. Vector search vs. keyword search Keyword search matches words. Vector search matches meaning. Both approaches have strengths, which is why vector search complements keyword search rather than replacing it. Exact-match search remains the best tool for structured queries such as order IDs, product codes and known document titles. Attribute Keyword search Vector search Matches on Exact words Meaning and context Handles synonyms Weak Strong Works across languages No Often yes Works on images and audio No Yes Best for exact terms (IDs, codes) Strong Weaker Typical method BM25 / TF-IDF Nearest-neighbor search

The strongest search systems combine both approaches. The hybrid search section below explains how. Vector search vs. semantic search Semantic search and vector search are closely related, but they are not the same thing. Semantic search is the outcome: helping users find relevant information based on meaning and context. Vector search is one of the most common techniques used to achieve it. Because semantic search describes an outcome rather than a specific technology, it can be implemented in different ways. In many modern systems, vector search is the primary engine behind semantic search. Dense vs. sparse vectors Dense and sparse vectors are designed for different kinds of search problems. Dense vectors capture overall meaning and context. They help systems recognize related ideas, synonyms and concepts even when different words are used. Generated by machine learning models, they are well suited for semantic and cross-language matching. Sparse vectors work more like traditional keyword search. Most values are zero, with nonzero values only for terms that appear in the content. Generated by algorithms such as BM25, they excel at exact-term matching. Product codes, proper names, and specific identifiers are where sparse vectors shine. Type What it captures Best for Dense vectors Overall meaning and context Semantic, synonym, and cross-language matching Sparse vectors Specific keywords and their weights Exact terms, names, and codes

Combining dense and sparse vectors is the basis for hybrid search. That combination often delivers the most reliable results in production. What is hybrid search? Hybrid search blends vector-based and keyword-based results into a single ranking. It’s often the practical default for production systems because it combines meaning-based and exact-match retrieval in one search experience. Vector search can miss exact product codes, names or identifiers because those terms do not always cluster closely in embedding space. Keyword search can miss relevant results that use different wording. Hybrid search addresses both challenges by combining the strengths of each approach. Most hybrid search systems also include a reranking step. Reranking is a second pass that reorders results to put the best matches on top. As a result, hybrid search often delivers more reliable relevance than either method alone. The...