What is Vector Search?

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.

1. 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
2. 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.
3. 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.

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.

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 Databricks Platform supports hybrid indexes that combine semantic and keyword search, with built-in reranking.

Common use cases for vector search

Vector search powers applications that depend on finding relevant results rather than exact keyword matches.

Retrieval-augmented generation (RAG)

Retrieval-augmented generation (RAG) helps AI assistants answer questions using current, relevant information rather than relying entirely on what the model already knows. Before responding, the assistant retrieves relevant content from a knowledge base. Vector search is the retrieval layer that finds that content.

Documents are broken into smaller chunks so the system can retrieve the most relevant passage, not just the most relevant document. This approach has made RAG one of the fastest-growing applications of vector search. Learn more about RAG and vector databases.

Semantic document and enterprise search

Internal knowledge bases, ticket systems and document repositories become more useful when people search by meaning rather than exact wording. A query like "parental leave policy" can surface the right HR document even if it is titled "family benefits guide." Vector search makes enterprise knowledge easier to find without requiring exact phrasing.

Recommendations and similarity search

Recommendation engines use vector search to find products, articles, videos and other content that are conceptually similar to what a user is already viewing. This powers familiar experiences such as "customers who liked this also liked" recommendations. The system simply finds items whose embeddings are closest to the current item.

Multimodal and cross-language search

Vector search can match across formats and languages. A text query like "hiking boots for wet terrain" can return relevant product images, even when the images contain no text. An English-language query can also retrieve relevant documents written in Spanish. This makes vector search useful for global organizations and applications that work with diverse content types.

Fraud and anomaly detection

Vector search can help identify unusual patterns by finding items that sit far from normal ones. In fraud detection, transactions that look significantly different from typical activity can be flagged for review. The same approach can be used to detect anomalies in network traffic, manufacturing systems and other operational data.

Do you need a vector database for vector search?

Not always. The right approach depends on your scale, performance requirements and existing infrastructure. Organizations typically implement vector search using a vector database, a search engine with vector features or a similarity search library. Learn more about vector databases.

Managed services can reduce setup time, operational overhead and ongoing maintenance. The Databricks Platform includes a built-in vector database through Databricks AI Search, which combines vector search, keyword search, metadata filtering and reranking in a single service. It also integrates with AI governance capabilities such as Unity Catalog, helping organizations apply consistent controls to AI applications.

Operational trade-offs and quality considerations

Implementing vector search in a live environment is just the first step. To ensure sustained reliability, performance, and accuracy in production, these systems require ongoing tuning and maintenance across several key operational areas.

Cost and performance

Large vector indexes consume memory and compute resources, which increases infrastructure costs. As datasets grow and query volumes rise, those costs increase as well. Faster, more accurate search often requires more resources, so teams need to balance performance, quality and cost.

Keeping the index fresh

Vector indexes do not automatically update when source content changes. When documents are added, edited or removed, the index must be refreshed to reflect those changes. Without regular updates, search results can become stale and miss important new information.

Evaluating quality

Before tuning a system, build a small test set of representative queries with known good results. Measuring quality means checking whether the right results appear near the top for those queries. This creates a baseline for evaluating future changes.

Two factors often have a significant impact on relevance. Metadata filters narrow results by tags, dates or categories before similarity ranking runs. This can improve precision when semantic matching returns too many results.

Embedding quality also depends on the model used to generate embeddings. General-purpose models may not capture specialized terminology well. For domains with industry-specific language, such as healthcare, legal or financial services, a domain-tuned model can improve retrieval quality.

AI Search on the Databricks Platform

Databricks AI Search is a managed vector database built into the Databricks Platform. It supports semantic search, keyword search, metadata filtering and reranking in a single service. Because it integrates with existing data, governance, and productivity tools, teams can build retrieval systems without stitching together separate platforms.

AI Search connects directly to data organizations already manage in Databricks, reducing the need to move or duplicate data. It also integrates with governance capabilities such as Unity Catalog, allowing existing access controls and lineage policies to extend to search workloads.

Frequently asked questions

How is vector search used in RAG applications?

In a RAG system, the user's query is converted into an embedding and matched against a library of document chunks in a vector index. The closest matches are retrieved and passed to the language model as context. The model then generates a response grounded in the organization's own data rather than relying solely on its training data.

When should you use hybrid search instead of vector search alone?

When users search for specific identifiers such as product codes, error codes or proper names, semantic search alone may miss them. Exact terms do not always align closely in embedding space. Hybrid search combines vector and keyword results, providing both semantic breadth and exact-match precision in a single ranking.

What makes vector search results more accurate?

Several factors influence retrieval quality. Embedding model quality determines how well meaning is captured. Metadata filters narrow the search space before similarity ranking runs. Index freshness ensures results reflect current content rather than outdated information.

What are the main trade-offs of vector search?

Vector search requires more memory and compute than keyword search. Large indexes carry real infrastructure costs. ANN search is approximate by design, creating a small precision trade-off compared with exhaustive search. Indexes also require regular refreshes to stay current as source data changes. Hybrid search adds complexity but often delivers more reliable results than either approach alone.

How does Databricks AI Search support vector search?

Databricks AI Search is a serverless, managed vector database built into the Databricks Platform. It supports hybrid search, metadata filtering, and built-in reranking. Automated sync pipelines keep indexes current as Delta table data changes, while Unity Catalog extends governance controls to search workloads.

Start building with governed vector search

Vector search helps AI systems find relevant information based on meaning rather than exact keywords. That capability powers everything from RAG applications and recommendation engines to enterprise search.

As AI systems become more dependent on retrieval, hybrid search and reliable search infrastructure are increasingly important. To learn more, explore the AI Search on the Databricks Platform.