Glossary · Beginner

What Is Vector Search? Definition and Example

Dipuck JonesLast reviewed 2026-10-30

Vector search finds the items whose embeddings are nearest to the embedding of a query, so results are matched by meaning rather than by shared keywords. It's the retrieval step behind many semantic search and retrieval-augmented generation systems.

On this page

Checked on 31 October 2026. "Our view" marks MentionX opinion. Ledgerline is a fictional company used for illustration.

How does vector search work?

  1. Each document or passage is converted into an embedding and stored.
  2. The query is embedded with the same model.
  3. The system returns the stored vectors closest to the query vector.

The open-source Postgres extension pgvector shows the moving parts. It supports "exact and approximate nearest neighbor search" with distance measures including "L2 distance, inner product, cosine distance" (pgvector). Its approximate HNSW index "creates a multilayer graph", and the docs warn that with approximate indexes "you will see different results for queries" (pgvector). OpenAI's embeddings guide ranks search results by cosine similarity (OpenAI).

What's an example?

Fictional example: a help centre stores 5,000 passages as embeddings. A customer types "can't log in after changing phone". Vector search returns a passage on "resetting two-factor authentication on a new device", which has no words in common with the query.

Why does it matter for GEO?

Engines don't publish their retrieval stacks. Still, our view is that vector-style retrieval rewards passages whose meaning closely matches a likely question. Exact-match keyword repetition matters less than it used to, and clear, specific answers matter more. Many systems combine vector and keyword search, so basic on-page relevance still counts.

  • Assuming every engine uses vector search alone. Hybrid approaches are common.
  • Treating approximate results as fixed rankings. They can vary with the index.
  • Stuffing synonyms into a passage. That blurs its meaning rather than sharpening it.

From MentionX data: what this means for measurement

Method. Read-only aggregate queries over MentionX scans from 22 August to 29 October 2026 (UTC): 2,217 engine runs (2,154 completed) across 18 anonymised projects and six engines.

MentionX can't see which retrieval method an engine uses. What it can see is variation: the same prompts produce different brands and different citations across engines and runs.

EngineBrand mentionedAnswers with no citations
Gemini47.3%50.7%
OpenAI42.9%30.2%
Perplexity31.5%4.1%
AI Mode31.3%12.6%
Copilot26.0%47.0%
DeepSeek24.0%99.0%

The wide spread in how often engines cite anything is a reminder that retrieval differs from system to system. Some engines nearly always show sources; others rarely do.

Our view: vector search is one of several reasons answers vary. Measure across repeated runs and several engines rather than drawing conclusions from one answer.

Limits. A convenience sample of mostly B2B software projects, run through engine APIs, which can behave differently from the consumer apps. Perplexity, Copilot and DeepSeek figures come from a single day (26 October 2026), so treat them as a snapshot, not a trend.

Ledgerline is a fictional accounts-payable tool. Figures are illustrative.

Ledgerline's support team notices that customers search the help centre for "supplier says we paid twice" and get no results, because the article is titled "Duplicate payment detection". The team tests a vector search tool on the help centre. The same query now returns the right article first, because the meanings are close even though the words differ.

The lesson for AI visibility is similar. Engines that retrieve by meaning can find Ledgerline's article for many phrasings, but only if the article clearly states what it covers.

Keyword search looks for documents containing the query's words, often weighted by how rare and how frequent they are. Vector search compares embeddings, so it can match synonyms and paraphrases. Each has weaknesses: keyword search misses paraphrases, while vector search can return passages that are related but not quite on point. That's why hybrid systems that use both are common.

What should marketers take from it?

Write passages that answer specific questions in plain language. Use the terms your buyers use, and include exact names, such as product names and features, because those still help keyword matching. Don't chase a theoretical "vector score"; you can't see it, and engines don't publish their retrieval stacks.

Common questions

Do all AI engines use vector search? They don't publish their full retrieval stacks. Many retrieval systems use vector search, often alongside keyword search.

Is vector search the same as RAG? No. Vector search is often the retrieval step inside retrieval-augmented generation.

Sources

Questions

How is vector search different from keyword search?

Keyword search matches words. Vector search compares embeddings, so it can match passages with the same meaning but different wording.

Is vector search exact?

Not always. Approximate indexes such as HNSW trade some recall for speed, as the pgvector documentation explains.