vector search
Also called: vector similarity search, semantic vector search, nearest neighbor search
Vector search retrieves results by semantic similarity instead of exact keywords. Content and queries are turned into embeddings (high-dimensional numeric vectors), and the engine returns the stored vectors closest to your query vector by distance, its nearest neighbors.
How the machine “reads” meaning
An embedding model maps a passage to a list of numbers, for example [0.02, -0.91, 0.44, ...], often hundreds or thousands of dimensions long. Two texts about the same topic land near each other in that space even when they share no words. A query gets the same treatment, then the engine measures distance (commonly cosine similarity or L2) and returns the closest vectors, the “nearest neighbors.”
Checking every stored vector (brute-force, exact search) is accurate but slow at scale. Production systems use approximate nearest neighbor (ANN) indexes such as inverted file (IVF), ScaNN, or HNSW to answer in milliseconds across millions of vectors. The tradeoff is recall loss: a few true neighbors get skipped in exchange for speed, as Google Cloud’s docs describe.
This is the retrieval layer under AI search. RAG (retrieval-augmented generation) embeds the user’s question, runs a vector search over a corpus, pulls the top matching chunks, and feeds them to the model as grounding before it writes an answer. AI Overviews, ChatGPT search, and Perplexity all lean on some form of embedding-based retrieval to decide which passages are worth pulling in and citing. Keyword search asks “does this string appear?” Vector search asks “does this mean the same thing?” That shift is why on-topic, self-contained passages get surfaced and loosely related ones do not.
How it affects your traffic
If an AI engine cannot retrieve your page, it cannot cite you, and vector search is the gate. Content that is clearly scoped, self-contained at the passage level, and semantically on-topic embeds close to the questions your buyers actually ask, so it surfaces as a nearest neighbor. Vague, padded, or off-topic sections drift away in vector space and never get pulled into the generated answer. Our AI SEO work restructures your pages into retrievable, quotable chunks so you show up when models assemble their responses.
Get AI SEO that moves the needle
We turn terms like this into ranked pages and qualified pipeline. Start with a free Initial SEO Strategy.