Embedding
A numerical vector representing the meaning of text, an image or another item, positioned so that semantically similar items lie close…
GEO AISemantic retrievalANN search
Vector search
Retrieval by nearest-neighbour similarity between embedding vectors rather than by keyword matching, usually implemented with approximate nearest-neighbour indexes.
In full
Vector search finds conceptually related content but can miss exact terms — product codes, names, rare identifiers — which is why production systems usually run hybrid retrieval combining vector similarity with BM25-style lexical scoring, then rerank. Understanding this explains a persistent GEO observation: pages can be retrieved for questions they never literally match, and can be missed for terms they contain but do not explain.
Example
A hybrid search returns a page about 'reducing churn' for the query 'stop customers cancelling', while lexical-only search returns nothing.
Related terms
Retrieval-augmented generation
An architecture where a system retrieves relevant documents or passages first and then has a language model generate an answer conditioned…
Semantic search
Retrieval based on the meaning of a query and documents rather than literal keyword matching, using entities, context and learned…
TF-IDF
A classical information-retrieval weighting that scores a term by how often it appears in a document offset by how common it is across the…
Chunk retrieval
Retrieving individual segments of a document rather than whole pages, which is how most AI search and RAG systems select what to feed a…
Where to read more
- Dense Passage Retrieval for Open-Domain Question Answering · Landmark Post · arxiv.org