Vector Search
Definition
Vector search finds similar content based on their representation as numerical vectors.
The search for vectors that are closest to a query according to a defined distance or similarity measure.
Simply explained
Texts or images get a kind of numerical address. The search finds entries whose addresses are close to your query.
Why it matters in practice
The term improves reliable access to data and knowledge as well as their usable linkage in digital systems.
Example from business
A technical search finds matching manual sections and combines them with BM25.
Limits and pitfalls
- Similarity is no guarantee of relevance; metric, normalization, filters, index recall, and permissions must be evaluated.
Origin & history
Vector search evolved from nearest neighbor methods and was widely used with neural embeddings.
Use in mid-sized companies
- Possible example: A technical search finds matching manual sections and combines them with BM25.
Frequently asked questions
What does vector search mean?
The search for vectors that are closest to a query according to a defined distance or similarity measure. What should be considered? Similarity is no guarantee of relevance; metric, normalization, filters, index recall, and permissions must be evaluated.
Sources
- Billion-scale similarity search with GPUs · Johnson et al., Billion-scale similarity search with GPUs
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: