Embeddings
Embeddings are numerical representations of data using vectors. AI uses them to interpret complex data and relationships, and vector databases store them for retrieval.
On this page
Source content checked . English translation published 2026-10-27.
In business automation
An embedding represents content as a vector that makes similarity comparisons possible. It is useful for searching by meaning even when the query and document use different words; similarity does not prove that a document answers the question correctly.
Examples
These examples illustrate a possible workflow. Adapt the connections and rules to your own systems.
Business process example
A customer writes that their package did not arrive. The query is converted into a representation, and passages about delivery delays are retrieved even though they use different wording. Before generating a reply, the system checks that the passages relate to the current shipping policy.
Relevant tools
- Cohere
- Provides embedding models for search and language applications; choose a model and input type that fit the use case.
What to check
- Keep the embedding model consistent for documents and queries. Changing models may require re-indexing.