embedding
An embedding represents data as a numerical vector for a particular model and task.
Use this when An application needs semantic retrieval or similarity matching.
without the term
“I want search that finds things by meaning, not exact words, like 'refund' should find 'money back'. How do I set that up?”
with the term · How do you ask a model for embedding?
Add embeddings for this retrieval task. Verify model compatibility, tenant filtering, and relevance on representative positive and negative examples.
What is embedding?
Embeddings let you find similar text by comparing vectors. Results depend on the embedding model, the preprocessing, and the distance measure, so only vectors from the same model can be compared.
Why does embedding matter when vibe coding?
Nearby vectors are not necessarily relevant, accurate, or authorized for the caller.
How do you do embedding?
Keep compatible vectors together and evaluate retrieval with permissions and real examples.
What goes wrong with embedding?
Mixing vectors from incompatible models in one similarity space.