Embeddings

An embedding is a list of numbers produced by a model that has learned to place similar meanings near each other in that numeric space. Two sentences that use different words but mean nearly the same thing end up close together; two sentences that share words but mean different things can end up far apart. That is what makes semantic search possible: search by meaning, not by matching keywords.

A regulated search over policies or case files written in inconsistent language needs to find the right passage even when the query and the document do not share vocabulary. Keyword search alone misses exactly the paraphrased or differently-worded version of the same rule.

A query about client onboarding retrieves a passage titled new account setup, because the two are close together in embedding space even though they share almost no words.

In Dynamiq, 8 embedding providers sit behind one interface, OpenAI, Cohere, Bedrock, Mistral, Gemini, Hugging Face, watsonx and Vertex AI, and the platform enforces the one rule that actually matters: whichever model embedded the documents at ingestion time is the same one used to embed the query at search time, automatically, inside a Knowledge Base.