Embeddings

The embedding model turns text into vectors. Queries, schema names, and extracted instances are embedded so later steps can match meaning, not only identical strings. Answer models use these vectors on the Middleware API. Schema refinement also uses them to find overlapping classes.

Configuration

Setting	How it is configured
What is embedded	Queries, class and property names, and instance labels in the workspace.
Similarity	Cosine similarity.
Retrieval	Hits are ranked literal first, then lexical, then semantic. An embedding-only hit is last, not first.
Schema	Near-duplicate classes and properties are proposed for merge using the same vectors.
Serving	AiQL selects the checkpoint and vector size. Customers do not pick an embedding model.

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Applications

Platform

Models

SDKs

Configuration

​Configuration

Configuration