Embeddings encode learned similarity
An embedding is a numeric representation produced by an embedding model. Text, images, or other objects are mapped into a vector space where the model has learned that certain relationships should correspond to geometric relationships.
Similar items end up close because the model's training objective encourages representations with useful similarity structure. For example, a sentence about cancelling an order and another sentence about requesting an order refund may receive nearby vectors if the model was trained to capture semantic relationships.
The important caveat is that 'close' does not mean universally similar. An embedding captures the notion of similarity learned by a particular model and task. Two models can produce vectors with different dimensions, scales, and semantic behavior, so embeddings from different models generally should not be mixed in the same vector space.
A common misconception is that Qdrant creates semantic meaning from raw text. Qdrant stores and compares vectors; the embedding model is responsible for transforming raw content into a meaningful representation.
The embedding model defines what semantic or task-specific similarity means
Vector proximity is geometric evidence of learned similarity, not a universal definition of meaning
Embeddings from incompatible models should not be mixed casually
Qdrant performs vector storage and retrieval; the model creates the representation
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