Match queryVector dimensions to the index and keep one embedding model per index
Context: MongoDB reference notes (grounded in the official Atlas Vector Search docs): the index definition declares the embedding field with its dimension count, for example { type: knnVector, dimensions: 1536, similarity: cosine }. The queryVector you send at query time must be an array of exactly that many numbers. You must also use the same embedding model that generated the indexed vectors; a query emb
Pin one embedding model for a collection and store its name and dimension count alongside the index definition. Validate every query vector's length against the declared dimensions client-side before sending the aggregation; fail fast on mismatch. If you change embedding models, re-embed the collection and recreate the index; never mix models in one index.Maintainer review
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