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chore: import upstream snapshot with attribution
2026-07-13 12:33:42 +08:00

36 lines
1.4 KiB
Go

package embedding
import "fmt"
// validateBatch checks that an embedding backend returned exactly one vector per
// input, that no vector is empty, and that every vector has the expected width.
//
// dims is the expected width. Pass a provider's known dimension (e.g. a Hugot
// model's 384) for a strict check; pass 0 when the width is not yet known — an
// API provider learns its width lazily from the first response — and the width
// is taken from the first returned vector so the batch is still checked for
// internal consistency (a ragged batch) without asserting an absolute size.
//
// A violation returns a descriptive error naming the offending index so a
// provider regression that returns nil, short, or mis-counted vectors surfaces
// as a loud, attributable failure instead of a silently empty or mis-dimensioned
// index.
func validateBatch(name string, texts []string, vecs [][]float32, dims int) error {
if len(vecs) != len(texts) {
return fmt.Errorf("%s returned %d vectors for %d inputs", name, len(vecs), len(texts))
}
expected := dims
if expected <= 0 && len(vecs) > 0 {
expected = len(vecs[0])
}
for i, v := range vecs {
if len(v) == 0 {
return fmt.Errorf("%s returned an empty vector at index %d of %d", name, i, len(vecs))
}
if expected > 0 && len(v) != expected {
return fmt.Errorf("%s returned a width-%d vector at index %d, want %d", name, len(v), i, expected)
}
}
return nil
}