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Input

  • output (str): The generated text to be evaluated.
  • expectedOutput (str): The reference or ground truth text.

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Output

  • Result (float): A distance score from 0 to infinity.

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Interpretation

  • 0: The embeddings are identical.
  • Higher scores: The texts are more semantically different.
    The score is sensitive to vector magnitude.

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Formula

Euclidean(x,y)=∑i(xi−yi)2\mathrm{Euclidean}(x, y) = \sqrt{\sum_i (x_i - y_i)^2} Where x_i and y_i are the components of the vectors at dimension i.
This is a distance metric. Lower scores indicate greater similarity.

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How It Works

The evaluator computes embeddings for both the generated and reference texts, then calculates the straight-line distance between these two vector points in the embedding space.

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Use Cases

  • Semantic distance measurement
  • Cluster analysis and nearest neighbor search
  • Quality threshold verification