> ## Documentation Index
> Fetch the complete documentation index at: https://www.getmaxim.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Euclidean Embedding Distance

> Calculates the straight-line L2 distance between two text embeddings, providing a natural measure of semantic difference in the vector space.

### Input

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

### Output

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

## Interpretation

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

## Formula

$$
\mathrm{Euclidean}(x, y) = \sqrt{\sum_i (x_i - y_i)^2}
$$

Where `x_i` and `y_i` are the components of the vectors at dimension `i`.

<Note>This is a **distance metric**. Lower scores indicate greater similarity.</Note>

## 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.

## Use Cases

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