> ## 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.

# Cosine Embedding Distance

> Measures the cosine of the angle between two embedding vectors to evaluate semantic similarity based on orientation, not magnitude.

### Input

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

### Output

* **`Result`** (float): A distance score between 0 and 1.

## Interpretation

* **`0`**: The vectors have the same orientation (perfectly similar).
* **`1`**: The vectors are orthogonal (no similarity).

<Note>A lower distance corresponds to greater semantic similarity, with smaller values indicating that the two outputs are closer in meaning </Note>

## Formula

$$
\mathrm{Cosine\ Distance} = 1 - \frac{A \cdot B}{\|A\| \times \|B\|}
$$

Where `A · B` is the dot product of the vectors and `||A||` is the vector's magnitude.

<Note>This is a **distance metric** <code>distance = 1 - cosine\_similarity</code>. Lower scores (closer to 0) indicate higher similarity</Note>

## How It Works

The evaluator computes embeddings for both texts and then calculates the angle between them. This provides a measure of similarity that is not affected by the length (magnitude) of the vectors.

## Use Cases

* Document similarity and text classification
* Information retrieval and search engines
* Paraphrase detection