> ## 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.

# Semantic Similarity

> Evaluates how close two texts are in meaning by comparing their vector embeddings, typically using cosine similarity. It captures meaning beyond exact word matches.

### Input

* **`output`** (str): The generated text.
* **`expectedOutput`** (str): The reference text.

### Output

* **`Result`** (float): A similarity score, typically between 0 and 1.

## Interpretation

* **`1`**: The texts are considered semantically identical.
* **`0`**: The texts have completely different meanings.
* Robust to paraphrasing and synonymous language.

## Formula

$$
\mathrm{Similarity} = \frac{A \cdot B}{\|A\| \times \|B\|}
$$

Where `A` and `B` are the embedding vectors.

<Note>This is a **similarity metric**. Higher scores (closer to 1) indicate greater semantic similarity.</Note>

## How It Works

Compute vector embeddings for the generated and reference texts, then measure their similarity (commonly cosine similarity). This captures shared meaning beyond exact word matches.

## Use Cases

* Evaluating chatbots and conversational AI
* Assessing the quality of abstractive summaries
* Measuring relevance in search and retrieval
* Paraphrase detection