> ## Documentation Index
> Fetch the complete documentation index at: https://futureagi.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Levenshtein Similarity

> Measures text similarity based on the minimum number of single-character edits required to transform one text into another.

<CodeGroup>
  ```python Python theme={null}
  result = evaluator.evaluate(
      eval_templates="levenshtein_similarity",
      inputs={
          "expected": "The Eiffel Tower is a famous landmark in Paris, built in 1889 for the World's Fair. It stands 324 meters tall.",
          "output": "The Eiffel Tower, located in Paris, was built in 1889 and is 324 meters high."
      },
      model_name="turing_flash"
  )

  print(result.eval_results[0].output)
  print(result.eval_results[0].reason)
  ```

  ```typescript JS/TS theme={null}
  import { Evaluator, Templates } from "@future-agi/ai-evaluation";

  const evaluator = new Evaluator();

  const result = await evaluator.evaluate(
    "levenshtein_similarity",
    {
      expected: "The Eiffel Tower is a famous landmark in Paris, built in 1889 for the World's Fair. It stands 324 meters tall.",
      output: "The Eiffel Tower, located in Paris, was built in 1889 and is 324 meters high."
    },
    {
      modelName: "turing_flash",
    }
  );

  console.log(result);
  ```
</CodeGroup>

| **Input** |                    |          |                                                                      |
| --------- | ------------------ | -------- | -------------------------------------------------------------------- |
|           | **Required Input** | **Type** | **Description**                                                      |
|           | `expected`         | `string` | Reference content for comparison against the model generated output. |
|           | `output`           | `string` | Model generated content to be evaluated for similarity.              |

| **Output** |            |                                                                   |
| ---------- | ---------- | ----------------------------------------------------------------- |
|            | **Field**  | **Description**                                                   |
|            | **Result** | Returns a score, where higher score indicates greater similarity. |
|            | **Reason** | Provides a detailed explanation of the similarity assessment.     |

***

### About Levenshtein Similarity

Levenshtein Similarity is a character-level metric that quantifies how similar two text sequences are by calculating the minimum number of operations needed to transform one sequence into the other. The output is normalized to a score between 0 and 1, where 1 indicates an exact match and 0 indicates maximum dissimilarity. This metric is useful for use-cases in spelling correction, OCR, and deterministic text matching.

### Edit Operations

* Possible operations that are allowed in Levenshtein calculation:
  * **Insertion**: Add a character (e.g., `kitten -> kitteng`)
  * **Deletion**: Remove a character (e.g., `kitten -> kiten`)
  * **Substitution**: Replace one character with another (e.g., `kitten -> sitten`)
* Each operation has a cost of 1. The final distance is the sum of all such operations needed to match the two strings.

### Normalized Levenshtein Score

$$
\hbox{Score} = 1 - { \hbox{Levenshtein Distance} \over \hbox{max(Length of Prediction, Length of Reference)} }
$$

* Score of **1** means the two strings are identical.
* Score of **0** means no characters are shared at corresponding positions.

***

### What to do If you get Undesired Results

If the Levenshtein similarity score is lower than expected:

* Consider case sensitivity - the comparison is typically case-sensitive
* Check for whitespace and punctuation differences, which count as edits
* For meaning-based comparison rather than exact character matching, consider using semantic similarity metrics
* For texts with similar meaning but different wording, consider metrics like ROUGE, BLEU, or embedding similarity
* Remember that this metric measures character-level similarity, not semantic similarity

***

### Comparing Levenshtein Similarity with Similar Evals

* [**Fuzzy Match**](/future-agi/get-started/evaluation/builtin-evals/fuzzy-match): While Levenshtein Similarity focuses on character-level edits, Fuzzy Match may use different algorithms for approximate string matching.
* [**Embedding Similarity**](/future-agi/get-started/evaluation/builtin-evals/embedding-similarity): Levenshtein Similarity measures character-level edits, whereas Embedding Similarity captures semantic similarity through vector representations.
* [**BLEU Score**](/future-agi/get-started/evaluation/builtin-evals/bleu): Levenshtein operates at character level, while BLEU focuses on n-gram precision between the candidate and reference texts.