> ## Documentation Index
> Fetch the complete documentation index at: https://arizeai-433a7140.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Levenshtein Distance

> Measure the edit distance between two strings.

Calculates the [Levenshtein (edit) distance](https://en.wikipedia.org/wiki/Levenshtein_distance) between two strings — the minimum number of single-character insertions, deletions, or substitutions needed to transform one string into the other. A score of `0` means the strings are identical; higher scores indicate more differences.

## Parameters

| Parameter        | Type    | Required | Default | Description                              |
| ---------------- | ------- | -------- | ------- | ---------------------------------------- |
| `expected`       | string  | Yes      | —       | The reference string                     |
| `actual`         | string  | Yes      | —       | The string to evaluate                   |
| `case_sensitive` | boolean | No       | `true`  | Whether the comparison is case-sensitive |

## Output

| Property     | Value       | Description                                       |
| ------------ | ----------- | ------------------------------------------------- |
| `score`      | Integer ≥ 0 | Number of edits required; `0` = identical strings |
| Optimization | Minimize    | Lower scores are better                           |

## Configuring Inputs

Each evaluator parameter can be set to either a **path** (a JSONPath expression that extracts a value from the evaluation parameters) or a **literal** (a fixed value typed directly). Use paths to pull from dataset inputs, task outputs, reference data, or metadata.

See [Input Mapping](/docs/phoenix/evaluation/server-evals/input-mapping) for full details on mapping modes, resolution order, and examples.

## Usage Examples

**Answer closeness** — A QA model where small paraphrasing is acceptable but significant divergence is not. **Actual** receives the model's text response; **Expected** receives the reference answer from your dataset, typically a path like `reference.answer`. Comparing average edit distance across experiment runs shows whether prompt changes are moving outputs closer to reference.

**Entity extraction quality** — A pipeline that extracts a specific named value (a product name, location, or identifier). **Actual** is the extracted value from the model's output — often a nested path like `output.entity` if the response is structured JSON. **Expected** is the ground-truth value per example in your dataset. Edit distance reveals whether extraction is improving as you iterate on prompts or model configuration.

**Comparative prompt evaluation** — Two prompt variants tested against the same dataset. **Actual** receives the response field from each run; **Expected** stays fixed, pointing to the same reference column. The variant with the lower average Levenshtein score is closer to the reference outputs.

## Notes

<Note>
  The algorithm runs in O(n×m) time, where n and m are the lengths of the two strings. Performance degrades quadratically on very long inputs. Keep inputs under a few thousand characters for predictable evaluation times.
</Note>

## See Also

* [Pre-Built Metrics Overview](/docs/phoenix/evaluation/server-evals/pre-built-metrics)
* [Exact Match](/docs/phoenix/evaluation/server-evals/pre-built-metrics/exact-match) — for strict equality when approximate matching is not needed
* [JSON Distance](/docs/phoenix/evaluation/server-evals/pre-built-metrics/json-distance) — measure structural differences between two JSON values