> ## 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.

# Exact Match

> Check whether two strings are identical.

Checks whether two strings are identical. Returns `true` if they match, `false` otherwise.

## Parameters

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

## Output

| Property     | Value             | Description                                     |
| ------------ | ----------------- | ----------------------------------------------- |
| `label`      | `true` or `false` | Whether the strings are identical               |
| `score`      | `1.0` or `0.0`    | Numeric score (`1.0` = match, `0.0` = no match) |
| Optimization | Maximize          | Higher 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. Use literals for fixed expected values that apply to every example.

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

## Usage Examples

**Classification label validation** — A model that must output exactly one of a fixed set of labels (e.g., `"positive"`, `"negative"`, `"neutral"`), where any deviation indicates a problem. **Actual** is the model's output — use `output` for a plain string response, or `output.label` if the response is a JSON object with a label field. **Expected** is the ground-truth label stored per-example in your dataset, typically a path like `reference.label` or `reference.expected`.

**Templated response checking** — A pipeline that should return a fixed string for certain inputs (a canned reply, a status code, or a pass-through value). **Actual** is the model's output; **Expected** can be typed as a literal value if every example uses the same target string, or mapped to a dataset field if the expected value varies per example.

## Notes

<Note>
  The comparison is whitespace-sensitive. Leading/trailing spaces and different line endings will cause a mismatch. If your dataset fields may have inconsistent whitespace, consider using `contains` or `regex` instead.
</Note>

## See Also

* [Pre-Built Metrics Overview](/docs/phoenix/evaluation/server-evals/pre-built-metrics)
* [Contains](/docs/phoenix/evaluation/server-evals/pre-built-metrics/contains) — check whether a text contains one or more words
* [Levenshtein Distance](/docs/phoenix/evaluation/server-evals/pre-built-metrics/levenshtein-distance) — measure edit distance between two strings when exact matching is too strict