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

# Custom Text Descriptor

> How to add a custom row-level text evaluator.

To run a check not available in Evidently, you can implement it as a custom function. Use this for building your own programmatic evaluators.

<Tip>
  You can also customize existing evals with parameters, such as defining custom [LLM judges ](/metrics/customize_llm_judge) or using regex-based metrics like `Contains` for word lists. See [available descriptors](/metrics/all_descriptors).
</Tip>

**Pre-requisites**:

* You know how to use built-in [descriptors](/docs/library/descriptors).

## Imports

```python theme={null}
import pandas as pd

from evidently import Dataset, DataDefinition
from evidently.core.datasets import DatasetColumn
from evidently.descriptors import CustomColumnDescriptor, CustomDescriptor
```

<Accordion title="Toy data to run the example" defaultOpen={false}>
  To generate toy data and create a Dataset object:

  ```python theme={null}
  data = [
      ["Can fish fly?", "no", ""],
      ["Is the sky blue?", "yes", "yes"],
      ["Is milk liquid??", "yes", "yes"]
  ]

  columns = ["question", "target_answer", "answer"]

  df = pd.DataFrame(data, columns=columns)

  eval_df = Dataset.from_pandas(
      df,
      data_definition=DataDefinition())
  ```
</Accordion>

## Single column check

You can define a `CustomColumnDescriptor` that will:

* take any column from your dataset to evaluate each value inside it

* return a single column with numerical (`num`) scores or categorical (`cat`) labels.

Implement it as a Python function that takes a Pandas Series as input and return a transformed Series. For example, to check if the column is empty:

```python theme={null}
def is_empty(data: DatasetColumn) -> DatasetColumn:
    return DatasetColumn(
        type="cat",
        data=pd.Series([
            "EMPTY" if val == "" else "NON EMPTY"
            for val in data.data]))
```

To use this descriptor on your data:

```python theme={null}
eval_df.add_descriptors(descriptors=[
    CustomColumnDescriptor("answer", is_empty, alias="is_empty"),
])
```

Publish to a dataframe:

```python theme={null}
eval_df.as_dataframe()
```

## Multi-column check

You can alternatively define a `CustomDescriptor` that:

* Takes one or many named columns from your dataset,

* Returns one or many transformed columns.

**Pairwise evaluation**. For example, to check exact match between `target_answer` and `answer` columns, and return a label:

```python theme={null}
def exact_match(dataset: Dataset) -> DatasetColumn:
    return DatasetColumn(
        type="cat",
        data=pd.Series([
            "MATCH" if val else "MISMATCH"
            for val in dataset.column("target_answer").data
            == dataset.column("answer").data]))
```

To use this descriptor on your data:

```python theme={null}
eval_df.add_descriptors(descriptors=[
    CustomDescriptor(exact_match, alias="exact"),
])
```

**Multiple scores**. You can also use `CustomDescriptor` to run evals for multiple columns and return multiple scores.

As a fun example, let's reverse all words in the `question` and `answer` columns:

```python theme={null}
from typing import Union, Dict

def reverse_text(dataset: Dataset) -> Union[DatasetColumn, Dict[str, DatasetColumn]]:
    return {
        "reversed_question": DatasetColumn(
            type="cat",
            data=pd.Series([
                value[::-1] for value in dataset.column("question").data])),
        "reversed_answer": DatasetColumn(
            type="cat",
            data=pd.Series([
                value[::-1] for value in dataset.column("answer").data]))}
```

To use this descriptor on your data:

```python theme={null}
eval_df.add_descriptors(descriptors=[
    CustomDescriptor(reverse_text),
])
```