# Application Metric Sets

The metric set helpers return an adaptive list of metrics, relevant to the application type. See the [dbnl.eval.metrics reference](/v0.24.x/reference/python-sdk/dbnl.eval.metrics.md) for details on all the metric functions available in the eval SDK.

### `text_metrics()`

Basic metrics for generic text comparison and monitoring

* `token_count`
* `word_count`
* `flesch_kincaid_grade`
* `automated_readability_index`
* `bleu`
* `levenshtein`
* `rouge1`
* `rouge2`
* `rougeL`
* `rougeLsum`
* `llm_text_toxicity_v0`
* `llm_sentiment_assessment_v0`
* `llm_reading_complexity_v0`
* `llm_grammar_accuracy_v0`
* `inner_product`
* `llm_text_similarity_v0`

### `question_and_answer_metrics()`

Basic metrics for RAG / question answering

* `llm_accuracy_v0`
* `llm_completeness_v0`
* `answer_similarity_v0`
* `faithfulness_v0`
* `mrr`
* `context_hit`

The metric set helpers are adaptive in that :

1. The metrics returned encode which columns of the dataframe are input to the metric computation\
   e.g.,  `rougeL_prediction__ground_truth` is the `rougeL` metric run with both the column named `prediction` and the column named `ground_truth` as input
2. The metrics returned support any additional optional column info and LLM-as-judge or embedding model clients. If any of this optional info is not provided, the metric set will exclude any metrics that depend on that information

```python
def text_metrics(
    prediction: str,
    target: Optional[str] = None,
    eval_llm_client: Optional[LLMClient] = None,
    eval_embedding_client: Optional[EmbeddingClient] = None,
) -> list[Metric]:
    """
    Returns a set of metrics relevant for a generic text application

    :param prediction: prediction column name (i.e. generated text)
    :param target: target column name (i.e. expected text)
    :return: list of metrics
    """
```

See the [How-To section](/v0.24.x/reference/python-sdk/eval-module/how-to-faq.md) for concrete examples of adaptive `text_metrics()` usage

See the [RAG example](/v0.24.x/reference/python-sdk/eval-module/rag-question-answer-example.md) for `question_and_answer_metrics()` usage


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