# Metrics

## What are Metrics?

Metrics are measurable properties that help quantify specific characteristics of your data. Metrics can be user-defined, by providing a numeric column computed from your source data alongside your application data. 

Alternatively, the Distributional SDK offers a comprehensive set of metrics for evaluating various aspects of text and LLM outputs. Using Distributional's methods for computing metrics will enable better data-exploration and application stability monitoring capabilities.

The [SDK documentation](/v0.25.x/reference/python-sdk/eval-module/quick-start.md) contains more details on Metrics including some example usage.

### Using Metrics

The SDK provides convenient functions for computing metrics from your data and reporting the results to Distributional:

```python
import dbnl
import dbnl.eval
import pandas as pd

# login to dbnl
dbnl.login()
project = dbnl.create_project(name="Metrics Project")

df = pd.DataFrame(
    {
        "id": [1, 2, 3],
        "question": [
            "What is the meaning of life?",
            "What is the airspeed velocity of an unladen swallow?",
            "What is the capital of Assyria?",
        ],
        "answer": [
            "To be happy and fulfilled.",
            "It's a question of aerodynamics.",
            "Nineveh was the capital of Assyria.",
        ],
        "expected_answer": [
            "42",
            "It's a question of aerodynamics.",
            "Nineveh was the capital of Assyria.",
        ],
    }
)

# Create individual metrics
metrics = [
    dbnl.eval.metrics.token_count("question"),
    dbnl.eval.metrics.word_count("question"),
    dbnl.eval.metrics.rouge1("answer", "expected_answer"),
]

# Compute metrics and report results to Distributional
run = dbnl.eval.report_run_with_results(
    project=project, column_data=df, metrics=metrics
)

```

<figure><img src="/files/0GiHC1JObQ8OEnLo9X3L" alt=""><figcaption><p>Summary statistics for the Run uploaded above</p></figcaption></figure>

See the [SDK documentation](/v0.25.x/reference/python-sdk.md) for a more complete list and description of available metrics.

### Convenience Functions

The SDK includes helper functions for creating common groups of related metrics based on consistent inputs.&#x20;

```python
import dbnl
import dbnl.eval
import pandas as pd

# login to DBNL
dbnl.login()
project = dbnl.create_project(name="Metrics Project")

df = pd.DataFrame(
    {
        "id": [1, 2, 3],
        "question": [
            "What is the meaning of life?",
            "What is the airspeed velocity of an unladen swallow?",
            "What is the capital of Assyria?",
        ],
        "answer": [
            "To be happy and fulfilled.",
            "It's a question of aerodynamics.",
            "Nineveh was the capital of Assyria.",
        ],
        "expected_answer": [
            "42",
            "It's a question of aerodynamics.",
            "Nineveh was the capital of Assyria.",
        ],
    }
)

# Get standard text evaluation metrics
text_eval_metrics = dbnl.eval.metrics.text_metrics(
    prediction="answer", target="expected_answer"
)

# Get comprehensive QA evaluation metrics
qa_metrics = dbnl.eval.metrics.question_and_answer_metrics(
    prediction="answer",
    target="expected_answer",
    input="question",
)

# Compute metrics and report results to Distributional
run = dbnl.eval.report_run_with_results(
    project=project, column_data=df, metrics=(text_eval_metrics + qa_metrics)
)
```

<figure><img src="/files/H6CDXJcjclhSTj80zUip" alt=""><figcaption><p>Summary statistics for the Run uploaded above</p></figcaption></figure>

See the [SDK documentation](/v0.25.x/reference/python-sdk.md) for a more complete list and description of available functions.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.dbnl.com/v0.25.x/using-distributional/metrics.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.