# Python Code Metrics

## Creating a Python-Based Metric in Dynamiq

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### The Power of Custom Python Metrics

One of Dynamiq's standout features is the ability to create **custom Python-based metrics**. This functionality provides you with unparalleled flexibility in evaluating your AI workflows. Instead of being confined to predefined evaluation criteria, you can implement tailored logic to effectively assess your specific use cases.

### Creating Your Python Metric

Follow these steps to create a Python metric in Dynamiq:

1. **Navigate to the Metric Creation Interface**:
   * Go to **Evaluations → Metrics → Create New Metric**.
2. **Select the Python Tab**:
   * Once you arrive at the **Python** tab, you will see an example metric designed to help you get started. For quick development, you can also choose from several pre-built templates, including:
     * **Exact Match**
     * **Email Presence**
     * **Phone Presence**
     * **String Presence**
     * **Arithmetic Sum**
     * **JSON Validity Check**

### How Python Metrics Work

Let’s look at a simple example using the **Exact Match** metric:

```python
def evaluate(answer: str, expected: str) -> int:
    if answer == expected:
        score = 1
    else:
        score = 0
    return score
```

This code checks if an answer matches the expected value exactly, returning a score of **1** for a match or **0** otherwise.

#### Important Note on Function Arguments

When defining a Python metric, Dynamiq automatically extracts the function arguments (in this case, `answer` and `expected`). During evaluation runs, you’ll need to map these arguments to specific fields from your dataset or workflow output.

This mapping system allows your metrics to dynamically access the relevant data during evaluation.

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### Technical Implementation Details

Under the hood, Dynamiq uses **RestrictedPython** to safely execute your metric code. This means that certain operations may be prohibited for security reasons.

For a detailed overview of allowed Python libraries and implementation details, you can review the [official Dynamiq GitHub repository](https://github.com/dynamiq-ai/dynamiq/blob/main/dynamiq/nodes/tools/python.py).

### Taking Your Evaluations to the Next Level

With the power of custom Python metrics at your disposal, you can create sophisticated evaluation pipelines tailored to your precise requirements. This functionality enables you to:

* Implement domain-specific evaluation logic.
* Create composite metrics that take multiple factors into account.
* Evaluate complex data structures and relationships.
* Build metrics that align directly with your business objectives.

By leveraging the full power of Python within your evaluation pipeline, you can gain deeper insights into your AI applications and make informed, data-driven improvements with confidence.

