Custom Alignment Optimizers

MLflow's alignment system is designed as a plugin architecture, allowing you to create custom optimizers for different alignment strategies. This extensibility enables you to implement domain-specific optimization approaches while leveraging MLflow's judge infrastructure.

Creating a Custom Optimizer

To create a custom alignment optimizer, extend the AlignmentOptimizer abstract base class:

python

from mlflow.genai.judges.base import AlignmentOptimizer, Judge
from mlflow.entities.trace import Trace


class MyCustomOptimizer(AlignmentOptimizer):
    """Custom optimizer implementation for judge alignment."""

    def __init__(self, model: str = None, **kwargs):
        """Initialize your optimizer with custom parameters."""
        self.model = model

    def align(self, judge: Judge, traces: list[Trace]) -> Judge:
        """
        Implement your alignment algorithm.

        Args:
            judge: The judge to be optimized
            traces: List of traces containing human feedback

        Returns:
            A new Judge instance with improved alignment
        """
        # Your custom alignment logic here
        # 1. Extract feedback from traces
        # 2. Analyze disagreements between judge and human
        # 3. Generate improved instructions
        # 4. Return new judge with better alignment

        from mlflow.genai.judges import make_judge

        improved_instructions = self._optimize_instructions(judge.instructions, traces)

        return make_judge(
            name=judge.name,
            instructions=improved_instructions,
            feedback_value_type=str,
            model=judge.model,
        )

    def _optimize_instructions(self, instructions: str, traces: list[Trace]) -> str:
        """Your custom optimization logic."""
        # Implement your optimization strategy
        pass

Using Custom Optimizers

Once implemented, use your custom optimizer just like the built-in ones:

python

# Create your custom optimizer
custom_optimizer = MyCustomOptimizer(model="your-model")

# Use it for alignment
aligned_judge = initial_judge.align(traces_with_feedback, custom_optimizer)

The plugin architecture ensures that new optimization strategies can be added without modifying the core judge system, promoting extensibility and experimentation with different alignment approaches.