analyze-mlflow-session

To debug an MLflow chat session, you can analyze trace spans to identify user and system inputs, assess conversation quality, and pinpoint root causes of issues using Python and MLflow tools.

The best way to inspect trace spans is to search session metadata using filter strings, retrieve full trace details, and reconstruct the session history to diagnose specific conversation issues.

You can extract input and output schemas by discovering the schema using the first trace in the MLflow session, then extracting necessary fields from all subsequent traces for in-depth analysis.

Yes, MLflow trace inspection supports assessment analysis to understand quality judgments, assessment signals, and errors found within the chat session traces for deeper insight.

You can reconstruct a full MLflow session history by grouping multiple traces, identifying user and system inputs, and outputting the data to diagnose specific conversation issues.

Analyzing MLflow sessions requires no specific external dependencies, relying solely on Python and MLflow tools to identify issues and improve overall conversation quality.