analyzing-mlflow-session

Debug multi-turn chat conversations by analyzing MLflow traces to reconstruct the interaction flow, discover input/output schemas, and pinpoint the exact turn where errors originated. This process requires MLflow traces with session IDs and specific attribute keys for inputs and outputs.

MLflow session analysis identifies the root cause of conversational errors by extracting fields across all session traces to reconstruct the conversation. It traces reported errors back to the originating turn, even if the failure appears later in the chat history.

Yes, you can review LLM chat assessment rationales from MLflow trace data. The analysis evaluates session-level and per-turn assessments to identify quality issues and explains the rationale behind those quality determinations.

To reconstruct chat history from MLflow traces, you need traces containing session IDs and specific attribute keys for inputs and outputs. The analysis uses these fields to map the multi-turn conversation flow and identify interaction failures.

The primary limitation of analyzing MLflow chat sessions is the strict dependency on trace data. You must have MLflow traces with session IDs and explicitly defined input/output attribute keys, or the conversation reconstruction and root cause analysis will fail.