Speech-To-Text-With-Databricks: Skill for Claude Code

Analyzing an MLflow Chat Session

What is a Session?

A session groups multiple traces that belong to the same chat conversation or user interaction. Each trace in the session represents one turn: the user's input and the system's response. Traces within a session are linked by a shared session ID stored in trace metadata.

The session ID is stored in trace metadata under the key mlflow.trace.session. This key contains dots, which affects filter syntax (see below). All traces sharing the same value for this key belong to the same session.

Reconstructing the Conversation

Reconstructing a session's conversation is a multi-step process: discover the input/output schema from the first trace, extract those fields efficiently across all session traces, then inspect specific turns as needed. Do NOT fetch full traces for every turn — use --extract-fields on the search command instead.

Step 1: Discover the schema. First, find a trace ID from the session, then fetch its full JSON to inspect the schema:

# Get the first trace in the session
mlflow traces search \
  --experiment-id <EXPERIMENT_ID> \
  --filter-string 'metadata.`mlflow.trace.session` = "<SESSION_ID>"' \
  --order-by "timestamp_ms ASC" \
  --extract-fields 'info.trace_id' \
  --output json \
  --max-results 1 > /tmp/first_trace.json

# Fetch the full trace (always outputs JSON, no --output flag needed)
mlflow traces get \
  --trace-id <TRACE_ID_FROM_ABOVE> > /tmp/trace_detail.json

Find the root span — the span with parent_span_id equal to null (i.e., it has no parent). This is the top-level operation in the trace:

# Find the root span
jq '.data.spans[] | select(.parent_span_id == null)' /tmp/trace_detail.json

Examine its attributes dict to identify which keys hold the user input and system output. These could be:

• MLflow standard attributes: mlflow.spanInputs and mlflow.spanOutputs (set by the MLflow Python client)
• Custom attributes: Application-specific keys set via @mlflow.trace or mlflow.start_span() with custom attribute logging
• Third-party OTel attributes: Keys following GenAI Semantic Conventions, OpenInference, or other instrumentation conventions