mlflow--mlflow/examples/open_webui

MLflow Filter Pipeline for Open WebUI

A filter pipeline that integrates MLflow tracing with Open WebUI, enabling observability for multi-turn chat sessions.

What It Does

• inlet: Captures the last user message and session context before each request
• outlet: Logs a complete trace per turn — user input, assistant response, model name, and token usage — grouped under the same session in the MLflow UI

Features

• Multi-turn session grouping — all turns of a conversation are linked via mlflow.trace.session, viewable with "Group by session" in the MLflow UI
• Per-turn tracing — each request/response pair is logged as a separate MLflow trace with latency and status
• Token usage tracking — input/output token counts are captured when provided by the backend, automatically aggregated at the trace level
• User attribution — traces are tagged with the authenticated user's email via mlflow.trace.user

Requirements

• MLflow tracking server running and accessible
• mlflow>=2.14.0

Configuration (Valves)

Valve	Default	Description
mlflow_tracking_uri	http://localhost:5000	MLflow tracking server URI
mlflow_experiment_name	open-webui	Experiment name in MLflow
debug	false	Enable debug logging

Setup

1. Start the MLflow server

mlflow server --disable-security-middleware

2. Start Open WebUI

open-webui serve

3. Launch the pipeline service via Docker

Build a custom Docker image with MLflow installed:

# Create Dockerfile.mlflow
cat > Dockerfile.mlflow <<'EOF'
FROM ghcr.io/open-webui/pipelines:main
RUN pip install --no-cache-dir mlflow
EOF

# Build image
docker build -f Dockerfile.mlflow -t pipelines-mlflow .

# Launch container (replace host.docker.internal:5000 with your MLflow server address)
docker run -p 9099:9099 \
  --add-host=host.docker.internal:host-gateway \
  -v pipelines:/app/pipelines \
  --name pipelines \
  --restart always \
  -e MLFLOW_TRACKING_URI=http://host.docker.internal:5000/ \
  -e DEBUG_MODE=true \
  pipelines-mlflow

4. Connect Open WebUI to the pipeline server

In Open WebUI, go to Admin Panel → Settings → Connections and add a new OpenAI API connection pointing to the pipeline server:

• URL: http://localhost:9099/
• Password: 0p3n-w3bu! (default credential)

5. Upload the pipeline in Open WebUI

Go to Admin Panel → Settings → Pipelines. Set the Pipelines listener address to http://host.docker.internal:9099, then upload mlflow_filter_pipeline.py using the file upload button. Then configure MLflow tracking URI and MLflow experiment name as follows:

6. Chat and observe traces

Start a conversation in Open WebUI:

Open the MLflow UI and enable "Group by session" to view full conversations as grouped traces.

Single turn traces:

Full chat session grouped view: