Add MLflow Pipelines Integration Example

[axc888]

Description

This PR adds a comprehensive MLflow integration pipeline for Open WebUI that enables real-time conversation tracking, analytics, and performance monitoring. The pipeline automatically logs all user-AI interactions to MLflow with detailed metrics and artifacts.

Features Added

• Real-time Conversation Tracking: Automatically logs all user-AI interactions to MLflow
• Comprehensive Metrics: Tracks tokens, response times, message lengths, and interaction counts
• Flexible Run Management: Choose between per-conversation or per-interaction tracking modes
• User Analytics: Monitor user behavior patterns and usage statistics
• Model Performance Tracking: Track model response times and token efficiency
• Artifact Storage: Save conversation history and individual messages as MLflow artifacts
• Rich Metadata: Extensive tagging and parameter tracking for easy filtering and analysis
• Robust Error Handling: Graceful degradation when MLflow server is unavailable

Type of Change

• New feature

Core Functionality

• Filter Pipeline: Implements both inlet() and outlet() methods to capture complete request/response cycle
• MLflow Integration: Creates experiments and runs with comprehensive logging
• Dual Tracking Modes:
  • Per-conversation: One MLflow run per chat session
  • Per-interaction: Separate MLflow run for each user-AI exchange
• Comprehensive Data Collection:
  • User inputs and AI responses as artifacts
  • Token usage metrics (input/output/total tokens)
  • Response timing measurements
  • Message length analytics
  • Model information and metadata

Configuration Options

The pipeline supports extensive configuration via environment variables and valves:

Environment Variable	Description	Default
MLFLOW_TRACKING_URI	MLflow server URL	http://localhost:5000
MLFLOW_EXPERIMENT_NAME	Experiment name	open-webui-experiments
SEPARATE_RUNS	Per-interaction tracking	false
USE_MODEL_NAME	Use model name vs ID	false
DEBUG_MODE	Enable debug logging	false

Data Structure

Tags: source, interface, user_id, chat_id, run_type, status, total_interactions

Parameters: model_id, model_name, user_email, chat_id, interface, task_type

Metrics: user_message_length, assistant_message_length, response_time, input_tokens, output_tokens, total_tokens

Artifacts: User inputs, AI responses, conversation history (JSON)

Requirements

• mlflow>=2.0.0
• requests>=2.25.0
• MLflow server (local or remote)

Usage Examples

Basic Setup

1. Install MLflow (version 2.0.0 or higher)

pip install "mlflow>=2.0.0"

2. Start the MLflow tracking server

mlflow server --host 0.0.0.0 --port 5000

3. Configure Open WebUI (or any client) to use MLflow

export MLFLOW_TRACKING_URI=http://localhost:5000
export MLFLOW_EXPERIMENT_NAME=my-conversations

Screenshots