Add Mixture of Models (MoM) Use Case Guide to Intelligent Route Tutorials

[Xunzhuo]

Is your feature request related to a problem? Please describe.

Currently, the semantic router has comprehensive Mixture of Models (MoM) functionality implemented, including semantic classification, model selection algorithms, and reasoning-aware routing. However, the intelligent route tutorials (website/docs/tutorials/intelligent-route/) lack practical use case guides that demonstrate how to implement and optimize MoM strategies for real-world scenarios.

Users struggle with:

1. Understanding practical MoM applications - While the overview exists in website/docs/overview/mixture-of-models.md, there's no hands-on tutorial showing step-by-step implementation
2. Configuring optimal model pools - No guidance on selecting and scoring models for different use cases
3. Cost-performance optimization - Missing practical examples of balancing cost, latency, and quality
4. Real-world deployment patterns - Lack of concrete examples for common business scenarios

This creates a gap between the powerful MoM capabilities and user adoption, especially for teams wanting to implement intelligent routing for production workloads.

Describe the solution you'd like

Create a comprehensive Mixture of Models Use Case Guide in website/docs/tutorials/intelligent-route/mixture-of-models.md that includes:

1. Practical Use Case Scenarios

• E-commerce Customer Support: Route simple order queries to lightweight models, complex issues to premium models
• Code Assistant Platform: Direct syntax questions to fast models, architecture reviews to specialized models
• Educational Content Platform: Route basic questions to efficient models, complex explanations to reasoning-capable models
• Financial Analysis Service: Balance cost-sensitive queries with high-accuracy requirements

2. Step-by-Step Implementation Guide

• Model pool selection and configuration
• Category definition and scoring strategies
• Confidence threshold optimization
• Fallback mechanism setup

3. Configuration Examples

• Complete config.yaml examples for each use case
• Model scoring matrices with performance data
• Reasoning family configurations
• Cost-performance trade-off examples

4. Performance Optimization Strategies

• Model selection algorithms in practice
• Confidence-based routing decisions
• Dynamic scaling patterns
• A/B testing configurations for model comparison

5. Monitoring and Analytics

• Key metrics for MoM performance evaluation
• Cost tracking and optimization
• Quality assurance patterns
• Routing decision analysis

6. Advanced Patterns

• Weighted routing configurations
• Multi-stage classification
• Provider diversity strategies
• Hybrid cloud/on-premise deployments

7. Troubleshooting and Best Practices

• Common routing issues and solutions
• Performance tuning guidelines
• Cost optimization techniques
• Quality assurance strategies

Additional context

Current Implementation Assets:

• MoM overview documentation (website/docs/overview/mixture-of-models.md)
• Model selection algorithms (src/semantic-router/pkg/extproc/model_selector.go)
• Classification system (src/semantic-router/pkg/utils/classification/classifier.go)
• Reasoning mode selection (src/semantic-router/pkg/extproc/reason_mode_selector.go)
• Example configurations (examples/semanticroute/)

Existing Tutorial Structure:

• website/docs/tutorials/intelligent-route/overview.md - Basic concepts
• website/docs/tutorials/intelligent-route/reasoning.md - Reasoning routing guide

Technical Features to Showcase:

• 14+ query categories with semantic classification
• Model scoring and selection algorithms
• Reasoning-aware routing with confidence thresholds
• Weighted routing and traffic splitting
• Fallback mechanisms and graceful degradation
• Integration with semantic cache and content safety

Real-World Examples to Include:

• Cost savings calculations (like the $165K/year example in overview)
• Performance benchmarks across different model combinations
• Latency vs. quality trade-offs
• Provider diversity and reliability patterns

Integration Points:

• Reference existing semantic cache tutorials for performance optimization
• Link to content safety tutorials for secure routing
• Connect with observability guides for monitoring MoM performance
• Cross-reference with Kubernetes deployment for production scaling

This tutorial will bridge the gap between the powerful MoM capabilities and practical implementation, enabling users to leverage intelligent routing for cost-effective, high-performance AI applications.

/area core user-experience docs
/milestone v0.1
/priority P1

[Xunzhuo]

/area core user-experience docs
/milestone v0.1
/priority P1

[samzong]

Hi, @Xunzhuo this issue already finished or someone working now?

if not I want take it.