Category Configuration

This guide covers how to configure categories in vLLM Semantic Router, including YAML syntax, parameter explanations, and best practices for optimal routing performance.

Configuration Overview

Categories are configured in the main config.yaml file under the categories section. Each category defines how queries of that type should be handled, including model preferences, reasoning settings, and routing behavior.

Basic Configuration Structure

categories:
  - name: "category_name"
    description: "Optional description"
    use_reasoning: true|false
    reasoning_description: "Why reasoning is needed"
    reasoning_effort: "low|medium|high"
    model_scores:
      - model: "model_name"
        score: 0.0-1.0

Configuration Parameters

Core Parameters

name (Required)

• Type: String
• Description: Unique identifier for the category
• Valid Values: Any string matching supported categories
• Example: "math", "computer science", "business"

categories:
  - name: "math"

description (Optional)

• Type: String
• Description: Human-readable description of the category
• Purpose: Documentation and debugging
• Example: "Mathematical problems and calculations"

categories:
  - name: "math"
    description: "Mathematical problems requiring step-by-step solutions"

Reasoning Configuration

use_reasoning (Required)

• Type: Boolean
• Description: Whether to enable reasoning mode for this category
• Default: false
• Impact: Enables step-by-step problem solving

categories:
  - name: "math"
    use_reasoning: true  # Enable reasoning for math problems

reasoning_description (Optional)

• Type: String
• Description: Explanation of why reasoning is needed
• Purpose: Documentation and model context
• Best Practice: Provide clear justification

categories:
  - name: "chemistry"
    use_reasoning: true
    reasoning_description: "Chemical reactions require systematic analysis"

reasoning_effort (Optional)

• Type: String
• Valid Values: "low", "medium", "high"
• Default: "medium"
• Description: Controls the depth of reasoning

categories:
  - name: "math"
    use_reasoning: true
    reasoning_effort: "high"  # Maximum reasoning depth

Reasoning Effort Levels:

• Low: Basic step-by-step thinking (1-3 steps)
• Medium: Moderate analysis (3-7 steps)
• High: Deep reasoning (7-15 steps)

Model Scoring

model_scores (Required)

• Type: Array of model-score pairs
• Description: Defines model preferences for this category
• Purpose: Intelligent model selection based on domain expertise

categories:
  - name: "math"
    model_scores:
      - model: "phi4"
        score: 1.0      # Highest preference
      - model: "mistral-small3.1"
        score: 0.8      # Second choice
      - model: "gemma3:27b"
        score: 0.6      # Fallback option

Score Guidelines:

• 1.0: Perfect match, primary choice
• 0.8-0.9: Excellent capability
• 0.6-0.7: Good capability
• 0.4-0.5: Adequate capability
• 0.0-0.3: Poor capability, avoid if possible

Complete Configuration Examples

Example 1: STEM Category (Reasoning Enabled)

categories:
  - name: "math"
    description: "Mathematical problems requiring step-by-step reasoning"
    use_reasoning: true
    reasoning_description: "Mathematical problems require systematic analysis"
    reasoning_effort: "high"
    model_scores:
      - model: "phi4"
        score: 1.0
      - model: "mistral-small3.1"
        score: 0.8
      - model: "gemma3:27b"
        score: 0.6

Example 2: Professional Category (Reasoning Disabled)

categories:
  - name: "business"
    description: "Business strategy and management discussions"
    use_reasoning: false
    reasoning_description: "Business content is typically conversational"
    reasoning_effort: "low"
    model_scores:
      - model: "phi4"
        score: 0.8
      - model: "gemma3:27b"
        score: 0.4
      - model: "mistral-small3.1"
        score: 0.2

Example 3: Multi-Category Configuration

categories:
  # Technical categories with reasoning
  - name: "computer science"
    use_reasoning: true
    reasoning_description: "Programming requires logical analysis"
    reasoning_effort: "medium"
    model_scores:
      - model: "gemma3:27b"
        score: 0.6
      - model: "mistral-small3.1"
        score: 0.6
      - model: "phi4"
        score: 0.0

  - name: "physics"
    use_reasoning: true
    reasoning_description: "Physics concepts need systematic thinking"
    reasoning_effort: "medium"
    model_scores:
      - model: "gemma3:27b"
        score: 0.4
      - model: "phi4"
        score: 0.4
      - model: "mistral-small3.1"
        score: 0.4

  # General categories without reasoning
  - name: "history"
    use_reasoning: false
    reasoning_description: "Historical content is narrative-based"
    model_scores:
      - model: "mistral-small3.1"
        score: 0.8
      - model: "phi4"
        score: 0.6
      - model: "gemma3:27b"
        score: 0.4

  - name: "other"
    use_reasoning: false
    reasoning_description: "General content doesn't require reasoning"
    model_scores:
      - model: "gemma3:27b"
        score: 0.8
      - model: "phi4"
        score: 0.6
      - model: "mistral-small3.1"
        score: 0.6

Configuration Best Practices

1. Model Score Optimization

Principle: Assign scores based on actual model performance for each domain.

# Good: Scores reflect model strengths
categories:
  - name: "math"
    model_scores:
      - model: "phi4"
        score: 1.0    # Excellent at math
      - model: "mistral-small3.1"
        score: 0.8    # Good at math
      - model: "gemma3:27b"
        score: 0.6    # Adequate at math

# Avoid: Uniform scores don't leverage model strengths
categories:
  - name: "math"
    model_scores:
      - model: "phi4"
        score: 0.8    # Underutilizes phi4's math strength
      - model: "mistral-small3.1"
        score: 0.8
      - model: "gemma3:27b"
        score: 0.8

2. Reasoning Configuration

Enable reasoning for complex domains:

# Reasoning recommended for:
categories:
  - name: "math"
    use_reasoning: true
    reasoning_effort: "high"

  - name: "computer science"
    use_reasoning: true
    reasoning_effort: "medium"

  - name: "chemistry"
    use_reasoning: true
    reasoning_effort: "high"

# Reasoning not needed for:
categories:
  - name: "business"
    use_reasoning: false

  - name: "history"
    use_reasoning: false

3. Performance Tuning

Balance accuracy vs. latency:

# High-performance setup (lower latency)
categories:
  - name: "math"
    use_reasoning: true
    reasoning_effort: "medium"  # Reduced from "high"
    model_scores:
      - model: "phi4"
        score: 1.0
      - model: "mistral-small3.1"
        score: 0.6  # Larger gap for faster selection

# High-accuracy setup (higher latency)
categories:
  - name: "math"
    use_reasoning: true
    reasoning_effort: "high"    # Maximum reasoning
    model_scores:
      - model: "phi4"
        score: 1.0
      - model: "mistral-small3.1"
        score: 0.9  # Close scores for better fallback

Classifier Configuration

Categories work with the classifier configuration:

classifier:
  category_model:
    model_id: "models/category_classifier_modernbert-base_model"
    use_modernbert: true
    threshold: 0.6              # Classification confidence threshold
    use_cpu: true
    category_mapping_path: "models/category_classifier_modernbert-base_model/category_mapping.json"

Threshold Tuning

• Higher threshold (0.7-0.9): More conservative, fewer false positives
• Lower threshold (0.4-0.6): More aggressive, better coverage
• Recommended: Start with 0.6 and adjust based on performance

Validation and Testing

Configuration Validation

# Test configuration syntax
make build

# Validate category mappings
curl -X GET http://localhost:8080/api/v1/models

Performance Testing

# Test category classification
curl -X POST http://localhost:8080/classify/intent \
  -H "Content-Type: application/json" \
  -d '{"text": "Solve x^2 + 5x + 6 = 0"}'

# Expected response for math category
{
  "classification": {
    "category": "math",
    "confidence": 0.95,
    "processing_time_ms": 45
  }
}

Common Issues and Solutions

Issue 1: Low Classification Accuracy

Symptoms: Queries routed to wrong categories

Solutions:

# Increase classification threshold
classifier:
  category_model:
    threshold: 0.7  # Increase from 0.6

# Add more specific model scores
categories:
  - name: "math"
    model_scores:
      - model: "phi4"
        score: 1.0
      - model: "other-models"
        score: 0.0  # Explicitly avoid poor performers

Issue 2: High Latency

Symptoms: Slow response times

Solutions:

# Reduce reasoning effort
categories:
  - name: "math"
    reasoning_effort: "medium"  # Reduce from "high"

# Optimize model selection
categories:
  - name: "math"
    model_scores:
      - model: "fast-model"
        score: 0.9
      - model: "slow-model"
        score: 0.1  # Deprioritize slow models

Issue 3: Poor Model Selection

Symptoms: Suboptimal model choices

Solutions:

# Review and adjust model scores based on benchmarks
categories:
  - name: "computer science"
    model_scores:
      - model: "code-specialized-model"
        score: 1.0  # Use specialized models
      - model: "general-model"
        score: 0.3  # Reduce general model preference

Migration Guide

From Legacy Configuration

# Old format (deprecated)
routing_rules:
  - category: "math"
    model: "phi4"
    reasoning: true

# New format (current)
categories:
  - name: "math"
    use_reasoning: true
    reasoning_effort: "high"
    model_scores:
      - model: "phi4"
        score: 1.0

Next Steps

• Supported Categories - Review all available categories
• Technical Details - Understand the implementation
• Category Overview - Learn about the category system