Domain Based Routing

This guide shows you how to use fine-tuned classification models for intelligent routing based on academic and professional domains. Domain routing uses specialized models (ModernBERT, Qwen3-Embedding, EmbeddingGemma) with LoRA adapters to classify queries into categories like math, physics, law, business, and more.

Key Advantages

• Efficient: Fine-tuned models with LoRA adapters provide fast inference (5-20ms) with high accuracy
• Specialized: Multiple model options (ModernBERT for English, Qwen3 for multilingual/long-context, Gemma for small footprint)
• Multi-task: LoRA enables running multiple classification tasks (domain + PII + jailbreak) with shared base model
• Cost-effective: Lower latency than LLM-based classification, no API costs

What Problem Does It Solve?

Generic classification approaches struggle with domain-specific terminology and nuanced differences between academic/professional fields. Domain routing provides:

• Accurate domain detection: Fine-tuned models distinguish between math, physics, chemistry, law, business, etc.
• Multi-task efficiency: LoRA adapters enable simultaneous domain classification, PII detection, and jailbreak detection with one base model pass
• Long-context support: Qwen3-Embedding handles up to 32K tokens (vs ModernBERT's 8K limit)
• Multilingual routing: Qwen3 trained on 100+ languages, ModernBERT optimized for English
• Resource optimization: Expensive reasoning only enabled for domains that benefit (math, physics, chemistry)

When to Use

• Educational platforms with diverse subject areas (STEM, humanities, social sciences)
• Professional services requiring domain expertise (legal, medical, financial)
• Enterprise knowledge bases spanning multiple departments
• Research assistance tools needing academic domain awareness
• Multi-domain products where classification accuracy is critical

Configuration

Configure the domain classifier in your config.yaml:

# Define domain signals
signals:
  domains:
    - name: "math"
      description: "Mathematics and quantitative reasoning"
      mmlu_categories: ["math"]

    - name: "physics"
      description: "Physics and physical sciences"
      mmlu_categories: ["physics"]

    - name: "computer_science"
      description: "Programming and computer science"
      mmlu_categories: ["computer_science"]

    - name: "business"
      description: "Business and management"
      mmlu_categories: ["business"]

    - name: "health"
      description: "Health and medical information"
      mmlu_categories: ["health"]

    - name: "law"
      description: "Legal and regulatory information"
      mmlu_categories: ["law"]

    - name: "other"
      description: "General queries"
      mmlu_categories: ["other"]

# Define decisions using domain signals
decisions:
  - name: math
    description: "Route mathematical queries"
    priority: 100
    rules:
      operator: "OR"
      conditions:
        - type: "domain"
          name: "math"
    modelRefs:
      - model: "openai/gpt-oss-120b"
        use_reasoning: true
    plugins:
      - type: "system_prompt"
        configuration:
          system_prompt: "You are a mathematics expert. Provide step-by-step solutions with clear explanations."

  - name: physics
    description: "Route physics queries"
    priority: 100
    rules:
      operator: "OR"
      conditions:
        - type: "domain"
          name: "physics"
    modelRefs:
      - model: "openai/gpt-oss-120b"
        use_reasoning: true
    plugins:
      - type: "system_prompt"
        configuration:
          system_prompt: "You are a physics expert with deep understanding of physical laws. Explain concepts clearly with mathematical derivations."

  - name: computer_science
    description: "Route computer science queries"
    priority: 100
    rules:
      operator: "OR"
      conditions:
        - type: "domain"
          name: "computer_science"
    modelRefs:
      - model: "openai/gpt-oss-120b"
        use_reasoning: false
    plugins:
      - type: "system_prompt"
        configuration:
          system_prompt: "You are a computer science expert with knowledge of algorithms and data structures. Provide clear code examples."

  - name: business
    description: "Route business queries"
    priority: 100
    rules:
      operator: "OR"
      conditions:
        - type: "domain"
          name: "business"
    modelRefs:
      - model: "openai/gpt-oss-120b"
        use_reasoning: false
    plugins:
      - type: "system_prompt"
        configuration:
          system_prompt: "You are a senior business consultant and strategic advisor."

  - name: health
    description: "Route health queries"
    priority: 100
    rules:
      operator: "OR"
      conditions:
        - type: "domain"
          name: "health"
    modelRefs:
      - model: "openai/gpt-oss-120b"
        use_reasoning: false
    plugins:
      - type: "system_prompt"
        configuration:
          system_prompt: "You are a health and medical information expert."
      - type: "semantic-cache"
        configuration:
          enabled: true
          similarity_threshold: 0.95

  - name: law
    description: "Route legal queries"
    priority: 100
    rules:
      operator: "OR"
      conditions:
        - type: "domain"
          name: "law"
    modelRefs:
      - model: "openai/gpt-oss-120b"
        use_reasoning: false
    plugins:
      - type: "system_prompt"
        configuration:
          system_prompt: "You are a knowledgeable legal expert."

  - name: general_route
    description: "Default fallback route for general queries"
    priority: 50
    rules:
      operator: "OR"
      conditions:
        - type: "domain"
          name: "other"
    modelRefs:
      - model: "openai/gpt-oss-120b"
        use_reasoning: false
    plugins:
      - type: "semantic-cache"
        configuration:
          enabled: true
          similarity_threshold: 0.85

Supported Domains

Academic: math, physics, chemistry, biology, computer science, engineering

Professional: business, law, economics, health, psychology

General: philosophy, history, other

Features

• PII Detection: Automatically detects and handles sensitive information
• Semantic Caching: Cache similar queries for faster responses
• Reasoning Control: Enable/disable reasoning per domain
• Custom Thresholds: Adjust cache sensitivity per category

Example Requests

# Math query (reasoning enabled)
curl -X POST http://localhost:8801/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "MoM",
    "messages": [{"role": "user", "content": "Solve: x^2 + 5x + 6 = 0"}]
  }'

# Business query (reasoning disabled)
curl -X POST http://localhost:8801/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "MoM",
    "messages": [{"role": "user", "content": "What is a SWOT analysis?"}]
  }'

# Health query (high cache threshold)
curl -X POST http://localhost:8801/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "MoM",
    "messages": [{"role": "user", "content": "What are symptoms of diabetes?"}]
  }'

Real-World Use Cases

1. Multi-Task Classification with LoRA (Efficient)

Problem: Need domain classification + PII detection + jailbreak detection on every request Solution: LoRA adapters run all 3 tasks with one base model pass instead of 3 separate models Impact: 3x faster than running 3 full models, <1% parameter overhead per task

2. Long Document Analysis (Specialized - Qwen3)

Problem: Research papers and legal documents exceed 8K token limit of ModernBERT Solution: Qwen3-Embedding supports up to 32K tokens without truncation Impact: Accurate classification on full documents, no information loss from truncation

3. Multilingual Education Platform (Specialized - Qwen3)

Problem: Students ask questions in 100+ languages, ModernBERT limited to English Solution: Qwen3-Embedding trained on 100+ languages handles multilingual routing Impact: Single model serves global users, consistent quality across languages

4. Edge Deployment (Specialized - Gemma)

Problem: Mobile/IoT devices can't run large classification models Solution: EmbeddingGemma-300M with Matryoshka embeddings (128-768 dims) Impact: 5x smaller model, runs on edge devices with <100MB memory

5. STEM Tutoring Platform (Efficient Reasoning Control)

Problem: Math/physics need reasoning, but history/literature don't Solution: Domain classifier routes STEM → reasoning models, humanities → fast models Impact: 2x better STEM accuracy, 60% cost savings on non-STEM queries

Domain-Specific Optimizations

STEM Domains (Reasoning Enabled)

decisions:
- name: math
  description: "Route mathematical queries"
  priority: 100
  rules:
    operator: "OR"
    conditions:
      - type: "domain"
        name: "math"
  modelRefs:
    - model: "openai/gpt-oss-120b"
      use_reasoning: true  # Step-by-step solutions
  plugins:
    - type: "system_prompt"
      configuration:
        system_prompt: "You are a mathematics expert. Provide step-by-step solutions."

- name: physics
  description: "Route physics queries"
  priority: 100
  rules:
    operator: "OR"
    conditions:
      - type: "domain"
        name: "physics"
  modelRefs:
    - model: "openai/gpt-oss-120b"
      use_reasoning: true  # Derivations and proofs
  plugins:
    - type: "system_prompt"
      configuration:
        system_prompt: "You are a physics expert. Explain concepts clearly with mathematical derivations."

- name: chemistry
  description: "Route chemistry queries"
  priority: 100
  rules:
    operator: "OR"
    conditions:
      - type: "domain"
        name: "chemistry"
  modelRefs:
    - model: "openai/gpt-oss-120b"
      use_reasoning: true  # Reaction mechanisms
  plugins:
    - type: "system_prompt"
      configuration:
        system_prompt: "You are a chemistry expert. Explain reaction mechanisms clearly."

Professional Domains (PII + Caching)

decisions:
- name: health
  description: "Route health queries"
  priority: 100
  rules:
    operator: "OR"
    conditions:
      - type: "domain"
        name: "health"
  modelRefs:
    - model: "openai/gpt-oss-120b"
      use_reasoning: false
  plugins:
    - type: "system_prompt"
      configuration:
        system_prompt: "You are a health and medical information expert."
    - type: "semantic-cache"
      configuration:
        enabled: true
        similarity_threshold: 0.95  # Very strict

- name: law
  description: "Route legal queries"
  priority: 100
  rules:
    operator: "OR"
    conditions:
      - type: "domain"
        name: "law"
  modelRefs:
    - model: "openai/gpt-oss-120b"
      use_reasoning: false
  plugins:
    - type: "system_prompt"
      configuration:
        system_prompt: "You are a knowledgeable legal expert."

General Domains (Fast + Cached)

decisions:
- name: business
  description: "Route business queries"
  priority: 100
  rules:
    operator: "OR"
    conditions:
      - type: "domain"
        name: "business"
  modelRefs:
    - model: "openai/gpt-oss-120b"
      use_reasoning: false  # Fast responses
  plugins:
    - type: "system_prompt"
      configuration:
        system_prompt: "You are a senior business consultant and strategic advisor."

- name: general_route
  description: "Default fallback route for general queries"
  priority: 50
  rules:
    operator: "OR"
    conditions:
      - type: "domain"
        name: "other"
  modelRefs:
    - model: "openai/gpt-oss-120b"
      use_reasoning: false
  plugins:
    - type: "semantic-cache"
      configuration:
        enabled: true
        similarity_threshold: 0.75  # Relaxed

Performance Characteristics

Domain	Reasoning	Cache Threshold	Avg Latency	Use Case
Math	✅	0.85	2-5s	Step-by-step solutions
Physics	✅	0.85	2-5s	Derivations
Chemistry	✅	0.85	2-5s	Mechanisms
Health	❌	0.95	500ms	Safety-critical
Law	❌	0.85	500ms	Compliance
Business	❌	0.80	300ms	Fast insights
Other	❌	0.75	200ms	General queries

Cost Optimization Strategy

1. Reasoning Budget: Enable only for STEM (30% of queries) → 60% cost reduction
2. Caching Strategy: High threshold for sensitive domains → 70% hit rate
3. Model Selection: Lower scores for low-value domains → cheaper models
4. PII Detection: Only for health/law → reduced processing overhead

Reference

See bert_classification.yaml for complete configuration.