Supported Categories

vLLM Semantic Router supports 14 predefined categories for automatic query classification. Each category represents a distinct domain that can be configured with custom routing rules, reasoning settings, and model preferences based on your specific needs.

Category Overview

Category	Domain	Typical Use Cases
Math	Mathematics	Calculations, equations, proofs, statistics
Computer Science	Programming & Technology	Coding, algorithms, software engineering
Physics	Physical Sciences	Mechanics, thermodynamics, quantum physics
Chemistry	Chemical Sciences	Reactions, molecular structures, formulas
Biology	Life Sciences	Genetics, ecology, anatomy, physiology
Engineering	Applied Sciences	Design, systems analysis, problem-solving
Business	Commerce & Management	Strategy, marketing, finance, operations
Law	Legal Domain	Regulations, jurisprudence, legal procedures
Economics	Economic Sciences	Markets, theory, macroeconomics, finance
Health	Medical & Wellness	Healthcare, anatomy, medical information
Psychology	Behavioral Sciences	Mental health, cognition, therapy
Philosophy	Philosophical Inquiry	Ethics, logic, metaphysics, reasoning
History	Historical Studies	Events, civilizations, historical analysis
Other	General Purpose	Miscellaneous queries, general knowledge

Detailed Category Descriptions

Math

Domain: Mathematics, calculations, equations, proofs, statistical analysis

Description: Handles mathematical queries ranging from basic arithmetic to advanced calculus, statistics, and mathematical proofs. This category is ideal for computational problems that benefit from step-by-step reasoning.

Typical Queries:

• "Solve the quadratic equation x² + 5x + 6 = 0"
• "Calculate the derivative of f(x) = x³ + 2x² - 5x + 1"
• "What is the probability of getting two heads in three coin flips?"
• "Prove that the square root of 2 is irrational"

Configuration Considerations:

• Often benefits from reasoning mode for complex problem-solving
• May require models with strong mathematical capabilities
• Consider higher reasoning effort for advanced mathematical proofs

Computer Science

Domain: Programming, algorithms, data structures, software engineering

Description: Covers programming languages, software development, algorithms, system design, and technical computing concepts. Suitable for both theoretical computer science and practical programming tasks.

Typical Queries:

• "Implement a binary search algorithm in Python"
• "Explain the time complexity of quicksort"
• "How do I optimize this SQL query?"
• "What's the difference between REST and GraphQL APIs?"

Configuration Considerations:

• Benefits from reasoning mode for algorithmic problem-solving
• Requires models with strong coding and technical knowledge
• May need specialized code generation capabilities

Physics

Domain: Physical concepts, mechanics, thermodynamics, electromagnetism

Description: Encompasses all areas of physics from classical mechanics to quantum physics and relativity. Handles both theoretical concepts and practical calculations involving physical phenomena.

Typical Queries:

• "Calculate the force needed to accelerate a 10kg mass at 5m/s²"
• "Explain Newton's laws of motion"
• "What is the relationship between voltage, current, and resistance?"
• "How does quantum entanglement work?"

Configuration Considerations:

• May benefit from reasoning mode for complex physics problems
• Requires models with strong scientific and mathematical knowledge
• Consider specialized physics-trained models for advanced topics

Chemistry

Domain: Chemical reactions, molecular structures, organic/inorganic chemistry

Description: Covers chemical processes, molecular interactions, reaction mechanisms, and chemical analysis. Suitable for both theoretical chemistry concepts and practical laboratory applications.

Typical Queries:

• "Balance the equation: C₆H₁₂O₆ + O₂ → CO₂ + H₂O"
• "Explain the mechanism of SN2 reactions"
• "What is the molecular geometry of SF₆?"
• "How do catalysts affect reaction rates?"

Configuration Considerations:

• Often benefits from reasoning mode for reaction mechanisms
• Requires models with strong chemistry and scientific knowledge
• May need specialized chemical notation and formula handling

Biology

Domain: Life sciences, genetics, ecology, anatomy, physiology

Description: Encompasses all biological sciences including molecular biology, genetics, ecology, evolution, and human biology. Handles both descriptive biological concepts and analytical processes.

Typical Queries:

• "Explain the process of photosynthesis"
• "How does DNA replication work?"
• "What are the stages of mitosis?"
• "Describe the structure and function of ribosomes"

Configuration Considerations:

• May benefit from reasoning mode for complex biological processes
• Requires models with comprehensive biological knowledge
• Consider models trained on scientific literature for accuracy

Engineering

Domain: Technical problem-solving, design, systems analysis

Description: Covers various engineering disciplines including mechanical, electrical, civil, and software engineering. Focuses on practical problem-solving and system design.

Typical Queries:

• "Design a load-bearing beam for a 20-foot span"
• "How do I calculate the efficiency of this heat exchanger?"
• "What are the trade-offs between different sorting algorithms?"
• "Explain the principles of feedback control systems"

Configuration Considerations:

• Often benefits from reasoning mode for design problems
• Requires models with technical and mathematical capabilities
• May need specialized engineering knowledge and calculations

Business

Domain: Business strategy, management, marketing, finance, entrepreneurship

Description: Covers business operations, strategic planning, management practices, marketing, finance, and entrepreneurship. Suitable for both theoretical business concepts and practical business advice.

Typical Queries:

• "What are the key components of a business plan?"
• "How do I improve team productivity?"
• "Explain different marketing strategies for startups"
• "What is the difference between B2B and B2C sales?"

Configuration Considerations:

• Typically conversational, may not require reasoning mode
• Benefits from models with business and management knowledge
• Consider models trained on business literature and case studies

Law

Domain: Legal concepts, regulations, jurisprudence, legal procedures

Description: Encompasses legal principles, regulations, court procedures, and jurisprudence across various legal domains. Handles both general legal concepts and specific legal questions.

Typical Queries:

• "What are the elements of a valid contract?"
• "Explain the difference between civil and criminal law"
• "What is intellectual property protection?"
• "How does the appeals process work?"

Configuration Considerations:

• Usually explanatory, may not require reasoning mode
• Requires models with comprehensive legal knowledge
• Important: Ensure disclaimers about not providing legal advice

Economics

Domain: Economic theory, markets, macroeconomics, microeconomics

Description: Covers economic principles, market analysis, fiscal policy, and economic theory. Handles both theoretical economic concepts and practical economic analysis.

Typical Queries:

• "Explain supply and demand curves"
• "What causes inflation and how is it measured?"
• "How do interest rates affect the economy?"
• "What is the difference between GDP and GNP?"

Configuration Considerations:

• Usually explanatory, may not require reasoning mode
• Benefits from models with strong economic and mathematical knowledge
• Consider models trained on economic literature and data

Health

Domain: Medical information, wellness, healthcare, anatomy

Description: Encompasses medical knowledge, health information, anatomy, physiology, and wellness topics. Covers both general health information and specific medical concepts.

Typical Queries:

• "What are the symptoms of diabetes?"
• "How does the immune system work?"
• "What are the benefits of regular exercise?"
• "Explain the structure of the human heart"

Configuration Considerations:

• Typically informational, may not require reasoning mode
• Requires models with medical and health knowledge
• Important: Ensure disclaimers about not providing medical advice

Psychology

Domain: Mental health, behavior, cognitive science, therapy

Description: Covers psychological concepts, mental health topics, cognitive processes, and therapeutic approaches. Handles both theoretical psychology and practical mental health information.

Typical Queries:

• "What are the stages of grief?"
• "Explain cognitive behavioral therapy techniques"
• "How does memory formation work?"
• "What is the difference between anxiety and depression?"

Configuration Considerations:

• Usually explanatory, may not require reasoning mode
• Benefits from models with psychology and mental health knowledge
• Important: Ensure disclaimers about not providing therapeutic advice

Philosophy

Domain: Philosophical discussions, ethics, logic, metaphysics

Description: Encompasses philosophical inquiry, ethical discussions, logical reasoning, and metaphysical concepts. Covers both historical philosophical thought and contemporary philosophical issues.

Typical Queries:

• "What is the meaning of life according to different philosophers?"
• "Explain the trolley problem in ethics"
• "What are the main arguments for and against free will?"
• "How do different cultures approach moral reasoning?"

Configuration Considerations:

• Typically conversational and exploratory
• May benefit from reasoning mode for complex philosophical arguments
• Requires models with broad philosophical knowledge

History

Domain: Historical events, narratives, civilizations, timelines

Description: Covers historical events, civilizations, cultural developments, and historical analysis across all time periods and regions. Handles both factual historical information and historical interpretation.

Typical Queries:

• "What were the causes of World War I?"
• "Explain the rise and fall of the Roman Empire"
• "How did the Industrial Revolution change society?"
• "What was the significance of the Renaissance?"

Configuration Considerations:

• Usually narrative-based, may not require reasoning mode
• Benefits from models with comprehensive historical knowledge
• Consider models trained on historical texts and sources

Other

Domain: General queries, miscellaneous topics, unclassified content

Description: Serves as a catch-all category for queries that don't fit into specific domains. Handles general knowledge questions, casual conversations, and miscellaneous topics.

Typical Queries:

• "What's the weather like today?"
• "How do I cook pasta?"
• "What are some good book recommendations?"
• "Tell me a joke"

Configuration Considerations:

• Usually doesn't require reasoning mode
• Benefits from models with broad general knowledge
• Often used as fallback when classification confidence is low

Configuration Guidelines

Reasoning Configuration

Each category can be configured to enable or disable reasoning based on your needs:

• STEM Categories (Math, Physics, Chemistry, Biology, Computer Science, Engineering): Often benefit from reasoning mode for complex problem-solving
• Professional Categories (Business, Law, Economics): May or may not require reasoning depending on query complexity
• Informational Categories (Health, Psychology, Philosophy, History): Typically explanatory, but reasoning can help with complex analysis
• General Category (Other): Usually doesn't require reasoning mode

Model Selection Strategy

Consider these factors when configuring model preferences:

• Domain Expertise: Choose models with strong knowledge in specific domains
• Reasoning Capability: Some models excel at step-by-step reasoning
• Performance Requirements: Balance accuracy with latency needs
• Cost Considerations: Optimize model selection based on computational costs

Best Practices

1. Start Simple: Begin with basic configurations and iterate based on performance
2. Test Thoroughly: Validate category classification accuracy with your specific queries
3. Monitor Performance: Track classification confidence and routing decisions
4. Customize Gradually: Adjust reasoning settings and model scores based on usage patterns

Performance Considerations

Classification Accuracy

The category classifier performance varies by domain complexity:

• STEM Categories: Generally high accuracy due to distinct technical vocabulary
• Professional Categories: Good accuracy with domain-specific terminology
• General Categories: May have lower confidence due to broader scope

Latency Impact

• Classification Time: <50ms average for category determination
• Reasoning Overhead: Additional 200-500ms when reasoning is enabled
• Model Selection: <10ms for routing decision based on configuration

Optimization Tips

• Adjust confidence thresholds based on your accuracy requirements
• Use reasoning mode selectively to balance quality and performance
• Monitor category distribution to identify potential classification issues
• Consider batch processing for high-volume scenarios

Future Roadmap

The vLLM Semantic Router category system is continuously evolving to support more sophisticated routing scenarios. Here are the planned category expansions:

Multimodal Categories

Vision + Text

• Image Analysis: Visual content understanding, image description, OCR
• Document Processing: PDF analysis, form extraction, diagram interpretation
• Medical Imaging: Radiology reports, medical image analysis
• Technical Diagrams: Engineering drawings, architectural plans, flowcharts

Audio + Text

• Speech Processing: Transcription, voice commands, audio analysis
• Music Theory: Musical composition, theory, instrument identification
• Audio Content: Podcast analysis, sound classification

RAG-Enhanced Categories

Knowledge-Intensive Domains

• Scientific Research: Literature review, research synthesis, citation analysis
• Legal Research: Case law analysis, statute interpretation, legal precedent
• Medical Research: Clinical studies, drug interactions, treatment protocols
• Technical Documentation: API references, software manuals, troubleshooting

Domain-Specific Knowledge Bases

• Enterprise Knowledge: Company policies, internal documentation, procedures
• Academic Research: Journal articles, thesis work, academic writing
• Regulatory Compliance: Industry standards, compliance requirements, auditing

Specialized Routing Categories

Intent-Based Routing

• Creative Writing: Story generation, poetry, creative content
• Code Generation: Specific programming languages, frameworks, libraries
• Data Analysis: Statistical analysis, data visualization, reporting
• Translation: Language pairs, cultural context, technical translation

Workflow-Based Categories

• Multi-Step Tasks: Complex procedures requiring sequential processing
• Collaborative Tasks: Multi-agent workflows, review processes
• Real-Time Processing: Streaming data, live analysis, immediate responses

Advanced Classification Features

Context-Aware Categories

• Conversation History: Context-dependent routing based on dialogue state
• User Profiles: Personalized routing based on user expertise and preferences
• Temporal Context: Time-sensitive routing for urgent vs. routine queries

Confidence-Based Routing

• Uncertainty Handling: Specialized routing for ambiguous queries
• Multi-Category Queries: Handling queries that span multiple domains
• Fallback Strategies: Intelligent degradation when classification confidence is low

Community Contributions

We welcome community input on category expansion:

• Feature Requests: Suggest new categories based on your use cases
• Domain Expertise: Contribute domain-specific knowledge for better classification
• Testing & Feedback: Help validate new categories in real-world scenarios
• Custom Categories: Share successful custom category implementations

Next Steps

• Configuration Guide - Learn how to configure category-based routing
• Technical Details - Deep dive into classifier implementation
• Category Overview - Understanding the category system architecture