Ai Query Service

Purpose

Routes LLM queries to optimal providers with caching and resilience

When to Use This Skill

1. All LLM Queries - Centralized access point
2. Cost Optimization - Route to cheapest capable model
3. Resilience - Automatic fallback on failures
4. Observability - Track LLM usage metrics

Responsibilities

1. Intelligent routing - (task type → best LLM)
2. Response caching - for identical queries
3. Rate limit - and retry handling
4. Token usage - optimization
5. Cost and - latency tracking

Integration with Pipeline

Communication

Receives:

• Input data specific to agent's purpose

Sends:

• Processed output and analysis results

Usage Examples

Standalone Usage

python3 ai_query_service.py --help

Programmatic Usage

from ai_query_service import AiQueryService

agent = AiQueryService()
result = agent.execute()

Configuration

Environment Variables

# Agent-specific configuration
ARTEMIS_AI_QUERY_SERVICE_ENABLED=true
ARTEMIS_LLM_PROVIDER=openai
ARTEMIS_LLM_MODEL=gpt-4o

Hydra Configuration (if applicable)

ai_query_service:
  enabled: true
  llm:
    provider: openai
    model: gpt-4o

Best Practices

1. Follow agent-specific guidelines
2. Monitor performance metrics
3. Handle errors gracefully
4. Log important events
5. Integrate with observability

Cost Considerations

Typical cost: $0.05-0.20 per operation depending on complexity

Limitations

• Depends on LLM quality
• Context window limits
• May require multiple iterations

References

• Artemis Documentation
• Agent Pattern

Version: 1.0.0

Maintained By: Artemis Pipeline Team

Last Updated: October 24, 2025