ADR-009: AI Integration Strategy
Status
Accepted - March 2025
Context
BookWorm aims to enhance user experience through intelligent features that leverage artificial intelligence capabilities. The system requires AI integration for several key scenarios:
- Conversational Support: Interactive chat support with natural language understanding
- Book Recommendations: Personalized book suggestions based on user preferences and behavior
- Content Search: Semantic search capabilities beyond traditional keyword matching
- Content Generation: Automated content creation for descriptions and summaries
- User Assistance: Intelligent help and guidance for user interactions
- Data Analytics: AI-powered insights from user behavior and system metrics
- Content Moderation: Automated review and moderation of user-generated content
- Performance Requirements: Sub-second response times for interactive features
- Cost Management: Balance between AI capabilities and operational costs
- Privacy and Security: Ensure user data protection in AI processing workflows
The choice of AI technologies and integration patterns significantly impacts user experience, operational costs, development complexity, and long-term maintainability.
Decision
Implement a comprehensive AI integration strategy using Ollama for local LLM hosting, Semantic Kernel for orchestration, Model Context Protocol (MCP) for standardized tooling, and multi-agent workflows for complex AI operations.
AI Architecture Components
Core AI Infrastructure
- Ollama Runtime: Local LLM hosting and model management
- Semantic Kernel: AI orchestration and workflow management
- MCP Tools: Standardized AI tool integration
- Vector Database: Qdrant for embedding storage and semantic search
- Agent Framework: Multi-agent coordination for complex workflows
Model Selection Strategy
- Chat Model: Gemma 3 for conversational interactions
- Embedding Model: Nomic Embed Text for semantic search
- Task-Specific Models: Specialized models for specific use cases
- Model Versioning: Controlled model updates and rollback capabilities
Service AI Integration Map
| Service | AI Capabilities | Models Used | Integration Pattern |
|---|---|---|---|
| Chat | Conversational AI, context understanding | Gemma 3 | Direct Ollama integration |
| Catalog | Semantic search, recommendation engine | Nomic Embed Text | Vector similarity search |
| Rating | Sentiment analysis, review insights | Gemma 3 | Batch processing |
| Ordering | Order assistance, problem resolution | Gemma 3 | Agent workflows |
| Finance | Fraud detection, transaction analysis | Custom models | ML.NET integration |
| Notification | Content personalization, timing optimization | Gemma 3 | Template generation |
Rationale
Why This AI Strategy?
Local-First AI Approach
- Data Privacy: Keep sensitive data within controlled infrastructure
- Cost Control: Avoid per-request charges from cloud AI services
- Performance: Reduce latency by eliminating external API calls
- Reliability: Independent operation without external service dependencies
- Customization: Full control over model fine-tuning and optimization
Ollama for LLM Runtime
- Ease of Deployment: Simple container-based LLM deployment
- Model Management: Easy model switching and version control
- Resource Efficiency: Optimized inference performance
- API Compatibility: OpenAI-compatible API for easy integration
- Local Operation: Complete on-premises operation
Semantic Kernel Orchestration
- .NET Integration: Native .NET AI orchestration framework
- Plugin System: Extensible plugin architecture for AI functions
- Planning Capabilities: Multi-step AI workflow planning
- Memory Management: Context and conversation state management
- Microsoft Support: Enterprise-grade support and documentation
AI Architecture Overview
Implementation Strategy
Ollama Configuration and Model Management
Model Deployment
# Ollama container configuration
FROM ollama/ollama:latest
# Pre-load models during build
RUN ollama pull gemma2:2b
RUN ollama pull nomic-embed-text
RUN ollama pull llama3.1:8b
# Custom model configuration
COPY ./models/bookworm-fine-tuned.gguf /root/.ollama/models/
Resource Management
Model Loading Strategy
- Lazy Loading: Load models only when needed
- Model Rotation: Unload unused models to free memory
- GPU Optimization: Utilize GPU acceleration when available
- Memory Management: Monitor and control memory usage
Security and Privacy
Data Privacy
- Local Processing: All AI processing happens within controlled infrastructure
- Data Minimization: Only process necessary data for AI features
- Anonymization: Remove or anonymize personal information where possible
- Audit Logging: Track all AI processing for compliance
Model Security
- Model Validation: Verify model integrity and authenticity
- Input Sanitization: Validate and sanitize all inputs to AI models
- Output Filtering: Filter AI outputs for inappropriate content
- Access Control: Restrict access to AI capabilities based on user roles
Consequences
Positive Outcomes
- Enhanced User Experience: Intelligent features improve user engagement
- Cost Control: Local hosting reduces per-request AI service costs
- Data Privacy: Keep sensitive data within controlled infrastructure
- Performance: Low-latency AI responses for interactive features
- Customization: Full control over AI behavior and fine-tuning
- Innovation: Platform for rapid AI feature development
Challenges and Considerations
- Resource Requirements: Significant compute resources for local AI hosting
- Model Management: Complexity of managing multiple AI models
- Development Expertise: Requires AI/ML expertise on development team
- Infrastructure Costs: Additional infrastructure for AI processing
- Model Updates: Managing model versions and updates
Risk Mitigation
- Fallback Strategies: Graceful degradation when AI services are unavailable
- Monitoring: Comprehensive monitoring of AI service health and performance
- Resource Scaling: Auto-scaling for AI workloads based on demand
- Model Governance: Clear processes for model updates and rollbacks
- Team Training: Invest in team AI/ML capabilities and knowledge
Success Metrics
- Response Quality: User satisfaction ratings for AI-generated responses
- Performance: Sub-second response times for 95% of AI requests
- Accuracy: High accuracy rates for recommendations and search results
- Cost Efficiency: 60% cost reduction compared to cloud AI services
- User Engagement: Increased user interaction with AI-powered features
Alternatives Considered
OpenAI API
- Pros: State-of-the-art models, excellent API, extensive capabilities
- Cons: High costs at scale, data privacy concerns, external dependency
- Decision: Rejected due to cost and privacy requirements
Azure Cognitive Services
- Pros: Enterprise features, Azure integration, managed services
- Cons: Azure lock-in, limited customization, ongoing costs
- Decision: Insufficient customization and control for requirements
AWS Bedrock
- Pros: Multiple model providers, managed infrastructure, enterprise features
- Cons: AWS lock-in, high costs, limited local deployment
- Decision: Cloud dependency conflicts with local-first strategy
Custom Model Training
- Pros: Complete control, optimized for specific use cases
- Cons: Significant development effort, expertise required, ongoing maintenance
- Decision: Too resource-intensive for initial implementation
Implementation Roadmap
Phase 1: Foundation (Months 1-2)
- Ollama deployment and model setup
- Basic Semantic Kernel integration
- Simple chat functionality
Phase 2: Core Features (Months 3-4)
- Vector search implementation
- Recommendation engine
- MCP tool standardization
Phase 3: Advanced Features (Months 5-6)
- Multi-agent workflows
- Content generation and analysis
- Performance optimization
Phase 4: Optimization (Months 7-8)
- Model fine-tuning
- Advanced caching strategies
- Comprehensive monitoring and analytics