Train Custom LLM for Smart Reminder Suggestions with API Exposure

[KaueReinbold]

Description

Train and deploy a custom LLM (Large Language Model) to provide intelligent reminder suggestions based on user input, historical data, and contextual patterns. The model will be exposed through API endpoints for consumption by the Reminders application and any AI client (including MCP servers, Copilot extensions, etc.).

Goals

• Train a custom LLM specialized in reminder management and suggestion generation
• Deploy the model as a service accessible via REST API
• Expose endpoints for AI clients to consume intelligent reminder suggestions
• Enable integration with MCP servers and other AI assistants

Architecture

Model Training & Deployment:

• Fine-tune an open-source LLM (e.g., Llama, Mistral, or GPT-Neo) on reminder-specific data
• Deploy using frameworks like vLLM, TGI (Text Generation Inference), or Ollama
• Host on infrastructure (Docker container, cloud GPU, or local GPU)

API Layer:

• REST API endpoints for suggestion generation
• OpenAI-compatible API format for broad compatibility
• Support for streaming responses
• Rate limiting and authentication

Data Pipeline:

• Collect and anonymize historical reminder data for training
• Create training dataset with examples of good reminder suggestions
• Implement feedback loop to improve model over time

Implementation Steps

Phase 1: Data Collection & Preparation

• Extract anonymized reminder data from existing database
• Create training dataset with prompt/completion pairs
• Define prompt templates for different suggestion types
• Validate and clean training data

Phase 2: Model Training

• Select base model (Llama 3, Mistral, or similar)
• Set up training environment (GPU infrastructure)
• Fine-tune model on reminder-specific tasks:
  • Reminder text completion
  • Priority suggestion
  • Due date recommendation
  • Category/tag suggestions
• Evaluate model performance and iterate

Phase 3: Model Deployment

• Package model in Docker container
• Deploy inference server (vLLM, Ollama, or TGI)
• Configure resource limits and scaling
• Set up health checks and monitoring

Phase 4: API Development

• Create inference API service (Python FastAPI or .NET)
• Implement endpoints:
  • POST /api/llm/suggest - Generate reminder suggestions
  • POST /api/llm/complete - Complete partial reminder text
  • POST /api/llm/analyze - Analyze reminder for improvements
  • GET /api/llm/health - Model health check
• Add OpenAI-compatible endpoint format
• Implement request/response validation
• Add authentication and API key management
• Configure rate limiting

Phase 5: Integration

• Integrate with existing Reminders API (.NET/Go/Python)
• Update frontend (React) with suggestion UI
• Create MCP server tool for AI client consumption
• Add webhook support for async processing
• Document API usage for external AI clients

Phase 6: Testing & Optimization

• Unit tests for API endpoints
• Load testing for inference performance
• A/B testing with users
• Optimize inference latency (< 1s response time)
• Monitor token usage and costs

API Endpoints

# Generate smart suggestions
POST /api/llm/suggest
{
  "input": "Need to prepare for meeting",
  "context": {
    "user_history": [...],
    "current_reminders": [...]
  }
}

Response:
{
  "suggestions": [
    {
      "title": "Prepare materials for quarterly review meeting",
      "description": "Review Q4 performance data and create presentation slides",
      "priority": "High",
      "suggested_date": "2026-02-01T09:00:00Z"
    }
  ]
}

# OpenAI-compatible format
POST /v1/chat/completions
{
  "model": "reminders-llm",
  "messages": [
    {"role": "system", "content": "You are a reminder assistant."},
    {"role": "user", "content": "Suggest a reminder for my dentist appointment"}
  ]
}

Expected Benefits

• Personalized: Model learns from user's reminder patterns
• Privacy-focused: Self-hosted model, no external API calls
• Cost-effective: No per-request API fees after initial setup
• Customizable: Full control over model behavior and training data
• AI-native: Direct consumption by MCP servers, Copilot, and other AI clients
• Offline capable: Can run locally without internet connectivity

Technical Considerations

Model Selection:

• Llama 3 8B/70B (Meta)
• Mistral 7B (Mistral AI)
• Phi-3 (Microsoft)
• Consider model size vs. performance tradeoffs

Infrastructure:

• GPU requirements (NVIDIA T4, A10, or similar)
• Memory requirements (16GB+ for 7B models)
• Docker Compose integration
• Optional: Cloud deployment (AWS SageMaker, Azure ML)

Training Data:

• Collect 10K+ reminder examples
• Include diverse use cases (work, personal, health, etc.)
• Augment with synthetic data if needed
• Implement data versioning

Security & Privacy:

• Anonymize user data for training
• Secure API endpoints with authentication
• Rate limiting to prevent abuse
• Audit logging for AI-generated suggestions

AI Client Integration

MCP Server:

// MCP tool definition
{
  "name": "suggest_reminder",
  "description": "Generate smart reminder suggestions using trained LLM",
  "parameters": {
    "input": "string",
    "context": "object"
  }
}

GitHub Copilot Extension:

• Expose as code completion API
• Integrate with VS Code extension

Direct API Access:

• Provide SDK for Python, JavaScript, .NET
• Documentation with examples

Success Metrics

• Model accuracy > 80% on validation set
• API response time < 1 second (p95)
• User acceptance rate > 60% for suggestions
• Support 100+ concurrent requests
• Model availability > 99%
• Cost < $50/month for inference infrastructure

References

• vLLM - Fast LLM Inference
• Ollama - Run LLMs locally
• Hugging Face TGI
• Fine-tuning LLMs Guide
• OpenAI API Spec

Labels

enhancement, api, learning, python