AI Integration Guide¶

Table of Contents¶

• Section 1
• Section 2

Documentation Version: 2.0
Last Updated: 2025-05-30
Status: Active
AI Label: [AIR-3][AIS-3][BPC-3]

📖 Table of Contents¶

• Overview
• Related Documentation
• Architecture
• Core Components
• Data Flow
• Getting Started
• Prerequisites
• Installation
• Quick Start
• Model Serving
• Deployment Options
• Scaling
• Monitoring
• Canary Deployments

Related Documentation¶

For a complete understanding of the AI system, please refer to:

• AI System Overview - High-level architecture and components
• AI Architecture - Technical implementation details
• AI Development Guide - Building and contributing to AI components
• AI Compliance - Regulatory and security requirements
• AI Metrics - Performance monitoring and metrics
• A/B Testing
• API Reference
• Best Practices
• Troubleshooting
• Examples
• Security Considerations
• Related Documents

Overview¶

Anya Core provides a flexible framework for integrating AI models and services. This guide covers the end-to-end process of integrating AI capabilities into the platform, from model development to production deployment.

Key Features¶

• Unified Model Serving: Consistent interface for deploying and managing ML models
• Scalable Architecture: Built-in support for horizontal scaling
• Comprehensive Monitoring: Real-time metrics and logging
• Security First: Built-in security best practices
• Extensible: Support for custom model types and frameworks

Architecture¶

Core Components¶

graph TD
    subgraph Anya Core
        AI[AI Module] -->|Manages| ModelRegistry[Model Registry]
        AI -->|Processes| InferenceEngine[Inference Engine]
        AI -->|Trains| TrainingService[Training Service]
        AI -->|Monitors| Monitoring[Monitoring]

        ModelRegistry -->|Stores| ModelMetadata[Model Metadata]
        ModelRegistry -->|Tracks| ModelVersions[Model Versions]

        InferenceEngine -->|Supports| RealTime[Real-time Inference]
        InferenceEngine -->|Supports| Batch[Batch Processing]

        TrainingService -->|Uses| TrainingData[Training Data]
        TrainingService -->|Produces| TrainedModels[Trained Models]

        Monitoring -->|Tracks| Metrics[Performance Metrics]
        Monitoring -->|Alerts| Anomalies[Anomaly Detection]
    end

    subgraph External Services
        Models[AI Models]
        Data[Data Sources]
        Storage[Model Storage]
        Clients[Client Applications]
    end

    AI -->|Publishes| API[API Endpoints]
    API -->|Serves| Clients

    ModelRegistry -->|Stores in| Storage
    InferenceEngine -->|Loads from| Models
    TrainingService -->|Reads from| Data
    TrainingService -->|Saves to| Storage

Data Flow¶

1. Model Registration
2. Models are registered in the Model Registry with metadata and versioning

3. Model artifacts are stored in the configured storage backend

4. Inference Request

5. Client sends request to API endpoint
6. Request is routed to appropriate model version
7. Inference Engine loads model (if not cached)

8. Prediction is generated and returned

9. Model Updates

10. New model versions are registered
11. Traffic is gradually shifted (canary deployment)
12. Rollback to previous version if issues detected

Getting Started¶

Prerequisites¶

Before you begin, ensure you have the following installed:

• Anya Core (v1.2.0 or later)
• Python 3.8+ or Rust 1.60+
• Docker (for containerized deployment)
• Kubernetes (for production deployment)
• GPU (recommended for production workloads)

Installation¶

Option 1: Using pip (Python)¶

# Install Anya Core with AI dependencies
pip install anya-core[ai]

# Or install from source
pip install -e .[ai]

Option 2: Using Cargo (Rust)¶

# Cargo.toml
[dependencies]
anya-core = { version = "1.2.0", features = ["ai"] }

Quick Start¶

1. Start the AI Service

bash # Start with default settings anya ai serve --model gpt-4 --port 8000

1. Make a Prediction

```python import requests

response = requests.post( "http://localhost:8000/predict", json={ "inputs": ["Hello, world!"], "parameters": { "max_length": 100, "temperature": 0.7 } } ) print(response.json()) ```

1. Check Service Health

bash curl http://localhost:8000/health

Configuration¶

Configure the AI service using environment variables or a YAML configuration file:

# config/ai.yaml
model:
  name: gpt-4
  version: latest
  device: cuda  # or cpu

server:
  port: 8000
  workers: 4
  log_level: info

monitoring:
  enabled: true
  metrics_port: 9090

cache:
  enabled: true
  ttl: 3600
  max_size: 1000

security:
  auth_required: true
  allowed_origins:
    - "https://app.anya.org"
    - "http://localhost:3000"

Model Serving¶

Anya Core provides a robust model serving infrastructure that supports various deployment patterns and scaling strategies. This section covers the key aspects of serving models in production.

Core Features¶

• Multiple Serving Endpoints: REST, gRPC, and WebSocket APIs
• Model Versioning: Seamless version management
• Dynamic Batching: Optimize throughput with automatic request batching
• Model Warmup: Pre-load models to reduce cold start latency
• Request Timeouts: Configurable timeouts for inference requests
• Concurrency Control: Limit concurrent requests per model

Deployment Options¶

1. Standalone Server¶

# Start a standalone server
anya ai serve --model gpt-4 --port 8000 --workers 4

2. Docker Compose¶

# docker-compose.yml
version: '3.8'
services:
  ai-service:
    image: anya/ai:latest
    ports:
      - "8000:8000"
      - "9090:9090"
    environment:
      - MODEL=gpt-4
      - DEVICE=cuda
      - WORKERS=4
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    volumes:
      - ./models:/app/models
      - ./config:/app/config

3. Kubernetes with Helm¶

# Install with Helm
helm install ai-service anya/ai \
  --set model.name=gpt-4 \
  --set replicaCount=3 \
  --set resources.limits.nvidia.com/gpu=1

Scaling¶

Horizontal Pod Autoscaling¶

# hpa.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: ai-service
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: ai-service
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: External
    external:
      metric:
        name: requests_per_second
        selector:
          matchLabels:
            app: ai-service
      target:
        type: AverageValue
        averageValue: 1000

GPU Sharing¶

# Enable GPU sharing with time-slicing
apiVersion: v1
kind: ConfigMap
metadata:
  name: time-slicing-config
data:
  anya-ai: |
    version: v1
    sharing:
      timeSlicing:
        resources:
        - name: nvidia.com/gpu
          replicas: 4  # Share 1 GPU among 4 models

Monitoring¶

Built-in Metrics¶

Anya Core exposes the following metrics:

• anya_ai_requests_total - Total number of requests
• anya_ai_request_duration_seconds - Request duration histogram
• anya_ai_model_load_count - Model load events
• anya_ai_batch_size - Current batch size
• anya_ai_queue_size - Current queue size

Grafana Dashboard¶

Import the following dashboard for monitoring:

{
  "annotations": {
    "list": [
      {
        "builtIn": 1,
        "datasource": "-- Grafana --",
        "enable": true,
        "hide": true,
        "iconColor": "rgba(0, 211, 255, 1)",
        "name": "Annotations & Alerts",
        "type": "dashboard"
      }
    ]
  },
  "editable": true,
  "gnetId": 13639,
  "graphTooltip": 0,
  "id": 2,
  "links": [],
  "panels": [
    /* Dashboard panels configuration */
  ],
  "schemaVersion": 27,
  "style": "dark",
  "tags": ["ai", "monitoring"],
  "templating": {
    "list": []
  },
  "time": {
    "from": "now-6h",
    "to": "now"
  },
  "timepicker": {},
  "timezone": "",
  "title": "Anya AI Monitoring",
  "uid": "anya-ai-dashboard",
  "version": 1
}

Canary Deployments¶

Gradually roll out new model versions:

# canary-deployment.yaml
apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
  name: gpt-4
  namespace: ai
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: gpt-4
  service:
    port: 8000
  analysis:
    interval: 1m
    threshold: 5
    stepWeight: 10
    metrics:
    - name: request-success-rate
      thresholdRange:
        min: 99
      interval: 1m
    - name: request-duration
      thresholdRange:
        max: 500
      interval: 1m

A/B Testing¶

Run experiments with different model versions:

from anya_ai.experiment import ABTest

# Define experiment
experiment = ABTest(
    name="model-comparison",
    variants=[
        {"name": "gpt-4", "weight": 70, "config": {"version": "1.0.0"}},
        {"name": "llama-2", "weight": 30, "config": {"model": "llama-2-70b"}}
    ],
    metrics=["accuracy", "latency"]
)

# Get variant for user
variant = experiment.get_variant(user_id="user123")
# Use variant config for inference
result = model.predict(..., **variant.config)
# Log metrics
experiment.log_metric("latency", variant.name, 0.15)

Best Practices¶

1. Resource Management
2. Set appropriate CPU/Memory limits
3. Use GPU for compute-intensive models

4. Implement request timeouts

5. Reliability

6. Use retries with exponential backoff
7. Implement circuit breakers

8. Set up health checks

9. Performance

10. Enable request batching
11. Use model warmup

12. Implement caching for frequent requests

13. Security

14. Enable authentication
15. Validate input data
16. Monitor for abuse

For more advanced deployment strategies, see the Model Serving Best Practices section below.

API Reference¶

REST API¶

Authentication¶

All API endpoints require authentication. Include your API key in the Authorization header:

Authorization: Bearer YOUR_API_KEY

Base URL¶

https://api.anya.ai/v1

Endpoints¶

1. Model Inference¶

POST /models/{model_id}/predict

Make predictions using a specific model.

Request

{
  "inputs": ["Hello, world!"],
  "parameters": {
    "max_length": 100,
    "temperature": 0.7
  }
}

Response

{
  "outputs": ["Generated text response"],
  "model": "gpt-4",
  "model_version": "1.0.0",
  "metadata": {
    "tokens_generated": 20,
    "tokens_processed": 5,
    "latency_ms": 150
  }
}

2. Model Information¶

GET /models/{model_id}

Get information about a specific model.

Response

{
  "id": "gpt-4",
  "version": "1.0.0",
  "framework": "pytorch",
  "input_schema": {
    "type": "string",
    "max_length": 2048
  },
  "output_schema": {
    "type": "string"
  },
  "parameters": {
    "max_length": {
      "type": "integer",
      "default": 100,
      "min": 1,
      "max": 4096
    },
    "temperature": {
      "type": "float",
      "default": 0.7,
      "min": 0.0,
      "max": 2.0
    }
  },
  "created_at": "2025-01-01T00:00:00Z",
  "updated_at": "2025-01-01T00:00:00Z"
}

Error Handling¶

All API errors follow the same format:

{
  "error": {
    "code": "invalid_request",
    "message": "The request was invalid",
    "details": {
      "field": "temperature",
      "issue": "Value must be between 0 and 2"
    }
  }
}

Common Error Codes¶

Status Code	Error Code	Description
400	invalid_request	The request was invalid
401	unauthorized	Authentication failed
403	forbidden	Insufficient permissions
404	not_found	The requested resource was not found
429	rate_limit_exceeded	Rate limit exceeded
500	internal_error	An internal server error occurred
503	service_unavailable	Service is currently unavailable

Rate Limiting¶

• Free Tier: 100 requests per minute
• Pro Tier: 1,000 requests per minute
• Enterprise: Custom limits available

Rate limit headers are included in all responses:

X-RateLimit-Limit: 100
X-RateLimit-Remaining: 97
X-RateLimit-Reset: 1625097600

For a complete reference of all available endpoints, see the API Reference.

Best Practices¶

1. Model Versioning¶

• Use semantic versioning for models
• Keep backward compatibility when possible
• Document breaking changes

2. Error Handling¶

• Use custom error types
• Provide meaningful error messages
• Log errors with context

3. Performance¶

• Use batching for inference
• Implement model quantization
• Profile and optimize critical paths

4. Security¶

• Validate all inputs
• Sanitize outputs
• Implement rate limiting

Troubleshooting¶

Common Issues¶

1. Model Loading Fails
2. Check model file paths
3. Verify model format

4. Check file permissions

5. Inference Performance Issues

6. Check batch size
7. Profile GPU/CPU usage

8. Check for memory leaks

9. Training Diverges

10. Check learning rate
11. Verify data preprocessing
12. Monitor loss curves

Debugging¶

Enable debug logging:

use tracing_subscriber;

fn init_logging() {
    tracing_subscriber::fmt()
        .with_max_level(tracing::Level::DEBUG)
        .init();
}

Examples¶

Text Generation¶

use anya_ai::{Model, TextGenerator};

let model = TextGenerator::load("gpt-4").await?;
let prompt = "Once upon a time";
let result = model.generate(prompt, 100).await?;
println!("Generated: {}", result);

Image Classification¶

use anya_ai::{Model, ImageClassifier};

let model = ImageClassifier::load("resnet50").await?;
let image = load_image("cat.jpg")?;
let class = model.classify(&image).await?;
println!("Predicted class: {}", class);

Security Considerations¶

1. Input Validation¶

• Validate all inputs
• Sanitize user-provided data
• Implement input length limits

2. Model Security¶

• Sign model files
• Verify model hashes
• Use secure model storage

3. API Security¶

• Authenticate API requests
• Implement rate limiting
• Use HTTPS

4. Data Privacy¶

• Anonymize training data
• Implement data access controls
• Log data access

Conclusion¶

This guide covers the essentials of AI integration in Anya Core. For more advanced usage, refer to the API documentation and examples.

See Also¶

• Related Document