---
title: "Integration"
description: "Documentation for Integration"
---
# AI Integration Guide
## Table of Contents
- [Section 1](#section-1)
- [Section 2](#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](#overview)
- [Related Documentation](#related-documentation)
- [Architecture](#architecture)
- [Core Components](#core-components)
- [Data Flow](#data-flow)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Model Serving](#model-serving)
- [Deployment Options](#deployment-options)
- [Scaling](#scaling)
- [Monitoring](#monitoring)
- [Canary Deployments](#canary-deployments)
## Related Documentation
For a complete understanding of the AI system, please refer to:
- [AI System Overview](OVERVIEW.md) - High-level architecture and components
- [AI Architecture](ARCHITECTURE.md) - Technical implementation details
- [AI Development Guide](DEVELOPMENT.md) - Building and contributing to AI components
- [AI Compliance](COMPLIANCE.md) - Regulatory and security requirements
- [AI Metrics](METRICS.md) - Performance monitoring and metrics
- [A/B Testing](#ab-testing)
- [API Reference](#api-reference)
- [Best Practices](#best-practices)
- [Troubleshooting](#troubleshooting)
- [Examples](#examples)
- [Security Considerations](#security-considerations)
- [Related Documents](#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
```mermaid
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**
- Models are registered in the Model Registry with metadata and versioning
- Model artifacts are stored in the configured storage backend
2. **Inference Request**
- Client sends request to API endpoint
- Request is routed to appropriate model version
- Inference Engine loads model (if not cached)
- Prediction is generated and returned
3. **Model Updates**
- New model versions are registered
- Traffic is gradually shifted (canary deployment)
- 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)
```bash
# Install Anya Core with AI dependencies
pip install anya-core[ai]
# Or install from source
pip install -e .[ai]
```
#### Option 2: Using Cargo (Rust)
```toml
# Cargo.toml
[dependencies]
anya-core = { version = "1.2.0", features = ["ai"] }
```
### Quick Start
1. **Start the AI Service**
```bash
anya ai serve --model gpt-4 --port 8000
```
2. **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())
```
3. **Check Service Health**
```bash
curl http://localhost:8000/health
```
### Configuration
Configure the AI service using environment variables or a YAML configuration file:
```yaml
# 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
```bash
# Start a standalone server
anya ai serve --model gpt-4 --port 8000 --workers 4
```
#### 2. Docker Compose
```yaml
# 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
```bash
# 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
```yaml
# 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
```yaml
# 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:
```json
{
"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:
```yaml
# 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:
```python
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**
- Set appropriate CPU/Memory limits
- Use GPU for compute-intensive models
- Implement request timeouts
2. **Reliability**
- Use retries with exponential backoff
- Implement circuit breakers
- Set up health checks
3. **Performance**
- Enable request batching
- Use model warmup
- Implement caching for frequent requests
4. **Security**
- Enable authentication
- Validate input data
- Monitor for abuse
For more advanced deployment strategies, see the [Model Serving Best Practices](#model-serving-best-practices) section below.
## API Reference
### REST API
#### Authentication
All API endpoints require authentication. Include your API key in the `Authorization` header:
```http
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**
```json
{
"inputs": ["Hello, world!"],
"parameters": {
"max_length": 100,
"temperature": 0.7
}
}
```
**Response**
```json
{
"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**
```json
{
"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:
```json
{
"error": {
"code": "invalid_request",
"message": "The request was invalid",
"details": {
"field": "temperature",
"issue": "Value must be between 0 and 2"
}
}
}
```
#### Common Error Codes
| 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](./API.md).
## 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**
- Check model file paths
- Verify model format
- Check file permissions
2. **Inference Performance Issues**
- Check batch size
- Profile GPU/CPU usage
- Check for memory leaks
3. **Training Diverges**
- Check learning rate
- Verify data preprocessing
- Monitor loss curves
### Debugging
Enable debug logging:
```rust
use tracing_subscriber;
fn init_logging() {
tracing_subscriber::fmt()
.with_max_level(tracing::Level::DEBUG)
.init();
}
```
## Examples
### Text Generation
```rust
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
```rust
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](https://docs.anya.org/api/ai) and [examples](https://github.com/anya-org/anya-core/examples).
## See Also
- [Related Document](#related-document)