docs/configuration.md

# Configuration Options

This document discusses the various configuration options available for Aurora AI.

## Overview

Aurora AI provides a comprehensive configuration framework supporting multi-tenancy, enterprise-grade security, and extensible integration patterns. The system employs a hierarchical configuration model with environment-specific overrides, schema validation, and runtime hot-reloading capabilities.

## Core Configuration Architecture

### Configuration Hierarchy

Aurora AI implements a cascading configuration system with the following precedence order:

1. **Runtime overrides** - Programmatic configuration via API
2. **Environment variables** - System-level configuration with `AURORA_` prefix
3. **Configuration files** - YAML/JSON/TOML format files
4. **Default values** - Embedded fallback configuration

### Configuration File Structure

```yaml
aurora:
    engine:
        inference_backend: "transformers"
        model_path: "/models/aurora-v3"
        device_map: "auto"
        quantization:
            enabled: true
            bits: 4
            scheme: "gptq"
    runtime:
        max_concurrent_requests: 128
        request_timeout_ms: 30000
        graceful_shutdown_timeout: 60
```

## Model Configuration

### Inference Engine Parameters

- **`model_path`**: Filesystem path or Hugging Face model identifier
- **`device_map`**: Hardware allocation strategy (`auto`, `balanced`, `sequential`, or custom JSON mapping)
- **`torch_dtype`**: Precision mode (`float32`, `float16`, `bfloat16`, `int8`, `int4`)
- **`attention_implementation`**: Mechanism selection (`flash_attention_2`, `sdpa`, `eager`)
- **`rope_scaling`**: Rotary Position Embedding interpolation configuration
- **`kv_cache_dtype`**: Key-value cache quantization type

### Quantization Strategies

Aurora AI supports multiple quantization backends:

- **GPTQ**: 4-bit grouped quantization with calibration datasets
- **AWQ**: Activation-aware weight quantization
- **GGUF**: CPU-optimized quantization format
- **BitsAndBytes**: Dynamic 8-bit and 4-bit quantization

## API Configuration

### REST API Settings

```yaml
api:
    host: "0.0.0.0"
    port: 8080
    workers: 4
    uvicorn:
        loop: "uvloop"
        http: "httptools"
        log_level: "info"
    cors:
        enabled: true
        origins: ["https://*.example.com"]
        allow_credentials: true
    rate_limiting:
        enabled: true
        requests_per_minute: 60
        burst_size: 10
```

### Authentication & Authorization

- **API Key Authentication**: Header-based (`X-API-Key`) or query parameter
- **OAuth 2.0**: Support for Authorization Code and Client Credentials flows
- **JWT Tokens**: RS256/ES256 signature verification with JWKS endpoints
- **mTLS**: Mutual TLS authentication for service-to-service communication

## Integration Patterns

### Vector Database Integration

Aurora AI integrates with enterprise vector stores:

```yaml
vector_store:
    provider: "pinecone"  # or "weaviate", "qdrant", "milvus", "chromadb"
    connection:
        api_key: "${PINECONE_API_KEY}"
        environment: "us-west1-gcp"
        index_name: "aurora-embeddings"
    embedding:
        model: "text-embedding-3-large"
        dimensions: 3072
        batch_size: 100
```

### Message Queue Integration

Asynchronous processing via message brokers:

- **RabbitMQ**: AMQP 0-9-1 protocol with exchange routing
- **Apache Kafka**: High-throughput event streaming with consumer groups
- **Redis Streams**: Lightweight pub/sub with consumer group support
- **AWS SQS/SNS**: Cloud-native queue and notification services

### Observability Stack

```yaml
observability:
    metrics:
        provider: "prometheus"
        port: 9090
        path: "/metrics"
    tracing:
        provider: "opentelemetry"
        exporter: "otlp"
        endpoint: "http://jaeger:4317"
        sampling_rate: 0.1
    logging:
        level: "INFO"
        format: "json"
        output: "stdout"
```

## Memory Management

### Cache Configuration

```yaml
cache:
    inference_cache:
        enabled: true
        backend: "redis"
        ttl_seconds: 3600
        max_size_mb: 2048
    prompt_cache:
        enabled: true
        strategy: "semantic_hash"
        similarity_threshold: 0.95
```

### Context Window Management

- **Sliding Window**: Maintains fixed-size context with FIFO eviction
- **Semantic Compression**: Entropy-based summarization for long contexts
- **Hierarchical Attention**: Multi-level context representation
- **External Memory**: Vector store-backed infinite context

## Distributed Deployment

### Kubernetes Configuration

```yaml
deployment:
    replicas: 3
    strategy: "RollingUpdate"
    resources:
        requests:
            cpu: "4000m"
            memory: "16Gi"
            nvidia.com/gpu: "1"
        limits:
            cpu: "8000m"
            memory: "32Gi"
            nvidia.com/gpu: "1"
    autoscaling:
        enabled: true
        min_replicas: 2
        max_replicas: 10
        target_cpu_utilization: 70
```

### Service Mesh Integration

Aurora AI supports Istio, Linkerd, and Consul service mesh architectures with:

- **Traffic management**: Weighted routing, circuit breaking, retries
- **Security**: mTLS encryption, authorization policies
- **Observability**: Distributed tracing, metrics aggregation

## Advanced Features

### Custom Plugin System

```yaml
plugins:
    enabled: true
    plugin_path: "/opt/aurora/plugins"
    plugins:
        - name: "custom_tokenizer"
            module: "aurora.plugins.tokenizers"
            config:
                vocab_size: 65536
        - name: "retrieval_augmentation"
            module: "aurora.plugins.rag"
            config:
                top_k: 5
                rerank: true
```

### Multi-Model Orchestration

Configure model routing and ensemble strategies:

- **Load-based routing**: Distribute requests based on model server load
- **A/B testing**: Traffic splitting for model evaluation
- **Cascade patterns**: Fallback to alternative models on failure
- **Ensemble voting**: Aggregate predictions from multiple models

## Security Hardening

- **Secrets management**: Integration with HashiCorp Vault, AWS Secrets Manager
- **Network policies**: Zero-trust networking with pod security policies
- **Input sanitization**: Prompt injection and jailbreak detection
- **Output filtering**: PII redaction and content safety validation
- **Audit logging**: Immutable logs with cryptographic verification