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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

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

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:

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

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

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

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

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