Cloud & Infrastructure Tools

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from aimatrix.tools.cloud import AWSCloudTool

# Initialize AWS management
aws = AWSCloudTool(
    regions=["us-east-1", "us-west-2", "eu-west-1", "ap-southeast-1"],
    services=["ec2", "s3", "rds", "lambda", "eks", "ecs"],
    cost_optimization=True,
    auto_scaling=True
)

# AI-powered infrastructure provisioning
cloud_agent = AIAgent(
    name="aws-infrastructure-agent",
    tools=[aws],
    instructions="""
    You are an AWS infrastructure expert. You can:
    1. Provision and manage AWS resources optimally
    2. Implement cost-effective architectures
    3. Ensure security best practices
    4. Automate scaling and disaster recovery
    5. Monitor and optimize performance
    """
)

# Example: Auto-scaling web application infrastructure
@cloud_agent.task("provision_web_app_infrastructure")
async def provision_scalable_web_app(app_requirements: dict):
    """Provision optimal infrastructure for a web application"""
    
    # Analyze requirements and select optimal architecture
    architecture = await aws.design_optimal_architecture(
        requirements={
            "expected_traffic": app_requirements["expected_users"],
            "performance_requirements": app_requirements["performance_sla"],
            "availability_requirements": app_requirements["uptime_sla"],
            "compliance_requirements": app_requirements.get("compliance", []),
            "budget_constraints": app_requirements.get("budget_limit")
        }
    )
    
    # Create VPC and networking
    vpc = await aws.create_vpc(
        cidr_block=architecture.networking.vpc_cidr,
        enable_dns_hostnames=True,
        enable_dns_support=True,
        tags={"Environment": app_requirements["environment"]}
    )
    
    # Create subnets across availability zones
    subnets = []
    for az_config in architecture.networking.availability_zones:
        # Public subnets for load balancers
        public_subnet = await aws.create_subnet(
            vpc_id=vpc.id,
            cidr_block=az_config.public_subnet_cidr,
            availability_zone=az_config.az_name,
            map_public_ip_on_launch=True,
            tags={"Type": "Public", "AZ": az_config.az_name}
        )
        
        # Private subnets for application servers
        private_subnet = await aws.create_subnet(
            vpc_id=vpc.id,
            cidr_block=az_config.private_subnet_cidr,
            availability_zone=az_config.az_name,
            map_public_ip_on_launch=False,
            tags={"Type": "Private", "AZ": az_config.az_name}
        )
        
        subnets.append({
            "public": public_subnet,
            "private": private_subnet,
            "az": az_config.az_name
        })
    
    # Create Application Load Balancer
    alb = await aws.create_application_load_balancer(
        name=f"{app_requirements['app_name']}-alb",
        scheme="internet-facing",
        subnets=[s["public"].id for s in subnets],
        security_groups=[await create_alb_security_group(vpc.id)]
    )
    
    # Create Auto Scaling Group with intelligent scaling
    launch_template = await aws.create_launch_template(
        name=f"{app_requirements['app_name']}-template",
        image_id=architecture.compute.ami_id,
        instance_type=architecture.compute.instance_type,
        security_group_ids=[await create_app_security_group(vpc.id)],
        user_data=generate_user_data_script(app_requirements)
    )
    
    asg = await aws.create_auto_scaling_group(
        name=f"{app_requirements['app_name']}-asg",
        launch_template_id=launch_template.id,
        min_size=architecture.compute.min_instances,
        max_size=architecture.compute.max_instances,
        desired_capacity=architecture.compute.desired_instances,
        vpc_zone_identifier=[s["private"].id for s in subnets],
        target_group_arns=[alb.target_groups[0].arn],
        health_check_type="ELB",
        health_check_grace_period=300
    )
    
    # Set up intelligent scaling policies
    await aws.create_predictive_scaling_policy(
        auto_scaling_group_name=asg.name,
        policy_name="predictive-scaling",
        target_utilization=70,
        scale_out_cooldown=300,
        scale_in_cooldown=900,
        ml_model="demand_forecasting"
    )
    
    # Create RDS database with read replicas
    if architecture.database.required:
        db_subnet_group = await aws.create_db_subnet_group(
            name=f"{app_requirements['app_name']}-db-subnet-group",
            subnet_ids=[s["private"].id for s in subnets],
            description="Database subnet group for application"
        )
        
        primary_db = await aws.create_rds_instance(
            db_instance_identifier=f"{app_requirements['app_name']}-db",
            db_instance_class=architecture.database.instance_class,
            engine=architecture.database.engine,
            allocated_storage=architecture.database.storage_gb,
            storage_encrypted=True,
            db_subnet_group_name=db_subnet_group.name,
            vpc_security_group_ids=[await create_db_security_group(vpc.id)],
            backup_retention_period=architecture.database.backup_retention_days,
            multi_az=architecture.database.multi_az
        )
        
        # Create read replicas for read-heavy workloads
        if architecture.database.read_replicas > 0:
            for i in range(architecture.database.read_replicas):
                await aws.create_db_read_replica(
                    db_instance_identifier=f"{app_requirements['app_name']}-read-{i+1}",
                    source_db_instance_identifier=primary_db.id,
                    db_instance_class=architecture.database.replica_instance_class
                )
    
    # Set up CloudWatch monitoring and alarms
    await setup_comprehensive_monitoring(
        resources={
            "alb": alb,
            "asg": asg,
            "database": primary_db if architecture.database.required else None
        },
        app_name=app_requirements["app_name"]
    )
    
    # Create CloudFront distribution for global performance
    if architecture.cdn.enabled:
        cdn = await aws.create_cloudfront_distribution(
            origin_domain_name=alb.dns_name,
            comment=f"CDN for {app_requirements['app_name']}",
            price_class=architecture.cdn.price_class,
            cache_behaviors=architecture.cdn.cache_behaviors
        )
    
    return {
        "vpc_id": vpc.id,
        "load_balancer": {"dns_name": alb.dns_name, "arn": alb.arn},
        "auto_scaling_group": {"name": asg.name, "arn": asg.arn},
        "database": {"endpoint": primary_db.endpoint if architecture.database.required else None},
        "cdn": {"domain_name": cdn.domain_name if architecture.cdn.enabled else None},
        "estimated_monthly_cost": architecture.cost_estimate,
        "deployment_status": "completed"
    }

# Automated disaster recovery
async def setup_disaster_recovery(primary_region: str, dr_region: str, resources: dict):
    """Set up automated disaster recovery across regions"""
    
    # Create disaster recovery plan
    dr_plan = await aws.create_disaster_recovery_plan(
        primary_region=primary_region,
        dr_region=dr_region,
        resources=resources,
        rto_minutes=30,  # Recovery Time Objective
        rpo_minutes=15   # Recovery Point Objective
    )
    
    # Set up cross-region replication
    for resource_type, resource_list in resources.items():
        if resource_type == "s3_buckets":
            for bucket in resource_list:
                # Set up cross-region replication
                await aws.setup_s3_cross_region_replication(
                    source_bucket=bucket["name"],
                    source_region=primary_region,
                    destination_region=dr_region,
                    replication_time_control=True
                )
        
        elif resource_type == "rds_instances":
            for db in resource_list:
                # Create cross-region read replica
                await aws.create_cross_region_read_replica(
                    source_db_identifier=db["identifier"],
                    source_region=primary_region,
                    target_region=dr_region,
                    auto_failover=True
                )
        
        elif resource_type == "dynamodb_tables":
            for table in resource_list:
                # Enable DynamoDB Global Tables
                await aws.enable_dynamodb_global_tables(
                    table_name=table["name"],
                    regions=[primary_region, dr_region]
                )
    
    # Create automated failover triggers
    await aws.create_failover_automation(
        dr_plan_id=dr_plan.id,
        triggers=[
            {"type": "health_check_failure", "threshold": 3},
            {"type": "region_outage", "detection_method": "service_health"},
            {"type": "manual_trigger", "authorized_users": ["ops-team"]}
        ]
    )
    
    return dr_plan

# Cost optimization automation
async def optimize_aws_costs():
    """Comprehensive AWS cost optimization"""
    
    # Analyze current spending
    cost_analysis = await aws.analyze_costs(
        timeframe="last_30_days",
        granularity="service",
        include_forecasting=True
    )
    
    optimizations = []
    
    # Identify underutilized EC2 instances
    underutilized_instances = await aws.identify_underutilized_ec2_instances(
        cpu_threshold=10,  # Less than 10% CPU utilization
        network_threshold=5,  # Less than 5% network utilization
        timeframe="7_days"
    )
    
    for instance in underutilized_instances:
        # Calculate potential savings
        current_cost = await aws.get_instance_cost(instance.id)
        
        if current_cost.monthly > 50:  # Only optimize instances costing $50+ per month
            # Get rightsizing recommendations
            recommendation = await aws.get_rightsizing_recommendation(instance.id)
            
            optimizations.append({
                "type": "ec2_rightsizing",
                "instance_id": instance.id,
                "current_type": instance.type,
                "recommended_type": recommendation.recommended_type,
                "monthly_savings": recommendation.monthly_savings,
                "action": "resize" if recommendation.recommended_type else "terminate"
            })
    
    # Analyze Reserved Instance opportunities
    ri_recommendations = await aws.analyze_reserved_instance_opportunities(
        lookback_period="60_days",
        payment_option="partial_upfront",
        term="1_year"
    )
    
    for ri_rec in ri_recommendations:
        if ri_rec.estimated_savings > 1000:  # Minimum $1000 annual savings
            optimizations.append({
                "type": "reserved_instance_purchase",
                "instance_type": ri_rec.instance_type,
                "region": ri_rec.region,
                "quantity": ri_rec.recommended_quantity,
                "annual_savings": ri_rec.estimated_savings,
                "upfront_cost": ri_rec.upfront_payment
            })
    
    # Identify unused EBS volumes and snapshots
    unused_storage = await aws.identify_unused_storage_resources()
    
    for storage in unused_storage:
        if storage.type == "ebs_volume" and not storage.attached:
            optimizations.append({
                "type": "delete_unused_ebs_volume",
                "volume_id": storage.id,
                "size_gb": storage.size,
                "monthly_savings": storage.monthly_cost
            })
        
        elif storage.type == "ebs_snapshot" and storage.age_days > 90:
            # Delete old snapshots
            optimizations.append({
                "type": "delete_old_snapshot",
                "snapshot_id": storage.id,
                "age_days": storage.age_days,
                "monthly_savings": storage.monthly_cost
            })
    
    # Auto-implement low-risk optimizations
    auto_implemented = []
    
    for optimization in optimizations:
        if (optimization["type"] in ["delete_unused_ebs_volume", "delete_old_snapshot"] and
            optimization.get("monthly_savings", 0) > 10):
            
            try:
                if optimization["type"] == "delete_unused_ebs_volume":
                    await aws.delete_ebs_volume(optimization["volume_id"])
                elif optimization["type"] == "delete_old_snapshot":
                    await aws.delete_ebs_snapshot(optimization["snapshot_id"])
                
                auto_implemented.append(optimization)
                
            except Exception as e:
                logger.warning(f"Failed to auto-implement {optimization}: {str(e)}")
    
    return {
        "total_optimizations": len(optimizations),
        "potential_monthly_savings": sum(
            opt.get("monthly_savings", opt.get("annual_savings", 0) / 12)
            for opt in optimizations
        ),
        "auto_implemented": len(auto_implemented),
        "manual_review_required": [
            opt for opt in optimizations
            if opt not in auto_implemented
        ]
    }

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from aimatrix.tools.cloud import GCPCloudTool

# Initialize GCP management
gcp = GCPCloudTool(
    project_id="your-project-id",
    regions=["us-central1", "europe-west1", "asia-southeast1"],
    services=["compute", "storage", "sql", "functions", "gke", "run"],
    ai_optimization=True
)

# Automated GKE cluster management
async def manage_gke_cluster(cluster_requirements: dict):
    """Create and manage Google Kubernetes Engine clusters"""
    
    # Design optimal GKE cluster configuration
    cluster_config = await gcp.design_gke_cluster(
        workload_requirements=cluster_requirements,
        optimization_goals=["cost", "performance", "reliability"]
    )
    
    # Create GKE cluster with autopilot or standard mode
    if cluster_requirements.get("autopilot", True):
        # Use GKE Autopilot for fully managed experience
        cluster = await gcp.create_gke_autopilot_cluster(
            name=cluster_requirements["name"],
            location=cluster_config.location,
            network=cluster_config.network,
            subnet=cluster_config.subnet,
            enable_private_nodes=True,
            master_ipv4_cidr_block=cluster_config.master_cidr
        )
    else:
        # Standard GKE with custom node pools
        cluster = await gcp.create_gke_cluster(
            name=cluster_requirements["name"],
            location=cluster_config.location,
            initial_node_count=0,  # Use node pools instead
            network=cluster_config.network,
            subnetwork=cluster_config.subnet
        )
        
        # Create optimized node pools
        for node_pool_config in cluster_config.node_pools:
            await gcp.create_gke_node_pool(
                cluster_name=cluster.name,
                node_pool_name=node_pool_config.name,
                machine_type=node_pool_config.machine_type,
                disk_size_gb=node_pool_config.disk_size,
                min_node_count=node_pool_config.min_nodes,
                max_node_count=node_pool_config.max_nodes,
                preemptible=node_pool_config.use_preemptible,
                auto_upgrade=True,
                auto_repair=True
            )
    
    # Set up cluster monitoring and logging
    await gcp.enable_gke_monitoring(
        cluster_name=cluster.name,
        location=cluster_config.location,
        enable_workload_monitoring=True,
        enable_managed_prometheus=True
    )
    
    # Install essential cluster add-ons
    addons = await gcp.install_gke_addons(
        cluster_name=cluster.name,
        addons=[
            "istio",  # Service mesh
            "knative",  # Serverless containers
            "cert-manager",  # Certificate management
            "external-dns",  # DNS automation
            "ingress-nginx"  # Ingress controller
        ]
    )
    
    # Set up GitOps with Config Sync
    await gcp.enable_config_sync(
        cluster_name=cluster.name,
        repo_url=cluster_requirements.get("gitops_repo"),
        branch="main",
        sync_directory="k8s-configs"
    )
    
    return {
        "cluster": cluster,
        "addons": addons,
        "monitoring_dashboard": f"https://console.cloud.google.com/monitoring/kubernetes/clusters/{cluster.name}",
        "estimated_monthly_cost": cluster_config.cost_estimate
    }

# Cloud Functions deployment and management
async def deploy_serverless_functions(functions_config: dict):
    """Deploy and manage Google Cloud Functions"""
    
    deployed_functions = []
    
    for function_config in functions_config["functions"]:
        # Optimize function configuration
        optimized_config = await gcp.optimize_function_config(
            runtime=function_config["runtime"],
            memory_mb=function_config.get("memory", 256),
            timeout_seconds=function_config.get("timeout", 60),
            expected_concurrency=function_config.get("expected_requests", 100)
        )
        
        # Deploy function with optimal settings
        function = await gcp.deploy_cloud_function(
            name=function_config["name"],
            source_archive_url=function_config["source_url"],
            entry_point=function_config["entry_point"],
            runtime=function_config["runtime"],
            memory_mb=optimized_config.memory,
            timeout=f"{optimized_config.timeout}s",
            environment_variables=function_config.get("env_vars", {}),
            trigger={
                "type": function_config["trigger_type"],  # http, pubsub, storage, etc.
                "resource": function_config.get("trigger_resource")
            }
        )
        
        # Set up monitoring and alerting
        await gcp.create_function_monitoring(
            function_name=function.name,
            alerts=[
                {"metric": "execution_count", "threshold": 1000, "period": "5m"},
                {"metric": "execution_time", "threshold": 30000, "period": "5m"},  # 30 seconds
                {"metric": "error_rate", "threshold": 0.05, "period": "5m"}  # 5% error rate
            ]
        )
        
        deployed_functions.append({
            "name": function.name,
            "trigger_url": function.trigger_url if function_config["trigger_type"] == "http" else None,
            "status": function.status,
            "memory": optimized_config.memory,
            "timeout": optimized_config.timeout
        })
    
    return deployed_functions

# BigQuery data warehouse automation
async def setup_bigquery_analytics(data_requirements: dict):
    """Set up automated BigQuery data pipeline"""
    
    # Create datasets for different data domains
    datasets = []
    
    for domain in data_requirements["data_domains"]:
        dataset = await gcp.create_bigquery_dataset(
            dataset_id=f"{domain['name']}_data",
            description=f"Data warehouse for {domain['description']}",
            location=data_requirements.get("location", "US"),
            default_table_expiration_ms=domain.get("retention_days", 365) * 24 * 60 * 60 * 1000
        )
        
        # Create tables based on schema
        for table_config in domain["tables"]:
            table = await gcp.create_bigquery_table(
                dataset_id=dataset.id,
                table_id=table_config["name"],
                schema=table_config["schema"],
                partitioning=table_config.get("partitioning"),
                clustering=table_config.get("clustering")
            )
        
        datasets.append(dataset)
    
    # Set up automated data ingestion pipelines
    if data_requirements.get("data_sources"):
        for source in data_requirements["data_sources"]:
            if source["type"] == "cloud_storage":
                # Set up Cloud Storage to BigQuery pipeline
                transfer_job = await gcp.create_bigquery_transfer_job(
                    source_uri=source["gcs_path"],
                    destination_dataset=source["target_dataset"],
                    destination_table=source["target_table"],
                    schedule=source.get("schedule", "daily"),
                    file_format=source.get("format", "JSON")
                )
            
            elif source["type"] == "pub_sub":
                # Set up Pub/Sub to BigQuery streaming
                subscription = await gcp.create_pubsub_bigquery_subscription(
                    topic_name=source["topic"],
                    table_name=f"{source['target_dataset']}.{source['target_table']}",
                    use_topic_schema=True,
                    write_metadata=True
                )
    
    # Create automated data transformation jobs
    if data_requirements.get("transformations"):
        for transformation in data_requirements["transformations"]:
            # Create scheduled SQL queries for transformations
            scheduled_query = await gcp.create_scheduled_query(
                name=transformation["name"],
                query=transformation["sql"],
                schedule=transformation.get("schedule", "0 2 * * *"),  # Daily at 2 AM
                destination_table=transformation.get("output_table")
            )
    
    # Set up data quality monitoring
    await gcp.setup_bigquery_data_quality_monitoring(
        datasets=[d.id for d in datasets],
        quality_checks=[
            "completeness",
            "uniqueness", 
            "validity",
            "consistency",
            "freshness"
        ]
    )
    
    return {
        "datasets": [{"id": d.id, "location": d.location} for d in datasets],
        "estimated_monthly_cost": await gcp.estimate_bigquery_costs(data_requirements)
    }

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from aimatrix.tools.cloud import AzureCloudTool

# Initialize Azure management
azure = AzureCloudTool(
    subscription_id="your-subscription-id",
    regions=["eastus", "westeurope", "southeastasia"],
    services=["compute", "storage", "sql", "functions", "aks", "container_instances"],
    governance_enabled=True
)

# Azure Kubernetes Service automation
async def deploy_aks_application(app_config: dict):
    """Deploy application to Azure Kubernetes Service"""
    
    # Create resource group
    resource_group = await azure.create_resource_group(
        name=f"{app_config['app_name']}-rg",
        location=app_config["location"],
        tags=app_config.get("tags", {})
    )
    
    # Create AKS cluster with best practices
    aks_cluster = await azure.create_aks_cluster(
        resource_group_name=resource_group.name,
        cluster_name=f"{app_config['app_name']}-aks",
        location=app_config["location"],
        kubernetes_version="1.28",
        node_pools=[
            {
                "name": "system",
                "vm_size": "Standard_D4s_v3",
                "node_count": 2,
                "mode": "System",
                "availability_zones": [1, 2, 3]
            },
            {
                "name": "user",
                "vm_size": app_config.get("node_vm_size", "Standard_D8s_v3"),
                "min_count": app_config.get("min_nodes", 2),
                "max_count": app_config.get("max_nodes", 10),
                "mode": "User",
                "enable_auto_scaling": True,
                "availability_zones": [1, 2, 3]
            }
        ],
        network_plugin="azure",
        network_policy="azure",
        enable_rbac=True,
        enable_managed_identity=True,
        enable_auto_scaler=True,
        enable_monitoring=True
    )
    
    # Connect to AKS cluster
    await azure.get_aks_credentials(
        resource_group_name=resource_group.name,
        cluster_name=aks_cluster.name
    )
    
    # Deploy application using Helm charts or YAML manifests
    if app_config.get("deployment_type") == "helm":
        # Deploy using Helm
        helm_release = await azure.deploy_helm_chart(
            cluster_name=aks_cluster.name,
            chart_name=app_config["helm_chart"],
            release_name=app_config["app_name"],
            values=app_config.get("helm_values", {}),
            create_namespace=True,
            namespace=app_config.get("namespace", "default")
        )
    else:
        # Deploy using Kubernetes manifests
        deployment_result = await azure.apply_kubernetes_manifests(
            cluster_name=aks_cluster.name,
            manifests=app_config["k8s_manifests"],
            namespace=app_config.get("namespace", "default")
        )
    
    # Set up ingress controller
    ingress_controller = await azure.install_nginx_ingress_controller(
        cluster_name=aks_cluster.name,
        enable_ssl=True,
        cert_manager=True
    )
    
    # Configure application gateway (if required)
    if app_config.get("use_application_gateway"):
        app_gateway = await azure.create_application_gateway(
            resource_group_name=resource_group.name,
            gateway_name=f"{app_config['app_name']}-appgw",
            location=app_config["location"],
            sku_name="WAF_v2",
            sku_tier="WAF_v2",
            sku_capacity=2,
            enable_waf=True
        )
        
        # Enable Application Gateway Ingress Controller
        await azure.enable_agic(
            cluster_name=aks_cluster.name,
            app_gateway_id=app_gateway.id
        )
    
    # Set up monitoring and logging
    await azure.enable_container_insights(
        resource_group_name=resource_group.name,
        cluster_name=aks_cluster.name
    )
    
    # Create Azure Monitor alerts
    alerts = await azure.create_aks_monitoring_alerts(
        resource_group_name=resource_group.name,
        cluster_name=aks_cluster.name,
        alerts=[
            {"metric": "node_cpu_usage", "threshold": 80, "operator": "GreaterThan"},
            {"metric": "node_memory_usage", "threshold": 85, "operator": "GreaterThan"},
            {"metric": "pod_ready_percentage", "threshold": 95, "operator": "LessThan"}
        ]
    )
    
    return {
        "resource_group": resource_group.name,
        "cluster_name": aks_cluster.name,
        "cluster_fqdn": aks_cluster.fqdn,
        "ingress_ip": ingress_controller.external_ip,
        "monitoring_enabled": True,
        "estimated_monthly_cost": await azure.estimate_aks_cost(aks_cluster)
    }

# Azure Functions serverless automation
async def create_function_app(function_config: dict):
    """Create and deploy Azure Functions"""
    
    # Create storage account for function app
    storage_account = await azure.create_storage_account(
        resource_group_name=function_config["resource_group"],
        account_name=f"{function_config['app_name']}storage",
        location=function_config["location"],
        sku_name="Standard_LRS",
        kind="StorageV2"
    )
    
    # Create Application Insights for monitoring
    app_insights = await azure.create_application_insights(
        resource_group_name=function_config["resource_group"],
        name=f"{function_config['app_name']}-insights",
        location=function_config["location"],
        application_type="web"
    )
    
    # Create Function App
    function_app = await azure.create_function_app(
        resource_group_name=function_config["resource_group"],
        name=function_config["app_name"],
        location=function_config["location"],
        storage_account_name=storage_account.name,
        runtime_stack=function_config.get("runtime", "python"),
        runtime_version=function_config.get("runtime_version", "3.9"),
        consumption_plan=function_config.get("consumption_plan", True),
        app_insights_key=app_insights.instrumentation_key
    )
    
    # Deploy functions
    deployed_functions = []
    
    for func in function_config["functions"]:
        # Package and deploy function
        deployment = await azure.deploy_function(
            function_app_name=function_app.name,
            function_name=func["name"],
            source_code=func["source_code"],
            trigger_type=func["trigger_type"],
            trigger_config=func.get("trigger_config", {}),
            environment_variables=func.get("env_vars", {})
        )
        
        deployed_functions.append({
            "name": func["name"],
            "trigger_type": func["trigger_type"],
            "status": deployment.status,
            "invoke_url": deployment.invoke_url if func["trigger_type"] == "http" else None
        })
    
    # Set up scaling rules
    if not function_config.get("consumption_plan"):
        await azure.configure_function_app_scaling(
            function_app_name=function_app.name,
            min_instances=function_config.get("min_instances", 1),
            max_instances=function_config.get("max_instances", 10),
            scaling_rules=function_config.get("scaling_rules", [])
        )
    
    return {
        "function_app_name": function_app.name,
        "functions": deployed_functions,
        "app_insights_name": app_insights.name,
        "estimated_monthly_cost": await azure.estimate_function_app_cost(function_config)
    }

# Azure DevOps integration for CI/CD
async def setup_azure_devops_pipeline(pipeline_config: dict):
    """Set up automated CI/CD pipeline with Azure DevOps"""
    
    # Create Azure DevOps project
    project = await azure.create_devops_project(
        organization=pipeline_config["organization"],
        name=pipeline_config["project_name"],
        description=pipeline_config.get("description", ""),
        visibility="private"
    )
    
    # Create repository
    repository = await azure.create_devops_repository(
        project_name=project.name,
        repo_name=pipeline_config["repo_name"],
        import_url=pipeline_config.get("import_url")
    )
    
    # Create service connections
    service_connections = []
    
    for connection_config in pipeline_config.get("service_connections", []):
        connection = await azure.create_service_connection(
            project_name=project.name,
            connection_name=connection_config["name"],
            connection_type=connection_config["type"],  # "azure", "docker", "kubernetes", etc.
            configuration=connection_config["config"]
        )
        service_connections.append(connection)
    
    # Create build pipeline
    build_pipeline = await azure.create_build_pipeline(
        project_name=project.name,
        pipeline_name=f"{pipeline_config['app_name']}-build",
        repository_id=repository.id,
        yaml_path=pipeline_config.get("build_yaml_path", "azure-pipelines.yml"),
        variables=pipeline_config.get("build_variables", {}),
        triggers=pipeline_config.get("build_triggers", ["main"])
    )
    
    # Create release pipeline for multi-stage deployments
    if pipeline_config.get("environments"):
        release_pipeline = await azure.create_release_pipeline(
            project_name=project.name,
            pipeline_name=f"{pipeline_config['app_name']}-release",
            artifact_source=build_pipeline.id,
            environments=pipeline_config["environments"]  # dev, staging, prod
        )
        
        # Set up approval gates for production
        for env in pipeline_config["environments"]:
            if env["name"] == "production":
                await azure.configure_deployment_approvals(
                    project_name=project.name,
                    environment_name=env["name"],
                    approvers=env.get("approvers", []),
                    approval_timeout_minutes=env.get("approval_timeout", 1440)  # 24 hours
                )
    
    # Set up monitoring and alerting for pipeline
    await azure.create_pipeline_monitoring(
        project_name=project.name,
        pipeline_id=build_pipeline.id,
        alerts=[
            {"event": "build_failed", "notification": "email"},
            {"event": "deployment_failed", "notification": "teams"},
            {"event": "security_scan_failed", "notification": "email"}
        ]
    )
    
    return {
        "project_name": project.name,
        "repository_url": repository.web_url,
        "build_pipeline_id": build_pipeline.id,
        "release_pipeline_id": release_pipeline.id if pipeline_config.get("environments") else None,
        "service_connections": [sc.name for sc in service_connections]
    }

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from aimatrix.tools.cloud import KubernetesTool

# Initialize Kubernetes management
k8s = KubernetesTool(
    clusters=["production", "staging", "development"],
    service_mesh="istio",
    observability_stack="prometheus",
    gitops_enabled=True
)

# Automated application deployment with best practices
async def deploy_microservices_application(app_manifest: dict):
    """Deploy complete microservices application with GitOps"""
    
    # Create namespace with security policies
    namespace = await k8s.create_namespace(
        name=app_manifest["namespace"],
        labels={
            "app": app_manifest["app_name"],
            "version": app_manifest["version"],
            "environment": app_manifest["environment"]
        },
        annotations={
            "security.istio.io/injection": "enabled"  # Enable Istio sidecar injection
        }
    )
    
    # Apply network policies
    network_policies = await k8s.create_network_policies(
        namespace=namespace.name,
        policies=[
            {
                "name": "default-deny-all",
                "policy_type": "Ingress",
                "selector": {}  # Apply to all pods
            },
            {
                "name": "allow-app-communication",
                "policy_type": "Ingress",
                "selector": {"app": app_manifest["app_name"]},
                "ingress_rules": app_manifest.get("network_rules", [])
            }
        ]
    )
    
    # Deploy each microservice
    deployed_services = []
    
    for service_config in app_manifest["services"]:
        # Create ConfigMaps and Secrets
        if service_config.get("config_maps"):
            for cm_config in service_config["config_maps"]:
                await k8s.create_config_map(
                    namespace=namespace.name,
                    name=cm_config["name"],
                    data=cm_config["data"]
                )
        
        if service_config.get("secrets"):
            for secret_config in service_config["secrets"]:
                await k8s.create_secret(
                    namespace=namespace.name,
                    name=secret_config["name"],
                    data=secret_config["data"],
                    secret_type=secret_config.get("type", "Opaque")
                )
        
        # Create Deployment with advanced features
        deployment = await k8s.create_deployment(
            namespace=namespace.name,
            name=service_config["name"],
            image=service_config["image"],
            replicas=service_config.get("replicas", 3),
            resources={
                "requests": {
                    "cpu": service_config.get("cpu_request", "100m"),
                    "memory": service_config.get("memory_request", "128Mi")
                },
                "limits": {
                    "cpu": service_config.get("cpu_limit", "500m"),
                    "memory": service_config.get("memory_limit", "512Mi")
                }
            },
            env_from=service_config.get("env_from", []),
            volumes=service_config.get("volumes", []),
            security_context={
                "run_as_non_root": True,
                "run_as_user": 10001,
                "fs_group": 20001,
                "security_context_constraints": "restricted"
            },
            liveness_probe=service_config.get("liveness_probe"),
            readiness_probe=service_config.get("readiness_probe"),
            startup_probe=service_config.get("startup_probe")
        )
        
        # Create Service
        service = await k8s.create_service(
            namespace=namespace.name,
            name=f"{service_config['name']}-service",
            selector={"app": service_config["name"]},
            ports=service_config["ports"],
            service_type=service_config.get("service_type", "ClusterIP")
        )
        
        # Create Horizontal Pod Autoscaler
        if service_config.get("auto_scaling"):
            hpa = await k8s.create_horizontal_pod_autoscaler(
                namespace=namespace.name,
                name=f"{service_config['name']}-hpa",
                target_deployment=deployment.name,
                min_replicas=service_config["auto_scaling"]["min_replicas"],
                max_replicas=service_config["auto_scaling"]["max_replicas"],
                metrics=service_config["auto_scaling"].get("metrics", [
                    {"type": "Resource", "resource": {"name": "cpu", "target": {"type": "Utilization", "averageUtilization": 70}}}
                ])
            )
        
        deployed_services.append({
            "name": service_config["name"],
            "deployment": deployment.name,
            "service": service.name,
            "status": "deployed"
        })
    
    # Create Ingress for external access
    if app_manifest.get("ingress"):
        ingress = await k8s.create_ingress(
            namespace=namespace.name,
            name=f"{app_manifest['app_name']}-ingress",
            rules=app_manifest["ingress"]["rules"],
            tls_config=app_manifest["ingress"].get("tls"),
            annotations={
                "kubernetes.io/ingress.class": "nginx",
                "cert-manager.io/cluster-issuer": "letsencrypt-prod",
                "nginx.ingress.kubernetes.io/rate-limit": "100"
            }
        )
    
    # Set up service mesh configuration
    if app_manifest.get("service_mesh_config"):
        # Create Istio VirtualService
        virtual_service = await k8s.create_istio_virtual_service(
            namespace=namespace.name,
            name=f"{app_manifest['app_name']}-vs",
            hosts=app_manifest["service_mesh_config"]["hosts"],
            http_routes=app_manifest["service_mesh_config"]["routes"]
        )
        
        # Create DestinationRule for traffic policies
        destination_rule = await k8s.create_istio_destination_rule(
            namespace=namespace.name,
            name=f"{app_manifest['app_name']}-dr",
            host=app_manifest["service_mesh_config"]["host"],
            traffic_policy=app_manifest["service_mesh_config"].get("traffic_policy", {
                "connection_pool": {
                    "tcp": {"max_connections": 10},
                    "http": {"http1_max_pending_requests": 10, "max_requests_per_connection": 2}
                },
                "outlier_detection": {
                    "consecutive_5xx_errors": 3,
                    "interval": "30s",
                    "base_ejection_time": "30s"
                }
            })
        )
    
    # Set up monitoring and observability
    await k8s.create_service_monitors(
        namespace=namespace.name,
        services=[s["name"] for s in deployed_services],
        metrics_path="/metrics",
        scrape_interval="30s"
    )
    
    # Create Grafana dashboard
    dashboard = await k8s.create_grafana_dashboard(
        name=f"{app_manifest['app_name']}-dashboard",
        services=deployed_services,
        metrics=[
            "request_rate",
            "error_rate", 
            "response_time",
            "resource_usage"
        ]
    )
    
    return {
        "namespace": namespace.name,
        "services": deployed_services,
        "ingress_url": ingress.hosts[0] if app_manifest.get("ingress") else None,
        "monitoring_dashboard": dashboard.url,
        "deployment_status": "completed"
    }

# Automated cluster upgrades
async def upgrade_kubernetes_cluster(cluster_name: str, target_version: str):
    """Safely upgrade Kubernetes cluster with zero downtime"""
    
    # Pre-upgrade checks
    upgrade_readiness = await k8s.check_upgrade_readiness(
        cluster_name=cluster_name,
        target_version=target_version
    )
    
    if not upgrade_readiness.ready:
        return {
            "status": "failed",
            "reason": "Cluster not ready for upgrade",
            "issues": upgrade_readiness.issues
        }
    
    # Backup cluster state
    backup_result = await k8s.backup_cluster_state(
        cluster_name=cluster_name,
        backup_name=f"pre-upgrade-{datetime.now().strftime('%Y%m%d-%H%M%S')}"
    )
    
    # Upgrade control plane first
    control_plane_upgrade = await k8s.upgrade_control_plane(
        cluster_name=cluster_name,
        target_version=target_version,
        drain_timeout=600  # 10 minutes
    )
    
    if not control_plane_upgrade.success:
        # Rollback control plane
        await k8s.rollback_control_plane(cluster_name, backup_result.version)
        return {
            "status": "failed",
            "reason": "Control plane upgrade failed",
            "error": control_plane_upgrade.error
        }
    
    # Upgrade node pools one by one
    node_pools = await k8s.get_node_pools(cluster_name)
    upgraded_pools = []
    
    for node_pool in node_pools:
        # Cordon and drain nodes safely
        await k8s.cordon_node_pool(cluster_name, node_pool.name)
        
        drain_result = await k8s.drain_node_pool(
            cluster_name=cluster_name,
            node_pool_name=node_pool.name,
            ignore_daemonsets=True,
            delete_local_data=True,
            timeout=900  # 15 minutes
        )
        
        if drain_result.success:
            # Upgrade node pool
            pool_upgrade = await k8s.upgrade_node_pool(
                cluster_name=cluster_name,
                node_pool_name=node_pool.name,
                target_version=target_version
            )
            
            if pool_upgrade.success:
                # Uncordon nodes
                await k8s.uncordon_node_pool(cluster_name, node_pool.name)
                upgraded_pools.append(node_pool.name)
            else:
                # Rollback this node pool
                await k8s.rollback_node_pool(cluster_name, node_pool.name, backup_result.version)
                break
    
    # Verify cluster health after upgrade
    health_check = await k8s.check_cluster_health(
        cluster_name=cluster_name,
        check_duration=300  # 5 minutes
    )
    
    if health_check.healthy:
        # Clean up old backups
        await k8s.cleanup_old_backups(cluster_name, keep_count=5)
        
        return {
            "status": "completed",
            "upgraded_version": target_version,
            "upgraded_node_pools": upgraded_pools,
            "upgrade_duration": health_check.total_duration
        }
    else:
        # Full rollback
        await k8s.rollback_cluster(cluster_name, backup_result)
        return {
            "status": "failed",
            "reason": "Cluster health check failed after upgrade",
            "rollback_completed": True
        }

# GitOps automation with ArgoCD
async def setup_gitops_deployment(gitops_config: dict):
    """Set up GitOps deployment pipeline with ArgoCD"""
    
    # Install ArgoCD
    argocd = await k8s.install_argocd(
        namespace="argocd",
        ha_mode=gitops_config.get("high_availability", True),
        ingress_config=gitops_config.get("ingress")
    )
    
    # Create ArgoCD applications for each environment
    applications = []
    
    for app_config in gitops_config["applications"]:
        app = await k8s.create_argocd_application(
            name=app_config["name"],
            project=app_config.get("project", "default"),
            repo_url=app_config["repo_url"],
            path=app_config["path"],
            target_revision=app_config.get("branch", "main"),
            destination_server=app_config.get("cluster_url", "https://kubernetes.default.svc"),
            destination_namespace=app_config["namespace"],
            sync_policy={
                "automated": {
                    "prune": app_config.get("auto_prune", True),
                    "self_heal": app_config.get("self_heal", True)
                },
                "sync_options": [
                    "CreateNamespace=true",
                    "PrunePropagationPolicy=foreground"
                ]
            }
        )
        applications.append(app)
    
    # Set up notification controllers
    if gitops_config.get("notifications"):
        await k8s.configure_argocd_notifications(
            triggers=gitops_config["notifications"].get("triggers", [
                "on-sync-succeeded",
                "on-sync-failed", 
                "on-health-degraded"
            ]),
            destinations=gitops_config["notifications"]["destinations"]  # slack, email, etc.
        )
    
    # Create RBAC policies
    await k8s.configure_argocd_rbac(
        policies=gitops_config.get("rbac_policies", []),
        default_policy="p, role:readonly, applications, get, */*, allow"
    )
    
    return {
        "argocd_url": argocd.server_url,
        "applications": [{"name": app.name, "status": app.status} for app in applications],
        "gitops_enabled": True
    }