Data & Storage Tools

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from aimatrix.tools.storage import FileSystemTool

# Initialize file system management
filesystem = FileSystemTool(
    local_systems=["windows", "macos", "linux"],
    network_protocols=["nfs", "smb", "ftp", "sftp"],
    cloud_filesystems=["s3fs", "gcsfuse", "azure_files"],
    ai_organization=True
)

# AI-powered file organization
file_agent = AIAgent(
    name="file-organization-agent",
    tools=[filesystem],
    instructions="""
    You are a file organization expert. You can:
    1. Organize files intelligently based on content and metadata
    2. Detect and remove duplicate files
    3. Optimize storage usage and performance
    4. Backup and sync files across systems
    5. Extract and index content for search
    """
)

# Example: Intelligent file organization
@file_agent.task("organize_files")
async def organize_files_intelligently(directory_path: str, organization_rules: dict):
    """Organize files based on AI analysis and custom rules"""
    
    # Scan directory and analyze all files
    file_analysis = await filesystem.analyze_directory(
        path=directory_path,
        recursive=True,
        include_metadata=True,
        content_analysis=True
    )
    
    # AI-powered file categorization
    categorized_files = await filesystem.categorize_files(
        files=file_analysis.files,
        categories=organization_rules.get("categories", [
            "documents", "images", "videos", "audio", "archives", "code", "data"
        ]),
        custom_rules=organization_rules.get("custom_rules", {})
    )
    
    # Create organized directory structure
    organization_plan = await filesystem.create_organization_plan(
        categorized_files=categorized_files,
        base_path=directory_path,
        structure_template=organization_rules.get("structure", "{category}/{year}/{month}")
    )
    
    # Execute organization with safety checks
    organization_results = []
    
    for file_operation in organization_plan.operations:
        # Create backup before moving important files
        if file_operation.file_size > 100 * 1024 * 1024:  # Files > 100MB
            backup_result = await filesystem.create_backup(
                file_path=file_operation.source_path,
                backup_location=organization_rules.get("backup_location", "./backups")
            )
        
        # Execute file operation
        try:
            operation_result = await filesystem.execute_operation(file_operation)
            organization_results.append({
                "file": file_operation.source_path,
                "destination": file_operation.destination_path,
                "operation": file_operation.operation_type,
                "status": "success",
                "size_saved": file_operation.size_optimization if hasattr(file_operation, 'size_optimization') else 0
            })
        except Exception as e:
            logger.error(f"Failed to organize {file_operation.source_path}: {str(e)}")
            organization_results.append({
                "file": file_operation.source_path,
                "operation": file_operation.operation_type,
                "status": "failed",
                "error": str(e)
            })
    
    # Update file index for search
    await filesystem.update_file_index(
        directory=directory_path,
        include_content=True,
        extract_metadata=True
    )
    
    # Generate organization summary
    summary = {
        "total_files_processed": len(organization_results),
        "successfully_organized": len([r for r in organization_results if r["status"] == "success"]),
        "total_size_optimized": sum(r.get("size_saved", 0) for r in organization_results),
        "categories_created": len(set(categorized_files.keys())),
        "organization_results": organization_results
    }
    
    return summary

# Duplicate file detection and management
async def manage_duplicate_files(scan_paths: list, action: str = "report"):
    """Find and manage duplicate files across multiple locations"""
    
    # Deep scan for duplicates using multiple algorithms
    duplicate_analysis = await filesystem.find_duplicates(
        paths=scan_paths,
        algorithms=["md5", "sha256", "content_similarity", "metadata_comparison"],
        minimum_size=1024,  # Ignore files smaller than 1KB
        include_similar_files=True  # Find similar but not identical files
    )
    
    duplicate_groups = []
    
    for duplicate_group in duplicate_analysis.duplicate_groups:
        # Analyze each group to determine the best file to keep
        best_file = await filesystem.select_best_duplicate(
            files=duplicate_group.files,
            criteria=[
                "highest_quality",
                "most_recent",
                "best_location",
                "most_metadata"
            ]
        )
        
        group_info = {
            "group_id": duplicate_group.id,
            "file_count": len(duplicate_group.files),
            "total_size": sum(f.size for f in duplicate_group.files),
            "potential_savings": sum(f.size for f in duplicate_group.files if f.path != best_file.path),
            "best_file": best_file.path,
            "duplicates": [f.path for f in duplicate_group.files if f.path != best_file.path],
            "similarity_score": duplicate_group.similarity_score
        }
        
        duplicate_groups.append(group_info)
    
    # Execute actions based on specified action
    if action == "remove_duplicates":
        removal_results = []
        
        for group in duplicate_groups:
            # Create safety backup of duplicates before removal
            for duplicate_path in group["duplicates"]:
                backup_path = await filesystem.create_safety_backup(duplicate_path)
                
                try:
                    await filesystem.remove_file(duplicate_path)
                    removal_results.append({
                        "file": duplicate_path,
                        "status": "removed",
                        "backup_location": backup_path,
                        "size_saved": await filesystem.get_file_size(duplicate_path)
                    })
                except Exception as e:
                    removal_results.append({
                        "file": duplicate_path,
                        "status": "failed",
                        "error": str(e)
                    })
        
        return {
            "duplicate_groups": duplicate_groups,
            "removal_results": removal_results,
            "total_space_saved": sum(r.get("size_saved", 0) for r in removal_results)
        }
    
    elif action == "create_hardlinks":
        # Create hardlinks to save space while preserving file structure
        hardlink_results = []
        
        for group in duplicate_groups:
            best_file_path = group["best_file"]
            
            for duplicate_path in group["duplicates"]:
                try:
                    # Create hardlink
                    await filesystem.create_hardlink(
                        source=best_file_path,
                        target=duplicate_path
                    )
                    
                    hardlink_results.append({
                        "original": duplicate_path,
                        "linked_to": best_file_path,
                        "status": "linked",
                        "space_saved": group["potential_savings"] / len(group["duplicates"])
                    })
                except Exception as e:
                    hardlink_results.append({
                        "original": duplicate_path,
                        "status": "failed",
                        "error": str(e)
                    })
        
        return {
            "duplicate_groups": duplicate_groups,
            "hardlink_results": hardlink_results,
            "total_space_saved": sum(r.get("space_saved", 0) for r in hardlink_results)
        }
    
    else:  # action == "report"
        return {
            "duplicate_groups": duplicate_groups,
            "total_duplicates": sum(g["file_count"] - 1 for g in duplicate_groups),
            "total_potential_savings": sum(g["potential_savings"] for g in duplicate_groups)
        }

# Network file system management
async def setup_network_file_access(network_config: dict):
    """Set up secure access to network file systems"""
    
    network_connections = []
    
    for connection_config in network_config["connections"]:
        if connection_config["protocol"] == "nfs":
            # Mount NFS share
            nfs_mount = await filesystem.mount_nfs(
                server=connection_config["server"],
                export_path=connection_config["export_path"],
                mount_point=connection_config["local_mount_point"],
                options=connection_config.get("mount_options", ["rw", "hard", "intr"]),
                version=connection_config.get("nfs_version", "4.1")
            )
            
            network_connections.append({
                "type": "nfs",
                "server": connection_config["server"],
                "mount_point": nfs_mount.mount_point,
                "status": nfs_mount.status
            })
        
        elif connection_config["protocol"] == "smb":
            # Mount SMB/CIFS share
            smb_mount = await filesystem.mount_smb(
                server=connection_config["server"],
                share_name=connection_config["share_name"],
                mount_point=connection_config["local_mount_point"],
                username=connection_config["username"],
                password=connection_config["password"],
                domain=connection_config.get("domain"),
                version=connection_config.get("smb_version", "3.0")
            )
            
            network_connections.append({
                "type": "smb",
                "server": connection_config["server"],
                "share": connection_config["share_name"],
                "mount_point": smb_mount.mount_point,
                "status": smb_mount.status
            })
        
        elif connection_config["protocol"] == "sftp":
            # Set up SFTP connection
            sftp_connection = await filesystem.create_sftp_connection(
                host=connection_config["host"],
                port=connection_config.get("port", 22),
                username=connection_config["username"],
                private_key_path=connection_config.get("private_key_path"),
                password=connection_config.get("password")
            )
            
            network_connections.append({
                "type": "sftp",
                "host": connection_config["host"],
                "connection_id": sftp_connection.id,
                "status": sftp_connection.status
            })
    
    # Set up automated synchronization
    if network_config.get("sync_rules"):
        sync_jobs = []
        
        for sync_rule in network_config["sync_rules"]:
            sync_job = await filesystem.create_sync_job(
                source=sync_rule["source"],
                destination=sync_rule["destination"],
                schedule=sync_rule.get("schedule", "hourly"),
                sync_type=sync_rule.get("type", "bidirectional"),  # or "push", "pull"
                conflict_resolution=sync_rule.get("conflict_resolution", "newest_wins"),
                exclude_patterns=sync_rule.get("exclude_patterns", [])
            )
            
            sync_jobs.append(sync_job)
    
    return {
        "network_connections": network_connections,
        "sync_jobs": sync_jobs if network_config.get("sync_rules") else [],
        "total_mounted_systems": len(network_connections)
    }

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from aimatrix.tools.storage import IPFSTool

# Initialize IPFS integration
ipfs = IPFSTool(
    node_type="full",  # or "light"
    gateway_enabled=True,
    pinning_service="pinata",  # or "web3.storage", "infura"
    content_addressing=True
)

# Decentralized file storage and retrieval
async def store_files_on_ipfs(files: list, metadata: dict = None):
    """Store files on IPFS with intelligent pinning strategy"""
    
    stored_files = []
    
    for file_path in files:
        # Analyze file for optimal storage strategy
        file_analysis = await ipfs.analyze_file(file_path)
        
        # Add file to IPFS
        ipfs_result = await ipfs.add_file(
            file_path=file_path,
            pin=True,
            wrap_with_directory=file_analysis.should_wrap,
            chunk_size=file_analysis.optimal_chunk_size
        )
        
        # Store metadata
        if metadata:
            metadata_hash = await ipfs.add_json({
                "file_hash": ipfs_result.hash,
                "original_name": os.path.basename(file_path),
                "file_size": file_analysis.size,
                "mime_type": file_analysis.mime_type,
                "upload_time": datetime.utcnow().isoformat(),
                **metadata
            })
        
        # Pin to multiple services for redundancy
        pinning_results = await ipfs.pin_to_services(
            hash=ipfs_result.hash,
            services=["pinata", "web3.storage", "infura"],
            metadata={"name": os.path.basename(file_path)}
        )
        
        stored_files.append({
            "original_path": file_path,
            "ipfs_hash": ipfs_result.hash,
            "metadata_hash": metadata_hash if metadata else None,
            "pinning_services": [p.service for p in pinning_results if p.success],
            "gateway_url": f"https://gateway.ipfs.io/ipfs/{ipfs_result.hash}",
            "size": file_analysis.size
        })
    
    return {
        "stored_files": stored_files,
        "total_files": len(stored_files),
        "total_size": sum(f["size"] for f in stored_files)
    }

# IPFS content discovery and retrieval
async def create_ipfs_content_index(directory_hash: str):
    """Create searchable index of IPFS content"""
    
    # Recursively explore IPFS directory
    content_tree = await ipfs.explore_directory(directory_hash, recursive=True)
    
    # Extract and index content
    indexed_content = []
    
    for item in content_tree.items:
        if item.type == "file":
            # Download and analyze content
            content = await ipfs.cat(item.hash, max_size=10*1024*1024)  # 10MB limit
            
            # Extract text content for indexing
            if item.mime_type.startswith("text/") or item.mime_type == "application/pdf":
                text_content = await extract_text_content(content, item.mime_type)
                
                # Create search index entry
                index_entry = {
                    "hash": item.hash,
                    "name": item.name,
                    "size": item.size,
                    "mime_type": item.mime_type,
                    "content_preview": text_content[:500],  # First 500 characters
                    "keywords": await extract_keywords(text_content),
                    "indexed_at": datetime.utcnow().isoformat()
                }
                
                indexed_content.append(index_entry)
    
    # Store index on IPFS
    index_hash = await ipfs.add_json({
        "directory_hash": directory_hash,
        "indexed_files": indexed_content,
        "total_files": len(indexed_content),
        "index_created": datetime.utcnow().isoformat()
    })
    
    return {
        "index_hash": index_hash,
        "indexed_files": len(indexed_content),
        "directory_hash": directory_hash
    }

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from aimatrix.tools.storage import CloudStorageTool

# Initialize multi-cloud storage
cloud_storage = CloudStorageTool(
    providers={
        "aws_s3": {"regions": ["us-east-1", "eu-west-1", "ap-southeast-1"]},
        "gcp_storage": {"regions": ["us-central1", "europe-west1", "asia-southeast1"]},
        "azure_blob": {"regions": ["eastus", "westeurope", "southeastasia"]},
        "dropbox": {"business_account": True},
        "box": {"enterprise_account": True}
    },
    intelligent_tiering=True,
    cross_provider_sync=True
)

# Intelligent storage optimization
async def optimize_cloud_storage():
    """Optimize storage across all cloud providers"""
    
    # Analyze storage usage across all providers
    storage_analysis = await cloud_storage.analyze_usage(
        include_costs=True,
        include_access_patterns=True,
        timeframe="last_90_days"
    )
    
    optimization_actions = []
    
    # Analyze each storage location
    for provider, buckets in storage_analysis.providers.items():
        for bucket in buckets:
            # Identify optimization opportunities
            bucket_optimization = await cloud_storage.analyze_bucket_optimization(
                provider=provider,
                bucket_name=bucket.name,
                optimization_goals=["cost", "performance", "compliance"]
            )
            
            # Storage class/tier optimization
            if bucket_optimization.tier_optimization:
                for file_group in bucket_optimization.tier_optimization.file_groups:
                    if file_group.potential_savings > 100:  # $100 minimum savings
                        # Implement tier transition
                        tier_result = await cloud_storage.transition_storage_tier(
                            provider=provider,
                            bucket_name=bucket.name,
                            objects=file_group.objects,
                            target_tier=file_group.recommended_tier
                        )
                        
                        optimization_actions.append({
                            "provider": provider,
                            "bucket": bucket.name,
                            "action": "tier_transition",
                            "objects_moved": len(file_group.objects),
                            "target_tier": file_group.recommended_tier,
                            "monthly_savings": file_group.potential_savings
                        })
            
            # Lifecycle policy optimization
            if not bucket.has_lifecycle_policy or bucket_optimization.lifecycle_improvement:
                lifecycle_policy = await cloud_storage.generate_optimal_lifecycle_policy(
                    provider=provider,
                    bucket_name=bucket.name,
                    access_patterns=bucket.access_patterns,
                    compliance_requirements=bucket.compliance_requirements
                )
                
                await cloud_storage.apply_lifecycle_policy(
                    provider=provider,
                    bucket_name=bucket.name,
                    policy=lifecycle_policy
                )
                
                optimization_actions.append({
                    "provider": provider,
                    "bucket": bucket.name,
                    "action": "lifecycle_policy",
                    "policy_rules": len(lifecycle_policy.rules),
                    "estimated_annual_savings": lifecycle_policy.estimated_savings
                })
            
            # Compression and deduplication
            if bucket_optimization.compression_opportunity:
                compression_result = await cloud_storage.compress_objects(
                    provider=provider,
                    bucket_name=bucket.name,
                    objects=bucket_optimization.compression_opportunity.candidates,
                    compression_algorithm="gzip"
                )
                
                optimization_actions.append({
                    "provider": provider,
                    "bucket": bucket.name,
                    "action": "compression",
                    "objects_compressed": compression_result.objects_processed,
                    "space_saved": compression_result.space_saved
                })
    
    # Cross-provider optimization
    cross_provider_opportunities = await cloud_storage.analyze_cross_provider_optimization(
        storage_analysis=storage_analysis,
        consider_data_transfer_costs=True
    )
    
    for opportunity in cross_provider_opportunities:
        if opportunity.annual_savings > 1000:  # $1000 minimum savings
            # Execute cross-provider migration
            migration_result = await cloud_storage.migrate_data(
                source_provider=opportunity.source_provider,
                source_bucket=opportunity.source_bucket,
                target_provider=opportunity.target_provider,
                target_bucket=opportunity.target_bucket,
                objects=opportunity.objects,
                preserve_metadata=True,
                verify_integrity=True
            )
            
            optimization_actions.append({
                "action": "cross_provider_migration",
                "source": f"{opportunity.source_provider}/{opportunity.source_bucket}",
                "target": f"{opportunity.target_provider}/{opportunity.target_bucket}",
                "objects_migrated": migration_result.objects_migrated,
                "annual_savings": opportunity.annual_savings
            })
    
    return {
        "optimization_actions": optimization_actions,
        "total_annual_savings": sum(
            action.get("monthly_savings", action.get("annual_savings", 0)) * 
            (12 if "monthly_savings" in action else 1)
            for action in optimization_actions
        ),
        "storage_optimized": True
    }

# Automated backup and sync
async def setup_intelligent_backup_strategy(backup_config: dict):
    """Set up intelligent backup strategy across multiple providers"""
    
    # Analyze data for backup strategy
    data_analysis = await cloud_storage.analyze_data_for_backup(
        sources=backup_config["sources"],
        requirements={
            "rpo": backup_config.get("recovery_point_objective", 4),  # hours
            "rto": backup_config.get("recovery_time_objective", 2),   # hours
            "retention_years": backup_config.get("retention_years", 7),
            "compliance_requirements": backup_config.get("compliance", [])
        }
    )
    
    # Design optimal backup architecture
    backup_strategy = await cloud_storage.design_backup_strategy(
        data_analysis=data_analysis,
        budget_constraints=backup_config.get("budget_limit"),
        geographic_distribution=backup_config.get("geo_distribution", True)
    )
    
    implemented_backups = []
    
    for backup_tier in backup_strategy.backup_tiers:
        if backup_tier.tier == "primary":
            # Primary backup - fast recovery, higher cost
            primary_backup = await cloud_storage.setup_primary_backup(
                sources=backup_tier.sources,
                target_provider=backup_tier.recommended_provider,
                target_region=backup_tier.recommended_region,
                backup_frequency=backup_tier.frequency,
                encryption=True,
                versioning=True
            )
            
            implemented_backups.append({
                "tier": "primary",
                "provider": backup_tier.recommended_provider,
                "frequency": backup_tier.frequency,
                "sources": len(backup_tier.sources),
                "estimated_cost": primary_backup.monthly_cost
            })
        
        elif backup_tier.tier == "secondary":
            # Secondary backup - balanced cost/recovery
            secondary_backup = await cloud_storage.setup_secondary_backup(
                sources=backup_tier.sources,
                target_provider=backup_tier.recommended_provider,
                target_region=backup_tier.recommended_region,
                backup_frequency=backup_tier.frequency,
                storage_class=backup_tier.storage_class
            )
            
            implemented_backups.append({
                "tier": "secondary",
                "provider": backup_tier.recommended_provider,
                "frequency": backup_tier.frequency,
                "sources": len(backup_tier.sources),
                "estimated_cost": secondary_backup.monthly_cost
            })
        
        elif backup_tier.tier == "archive":
            # Archive backup - long-term retention, lowest cost
            archive_backup = await cloud_storage.setup_archive_backup(
                sources=backup_tier.sources,
                target_provider=backup_tier.recommended_provider,
                storage_class="glacier_deep_archive",
                retention_years=backup_config.get("retention_years", 7)
            )
            
            implemented_backups.append({
                "tier": "archive",
                "provider": backup_tier.recommended_provider,
                "retention": f"{backup_config.get('retention_years', 7)} years",
                "sources": len(backup_tier.sources),
                "estimated_cost": archive_backup.monthly_cost
            })
    
    # Set up monitoring and alerting
    backup_monitoring = await cloud_storage.setup_backup_monitoring(
        backup_jobs=[b["job_id"] for b in implemented_backups if "job_id" in b],
        alerts=[
            {"event": "backup_failed", "notification": "email"},
            {"event": "backup_delayed", "threshold_hours": 2},
            {"event": "storage_quota_exceeded", "threshold_percent": 90}
        ]
    )
    
    # Create disaster recovery plan
    dr_plan = await cloud_storage.create_disaster_recovery_plan(
        backup_strategy=backup_strategy,
        recovery_procedures=backup_config.get("recovery_procedures", {}),
        test_schedule=backup_config.get("dr_test_schedule", "quarterly")
    )
    
    return {
        "backup_tiers": implemented_backups,
        "total_monthly_cost": sum(b["estimated_cost"] for b in implemented_backups),
        "monitoring_enabled": True,
        "disaster_recovery_plan": dr_plan.id,
        "estimated_rpo": backup_strategy.achieved_rpo,
        "estimated_rto": backup_strategy.achieved_rto
    }

# Content delivery network optimization
async def optimize_cdn_strategy(content_config: dict):
    """Optimize content delivery across global CDN networks"""
    
    # Analyze content and user distribution
    content_analysis = await cloud_storage.analyze_content_for_cdn(
        content_sources=content_config["content_sources"],
        user_analytics=content_config.get("user_analytics"),
        performance_requirements=content_config.get("performance_sla", {"p95_latency": 200})
    )
    
    # Select optimal CDN providers
    cdn_recommendations = await cloud_storage.recommend_cdn_providers(
        content_analysis=content_analysis,
        budget_constraints=content_config.get("budget_limit"),
        geographic_requirements=content_config.get("geo_requirements", [])
    )
    
    deployed_cdns = []
    
    for cdn_config in cdn_recommendations.recommended_cdns:
        # Deploy CDN configuration
        cdn_deployment = await cloud_storage.deploy_cdn(
            provider=cdn_config.provider,
            origins=cdn_config.origins,
            cache_behaviors=cdn_config.cache_behaviors,
            geographic_distribution=cdn_config.edge_locations,
            ssl_config=content_config.get("ssl_config", {"enable": True}),
            compression=cdn_config.compression_settings
        )
        
        # Configure intelligent caching rules
        caching_rules = await cloud_storage.optimize_caching_rules(
            cdn_id=cdn_deployment.id,
            content_types=content_analysis.content_types,
            access_patterns=content_analysis.access_patterns
        )
        
        deployed_cdns.append({
            "provider": cdn_config.provider,
            "distribution_id": cdn_deployment.id,
            "domain_name": cdn_deployment.domain_name,
            "edge_locations": len(cdn_config.edge_locations),
            "caching_rules": len(caching_rules),
            "estimated_monthly_cost": cdn_deployment.estimated_cost
        })
    
    # Set up CDN performance monitoring
    performance_monitoring = await cloud_storage.setup_cdn_monitoring(
        cdns=deployed_cdns,
        metrics=["cache_hit_ratio", "origin_latency", "edge_latency", "error_rate"],
        alerts=[
            {"metric": "cache_hit_ratio", "threshold": 85, "operator": "less_than"},
            {"metric": "edge_latency_p95", "threshold": 200, "operator": "greater_than"},
            {"metric": "error_rate", "threshold": 0.01, "operator": "greater_than"}
        ]
    )
    
    return {
        "deployed_cdns": deployed_cdns,
        "total_edge_locations": sum(cdn["edge_locations"] for cdn in deployed_cdns),
        "estimated_monthly_cost": sum(cdn["estimated_monthly_cost"] for cdn in deployed_cdns),
        "performance_monitoring": True,
        "global_coverage": cdn_recommendations.geographic_coverage
    }

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from aimatrix.tools.storage import DatabaseTool

# Initialize database management
database = DatabaseTool(
    sql_databases=["postgresql", "mysql", "sql_server", "oracle"],
    nosql_databases=["mongodb", "cassandra", "dynamodb", "redis"],
    graph_databases=["neo4j", "amazon_neptune", "arangodb"],
    vector_databases=["pinecone", "weaviate", "chroma", "qdrant"],
    ai_optimization=True
)

# Intelligent database optimization
async def optimize_database_performance(db_config: dict):
    """Comprehensive database performance optimization"""
    
    # Connect to database and analyze performance
    db_connection = await database.connect(
        database_type=db_config["type"],
        connection_string=db_config["connection_string"],
        credentials=db_config["credentials"]
    )
    
    # Comprehensive performance analysis
    performance_analysis = await database.analyze_performance(
        connection=db_connection,
        analysis_duration=db_config.get("analysis_hours", 24),
        include_query_patterns=True,
        include_resource_usage=True,
        include_index_analysis=True
    )
    
    optimization_actions = []
    
    # Query optimization
    if performance_analysis.slow_queries:
        for slow_query in performance_analysis.slow_queries:
            # AI-powered query optimization
            optimized_query = await database.optimize_query(
                original_query=slow_query.query,
                schema=performance_analysis.schema,
                statistics=performance_analysis.table_statistics
            )
            
            if optimized_query.estimated_improvement > 50:  # 50% improvement minimum
                optimization_actions.append({
                    "type": "query_optimization",
                    "original_query_id": slow_query.id,
                    "optimized_query": optimized_query.sql,
                    "estimated_improvement": optimized_query.estimated_improvement,
                    "execution_time_before": slow_query.avg_execution_time,
                    "execution_time_after": optimized_query.estimated_execution_time
                })
    
    # Index optimization
    index_recommendations = await database.analyze_index_optimization(
        connection=db_connection,
        query_workload=performance_analysis.query_patterns,
        table_statistics=performance_analysis.table_statistics
    )
    
    for index_rec in index_recommendations:
        if index_rec.impact_score > 70:  # High impact indexes only
            if index_rec.action == "create":
                # Create new index
                create_result = await database.create_index(
                    connection=db_connection,
                    table_name=index_rec.table,
                    columns=index_rec.columns,
                    index_type=index_rec.index_type,
                    include_columns=index_rec.include_columns
                )
                
                optimization_actions.append({
                    "type": "index_creation",
                    "table": index_rec.table,
                    "columns": index_rec.columns,
                    "index_name": create_result.index_name,
                    "estimated_improvement": index_rec.performance_improvement
                })
            
            elif index_rec.action == "drop":
                # Remove unused index
                drop_result = await database.drop_index(
                    connection=db_connection,
                    index_name=index_rec.index_name,
                    safety_check=True
                )
                
                optimization_actions.append({
                    "type": "index_removal",
                    "index_name": index_rec.index_name,
                    "space_saved": index_rec.space_savings,
                    "maintenance_cost_reduction": index_rec.maintenance_savings
                })
    
    # Configuration optimization
    config_optimization = await database.optimize_configuration(
        connection=db_connection,
        workload_characteristics=performance_analysis.workload_profile,
        hardware_specs=db_config.get("hardware_specs"),
        performance_goals=db_config.get("performance_goals", ["throughput", "latency"])
    )
    
    for config_change in config_optimization.recommended_changes:
        if config_change.safety_level == "safe":
            # Apply safe configuration changes
            await database.apply_configuration_change(
                connection=db_connection,
                parameter=config_change.parameter,
                value=config_change.recommended_value,
                restart_required=config_change.restart_required
            )
            
            optimization_actions.append({
                "type": "configuration_change",
                "parameter": config_change.parameter,
                "old_value": config_change.current_value,
                "new_value": config_change.recommended_value,
                "expected_improvement": config_change.expected_improvement
            })
    
    # Resource scaling recommendations
    scaling_analysis = await database.analyze_scaling_needs(
        performance_metrics=performance_analysis.metrics,
        growth_projections=db_config.get("growth_projections"),
        cost_constraints=db_config.get("cost_constraints")
    )
    
    if scaling_analysis.scaling_needed:
        optimization_actions.append({
            "type": "scaling_recommendation",
            "current_resources": scaling_analysis.current_resources,
            "recommended_resources": scaling_analysis.recommended_resources,
            "scaling_type": scaling_analysis.scaling_type,  # "vertical", "horizontal", "storage"
            "estimated_cost_impact": scaling_analysis.cost_impact,
            "performance_improvement": scaling_analysis.performance_improvement
        })
    
    # Generate optimization report
    optimization_report = await database.generate_optimization_report(
        actions=optimization_actions,
        performance_baseline=performance_analysis.baseline_metrics,
        estimated_improvements=sum(action.get("estimated_improvement", 0) for action in optimization_actions)
    )
    
    return {
        "optimization_actions": optimization_actions,
        "total_actions_applied": len([a for a in optimization_actions if a.get("applied", True)]),
        "estimated_performance_improvement": optimization_report.total_improvement,
        "optimization_report": optimization_report.summary
    }

# Database migration and modernization
async def modernize_database_architecture(migration_config: dict):
    """Migrate and modernize database architecture"""
    
    # Analyze source database
    source_analysis = await database.analyze_database_for_migration(
        source_config=migration_config["source"],
        target_options=migration_config["target_options"],
        migration_goals=migration_config.get("goals", ["performance", "cost", "scalability"])
    )
    
    # Select optimal target architecture
    target_recommendation = await database.recommend_target_architecture(
        source_analysis=source_analysis,
        requirements=migration_config["requirements"],
        constraints=migration_config.get("constraints", {})
    )
    
    # Create migration plan
    migration_plan = await database.create_migration_plan(
        source_config=migration_config["source"],
        target_config=target_recommendation.recommended_config,
        migration_strategy=migration_config.get("strategy", "online"),  # or "offline"
        data_validation_level=migration_config.get("validation_level", "comprehensive")
    )
    
    migration_results = []
    
    # Execute migration phases
    for phase in migration_plan.phases:
        if phase.type == "schema_migration":
            # Migrate database schema
            schema_result = await database.migrate_schema(
                source_connection=migration_plan.source_connection,
                target_connection=migration_plan.target_connection,
                schema_mapping=phase.schema_mapping,
                validation_enabled=True
            )
            
            migration_results.append({
                "phase": "schema_migration",
                "status": schema_result.status,
                "tables_migrated": len(schema_result.migrated_tables),
                "indexes_created": len(schema_result.created_indexes),
                "constraints_applied": len(schema_result.applied_constraints)
            })
        
        elif phase.type == "data_migration":
            # Migrate data with validation
            data_result = await database.migrate_data(
                source_connection=migration_plan.source_connection,
                target_connection=migration_plan.target_connection,
                tables=phase.tables,
                batch_size=phase.batch_size,
                parallel_threads=phase.parallel_threads,
                validation_enabled=True,
                progress_callback=lambda progress: print(f"Migration progress: {progress}%")
            )
            
            migration_results.append({
                "phase": "data_migration",
                "status": data_result.status,
                "records_migrated": data_result.total_records,
                "data_size_migrated": data_result.total_size,
                "validation_errors": len(data_result.validation_errors)
            })
        
        elif phase.type == "application_cutover":
            # Application cutover with minimal downtime
            cutover_result = await database.execute_application_cutover(
                cutover_plan=phase.cutover_plan,
                rollback_plan=phase.rollback_plan,
                downtime_window=phase.max_downtime_minutes
            )
            
            migration_results.append({
                "phase": "application_cutover",
                "status": cutover_result.status,
                "actual_downtime": cutover_result.actual_downtime_minutes,
                "applications_migrated": len(cutover_result.migrated_applications)
            })
    
    # Post-migration optimization
    post_migration_optimization = await database.optimize_migrated_database(
        target_connection=migration_plan.target_connection,
        workload_characteristics=source_analysis.workload_profile,
        optimization_goals=migration_config.get("post_migration_goals", ["performance"])
    )
    
    # Validation and testing
    migration_validation = await database.validate_migration(
        source_connection=migration_plan.source_connection,
        target_connection=migration_plan.target_connection,
        validation_tests=["data_integrity", "performance_comparison", "functionality_test"]
    )
    
    return {
        "migration_phases": migration_results,
        "total_downtime": sum(r.get("actual_downtime", 0) for r in migration_results),
        "data_migrated": sum(r.get("records_migrated", 0) for r in migration_results),
        "migration_successful": all(r["status"] == "success" for r in migration_results),
        "post_optimization": post_migration_optimization.summary,
        "validation_results": migration_validation.summary,
        "target_architecture": target_recommendation.architecture_summary
    }

# Vector database for AI applications
async def setup_vector_database_for_ai(vector_config: dict):
    """Set up vector database for AI/ML applications"""
    
    # Analyze embedding requirements
    embedding_analysis = await database.analyze_embedding_requirements(
        data_sources=vector_config["data_sources"],
        use_cases=vector_config["use_cases"],
        expected_scale=vector_config.get("expected_scale", {"documents": 1000000, "queries_per_second": 100})
    )
    
    # Select optimal vector database
    vector_db_recommendation = await database.recommend_vector_database(
        requirements=embedding_analysis,
        budget_constraints=vector_config.get("budget_limit"),
        performance_requirements=vector_config.get("performance_sla")
    )
    
    # Deploy vector database
    vector_db = await database.deploy_vector_database(
        provider=vector_db_recommendation.recommended_provider,
        configuration=vector_db_recommendation.optimal_config,
        scaling_config={
            "min_replicas": vector_config.get("min_replicas", 1),
            "max_replicas": vector_config.get("max_replicas", 5),
            "auto_scaling": True
        }
    )
    
    # Set up embedding pipeline
    embedding_pipeline = await database.create_embedding_pipeline(
        vector_db=vector_db,
        data_sources=vector_config["data_sources"],
        embedding_model=vector_config.get("embedding_model", "sentence-transformers/all-MiniLM-L6-v2"),
        chunk_strategy=vector_config.get("chunk_strategy", {"size": 512, "overlap": 50}),
        batch_size=vector_config.get("batch_size", 100)
    )
    
    # Process and index data
    indexing_results = []
    
    for data_source in vector_config["data_sources"]:
        indexing_result = await database.index_data_source(
            vector_db=vector_db,
            data_source=data_source,
            embedding_pipeline=embedding_pipeline,
            metadata_extraction=True,
            progress_callback=lambda progress: print(f"Indexing {data_source['name']}: {progress}%")
        )
        
        indexing_results.append({
            "data_source": data_source["name"],
            "documents_indexed": indexing_result.documents_processed,
            "vectors_created": indexing_result.vectors_created,
            "index_size": indexing_result.index_size,
            "indexing_time": indexing_result.processing_time
        })
    
    # Set up search and retrieval API
    search_api = await database.create_vector_search_api(
        vector_db=vector_db,
        search_features=[
            "semantic_search",
            "hybrid_search",  # Combines vector and keyword search
            "filtered_search",
            "multi_modal_search"
        ],
        rate_limiting=vector_config.get("rate_limits", {"requests_per_minute": 1000})
    )
    
    # Configure monitoring and alerts
    monitoring = await database.setup_vector_db_monitoring(
        vector_db=vector_db,
        metrics=["query_latency", "index_size", "memory_usage", "search_accuracy"],
        alerts=[
            {"metric": "query_latency_p95", "threshold": 100, "unit": "ms"},
            {"metric": "search_accuracy", "threshold": 0.85, "operator": "less_than"},
            {"metric": "memory_usage", "threshold": 80, "unit": "percent"}
        ]
    )
    
    return {
        "vector_database": {
            "provider": vector_db.provider,
            "instance_id": vector_db.id,
            "endpoint": vector_db.endpoint
        },
        "indexing_results": indexing_results,
        "total_vectors": sum(r["vectors_created"] for r in indexing_results),
        "search_api": {
            "endpoint": search_api.endpoint,
            "features": search_api.available_features
        },
        "monitoring_enabled": True,
        "estimated_monthly_cost": vector_db.estimated_monthly_cost
    }