use crate::error::Result;
pub struct ReportGenerationExample;
impl ReportGenerationExample {
pub async fn cost_analysis_report() -> Result<()> {
use crate::reports::{CostAnalysisReport, ReportFormat, ReportGenerator, ReportType};
println!("=== Cost Analysis Report Generation ===");
println!();
let mut report = CostAnalysisReport::new(
"AI Operations Cost Analysis - Q1 2026".to_string(),
"January - March 2026".to_string(),
);
report.add_provider_cost("OpenAI".to_string(), 1_850.50);
report.add_provider_cost("Anthropic".to_string(), 1_420.75);
report.add_provider_cost("Gemini".to_string(), 345.00);
report.add_provider_cost("DeepSeek".to_string(), 125.25);
report.add_provider_cost("Ollama".to_string(), 0.0);
report.add_operation_cost("Code Evaluation".to_string(), 1_680.00);
report.add_operation_cost("Commitment Verification".to_string(), 895.75);
report.add_operation_cost("Fraud Detection".to_string(), 540.50);
report.add_operation_cost("Plagiarism Detection".to_string(), 425.25);
report.add_operation_cost("Document Analysis".to_string(), 200.00);
report.set_total_requests(15_450);
report.cost_trend = Some(-12.5);
report.add_recommendation(
"Switch 40% of simple tasks to Gemini Flash to reduce costs by ~$600/month".to_string(),
);
report.add_recommendation(
"Use Ollama for development and testing to eliminate dev environment costs".to_string(),
);
report.add_recommendation(
"Implement request batching to reduce API calls by 25%".to_string(),
);
report.add_recommendation(
"Enable caching for repetitive operations (estimated 15% cost savings)".to_string(),
);
println!("Report Summary:");
println!(" * Total Cost: ${:.2}", report.total_cost);
println!(" * Total Requests: {}", report.total_requests);
println!(" * Avg Cost/Request: ${:.4}", report.avg_cost_per_request);
println!(" * Cost Trend: down 12.5%");
println!();
println!("=== Markdown Report ===");
let markdown = ReportGenerator::generate(
ReportType::CostAnalysis(report.clone()),
ReportFormat::Markdown,
)?;
println!("{markdown}");
println!("=== JSON Report (Preview) ===");
let json = ReportGenerator::generate(
ReportType::CostAnalysis(report.clone()),
ReportFormat::Json,
)?;
println!("{}...{}", &json[..200], &json[json.len() - 50..]);
println!();
println!("=== CSV Report ===");
let csv = ReportGenerator::generate(ReportType::CostAnalysis(report), ReportFormat::Csv)?;
let lines: Vec<&str> = csv.lines().take(8).collect();
println!("{}", lines.join("\n"));
println!("...");
println!();
println!("Cost analysis report generated in 3 formats");
Ok(())
}
pub async fn performance_benchmark_report() -> Result<()> {
use crate::reports::{
OperationBenchmark, PerformanceBenchmarkReport, ReportFormat, ReportGenerator,
ReportType,
};
println!("=== Performance Benchmark Report Generation ===");
println!();
let mut report = PerformanceBenchmarkReport::new(
"AI Services Performance Benchmarks".to_string(),
"2026-01-09".to_string(),
);
report.add_operation(
"code_evaluation".to_string(),
OperationBenchmark {
name: "Code Evaluation".to_string(),
avg_latency_ms: 285.5,
median_latency_ms: 265.0,
p95_latency_ms: 420.0,
p99_latency_ms: 580.0,
total_ops: 5_420,
success_rate: 99.2,
},
);
report.add_operation(
"commitment_verification".to_string(),
OperationBenchmark {
name: "Commitment Verification".to_string(),
avg_latency_ms: 320.2,
median_latency_ms: 295.0,
p95_latency_ms: 495.0,
p99_latency_ms: 650.0,
total_ops: 3_850,
success_rate: 98.7,
},
);
report.add_operation(
"fraud_detection".to_string(),
OperationBenchmark {
name: "Fraud Detection".to_string(),
avg_latency_ms: 195.8,
median_latency_ms: 180.0,
p95_latency_ms: 280.0,
p99_latency_ms: 385.0,
total_ops: 2_240,
success_rate: 99.5,
},
);
report.add_operation(
"plagiarism_detection".to_string(),
OperationBenchmark {
name: "Plagiarism Detection".to_string(),
avg_latency_ms: 425.3,
median_latency_ms: 390.0,
p95_latency_ms: 620.0,
p99_latency_ms: 820.0,
total_ops: 1_650,
success_rate: 97.8,
},
);
report.add_operation(
"document_analysis".to_string(),
OperationBenchmark {
name: "Document Analysis".to_string(),
avg_latency_ms: 520.7,
median_latency_ms: 475.0,
p95_latency_ms: 780.0,
p99_latency_ms: 1050.0,
total_ops: 890,
success_rate: 98.3,
},
);
report.calculate_summary();
println!("Performance Summary:");
println!(" * Total Operations: {}", report.summary.total_operations);
println!(
" * Overall Avg Latency: {:.2}ms",
report.summary.overall_avg_latency_ms
);
println!(
" * Overall Success Rate: {:.1}%",
report.summary.overall_success_rate
);
if let Some(ref fastest) = report.summary.fastest_operation {
println!(" * Fastest Operation: {fastest}");
}
if let Some(ref slowest) = report.summary.slowest_operation {
println!(" * Slowest Operation: {slowest}");
}
println!();
println!("=== Markdown Report ===");
let markdown = ReportGenerator::generate(
ReportType::PerformanceBenchmark(report.clone()),
ReportFormat::Markdown,
)?;
println!("{markdown}");
println!("=== JSON Report (Preview) ===");
let json = ReportGenerator::generate(
ReportType::PerformanceBenchmark(report),
ReportFormat::Json,
)?;
let lines: Vec<&str> = json.lines().take(10).collect();
println!("{}", lines.join("\n"));
println!("...");
println!();
println!("Performance benchmark report generated");
Ok(())
}
pub async fn fraud_summary_report() -> Result<()> {
use crate::fraud::RiskLevel;
use crate::reports::{FraudSummaryReport, ReportFormat, ReportGenerator, ReportType};
println!("=== Fraud Detection Summary Report Generation ===");
println!();
let mut report = FraudSummaryReport::new(
"Fraud Detection Analysis - Q1 2026".to_string(),
"January - March 2026".to_string(),
);
for _ in 0..125 {
report.add_case(RiskLevel::Low);
}
for _ in 0..48 {
report.add_case(RiskLevel::Medium);
}
for _ in 0..23 {
report.add_case(RiskLevel::High);
}
for _ in 0..7 {
report.add_case(RiskLevel::Critical);
}
report.set_common_fraud_types(vec![
("Sybil Attack".to_string(), 45),
("Wash Trading".to_string(), 28),
("Reputation Gaming".to_string(), 22),
("Image Manipulation".to_string(), 18),
("Content Plagiarism".to_string(), 15),
]);
report.accuracy = Some(96.8);
report.add_insight(
"Sybil attacks increased by 32% compared to Q4 2026, primarily from newly registered accounts".to_string(),
);
report.add_insight(
"Average detection time improved by 45% due to enhanced ML models".to_string(),
);
report.add_insight(
"Image manipulation attempts decreased by 18% after implementing perceptual hashing"
.to_string(),
);
report.add_insight(
"Critical risk cases require average 15 minutes for manual review and final decision"
.to_string(),
);
println!("Fraud Detection Summary:");
println!(" * Total Cases Analyzed: {}", report.total_cases);
println!(" * Detection Accuracy: {:.1}%", report.accuracy.unwrap());
println!(" * Most Common: Sybil Attack (45 cases)");
println!(" * Critical Cases: 7");
println!();
println!("=== Markdown Report ===");
let markdown = ReportGenerator::generate(
ReportType::FraudSummary(report.clone()),
ReportFormat::Markdown,
)?;
println!("{markdown}");
println!("=== JSON Report (Preview) ===");
let json = ReportGenerator::generate(ReportType::FraudSummary(report), ReportFormat::Json)?;
let lines: Vec<&str> = json.lines().take(15).collect();
println!("{}", lines.join("\n"));
println!("...");
println!();
println!("Fraud detection summary report generated");
Ok(())
}
pub async fn multi_format_export() -> Result<()> {
use crate::reports::{CostAnalysisReport, ReportFormat, ReportGenerator, ReportType};
println!("=== Multi-Format Report Export ===");
println!();
let mut report = CostAnalysisReport::new(
"Weekly Cost Report".to_string(),
"Week of Jan 9, 2026".to_string(),
);
report.add_provider_cost("OpenAI".to_string(), 425.00);
report.add_provider_cost("Anthropic".to_string(), 325.00);
report.add_provider_cost("Gemini".to_string(), 85.00);
report.set_total_requests(3_500);
println!("Exporting report in 3 formats...");
println!();
let markdown = ReportGenerator::generate(
ReportType::CostAnalysis(report.clone()),
ReportFormat::Markdown,
)?;
println!("Markdown export: {} bytes", markdown.len());
println!(" Use case: Documentation, GitHub issues, Slack messages");
println!();
let json = ReportGenerator::generate(
ReportType::CostAnalysis(report.clone()),
ReportFormat::Json,
)?;
println!("JSON export: {} bytes", json.len());
println!(" Use case: API responses, data storage, dashboard integration");
println!();
let csv = ReportGenerator::generate(ReportType::CostAnalysis(report), ReportFormat::Csv)?;
println!("CSV export: {} bytes", csv.len());
println!(" Use case: Excel analysis, data science, financial reporting");
println!();
println!("All reports generated successfully!");
println!();
println!("Integration tips:");
println!(" * Save to files: write reports to disk for archival");
println!(" * Send via email: attach reports to automated email notifications");
println!(" * Post to Slack: use Markdown format for rich formatting");
println!(" * Store in DB: save JSON format for queryable storage");
println!(" * Generate dashboards: parse JSON data for real-time visualizations");
Ok(())
}
pub async fn automated_reporting() -> Result<()> {
use crate::reports::{CostAnalysisReport, ReportFormat, ReportGenerator, ReportType};
println!("=== Automated Report Scheduling ===");
println!();
println!("Example: Daily cost monitoring workflow");
println!();
let mut report = CostAnalysisReport::new(
"Daily Cost Report".to_string(),
format!("Date: {}", chrono::Local::now().format("%Y-%m-%d")),
);
report.add_provider_cost("OpenAI".to_string(), 125.50);
report.add_provider_cost("Anthropic".to_string(), 95.25);
report.set_total_requests(1_250);
report.cost_trend = Some(5.2);
if report.total_cost > 200.0 {
println!("ALERT: Daily cost exceeds $200 threshold!");
println!(" Current: ${:.2}", report.total_cost);
println!(" Trend: +{:.1}%", report.cost_trend.unwrap());
println!();
}
if let Some(trend) = report.cost_trend {
if trend > 10.0 {
report.add_recommendation(
"Cost increased significantly. Review recent usage patterns".to_string(),
);
}
}
let _markdown =
ReportGenerator::generate(ReportType::CostAnalysis(report), ReportFormat::Markdown)?;
println!("Report generated and ready for distribution:");
println!(" * Post to Slack #finance channel");
println!(" * Email to finance@company.com");
println!(" * Archive in S3 bucket");
println!(" * Update monitoring dashboard");
println!();
println!("Scheduling options:");
println!(" * Use cron: Schedule daily/weekly/monthly reports");
println!(" * Use tokio interval: In-app scheduled reporting");
println!(" * Use GitHub Actions: Automated CI/CD reports");
println!(" * Use AWS Lambda: Serverless scheduled reports");
Ok(())
}
}
pub struct DashboardIntegrationExample;
impl DashboardIntegrationExample {
pub async fn prometheus_export() -> Result<()> {
use crate::dashboard::DashboardMetrics;
println!("=== Prometheus Metrics Export ===");
println!();
let mut metrics = DashboardMetrics::new();
metrics.request_count = 15_450;
metrics.total_cost = 3_741.50;
metrics.avg_latency_ms = 285.5;
metrics.error_count = 127;
metrics.success_rate = 99.18;
metrics.cache_hit_rate = 42.5;
metrics.circuit_breaker_open = 3;
metrics.budget_utilization = 67.8;
metrics.active_providers = vec![
"openai".to_string(),
"anthropic".to_string(),
"gemini".to_string(),
];
metrics.add_custom_metric("fraud_detection_accuracy".to_string(), 96.8);
metrics.add_custom_metric("plagiarism_checks_total".to_string(), 1_250.0);
println!("Current Metrics:");
println!(" * Total Requests: {}", metrics.request_count);
println!(" * Total Cost: ${:.2}", metrics.total_cost);
println!(" * Avg Latency: {:.2}ms", metrics.avg_latency_ms);
println!(" * Success Rate: {:.2}%", metrics.success_rate);
println!(" * Cache Hit Rate: {:.2}%", metrics.cache_hit_rate);
println!();
let prometheus_metrics = metrics.to_prometheus();
println!("=== Prometheus Format Output ===");
println!();
for metric in prometheus_metrics.iter().take(3) {
println!("{}", metric.to_prometheus_format());
}
println!("... {} more metrics", prometheus_metrics.len() - 3);
println!();
println!("Integration:");
println!(" * Expose this endpoint at /metrics");
println!(" * Configure Prometheus to scrape this endpoint");
println!(" * Default scrape interval: 15s");
println!();
println!("Example Prometheus Configuration:");
println!("```yaml");
println!("scrape_configs:");
println!(" - job_name: 'kaccy-ai'");
println!(" static_configs:");
println!(" - targets: ['localhost:8080']");
println!(" metrics_path: '/metrics'");
println!(" scrape_interval: 15s");
println!("```");
Ok(())
}
pub async fn grafana_integration() -> Result<()> {
use crate::dashboard::{DashboardMetrics, to_grafana_format};
println!("=== Grafana Dashboard Integration ===");
println!();
let mut metrics = DashboardMetrics::new();
metrics.request_count = 8_500;
metrics.total_cost = 1_847.25;
metrics.avg_latency_ms = 320.5;
metrics.success_rate = 98.9;
metrics.cache_hit_rate = 38.2;
metrics.budget_utilization = 52.1;
let datapoints = to_grafana_format(&metrics);
println!("Grafana Data Points (JSON Simple Format):");
println!();
for dp in datapoints.iter().take(3) {
let json = dp.to_json()?;
let lines: Vec<&str> = json.lines().take(5).collect();
println!("{}", lines.join("\n"));
println!(" ...");
println!();
}
println!("Dashboard Setup:");
println!(" 1. Add JSON data source in Grafana");
println!(" 2. Configure endpoint: http://your-api/grafana-metrics");
println!(" 3. Set refresh interval: 10s");
println!(" 4. Create panels for each metric");
println!();
println!("Recommended Grafana Panels:");
println!(" * Request Rate (Time Series)");
println!(" * Cost Tracking (Stat + Time Series)");
println!(" * Latency Distribution (Histogram)");
println!(" * Success Rate (Gauge)");
println!(" * Cache Hit Rate (Bar Gauge)");
println!(" * Budget Utilization (Gauge with thresholds)");
Ok(())
}
pub async fn health_check_endpoint() -> Result<()> {
use crate::dashboard::{ComponentHealth, HealthCheckStatus};
use std::time::{SystemTime, UNIX_EPOCH};
println!("=== Health Check Endpoint ===");
println!();
let mut status = HealthCheckStatus::new();
let llm_health = ComponentHealth {
healthy: true,
last_check: SystemTime::now()
.duration_since(UNIX_EPOCH)
.unwrap()
.as_secs(),
response_time_ms: Some(45.2),
error: None,
};
status.add_component("llm_client".to_string(), llm_health);
let cache_health = ComponentHealth {
healthy: true,
last_check: SystemTime::now()
.duration_since(UNIX_EPOCH)
.unwrap()
.as_secs(),
response_time_ms: Some(2.1),
error: None,
};
status.add_component("cache".to_string(), cache_health);
let db_health = ComponentHealth {
healthy: true,
last_check: SystemTime::now()
.duration_since(UNIX_EPOCH)
.unwrap()
.as_secs(),
response_time_ms: Some(125.8),
error: None,
};
status.add_component("database".to_string(), db_health);
println!("Health Check Status:");
println!(
" * Overall: {}",
if status.healthy {
"HEALTHY"
} else {
"UNHEALTHY"
}
);
println!(" * Components: {}", status.components.len());
println!();
let json = status.to_json()?;
println!("=== JSON Response ===");
let lines: Vec<&str> = json.lines().take(15).collect();
println!("{}", lines.join("\n"));
println!(" ...");
println!();
println!("Integration:");
println!(" * Expose at /health or /healthz");
println!(" * Used by Kubernetes liveness/readiness probes");
println!(" * Monitored by uptime services (StatusPage, Pingdom)");
println!(" * Return HTTP 200 if healthy, 503 if unhealthy");
Ok(())
}
pub async fn realtime_monitoring() -> Result<()> {
use crate::dashboard::DashboardMetrics;
println!("=== Real-Time Metrics Monitoring ===");
println!();
println!("Simulating real-time metric updates...");
println!();
let mut metrics_history: Vec<DashboardMetrics> = Vec::new();
for i in 0..5 {
let mut metrics = DashboardMetrics::new();
metrics.request_count = 1000 + i * 250;
metrics.total_cost = 250.0 + (i as f64 * 62.5);
metrics.avg_latency_ms = 280.0 + (i as f64 * 10.0);
metrics.success_rate = 99.5 - (i as f64 * 0.1);
metrics.cache_hit_rate = 40.0 + (i as f64 * 2.5);
metrics_history.push(metrics);
}
println!("+---------+-----------+------------+------------+-------------+");
println!("| Time | Requests | Cost | Latency | Success % |");
println!("+---------+-----------+------------+------------+-------------+");
for (i, m) in metrics_history.iter().enumerate() {
println!(
"| T+{}min | {:>9} | ${:>9.2} | {:>8.1}ms | {:>10.2}% |",
i * 5,
m.request_count,
m.total_cost,
m.avg_latency_ms,
m.success_rate
);
}
println!("+---------+-----------+------------+------------+-------------+");
println!();
println!("Monitoring Strategy:");
println!(" * Update metrics every 10-30 seconds");
println!(" * Store historical data for trend analysis");
println!(" * Alert on threshold violations:");
println!(" - Success rate < 95%");
println!(" - Latency > 500ms (P95)");
println!(" - Cost growth > 20% per hour");
println!(" - Error rate > 5%");
println!();
println!("Alert Channels:");
println!(" * PagerDuty for critical alerts");
println!(" * Slack for warnings");
println!(" * Email for daily summaries");
println!(" * Webhook for custom integrations");
Ok(())
}
pub async fn custom_instrumentation() -> Result<()> {
use crate::dashboard::{DashboardMetrics, MetricType, PrometheusMetric, TimeSeriesPoint};
println!("=== Custom Metrics and Instrumentation ===");
println!();
let mut metrics = DashboardMetrics::new();
metrics.add_custom_metric("code_evaluations_completed".to_string(), 5_420.0);
metrics.add_custom_metric("fraud_cases_detected".to_string(), 127.0);
metrics.add_custom_metric("plagiarism_similarity_avg".to_string(), 15.3);
metrics.add_custom_metric("github_verifications".to_string(), 3_850.0);
metrics.add_custom_metric("document_pages_analyzed".to_string(), 12_450.0);
println!("Custom Metrics Registered:");
for (name, value) in &metrics.custom_metrics {
println!(" * {name}: {value}");
}
println!();
let mut provider_latency = PrometheusMetric::new(
"kaccy_ai_provider_latency_ms".to_string(),
MetricType::Histogram,
"LLM provider latency in milliseconds".to_string(),
);
provider_latency.add_point(
TimeSeriesPoint::now(285.5)
.with_label("provider".to_string(), "openai".to_string())
.with_label("model".to_string(), "gpt-4-turbo".to_string()),
);
provider_latency.add_point(
TimeSeriesPoint::now(320.2)
.with_label("provider".to_string(), "anthropic".to_string())
.with_label("model".to_string(), "claude-3-opus".to_string()),
);
provider_latency.add_point(
TimeSeriesPoint::now(195.8)
.with_label("provider".to_string(), "gemini".to_string())
.with_label("model".to_string(), "gemini-1.5-flash".to_string()),
);
println!("=== Labeled Metrics (Prometheus Format) ===");
println!("{}", provider_latency.to_prometheus_format());
println!("Benefits of Custom Metrics:");
println!(" * Track domain-specific KPIs");
println!(" * Monitor business logic performance");
println!(" * Identify bottlenecks and optimization opportunities");
println!(" * Support data-driven decision making");
println!(" * Enable advanced alerting rules");
println!();
println!("Best Practices:");
println!(" * Use meaningful metric names (snake_case)");
println!(" * Add appropriate labels for filtering");
println!(" * Choose correct metric type (Counter/Gauge/Histogram)");
println!(" * Document metrics in code comments");
println!(" * Set up retention policies for historical data");
Ok(())
}
}