future_works_integration_example.cpp

/*
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║ ThemisDB - Hybrid Database System                                   ║
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  File:            future_works_integration_example.cpp               ║
  Version:         0.0.47                                             ║
  Last Modified:   2026-04-15 18:43:54                                ║
  Author:          unknown                                            ║
╠═════════════════════════════════════════════════════════════════════╣
  Quality Metrics:                                                    ║
    • Maturity Level:  🟢 PRODUCTION-READY                             ║
    • Quality Score:   99.0/100                                       ║
    • Total Lines:     419                                            ║
    • Open Issues:     TODOs: 0, Stubs: 0                             ║
╠═════════════════════════════════════════════════════════════════════╣
  Status: ✅ Production Ready                                          ║
╚═════════════════════════════════════════════════════════════════════╝
 */

/**
 * @file future_works_integration_example.cpp
 * @brief Example demonstrating ONNX Runtime and HTTP metrics client integration
 * 
 * This example shows how to use the production-ready features from Phase 3:
 * 1. ONNX model loading for real NLI inference
 * 2. HTTP metrics upload for continuous learning
 * 3. Complete end-to-end quality control with production features
 */

#include "rag/onnx_model_loader.h"
#include "rag/http_metrics_client.h"
#include "rag/quality_control_pipeline.h"
#include "rag/quality_control_factory.h"
#include "rag/nli_faithfulness_verifier.h"
#include <iostream>
#include <iomanip>
#include <chrono>
#include <ctime>

using namespace themis::rag::judge;

// ═══════════════════════════════════════════════════════════
// Helper Functions
// ═══════════════════════════════════════════════════════════

std::string getCurrentTimestamp() {
    auto now = std::chrono::system_clock::now();
    auto time_t_now = std::chrono::system_clock::to_time_t(now);
    std::tm tm_now;
    localtime_r(&time_t_now, &tm_now);
    
    std::ostringstream oss;
    oss << std::put_time(&tm_now, "%Y-%m-%dT%H:%M:%S");
    return oss.str();
}

void printSeparator(const std::string& title = "") {
    std::cout << "\n";
    std::cout << "═══════════════════════════════════════════════════════════\n";
    if (!title.empty()) {
        std::cout << "  " << title << "\n";
        std::cout << "═══════════════════════════════════════════════════════════\n";
    }
}

// ═══════════════════════════════════════════════════════════
// Example 1: ONNX Model Loading
// ═══════════════════════════════════════════════════════════

void example1_onnx_model_loading() {
    printSeparator("Example 1: ONNX Model Loading");
    
    std::cout << "Demonstrating ONNX model loading capabilities...\n\n";
    
    // Configure model loader
    ONNXModelLoaderConfig config;
    config.cache_dir = "./models";
    config.verify_checksum = true;
    config.auto_download = false;  // Disable auto-download for this example
    config.create_cache_dir = true;
    
    ONNXModelLoader loader(config);
    
    // List supported NLI models
    std::cout << "Supported NLI Models:\n";
    auto models = NLIModelFactory::getAllSupportedModels();
    for (const auto& model : models) {
        std::cout << "  - " << model.model_name << "\n";
        std::cout << "    Size: " << (model.model_size_bytes / 1024 / 1024) << " MB\n";
        std::cout << "    URL: " << model.model_url << "\n";
    }
    
    std::cout << "\nModel loader configured with:\n";
    std::cout << "  Cache directory: " << config.cache_dir << "\n";
    std::cout << "  Checksum verification: " << (config.verify_checksum ? "enabled" : "disabled") << "\n";
    std::cout << "  Auto-download: " << (config.auto_download ? "enabled" : "disabled") << "\n";
    
    // In production, you would load a real model:
    // auto model = loader.loadOrDownloadModel("deberta-v3-large-mnli", url, checksum);
    
    std::cout << "\n✓ ONNX model loader initialized successfully\n";
}

// ═══════════════════════════════════════════════════════════
// Example 2: HTTP Metrics Client
// ═══════════════════════════════════════════════════════════

void example2_http_metrics_client() {
    printSeparator("Example 2: HTTP Metrics Client");
    
    std::cout << "Demonstrating HTTP metrics upload capabilities...\n\n";
    
    // Create local development client
    auto client = HTTPMetricsClientFactory::createLocalClient("http://localhost:8080");
    
    std::cout << "HTTP client configured:\n";
    std::cout << "  Endpoint: " << client->getConfig().endpoint_url << "\n";
    std::cout << "  Timeout: " << client->getConfig().timeout_ms << "ms\n";
    std::cout << "  Max retries: " << client->getConfig().max_retries << "\n";
    std::cout << "  SSL verification: " << (client->getConfig().verify_ssl ? "enabled" : "disabled") << "\n";
    
    // Create sample metric payload
    QualityMetricPayload metric;
    metric.query = "What is the capital of France?";
    metric.faithfulness_score = 0.95;
    metric.relevance_score = 0.98;
    metric.completeness_score = 0.90;
    metric.coherence_score = 0.92;
    metric.overall_score = 0.94;
    metric.decision = "ACCEPT";
    metric.latency_ms = 150;
    metric.mode = "BALANCED";
    metric.timestamp = getCurrentTimestamp();
    metric.metadata["user_id"] = "user123";
    metric.metadata["session_id"] = "session456";
    
    std::cout << "\nSample metric payload:\n";
    std::cout << "  Query: " << metric.query << "\n";
    std::cout << "  Faithfulness: " << metric.faithfulness_score << "\n";
    std::cout << "  Relevance: " << metric.relevance_score << "\n";
    std::cout << "  Overall: " << metric.overall_score << "\n";
    std::cout << "  Decision: " << metric.decision << "\n";
    std::cout << "  Latency: " << metric.latency_ms << "ms\n";
    
    // Note: In production, this would send to a real endpoint
    // auto response = client->sendMetric(metric);
    
    std::cout << "\n✓ HTTP metrics client ready to send metrics\n";
    std::cout << "  (Would send to: " << client->getConfig().endpoint_url << "/metrics)\n";
}

// ═══════════════════════════════════════════════════════════
// Example 3: Batch Metrics Upload
// ═══════════════════════════════════════════════════════════

void example3_batch_metrics_upload() {
    printSeparator("Example 3: Batch Metrics Upload");
    
    std::cout << "Demonstrating batch metric upload capabilities...\n\n";
    
    auto client = HTTPMetricsClientFactory::createLocalClient("http://localhost:8080");
    
    // Create batch of metrics
    std::vector<QualityMetricPayload> metrics_batch;
    
    std::vector<std::string> queries = {
        "What is machine learning?",
        "Explain quantum computing",
        "How does photosynthesis work?"
    };
    
    for (size_t i = 0; i < queries.size(); i++) {
        QualityMetricPayload metric;
        metric.query = queries[i];
        metric.faithfulness_score = 0.85 + (i * 0.05);
        metric.relevance_score = 0.90 + (i * 0.03);
        metric.completeness_score = 0.88 + (i * 0.04);
        metric.coherence_score = 0.92;
        metric.overall_score = (metric.faithfulness_score + metric.relevance_score + 
                               metric.completeness_score + metric.coherence_score) / 4.0;
        metric.decision = "ACCEPT";
        metric.latency_ms = 100 + (i * 20);
        metric.mode = "BALANCED";
        metric.timestamp = getCurrentTimestamp();
        
        metrics_batch.push_back(metric);
    }
    
    std::cout << "Batch contains " << metrics_batch.size() << " metrics:\n";
    for (size_t i = 0; i < metrics_batch.size(); i++) {
        std::cout << "  " << (i+1) << ". " << metrics_batch[i].query << "\n";
        std::cout << "     Overall score: " << std::fixed << std::setprecision(2) 
                  << metrics_batch[i].overall_score << "\n";
    }
    
    // Note: In production, this would send the batch
    // auto response = client->sendMetricsBatch(metrics_batch);
    
    std::cout << "\n✓ Batch ready to upload\n";
    std::cout << "  Batch size: " << metrics_batch.size() << " metrics\n";
    std::cout << "  Max batch size: " << client->getConfig().max_batch_size << "\n";
}

// ═══════════════════════════════════════════════════════════
// Example 4: Production Configuration
// ═══════════════════════════════════════════════════════════

void example4_production_configuration() {
    printSeparator("Example 4: Production Configuration");
    
    std::cout << "Demonstrating production-ready configuration...\n\n";
    
    // Production HTTP client with authentication
    std::cout << "1. HTTP Client (Production):\n";
    auto http_client = HTTPMetricsClientFactory::createProductionClient(
        "https://api.continuous-learning.example.com",
        "your_auth_token_here"
    );
    
    std::cout << "  Endpoint: " << http_client->getConfig().endpoint_url << "\n";
    std::cout << "  Authentication: Bearer token (configured)\n";
    std::cout << "  SSL verification: enabled\n";
    std::cout << "  Compression: enabled\n";
    std::cout << "  Max retries: " << http_client->getConfig().max_retries << "\n";
    
    // ONNX Model Loader (Production)
    std::cout << "\n2. ONNX Model Loader (Production):\n";
    ONNXModelLoaderConfig model_config;
    model_config.cache_dir = "/var/lib/themisdb/models";
    model_config.verify_checksum = true;
    model_config.auto_download = true;
    model_config.download_timeout_sec = 600;  // 10 minutes for large models
    
    ONNXModelLoader loader(model_config);
    
    std::cout << "  Cache directory: " << model_config.cache_dir << "\n";
    std::cout << "  Auto-download: enabled\n";
    std::cout << "  Checksum verification: enabled\n";
    std::cout << "  Download timeout: " << model_config.download_timeout_sec << "s\n";
    
    // Quality Control Pipeline with production features
    std::cout << "\n3. Quality Control Pipeline (Production):\n";
    QualityControlFactory::SetupConfig qc_config;
    qc_config.enable_continuous_learning = true;
    qc_config.cl_endpoint = "https://api.continuous-learning.example.com";
    qc_config.nli_model_path = "/var/lib/themisdb/models/deberta-v3-large-mnli.onnx";
    
    std::cout << "  Continuous learning: enabled\n";
    std::cout << "  CL endpoint: " << qc_config.cl_endpoint << "\n";
    std::cout << "  NLI model: " << qc_config.nli_model_path << "\n";
    
    std::cout << "\n✓ Production configuration complete\n";
}

// ═══════════════════════════════════════════════════════════
// Example 5: End-to-End Integration
// ═══════════════════════════════════════════════════════════

void example5_end_to_end_integration() {
    printSeparator("Example 5: End-to-End Integration");
    
    std::cout << "Demonstrating complete quality control with future works...\n\n";
    
    // Step 1: Load ONNX model
    std::cout << "Step 1: Load ONNX model for NLI verification\n";
    ONNXModelLoaderConfig model_config;
    model_config.cache_dir = "./models";
    model_config.verify_checksum = true;
    ONNXModelLoader loader(model_config);
    
    // In production, load real model:
    // auto model = loader.loadOrDownloadModel("deberta-v3-large-mnli", url);
    std::cout << "  ✓ Model loader initialized\n";
    
    // Step 2: Create quality control pipeline
    std::cout << "\nStep 2: Create quality control pipeline\n";
    auto pipeline = QualityControlFactory::createBasic();
    std::cout << "  ✓ QC pipeline created\n";
    
    // Step 3: Setup HTTP metrics client
    std::cout << "\nStep 3: Setup HTTP metrics client\n";
    auto http_client = HTTPMetricsClientFactory::createLocalClient("http://localhost:8080");
    std::cout << "  ✓ HTTP client initialized\n";
    std::cout << "  Endpoint: " << http_client->getConfig().endpoint_url << "\n";
    
    // Step 4: Run quality control
    std::cout << "\nStep 4: Run quality control\n";
    std::string query = "What are the benefits of renewable energy?";
    std::vector<std::string> documents = {
        "Renewable energy sources like solar and wind power reduce carbon emissions.",
        "Solar panels convert sunlight directly into electricity.",
        "Wind turbines generate clean energy from wind."
    };
    std::string answer = "Renewable energy reduces carbon emissions and provides clean power.";
    
    auto qc_result = pipeline->runQualityControl(query, documents, answer);
    
    std::cout << "  Query: " << query << "\n";
    std::cout << "  Answer: " << answer << "\n";
    std::cout << "  Decision: ";
    switch (qc_result.decision) {
        case QCDecision::ACCEPT: std::cout << "ACCEPT"; break;
        case QCDecision::REJECT: std::cout << "REJECT"; break;
        case QCDecision::WARN: std::cout << "WARN"; break;
        case QCDecision::RETRY: std::cout << "RETRY"; break;
    }
    std::cout << "\n";
    std::cout << "  Overall Score: " << std::fixed << std::setprecision(2) 
              << qc_result.overall_score << "\n";
    
    // Step 5: Upload metrics
    std::cout << "\nStep 5: Upload metrics to continuous learning\n";
    QualityMetricPayload metric;
    metric.query = query;
    metric.faithfulness_score = qc_result.dimension_scores["faithfulness"];
    metric.relevance_score = qc_result.dimension_scores["relevance"];
    metric.completeness_score = qc_result.dimension_scores["completeness"];
    metric.coherence_score = qc_result.dimension_scores["coherence"];
    metric.overall_score = qc_result.overall_score;
    metric.decision = "ACCEPT";
    metric.latency_ms = static_cast<int>(qc_result.latency_ms);
    metric.mode = "FAST";
    metric.timestamp = getCurrentTimestamp();
    
    // In production: auto response = http_client->sendMetric(metric);
    std::cout << "  ✓ Metric prepared for upload\n";
    std::cout << "  Faithfulness: " << metric.faithfulness_score << "\n";
    std::cout << "  Relevance: " << metric.relevance_score << "\n";
    std::cout << "  Latency: " << metric.latency_ms << "ms\n";
    
    std::cout << "\n✓ End-to-end integration complete!\n";
    std::cout << "  Quality check passed ✓\n";
    std::cout << "  Metrics ready for continuous learning ✓\n";
}

// ═══════════════════════════════════════════════════════════
// Example 6: Statistics and Monitoring
// ═══════════════════════════════════════════════════════════

void example6_statistics_and_monitoring() {
    printSeparator("Example 6: Statistics and Monitoring");
    
    std::cout << "Demonstrating statistics tracking and monitoring...\n\n";
    
    auto client = HTTPMetricsClientFactory::createLocalClient("http://localhost:8080");
    
    // Simulate some requests
    std::cout << "Simulating metric uploads...\n";
    for (int i = 0; i < 5; i++) {
        QualityMetricPayload metric;
        metric.query = "Sample query " + std::to_string(i + 1);
        metric.overall_score = 0.85 + (i * 0.02);
        metric.decision = "ACCEPT";
        metric.latency_ms = 100 + (i * 10);
        metric.timestamp = getCurrentTimestamp();
        
        // In production: client->sendMetric(metric);
        std::cout << "  - Metric " << (i+1) << ": " << metric.query << "\n";
    }
    
    // Get statistics
    auto stats = client->getStatistics();
    
    std::cout << "\nHTTP Client Statistics:\n";
    std::cout << "  Requests sent: " << stats.requests_sent << "\n";
    std::cout << "  Requests succeeded: " << stats.requests_succeeded << "\n";
    std::cout << "  Requests failed: " << stats.requests_failed << "\n";
    std::cout << "  Metrics sent: " << stats.metrics_sent << "\n";
    std::cout << "  Retries attempted: " << stats.retries_attempted << "\n";
    std::cout << "  Average latency: " << stats.avg_latency.count() << "ms\n";
    
    std::cout << "\n✓ Statistics tracking enabled\n";
    std::cout << "  Use for monitoring and alerting\n";
}

// ═══════════════════════════════════════════════════════════
// Main
// ═══════════════════════════════════════════════════════════

int main() {
    std::cout << "╔═══════════════════════════════════════════════════════════╗\n";
    std::cout << "║  ThemisDB Quality Control - Future Works Integration     ║\n";
    std::cout << "║  Demonstrating ONNX Runtime & HTTP Metrics Client        ║\n";
    std::cout << "╚═══════════════════════════════════════════════════════════╝\n";
    
    try {
        example1_onnx_model_loading();
        example2_http_metrics_client();
        example3_batch_metrics_upload();
        example4_production_configuration();
        example5_end_to_end_integration();
        example6_statistics_and_monitoring();
        
        printSeparator("Summary");
        std::cout << "All examples completed successfully!\n\n";
        std::cout << "Key Features Demonstrated:\n";
        std::cout << "  ✓ ONNX model loading and management\n";
        std::cout << "  ✓ HTTP metrics client with retry logic\n";
        std::cout << "  ✓ Batch metrics upload\n";
        std::cout << "  ✓ Production configuration\n";
        std::cout << "  ✓ End-to-end quality control integration\n";
        std::cout << "  ✓ Statistics tracking and monitoring\n";
        std::cout << "\nNext Steps:\n";
        std::cout << "  1. Configure real ONNX models in production\n";
        std::cout << "  2. Setup continuous learning endpoint\n";
        std::cout << "  3. Enable automatic metric uploads\n";
        std::cout << "  4. Monitor statistics and optimize\n";
        std::cout << "\n";
        
    } catch (const std::exception& e) {
        std::cerr << "Error: " << e.what() << "\n";
        return 1;
    }
    
    return 0;
}