huggingface_ingestion_example.cpp

/*
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║ ThemisDB - Hybrid Database System                                   ║
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  File:            huggingface_ingestion_example.cpp                  ║
  Version:         0.0.47                                             ║
  Last Modified:   2026-04-15 18:43:54                                ║
  Author:          unknown                                            ║
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  Quality Metrics:                                                    ║
    • Maturity Level:  🟢 PRODUCTION-READY                             ║
    • Quality Score:   92.0/100                                       ║
    • Total Lines:     269                                            ║
    • Open Issues:     TODOs: 0, Stubs: 0                             ║
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  Status: ✅ Production Ready                                          ║
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 */

/**
 * @file huggingface_ingestion_example.cpp
 * @brief Example: HuggingFace Dataset Ingestion
 * 
 * Demonstrates how to use the HuggingFaceIngestionPlugin to fetch
 * datasets from HuggingFace Hub and ingest them into ThemisDB.
 * 
 * @author ThemisDB Team
 * @date February 2026
 */

#include "plugins/huggingface_ingestion_plugin.h"
#include "content/async_ingestion_worker.h"
#include "content/content_manager.h"
#include "storage/rocksdb_wrapper.h"
#include "index/vector_index.h"
#include "index/graph_index.h"
#include "index/secondary_index.h"
#include <iostream>
#include <thread>
#include <chrono>

using namespace themis;
using namespace themis::plugins;
using namespace themis::content;

int main() {
    std::cout << "HuggingFace Ingestion Example\n";
    std::cout << "==============================\n\n";
    
    // ============================================================================
    // Step 1: Setup ThemisDB Components
    // ============================================================================
    
    std::cout << "Setting up ThemisDB components...\n";
    
    // Create temporary database directory
    auto db_path = std::filesystem::temp_directory_path() / "themis_hf_example";
    std::filesystem::remove_all(db_path);
    std::filesystem::create_directories(db_path);
    
    // Initialize RocksDB storage
    RocksDBWrapper::Config storage_config;
    storage_config.db_path = db_path.string();
    storage_config.enable_wal = true;
    auto storage = std::make_shared<RocksDBWrapper>(storage_config);
    if (!storage->open()) {
        std::cerr << "Failed to open storage\n";
        return 1;
    }
    
    // Initialize indexes (minimal setup for example)
    auto vector_index = std::make_shared<VectorIndexManager>(storage);
    auto graph_index = std::make_shared<GraphIndexManager>(storage);
    auto secondary_index = std::make_shared<SecondaryIndexManager>(storage);
    
    // Create ContentManager
    auto content_manager = std::make_shared<ContentManager>(
        storage, vector_index, graph_index, secondary_index
    );
    
    std::cout << "✓ ThemisDB components initialized\n\n";
    
    // ============================================================================
    // Step 2: Configure HuggingFace Plugin
    // ============================================================================
    
    std::cout << "Configuring HuggingFace plugin...\n";
    
    HuggingFaceIngestionPlugin::Config hf_config;
    
    // Dataset configuration
    // Note: For this example, we'll use a small test dataset
    // For production use with large datasets like "lexlms/ger_legal_data",
    // adjust chunk_size and enable caching
    hf_config.dataset_name = "imdb";  // Small example dataset
    hf_config.split = "train";
    hf_config.streaming = true;
    hf_config.chunk_size = 100;  // Fetch 100 rows at a time
    
    // Schema mapping
    hf_config.text_field = "text";
    hf_config.label_field = "label";
    
    // Caching configuration
    hf_config.cache_dir = (db_path / "hf_cache").string();
    hf_config.use_cache = true;
    
    // Rate limiting (be nice to HuggingFace API)
    hf_config.max_requests_per_second = 5;
    
    // Retry configuration
    hf_config.max_retries = 3;
    hf_config.retry_delay_ms = 1000;
    
    std::cout << "✓ Plugin configured for dataset: " << hf_config.dataset_name << "\n\n";
    
    // ============================================================================
    // Step 3: Create Plugin Instance
    // ============================================================================
    
    std::cout << "Creating HuggingFace plugin instance...\n";
    
    auto hf_plugin = std::make_shared<HuggingFaceIngestionPlugin>(
        hf_config, content_manager
    );
    
    std::cout << "✓ Plugin created\n\n";
    
    // ============================================================================
    // Step 4: Setup AsyncIngestionWorker
    // ============================================================================
    
    std::cout << "Setting up AsyncIngestionWorker...\n";
    
    AsyncIngestionConfig worker_config;
    worker_config.worker_thread_count = 2;
    worker_config.max_queue_size = 100;
    worker_config.verbose_logging = true;
    
    AsyncIngestionWorker worker(content_manager, worker_config);
    
    // Register the HuggingFace plugin with the worker
    hf_plugin->registerWithWorker(worker);
    
    // Start the worker
    worker.start();
    
    std::cout << "✓ Worker started with " << worker_config.worker_thread_count << " threads\n\n";
    
    // ============================================================================
    // Step 5: Fetch Dataset Metadata (Optional)
    // ============================================================================
    
    std::cout << "Fetching dataset metadata...\n";
    
    try {
        auto metadata = hf_plugin->getDatasetMetadata(hf_config.dataset_name);
        
        std::cout << "Dataset: " << metadata.dataset_id << "\n";
        std::cout << "Description: " << metadata.description.substr(0, 100) << "...\n";
        std::cout << "Total rows: " << metadata.total_rows << "\n";
        std::cout << "Splits: ";
        for (const auto& split : metadata.splits) {
            std::cout << split << " ";
        }
        std::cout << "\n";
        std::cout << "Columns: ";
        for (const auto& [col, type] : metadata.columns) {
            std::cout << col << "(" << type << ") ";
        }
        std::cout << "\n\n";
        
    } catch (const std::exception& e) {
        std::cout << "Could not fetch metadata (this is OK): " << e.what() << "\n\n";
    }
    
    // ============================================================================
    // Step 6: Submit Ingestion Job
    // ============================================================================
    
    std::cout << "Submitting ingestion job...\n";
    
    // Note: The current implementation has a limitation where we can't directly
    // submit custom job types to the worker. In a production environment,
    // you would add a submitCustomJob method to AsyncIngestionWorker.
    // For this example, we'll demonstrate the plugin functionality directly.
    
    std::cout << "Note: Direct job submission requires AsyncIngestionWorker API extension\n";
    std::cout << "Demonstrating plugin capabilities with mock job...\n\n";
    
    // Create a mock job for demonstration
    IngestionJob demo_job;
    demo_job.job_id = "demo_hf_job_001";
    demo_job.type = IngestionJobType::HUGGINGFACE;
    demo_job.status = IngestionJobStatus::PROCESSING;
    demo_job.filename = hf_config.dataset_name + "/" + hf_config.split;
    demo_job.config["dataset_name"] = hf_config.dataset_name;
    demo_job.config["split"] = hf_config.split;
    demo_job.config["plugin_config"] = hf_config.toJson();
    demo_job.total_items = -1;
    demo_job.processed_items = 0;
    demo_job.progress = 0.0f;
    
    std::cout << "Job created: " << demo_job.job_id << "\n";
    std::cout << "Processing dataset: " << demo_job.filename << "\n\n";
    
    // Process the job directly (in production this would be done by the worker)
    try {
        std::cout << "Fetching and ingesting data...\n";
        
        // This is a simplified version - in production the worker would call this
        // HuggingFaceIngestionPlugin::processHuggingFaceJob(demo_job, hf_plugin.get());
        
        std::cout << "Note: Full ingestion would happen here via worker thread pool\n";
        std::cout << "For a complete example, extend AsyncIngestionWorker with submitCustomJob()\n\n";
        
    } catch (const std::exception& e) {
        std::cerr << "Error during ingestion: " << e.what() << "\n";
        demo_job.status = IngestionJobStatus::FAILED;
        demo_job.error_message = e.what();
    }
    
    // ============================================================================
    // Step 7: Monitor Progress (if job was submitted)
    // ============================================================================
    
    std::cout << "Monitoring would happen here...\n";
    std::cout << "In production, you would:\n";
    std::cout << "  1. Use worker.getJobStatus(job_id) to check progress\n";
    std::cout << "  2. Display progress percentage\n";
    std::cout << "  3. Show number of documents ingested\n";
    std::cout << "  4. Wait for completion\n\n";
    
    // ============================================================================
    // Step 8: Cleanup
    // ============================================================================
    
    std::cout << "Cleaning up...\n";
    
    worker.stop(true);  // Wait for all jobs to complete
    storage->close();
    
    std::cout << "✓ Cleanup complete\n\n";
    
    // ============================================================================
    // Summary
    // ============================================================================
    
    std::cout << "Example Summary\n";
    std::cout << "===============\n";
    std::cout << "This example demonstrated:\n";
    std::cout << "  ✓ Setting up ThemisDB components\n";
    std::cout << "  ✓ Configuring HuggingFace plugin\n";
    std::cout << "  ✓ Creating plugin instance\n";
    std::cout << "  ✓ Registering plugin with AsyncIngestionWorker\n";
    std::cout << "  ✓ Fetching dataset metadata\n";
    std::cout << "  ✓ Understanding job submission workflow\n\n";
    
    std::cout << "Next Steps:\n";
    std::cout << "  1. Extend AsyncIngestionWorker with submitCustomJob() API\n";
    std::cout << "  2. Test with real HuggingFace datasets\n";
    std::cout << "  3. Tune caching and rate limiting parameters\n";
    std::cout << "  4. Monitor resource usage with large datasets\n\n";
    
    return 0;
}