oxirs-arq 0.2.4

//! Vector-Aware SPARQL Query Optimization Example
//!
//! This example demonstrates how to use the vector-aware query optimization
//! capabilities in oxirs-arq to enhance SPARQL query performance with semantic
//! vector search integration.

#![allow(
    unused_imports,
    unused_variables,
    dead_code,
    clippy::uninlined_format_args,
    clippy::print_literal
)]

use anyhow::Result;
use oxirs_arq::{
    algebra::{Term, TriplePattern},
    executor::QueryExecutor,
    integrated_query_planner::IntegratedPlannerConfig,
    vector_query_optimizer::{
        IndexAccuracyStats, IndexPerformanceStats, VectorDistanceMetric, VectorIndexInfo,
        VectorIndexType, VectorOptimizerConfig,
    },
};
use std::collections::HashMap;
use std::time::{Duration, Instant};

fn main() -> Result<()> {
    // Initialize logging
    tracing_subscriber::fmt::init();

    println!("🚀 Vector-Aware SPARQL Query Optimization Example");

    // 1. Create query executor with vector optimization
    let executor = create_vector_enabled_executor()?;

    // 2. Register vector indices
    register_sample_vector_indices(&executor)?;

    // 3. Execute sample queries with vector optimization
    execute_sample_queries(&executor)?;

    // 4. Show performance metrics
    show_performance_metrics(&executor)?;

    println!("✅ Vector optimization example completed successfully!");
    Ok(())
}

/// Create a query executor with vector optimization enabled
fn create_vector_enabled_executor() -> Result<QueryExecutor> {
    println!("\n📊 Creating vector-enabled query executor...");

    // Create base query executor
    let executor = QueryExecutor::new();

    // Configure vector optimization
    let _vector_config = VectorOptimizerConfig {
        enable_vector_optimization: true,
        similarity_threshold: 0.8,
        max_vector_candidates: 1000,
        vector_cache_size: 10_000,
        enable_hybrid_search: true,
        embedding_dimension: 768, // BERT-like embeddings
        distance_metric: VectorDistanceMetric::Cosine,
        preferred_index_types: vec![
            VectorIndexType::Hnsw,
            VectorIndexType::IvfPq,
            VectorIndexType::IvfFlat,
        ],
        complexity_threshold: 5.0,
    };

    // Configure integrated planner
    let _planner_config = IntegratedPlannerConfig {
        adaptive_optimization: true,
        cross_query_optimization: true,
        streaming_threshold: 256 * 1024 * 1024, // 256MB
        ml_cost_estimation: true,
        plan_cache_size: 1000,
        parallel_planning: true,
        stats_collection_interval: Duration::from_secs(30),
        advanced_index_recommendations: true,
    };

    // Note: Vector optimization would be configured through the executor
    // For demonstration purposes, we'll show what the configuration would look like

    println!("✅ Vector optimization configured");
    println!("   - Similarity threshold: 0.8");
    println!("   - Max candidates: 1000");
    println!("   - Distance metric: Cosine");
    println!("   - Embedding dimension: 768");

    Ok(executor)
}

/// Register sample vector indices for demonstration
fn register_sample_vector_indices(_executor: &QueryExecutor) -> Result<()> {
    println!("\n📝 Registering vector indices...");

    // Register HNSW index for entities
    let _entity_index = VectorIndexInfo {
        index_type: VectorIndexType::Hnsw,
        dimension: 768,
        size: 1_000_000, // 1M entities
        distance_metric: VectorDistanceMetric::Cosine,
        build_time: Duration::from_secs(300), // 5 minutes build time
        last_updated: Instant::now(),
        accuracy_stats: IndexAccuracyStats {
            recall_at_k: {
                let mut map = HashMap::new();
                map.insert(1, 0.98);
                map.insert(5, 0.95);
                map.insert(10, 0.92);
                map.insert(50, 0.88);
                map
            },
            precision_at_k: {
                let mut map = HashMap::new();
                map.insert(10, 0.89);
                map.insert(50, 0.85);
                map
            },
            average_distance_error: 0.02,
            query_count: 50000,
        },
        performance_stats: IndexPerformanceStats {
            average_query_time: Duration::from_micros(150), // 150μs average
            queries_per_second: 6666.0,
            memory_usage: 2 * 1024 * 1024 * 1024, // 2GB
            cache_hit_rate: 0.85,
            index_efficiency: 0.92,
        },
    };

    // Note: In a real implementation, vector indices would be registered with the executor
    println!("   - Would register entity_embeddings index with executor");
    println!("✅ Registered HNSW entity index (1M vectors, 768-dim)");

    // Register IVF-PQ index for properties
    let _property_index = VectorIndexInfo {
        index_type: VectorIndexType::IvfPq,
        dimension: 384,
        size: 500_000, // 500K properties
        distance_metric: VectorDistanceMetric::Cosine,
        build_time: Duration::from_secs(120), // 2 minutes build time
        last_updated: Instant::now(),
        accuracy_stats: IndexAccuracyStats {
            recall_at_k: {
                let mut map = HashMap::new();
                map.insert(1, 0.94);
                map.insert(5, 0.90);
                map.insert(10, 0.87);
                map.insert(50, 0.82);
                map
            },
            precision_at_k: {
                let mut map = HashMap::new();
                map.insert(10, 0.84);
                map.insert(50, 0.79);
                map
            },
            average_distance_error: 0.05,
            query_count: 25000,
        },
        performance_stats: IndexPerformanceStats {
            average_query_time: Duration::from_micros(80), // 80μs average
            queries_per_second: 12500.0,
            memory_usage: 512 * 1024 * 1024, // 512MB
            cache_hit_rate: 0.78,
            index_efficiency: 0.88,
        },
    };

    // Note: In a real implementation, vector indices would be registered with the executor
    println!("   - Would register property_embeddings index with executor");
    println!("✅ Registered IVF-PQ property index (500K vectors, 384-dim)");

    // Register flat index for literals (smaller, exact search)
    let _literal_index = VectorIndexInfo {
        index_type: VectorIndexType::FlatIndex,
        dimension: 256,
        size: 100_000, // 100K literals
        distance_metric: VectorDistanceMetric::Cosine,
        build_time: Duration::from_secs(10), // 10 seconds build time
        last_updated: Instant::now(),
        accuracy_stats: IndexAccuracyStats {
            recall_at_k: {
                let mut map = HashMap::new();
                map.insert(1, 1.0); // Exact search
                map.insert(5, 1.0);
                map.insert(10, 1.0);
                map.insert(50, 1.0);
                map
            },
            precision_at_k: {
                let mut map = HashMap::new();
                map.insert(10, 1.0);
                map.insert(50, 1.0);
                map
            },
            average_distance_error: 0.0, // Exact
            query_count: 10000,
        },
        performance_stats: IndexPerformanceStats {
            average_query_time: Duration::from_micros(500), // 500μs average
            queries_per_second: 2000.0,
            memory_usage: 100 * 1024 * 1024, // 100MB
            cache_hit_rate: 0.95,
            index_efficiency: 1.0, // Exact search
        },
    };

    // Note: In a real implementation, vector indices would be registered with the executor
    println!("   - Would register literal_embeddings index with executor");
    println!("✅ Registered Flat literal index (100K vectors, 256-dim)");

    Ok(())
}

/// Execute sample SPARQL queries with vector optimization
fn execute_sample_queries(executor: &QueryExecutor) -> Result<()> {
    println!("\n🔍 Executing sample queries with vector optimization...");

    // Mock dataset - in a real implementation, this would be a proper dataset
    struct MockDataset;
    impl oxirs_arq::executor::Dataset for MockDataset {
        fn find_triples(&self, _pattern: &TriplePattern) -> Result<Vec<(Term, Term, Term)>> {
            Ok(vec![]) // Return empty for mock implementation
        }

        fn contains_triple(
            &self,
            _subject: &Term,
            _predicate: &Term,
            _object: &Term,
        ) -> Result<bool> {
            Ok(false) // Mock implementation always returns false
        }

        fn subjects(&self) -> Result<Vec<Term>> {
            Ok(vec![]) // Return empty for mock implementation
        }

        fn predicates(&self) -> Result<Vec<Term>> {
            Ok(vec![]) // Return empty for mock implementation
        }

        fn objects(&self) -> Result<Vec<Term>> {
            Ok(vec![]) // Return empty for mock implementation
        }
    }

    let dataset = MockDataset;

    // Sample queries that could benefit from vector optimization
    let sample_queries = vec![
        (
            "Semantic Entity Search",
            r#"
            PREFIX vec: <http://example.org/vector/>
            SELECT ?entity ?similarity WHERE {
                ?entity vec:similar "artificial intelligence" .
                ?entity vec:similarity ?similarity .
                FILTER (?similarity > 0.8)
            }
            ORDER BY DESC(?similarity)
            LIMIT 10
            "#,
        ),
        (
            "Hybrid Text-Vector Search",
            r#"
            PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
            PREFIX vec: <http://example.org/vector/>
            SELECT ?concept ?label ?score WHERE {
                ?concept rdfs:label ?label .
                ?concept vec:search "machine learning concepts" .
                ?concept vec:similarity ?score .
                FILTER (regex(?label, "learning", "i") && ?score > 0.7)
            }
            ORDER BY DESC(?score)
            LIMIT 20
            "#,
        ),
        (
            "Cross-Modal Similarity",
            r#"
            PREFIX vec: <http://example.org/vector/>
            PREFIX ex: <http://example.org/>
            SELECT ?image ?text ?similarity WHERE {
                ?image a ex:Image .
                ?text a ex:TextDocument .
                ?image vec:crossModalSimilarity ?text .
                ?image vec:similarity ?similarity .
                FILTER (?similarity > 0.6)
            }
            ORDER BY DESC(?similarity)
            LIMIT 15
            "#,
        ),
        (
            "Semantic Property Expansion",
            r#"
            PREFIX vec: <http://example.org/vector/>
            PREFIX ex: <http://example.org/>
            SELECT ?subject ?property ?object WHERE {
                ?subject ?property ?object .
                ?property vec:semanticallyRelated ex:hasSkill .
                FILTER (?subject = ex:person123)
            }
            "#,
        ),
    ];

    for (query_name, query_str) in sample_queries {
        println!("\n🔸 Executing: {}", query_name);
        println!(
            "   Query preview: {}",
            query_str.lines().nth(3).unwrap_or("").trim()
        );

        let start_time = Instant::now();

        // For this example, we'll simulate the vector optimization process
        let execution_time = start_time.elapsed();

        // Simulate successful execution for demonstration
        println!("   ✅ Query analysis completed");
        println!("   📊 Vector strategy identified");
        println!("   ⏱️  Analysis time: {:?}", execution_time);
        println!("   💾 Estimated optimizations available");

        // Simulate vector optimization feedback
        println!("   🎯 Vector strategy detected and cost estimated");
        println!("   📈 Query plan enhanced with vector awareness");
    }

    Ok(())
}

/// Show performance metrics from vector optimization
fn show_performance_metrics(executor: &QueryExecutor) -> Result<()> {
    println!("\n📈 Vector Optimization Performance Metrics");

    // Simulate vector performance metrics display
    {
        println!("╭─────────────────────────────────────────╮");
        println!("│           Vector Metrics                │");
        println!("├─────────────────────────────────────────┤");
        println!("│ Vector queries optimized: {:>13} │", 125);
        println!("│ Hybrid queries optimized: {:>13} │", 38);
        println!("│ Semantic expansions:      {:>13} │", 42);
        println!("│ Average speedup:          {:>13.2}x │", 2.5);
        println!("│ Vector cache hit rate:    {:>13.1}% │", 85.0);
        println!(
            "│ Embedding gen time:       {:>13?} │",
            Duration::from_micros(200)
        );
        println!(
            "│ Total optimization time:  {:>13?} │",
            Duration::from_millis(50)
        );
        println!("╰─────────────────────────────────────────╯");
    }

    // Show integration status
    println!("\n🔧 Integration Status");
    println!("├─ Vector optimization: {}", "✅ Enabled");
    println!("├─ Integrated planning: {}", "✅ Enabled");
    println!("└─ Vector indices: 3 registered (HNSW, IVF-PQ, Flat)");

    Ok(())
}

/// Calculate a simple hash for query string (for demonstration)
fn calculate_query_hash(query: &str) -> u64 {
    use std::collections::hash_map::DefaultHasher;
    use std::hash::{Hash, Hasher};

    let mut hasher = DefaultHasher::new();
    query.hash(&mut hasher);
    hasher.finish()
}

/// Additional example: Programmatic vector index optimization
#[allow(dead_code)]
fn demonstrate_index_optimization(executor: &QueryExecutor) -> Result<()> {
    println!("\n🎯 Vector Index Optimization Recommendations");

    // Get index recommendations (would come from integrated planner)
    let recommendations: Vec<String> = vec![]; // Mock empty recommendations

    if recommendations.is_empty() {
        println!("📊 Analysis: Current vector indices are well-optimized");
        println!("   - Entity HNSW index: High performance for similarity search");
        println!("   - Property IVF-PQ index: Good balance of speed and memory");
        println!("   - Literal Flat index: Perfect for exact small-scale search");

        println!("\n💡 Optimization Tips:");
        println!("   1. Consider HNSW for properties if query volume increases");
        println!("   2. Monitor cache hit rates and adjust cache sizes");
        println!("   3. Use hybrid search for complex queries");
        println!("   4. Enable GPU acceleration for large vector operations");
    } else {
        println!("📋 Recommendations found:");
        for (i, rec) in recommendations.iter().enumerate() {
            println!("   {}. {}", i + 1, rec);
        }
    }

    Ok(())
}

/// Example of vector-specific SPARQL functions
#[allow(dead_code)]
fn demonstrate_vector_functions() {
    println!("\n🔧 Vector-Specific SPARQL Functions");

    println!("Available vector functions:");
    println!("├─ vec:similarity(?a, ?b)     - Calculate similarity between vectors");
    println!("├─ vec:similar(?entity, k)    - Find k most similar entities");
    println!("├─ vec:search(?text, limit)   - Semantic text search");
    println!("├─ vec:searchIn(?query, ?graph) - Graph-scoped vector search");
    println!("├─ vec:distance(?a, ?b)       - Calculate vector distance");
    println!("├─ vec:embed(?text)           - Generate embedding for text");
    println!("├─ vec:cosine(?a, ?b)         - Cosine similarity");
    println!("├─ vec:euclidean(?a, ?b)      - Euclidean distance");
    println!("├─ vec:cluster(?entities, k)  - K-means clustering");
    println!("└─ vec:recommend(?entity, k)  - Entity recommendations");

    println!("\nExample usage in SPARQL:");
    println!("  SELECT ?similar WHERE {{");
    println!("    ?entity vec:similar \"machine learning\" .");
    println!("    ?entity vec:similarity ?similar .");
    println!("    FILTER(?similar > 0.8)");
    println!("  }} ORDER BY DESC(?similar)");
}