rrag 0.1.0-alpha.2

//! # Vector Update Manager
//!
//! Manages incremental updates to vector indexes without requiring full rebuilds.
//! Handles embedding updates, index optimization, and performance monitoring.

use crate::{Embedding, RragResult};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::Arc;
use tokio::sync::RwLock;
use uuid::Uuid;

/// Vector update configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VectorUpdateConfig {
    /// Enable batch processing for vector updates
    pub enable_batch_processing: bool,

    /// Maximum batch size for vector operations
    pub max_batch_size: usize,

    /// Batch timeout in milliseconds
    pub batch_timeout_ms: u64,

    /// Index update strategy
    pub update_strategy: IndexUpdateStrategy,

    /// Enable index optimization
    pub enable_optimization: bool,

    /// Optimization interval in seconds
    pub optimization_interval_secs: u64,

    /// Enable similarity threshold updates
    pub enable_similarity_updates: bool,

    /// Similarity update threshold
    pub similarity_threshold: f32,

    /// Maximum concurrent operations
    pub max_concurrent_operations: usize,

    /// Performance monitoring settings
    pub monitoring: VectorMonitoringConfig,
}

/// Index update strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum IndexUpdateStrategy {
    /// Immediate update on each change
    Immediate,
    /// Batch updates periodically
    Batch,
    /// Lazy updates on query
    Lazy,
    /// Adaptive based on load
    Adaptive,
    /// Custom strategy
    Custom(String),
}

/// Vector monitoring configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VectorMonitoringConfig {
    /// Enable performance tracking
    pub enable_performance_tracking: bool,

    /// Enable memory usage monitoring
    pub enable_memory_monitoring: bool,

    /// Enable index quality metrics
    pub enable_quality_metrics: bool,

    /// Metrics collection interval in seconds
    pub metrics_interval_secs: u64,
}

impl Default for VectorUpdateConfig {
    fn default() -> Self {
        Self {
            enable_batch_processing: true,
            max_batch_size: 1000,
            batch_timeout_ms: 5000,
            update_strategy: IndexUpdateStrategy::Batch,
            enable_optimization: true,
            optimization_interval_secs: 3600, // 1 hour
            enable_similarity_updates: true,
            similarity_threshold: 0.7,
            max_concurrent_operations: 10,
            monitoring: VectorMonitoringConfig::default(),
        }
    }
}

impl Default for VectorMonitoringConfig {
    fn default() -> Self {
        Self {
            enable_performance_tracking: true,
            enable_memory_monitoring: true,
            enable_quality_metrics: true,
            metrics_interval_secs: 300, // 5 minutes
        }
    }
}

/// Types of vector operations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum VectorOperation {
    /// Add new embeddings to index
    Add {
        embeddings: Vec<Embedding>,
        index_name: String,
    },

    /// Update existing embeddings
    Update {
        embedding_updates: Vec<EmbeddingUpdate>,
        index_name: String,
    },

    /// Remove embeddings from index
    Remove {
        embedding_ids: Vec<String>,
        index_name: String,
    },

    /// Optimize index structure
    Optimize {
        index_name: String,
        optimization_type: OptimizationType,
    },

    /// Rebuild index from scratch
    Rebuild {
        index_name: String,
        embeddings: Vec<Embedding>,
    },

    /// Update similarity thresholds
    UpdateThresholds {
        index_name: String,
        new_threshold: f32,
    },
}

/// Embedding update information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmbeddingUpdate {
    /// Embedding ID to update
    pub embedding_id: String,

    /// New embedding data
    pub new_embedding: Embedding,

    /// Update reason
    pub update_reason: UpdateReason,

    /// Update metadata
    pub metadata: HashMap<String, serde_json::Value>,
}

/// Reasons for embedding updates
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum UpdateReason {
    /// Content changed
    ContentChanged,
    /// Model updated
    ModelUpdated,
    /// Quality improvement
    QualityImprovement,
    /// Metadata updated
    MetadataUpdated,
    /// Error correction
    ErrorCorrection,
    /// Manual update
    Manual,
}

/// Types of index optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationType {
    /// Compact index structure
    Compact,
    /// Rebuild index trees
    RebuildTrees,
    /// Update clustering
    UpdateClustering,
    /// Optimize for query performance
    QueryOptimization,
    /// Memory optimization
    MemoryOptimization,
    /// Full optimization
    Full,
}

/// Vector batch operation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VectorBatch {
    /// Batch ID
    pub batch_id: String,

    /// Operations in this batch
    pub operations: Vec<VectorOperation>,

    /// Batch creation timestamp
    pub created_at: chrono::DateTime<chrono::Utc>,

    /// Target index name
    pub index_name: String,

    /// Batch priority
    pub priority: u8,

    /// Expected processing time
    pub estimated_duration_ms: u64,

    /// Batch metadata
    pub metadata: HashMap<String, serde_json::Value>,
}

/// Result of vector operations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VectorOperationResult {
    /// Operation ID
    pub operation_id: String,

    /// Whether operation succeeded
    pub success: bool,

    /// Number of embeddings processed
    pub embeddings_processed: usize,

    /// Processing time in milliseconds
    pub processing_time_ms: u64,

    /// Index statistics after operation
    pub index_stats: Option<IndexStats>,

    /// Performance metrics
    pub performance_metrics: OperationMetrics,

    /// Errors encountered
    pub errors: Vec<String>,

    /// Operation metadata
    pub metadata: HashMap<String, serde_json::Value>,
}

/// Index statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IndexStats {
    /// Index name
    pub index_name: String,

    /// Number of embeddings in index
    pub embedding_count: usize,

    /// Index size in bytes
    pub size_bytes: u64,

    /// Index dimensions
    pub dimensions: usize,

    /// Index type/algorithm
    pub index_type: String,

    /// Memory usage in bytes
    pub memory_usage_bytes: u64,

    /// Last optimization timestamp
    pub last_optimized_at: Option<chrono::DateTime<chrono::Utc>>,

    /// Index quality metrics
    pub quality_metrics: IndexQualityMetrics,

    /// Performance metrics
    pub performance_metrics: IndexPerformanceMetrics,
}

/// Index quality metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IndexQualityMetrics {
    /// Average recall at k=10
    pub recall_at_10: f32,

    /// Average precision
    pub precision: f32,

    /// Index freshness (how up-to-date it is)
    pub freshness_score: f32,

    /// Clustering quality
    pub clustering_quality: f32,

    /// Distribution balance
    pub distribution_balance: f32,
}

/// Index performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IndexPerformanceMetrics {
    /// Average query time in milliseconds
    pub avg_query_time_ms: f32,

    /// 95th percentile query time
    pub p95_query_time_ms: f32,

    /// Throughput (queries per second)
    pub queries_per_second: f32,

    /// Index build time in milliseconds
    pub build_time_ms: u64,

    /// Memory efficiency score
    pub memory_efficiency: f32,
}

/// Operation performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OperationMetrics {
    /// CPU time used
    pub cpu_time_ms: u64,

    /// Memory peak usage
    pub peak_memory_mb: f32,

    /// I/O operations performed
    pub io_operations: u64,

    /// Cache hit rate
    pub cache_hit_rate: f32,

    /// Throughput (embeddings per second)
    pub throughput_eps: f32,
}

/// Vector update manager
pub struct VectorUpdateManager {
    /// Configuration
    config: VectorUpdateConfig,

    /// Pending operations queue
    pending_operations: Arc<RwLock<VecDeque<VectorOperation>>>,

    /// Active batches
    active_batches: Arc<RwLock<HashMap<String, VectorBatch>>>,

    /// Index metadata
    index_metadata: Arc<RwLock<HashMap<String, IndexStats>>>,

    /// Operation history
    operation_history: Arc<RwLock<VecDeque<VectorOperationResult>>>,

    /// Performance metrics
    metrics: Arc<RwLock<VectorUpdateMetrics>>,

    /// Background task handles
    task_handles: Arc<tokio::sync::Mutex<Vec<tokio::task::JoinHandle<()>>>>,
}

/// Vector update system metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VectorUpdateMetrics {
    /// Total operations processed
    pub total_operations: u64,

    /// Operations by type
    pub operations_by_type: HashMap<String, u64>,

    /// Success rate
    pub success_rate: f32,

    /// Average processing time
    pub avg_processing_time_ms: f32,

    /// Total embeddings processed
    pub total_embeddings_processed: u64,

    /// Index statistics
    pub index_stats: HashMap<String, IndexStats>,

    /// System performance
    pub system_performance: SystemPerformanceMetrics,

    /// Last updated
    pub last_updated: chrono::DateTime<chrono::Utc>,
}

/// System-wide performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SystemPerformanceMetrics {
    /// Overall throughput
    pub overall_throughput_eps: f32,

    /// Memory usage
    pub memory_usage_mb: f32,

    /// CPU utilization
    pub cpu_utilization_percent: f32,

    /// Queue depth
    pub queue_depth: usize,

    /// Active operations count
    pub active_operations: usize,

    /// System health score
    pub health_score: f32,
}

impl VectorUpdateManager {
    /// Create new vector update manager
    pub async fn new(config: VectorUpdateConfig) -> RragResult<Self> {
        let manager = Self {
            config: config.clone(),
            pending_operations: Arc::new(RwLock::new(VecDeque::new())),
            active_batches: Arc::new(RwLock::new(HashMap::new())),
            index_metadata: Arc::new(RwLock::new(HashMap::new())),
            operation_history: Arc::new(RwLock::new(VecDeque::new())),
            metrics: Arc::new(RwLock::new(VectorUpdateMetrics {
                total_operations: 0,
                operations_by_type: HashMap::new(),
                success_rate: 1.0,
                avg_processing_time_ms: 0.0,
                total_embeddings_processed: 0,
                index_stats: HashMap::new(),
                system_performance: SystemPerformanceMetrics {
                    overall_throughput_eps: 0.0,
                    memory_usage_mb: 0.0,
                    cpu_utilization_percent: 0.0,
                    queue_depth: 0,
                    active_operations: 0,
                    health_score: 1.0,
                },
                last_updated: chrono::Utc::now(),
            })),
            task_handles: Arc::new(tokio::sync::Mutex::new(Vec::new())),
        };

        manager.start_background_tasks().await?;
        Ok(manager)
    }

    /// Submit vector operation for processing
    pub async fn submit_operation(&self, operation: VectorOperation) -> RragResult<String> {
        let operation_id = Uuid::new_v4().to_string();

        if self.config.enable_batch_processing {
            match self.config.update_strategy {
                IndexUpdateStrategy::Batch => {
                    self.add_to_batch(operation).await?;
                }
                IndexUpdateStrategy::Immediate => {
                    self.process_immediate(operation).await?;
                }
                _ => {
                    let mut pending = self.pending_operations.write().await;
                    pending.push_back(operation);
                }
            }
        } else {
            self.process_immediate(operation).await?;
        }

        Ok(operation_id)
    }

    /// Process embedding updates
    pub async fn process_embedding_updates(
        &self,
        updates: Vec<EmbeddingUpdate>,
        index_name: &str,
    ) -> RragResult<VectorOperationResult> {
        let start_time = std::time::Instant::now();
        let operation_id = Uuid::new_v4().to_string();

        // Process each embedding update
        let mut processed_count = 0;
        let mut errors = Vec::new();

        for update in &updates {
            match self
                .process_single_embedding_update(update, index_name)
                .await
            {
                Ok(_) => processed_count += 1,
                Err(e) => errors.push(e.to_string()),
            }
        }

        let processing_time = start_time.elapsed().as_millis() as u64;
        let success = processed_count > 0;

        // Update index statistics
        let index_stats = self.update_index_stats(index_name, processed_count).await?;

        let result = VectorOperationResult {
            operation_id,
            success,
            embeddings_processed: processed_count,
            processing_time_ms: processing_time,
            index_stats: Some(index_stats),
            performance_metrics: OperationMetrics {
                cpu_time_ms: processing_time,
                peak_memory_mb: 10.0, // Would be measured
                io_operations: processed_count as u64,
                cache_hit_rate: 0.8,
                throughput_eps: processed_count as f32 / (processing_time as f32 / 1000.0),
            },
            errors,
            metadata: HashMap::new(),
        };

        // Store result
        self.store_operation_result(result.clone()).await?;

        Ok(result)
    }

    /// Optimize vector index
    pub async fn optimize_index(
        &self,
        index_name: &str,
        optimization_type: OptimizationType,
    ) -> RragResult<VectorOperationResult> {
        let start_time = std::time::Instant::now();
        let operation_id = Uuid::new_v4().to_string();

        // Perform optimization based on type
        let optimization_result = self
            .perform_optimization(index_name, &optimization_type)
            .await?;
        let processing_time = start_time.elapsed().as_millis() as u64;

        // Update index metadata
        let mut index_metadata = self.index_metadata.write().await;
        if let Some(stats) = index_metadata.get_mut(index_name) {
            stats.last_optimized_at = Some(chrono::Utc::now());
            stats.quality_metrics = optimization_result.new_quality_metrics;
            stats.performance_metrics = optimization_result.new_performance_metrics;
        }

        let result = VectorOperationResult {
            operation_id,
            success: optimization_result.success,
            embeddings_processed: optimization_result.embeddings_affected,
            processing_time_ms: processing_time,
            index_stats: index_metadata.get(index_name).cloned(),
            performance_metrics: OperationMetrics {
                cpu_time_ms: processing_time,
                peak_memory_mb: optimization_result.peak_memory_usage,
                io_operations: optimization_result.io_operations,
                cache_hit_rate: 0.9,
                throughput_eps: 0.0, // Not applicable for optimization
            },
            errors: optimization_result.errors,
            metadata: optimization_result.metadata,
        };

        self.store_operation_result(result.clone()).await?;
        Ok(result)
    }

    /// Get index statistics
    pub async fn get_index_stats(&self, index_name: &str) -> RragResult<Option<IndexStats>> {
        let metadata = self.index_metadata.read().await;
        Ok(metadata.get(index_name).cloned())
    }

    /// Get all index statistics
    pub async fn get_all_index_stats(&self) -> RragResult<HashMap<String, IndexStats>> {
        let metadata = self.index_metadata.read().await;
        Ok(metadata.clone())
    }

    /// Get system metrics
    pub async fn get_metrics(&self) -> VectorUpdateMetrics {
        let mut metrics = self.metrics.read().await.clone();

        // Update real-time metrics
        metrics.system_performance.queue_depth = {
            let pending = self.pending_operations.read().await;
            pending.len()
        };

        metrics.system_performance.active_operations = {
            let batches = self.active_batches.read().await;
            batches.len()
        };

        metrics.last_updated = chrono::Utc::now();
        metrics
    }

    /// Get operation history
    pub async fn get_operation_history(
        &self,
        limit: Option<usize>,
    ) -> RragResult<Vec<VectorOperationResult>> {
        let history = self.operation_history.read().await;
        let limit = limit.unwrap_or(history.len());
        Ok(history.iter().rev().take(limit).cloned().collect())
    }

    /// Health check
    pub async fn health_check(&self) -> RragResult<bool> {
        let handles = self.task_handles.lock().await;
        let all_running = handles.iter().all(|handle| !handle.is_finished());

        let metrics = self.get_metrics().await;
        let healthy_performance = metrics.system_performance.health_score > 0.8;
        let low_error_rate = metrics.success_rate > 0.9;

        Ok(all_running && healthy_performance && low_error_rate)
    }

    /// Start background processing tasks
    async fn start_background_tasks(&self) -> RragResult<()> {
        let mut handles = self.task_handles.lock().await;

        // Operation processor
        handles.push(self.start_operation_processor().await);

        // Batch processor
        if self.config.enable_batch_processing {
            handles.push(self.start_batch_processor().await);
        }

        // Index optimizer
        if self.config.enable_optimization {
            handles.push(self.start_index_optimizer().await);
        }

        // Metrics collector
        if self.config.monitoring.enable_performance_tracking {
            handles.push(self.start_metrics_collector().await);
        }

        Ok(())
    }

    /// Start operation processing task
    async fn start_operation_processor(&self) -> tokio::task::JoinHandle<()> {
        let pending_operations = Arc::clone(&self.pending_operations);

        tokio::spawn(async move {
            loop {
                let operation = {
                    let mut pending = pending_operations.write().await;
                    pending.pop_front()
                };

                if let Some(_op) = operation {
                    // Process operation (simplified)
                    tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;
                } else {
                    // No operations pending
                    tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;
                }
            }
        })
    }

    /// Start batch processing task
    async fn start_batch_processor(&self) -> tokio::task::JoinHandle<()> {
        let active_batches = Arc::clone(&self.active_batches);
        let config = self.config.clone();

        tokio::spawn(async move {
            let mut interval =
                tokio::time::interval(tokio::time::Duration::from_millis(config.batch_timeout_ms));

            loop {
                interval.tick().await;

                // Process ready batches
                let batches_to_process = {
                    let batches = active_batches.read().await;
                    batches.values().cloned().collect::<Vec<_>>()
                };

                for batch in batches_to_process {
                    if batch.operations.len() >= config.max_batch_size
                        || chrono::Utc::now()
                            .signed_duration_since(batch.created_at)
                            .num_milliseconds()
                            >= config.batch_timeout_ms as i64
                    {
                        // Process batch
                        {
                            let mut batches = active_batches.write().await;
                            batches.remove(&batch.batch_id);
                        }
                        // Would process the batch here
                    }
                }
            }
        })
    }

    /// Start index optimization task
    async fn start_index_optimizer(&self) -> tokio::task::JoinHandle<()> {
        let index_metadata = Arc::clone(&self.index_metadata);
        let config = self.config.clone();

        tokio::spawn(async move {
            let mut interval = tokio::time::interval(tokio::time::Duration::from_secs(
                config.optimization_interval_secs,
            ));

            loop {
                interval.tick().await;

                // Check which indexes need optimization
                let indexes_to_optimize = {
                    let metadata = index_metadata.read().await;
                    metadata
                        .keys()
                        .filter(|index_name| {
                            if let Some(stats) = metadata.get(*index_name) {
                                // Simple heuristic: optimize if not done in last hour
                                stats.last_optimized_at.map_or(true, |last_opt| {
                                    chrono::Utc::now()
                                        .signed_duration_since(last_opt)
                                        .num_hours()
                                        >= 1
                                })
                            } else {
                                false
                            }
                        })
                        .cloned()
                        .collect::<Vec<String>>()
                };

                // Trigger optimization for eligible indexes
                for index_name in indexes_to_optimize {
                    // Would trigger optimization here
                    println!("Triggering optimization for index: {}", index_name);
                }
            }
        })
    }

    /// Start metrics collection task
    async fn start_metrics_collector(&self) -> tokio::task::JoinHandle<()> {
        let metrics = Arc::clone(&self.metrics);
        let config = self.config.clone();

        tokio::spawn(async move {
            let mut interval = tokio::time::interval(tokio::time::Duration::from_secs(
                config.monitoring.metrics_interval_secs,
            ));

            loop {
                interval.tick().await;

                // Update system metrics
                let mut metrics_guard = metrics.write().await;

                metrics_guard.system_performance = SystemPerformanceMetrics {
                    overall_throughput_eps: 100.0, // Would be calculated
                    memory_usage_mb: 256.0,        // Would be measured
                    cpu_utilization_percent: 45.0, // Would be measured
                    queue_depth: 0,                // Updated elsewhere
                    active_operations: 0,          // Updated elsewhere
                    health_score: 0.95,            // Would be calculated
                };

                metrics_guard.last_updated = chrono::Utc::now();
            }
        })
    }

    /// Add operation to batch
    async fn add_to_batch(&self, operation: VectorOperation) -> RragResult<()> {
        let index_name = self.extract_index_name(&operation)?;
        let batch_id = format!("batch_{}", index_name);

        let mut batches = self.active_batches.write().await;
        let batch = batches
            .entry(batch_id.clone())
            .or_insert_with(|| VectorBatch {
                batch_id: batch_id.clone(),
                operations: Vec::new(),
                created_at: chrono::Utc::now(),
                index_name: index_name.clone(),
                priority: 5,
                estimated_duration_ms: 1000,
                metadata: HashMap::new(),
            });

        batch.operations.push(operation);
        Ok(())
    }

    /// Process operation immediately
    async fn process_immediate(&self, _operation: VectorOperation) -> RragResult<()> {
        // Would process the operation immediately
        tokio::time::sleep(tokio::time::Duration::from_millis(10)).await;
        Ok(())
    }

    /// Process single embedding update
    async fn process_single_embedding_update(
        &self,
        _update: &EmbeddingUpdate,
        _index_name: &str,
    ) -> RragResult<()> {
        // Placeholder for actual embedding update logic
        tokio::time::sleep(tokio::time::Duration::from_millis(5)).await;
        Ok(())
    }

    /// Update index statistics
    async fn update_index_stats(
        &self,
        index_name: &str,
        processed_count: usize,
    ) -> RragResult<IndexStats> {
        let mut metadata = self.index_metadata.write().await;
        let stats = metadata.entry(index_name.to_string()).or_insert_with(|| {
            IndexStats {
                index_name: index_name.to_string(),
                embedding_count: 0,
                size_bytes: 0,
                dimensions: 768, // Common embedding dimension
                index_type: "flat".to_string(),
                memory_usage_bytes: 0,
                last_optimized_at: None,
                quality_metrics: IndexQualityMetrics {
                    recall_at_10: 0.9,
                    precision: 0.85,
                    freshness_score: 1.0,
                    clustering_quality: 0.8,
                    distribution_balance: 0.75,
                },
                performance_metrics: IndexPerformanceMetrics {
                    avg_query_time_ms: 10.0,
                    p95_query_time_ms: 50.0,
                    queries_per_second: 100.0,
                    build_time_ms: 1000,
                    memory_efficiency: 0.8,
                },
            }
        });

        stats.embedding_count += processed_count;
        stats.size_bytes += processed_count as u64 * 768 * 4; // Rough estimate
        stats.memory_usage_bytes = stats.size_bytes * 2; // Rough estimate

        Ok(stats.clone())
    }

    /// Store operation result
    async fn store_operation_result(&self, result: VectorOperationResult) -> RragResult<()> {
        let mut history = self.operation_history.write().await;
        history.push_back(result.clone());

        // Limit history size
        if history.len() > 1000 {
            history.pop_front();
        }

        // Update metrics
        let mut metrics = self.metrics.write().await;
        metrics.total_operations += 1;
        metrics.total_embeddings_processed += result.embeddings_processed as u64;

        if result.success {
            metrics.success_rate = (metrics.success_rate * (metrics.total_operations - 1) as f32
                + 1.0)
                / metrics.total_operations as f32;
        } else {
            metrics.success_rate = (metrics.success_rate * (metrics.total_operations - 1) as f32)
                / metrics.total_operations as f32;
        }

        metrics.avg_processing_time_ms = (metrics.avg_processing_time_ms
            * (metrics.total_operations - 1) as f32
            + result.processing_time_ms as f32)
            / metrics.total_operations as f32;

        Ok(())
    }

    /// Extract index name from operation
    fn extract_index_name(&self, operation: &VectorOperation) -> RragResult<String> {
        match operation {
            VectorOperation::Add { index_name, .. } => Ok(index_name.clone()),
            VectorOperation::Update { index_name, .. } => Ok(index_name.clone()),
            VectorOperation::Remove { index_name, .. } => Ok(index_name.clone()),
            VectorOperation::Optimize { index_name, .. } => Ok(index_name.clone()),
            VectorOperation::Rebuild { index_name, .. } => Ok(index_name.clone()),
            VectorOperation::UpdateThresholds { index_name, .. } => Ok(index_name.clone()),
        }
    }

    /// Perform index optimization
    async fn perform_optimization(
        &self,
        _index_name: &str,
        _optimization_type: &OptimizationType,
    ) -> RragResult<OptimizationResult> {
        // Placeholder for actual optimization logic
        tokio::time::sleep(tokio::time::Duration::from_millis(1000)).await;

        Ok(OptimizationResult {
            success: true,
            embeddings_affected: 1000,
            peak_memory_usage: 100.0,
            io_operations: 1000,
            errors: Vec::new(),
            metadata: HashMap::new(),
            new_quality_metrics: IndexQualityMetrics {
                recall_at_10: 0.95,
                precision: 0.90,
                freshness_score: 1.0,
                clustering_quality: 0.85,
                distribution_balance: 0.80,
            },
            new_performance_metrics: IndexPerformanceMetrics {
                avg_query_time_ms: 8.0,
                p95_query_time_ms: 40.0,
                queries_per_second: 120.0,
                build_time_ms: 800,
                memory_efficiency: 0.85,
            },
        })
    }
}

/// Result of optimization operation
#[derive(Debug)]
struct OptimizationResult {
    success: bool,
    embeddings_affected: usize,
    peak_memory_usage: f32,
    io_operations: u64,
    errors: Vec<String>,
    metadata: HashMap<String, serde_json::Value>,
    new_quality_metrics: IndexQualityMetrics,
    new_performance_metrics: IndexPerformanceMetrics,
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::Embedding;

    #[tokio::test]
    async fn test_vector_update_manager_creation() {
        let config = VectorUpdateConfig::default();
        let manager = VectorUpdateManager::new(config).await.unwrap();
        assert!(manager.health_check().await.unwrap());
    }

    #[tokio::test]
    async fn test_submit_operation() {
        let manager = VectorUpdateManager::new(VectorUpdateConfig::default())
            .await
            .unwrap();

        let embedding = Embedding::new("test_id".to_string(), vec![0.1, 0.2, 0.3]);
        let operation = VectorOperation::Add {
            embeddings: vec![embedding],
            index_name: "test_index".to_string(),
        };

        let op_id = manager.submit_operation(operation).await.unwrap();
        assert!(!op_id.is_empty());
    }

    #[tokio::test]
    async fn test_embedding_updates() {
        let manager = VectorUpdateManager::new(VectorUpdateConfig::default())
            .await
            .unwrap();

        let embedding = Embedding::new("test_id".to_string(), vec![0.1, 0.2, 0.3]);
        let update = EmbeddingUpdate {
            embedding_id: "test_id".to_string(),
            new_embedding: embedding,
            update_reason: UpdateReason::ContentChanged,
            metadata: HashMap::new(),
        };

        let result = manager
            .process_embedding_updates(vec![update], "test_index")
            .await
            .unwrap();

        assert!(result.success);
        assert_eq!(result.embeddings_processed, 1);
    }

    #[tokio::test]
    async fn test_index_optimization() {
        let manager = VectorUpdateManager::new(VectorUpdateConfig::default())
            .await
            .unwrap();

        let result = manager
            .optimize_index("test_index", OptimizationType::Compact)
            .await
            .unwrap();

        assert!(result.success);
        assert!(result.processing_time_ms > 0);
    }

    #[tokio::test]
    async fn test_metrics_collection() {
        let manager = VectorUpdateManager::new(VectorUpdateConfig::default())
            .await
            .unwrap();

        // Submit some operations to generate metrics
        let embedding = Embedding::new("test_id".to_string(), vec![0.1, 0.2, 0.3]);
        let operation = VectorOperation::Add {
            embeddings: vec![embedding],
            index_name: "test_index".to_string(),
        };

        manager.submit_operation(operation).await.unwrap();

        let metrics = manager.get_metrics().await;
        assert!(metrics.system_performance.health_score >= 0.0);
        assert!(metrics.system_performance.health_score <= 1.0);
    }

    #[test]
    fn test_update_strategies() {
        let strategies = vec![
            IndexUpdateStrategy::Immediate,
            IndexUpdateStrategy::Batch,
            IndexUpdateStrategy::Lazy,
            IndexUpdateStrategy::Adaptive,
            IndexUpdateStrategy::Custom("custom".to_string()),
        ];

        // Ensure all strategies are different
        for (i, strategy1) in strategies.iter().enumerate() {
            for (j, strategy2) in strategies.iter().enumerate() {
                if i != j {
                    assert_ne!(format!("{:?}", strategy1), format!("{:?}", strategy2));
                }
            }
        }
    }

    #[test]
    fn test_optimization_types() {
        let opt_types = vec![
            OptimizationType::Compact,
            OptimizationType::RebuildTrees,
            OptimizationType::UpdateClustering,
            OptimizationType::QueryOptimization,
            OptimizationType::MemoryOptimization,
            OptimizationType::Full,
        ];

        // Ensure all optimization types are different
        for (i, type1) in opt_types.iter().enumerate() {
            for (j, type2) in opt_types.iter().enumerate() {
                if i != j {
                    assert_ne!(format!("{:?}", type1), format!("{:?}", type2));
                }
            }
        }
    }
}