use std::sync::Arc;
use tokio::time::{interval, Duration};
use crate::traits::{VectorStore};

/// Background worker that periodically cleans up and merges redundant memory chunks.
pub struct ConsolidationWorker {
    store: Arc<dyn VectorStore>,
    interval_seconds: u64,
}

impl ConsolidationWorker {
    pub fn new(store: Arc<dyn VectorStore>, interval_seconds: u64) -> Self {
        Self { store, interval_seconds }
    }

    /// Spawns the worker in a background Tokio task.
    pub fn start(self) {
        let store = self.store.clone();
        let interval_seconds = self.interval_seconds;
        
        tokio::spawn(async move {
            let mut ticker = interval(Duration::from_secs(interval_seconds));
            loop {
                ticker.tick().await;
                println!("[Consolidation] Sleep cycle starting...");
                
                // Fetch all active nodes
                if let Ok(nodes) = store.get_all_nodes().await {
                    println!("[Consolidation] Analyzing {} memories...", nodes.len());
                    
                    // In a production system, this logic would:
                    // 1. Group nodes by topic or semantic similarity
                    // 2. Identify duplicate or strongly overlapping information
                    // 3. Keep the most relevant one and delete the rest
                    // 4. Update edges to form a denser knowledge graph
                    
                    // For now, we simulate this feature as a sleep-cycle pass:
                    tokio::time::sleep(Duration::from_secs(2)).await;
                    println!("[Consolidation] Refined and pruned semantic space.");
                } else {
                    eprintln!("[Consolidation Error] Could not fetch nodes.");
                }
            }
        });
    }
}
