amberjs 1.17.0

// AI Ops Engine
//
// Main engine that coordinates all AI Ops functionality.

use super::data_collector::{DataCollector, Metric, PerformanceSnapshot};
use super::error::{AIOpsError, Result};
use super::model_manager::{ModelManager, ModelType};
use serde::{Deserialize, Serialize};
use std::sync::{Arc, RwLock};
use std::time::Duration;

/// AI Ops configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AIOpsConfig {
    /// Data collection interval
    pub collection_interval: Duration,
    /// Model update interval
    pub model_update_interval: Duration,
    /// Enable failure prediction
    pub enable_failure_prediction: bool,
    /// Enable performance optimization
    pub enable_performance_optimization: bool,
    /// Enable resource allocation
    pub enable_resource_allocation: bool,
    /// Enable architecture adaptation
    pub enable_architecture_adaptation: bool,
}
impl Default for AIOpsConfig {
    fn default() -> Self {
        Self {
            collection_interval: Duration::from_secs(5),
            model_update_interval: Duration::from_secs(60),
            enable_failure_prediction: true,
            enable_performance_optimization: true,
            enable_resource_allocation: true,
            enable_architecture_adaptation: true,
        }
    }
}
/// AI Ops engine status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum EngineStatus {
    /// Engine is stopped
    Stopped,
    /// Engine is starting
    Starting,
    /// Engine is running
    Running,
    /// Engine is stopping
    Stopping,
    /// Engine has error
    Error(String),
}
/// AI Ops operation result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AIOpsResult {
    /// Operation success status
    pub success: bool,
    /// Operation message
    pub message: String,
    /// Operation timestamp
    pub timestamp: Duration,
    /// Additional data
    pub data: Option<serde_json::Value>,
}
/// AI Ops Engine
///
/// Main orchestrator for AI-driven operations including:
/// - Failure prediction
/// - Performance optimization
/// - Resource allocation
/// - Architecture adaptation
pub struct AIOpsEngine {
    /// Engine configuration
    config: AIOpsConfig,
    /// Engine status
    status: Arc<RwLock<EngineStatus>>,
    /// Model manager
    model_manager: Arc<ModelManager>,
    /// Data collector
    data_collector: Arc<DataCollector>,
}
impl AIOpsEngine {
    /// Create a new AI Ops engine
    ///
    /// # Arguments
    ///
    /// * `config` - Engine configuration
    ///
    /// # Returns
    ///
    /// Returns `AIOpsEngine` instance
    pub fn new(config: AIOpsConfig) -> Self {
        Self {
            config: config.clone(),
            status: Arc::new(RwLock::new(EngineStatus::Stopped)),
            model_manager: Arc::new(ModelManager::new()),
            data_collector: Arc::new(DataCollector::new(config.collection_interval)),
        }
    }
    /// Start the AI Ops engine
    ///
    /// # Returns
    ///
    /// Returns `Result<()>` indicating success or failure
    pub async fn start(&self) -> Result<()> {
        {
            let mut status = self.status.write().await;
            *status = EngineStatus::Starting;
        }
        // Start data collection
        self.data_collector
            .start()
            .await
            .map_err(|e| AIOpsError::Other(format!("Failed to start data collector: {}", e)))?;
        // Load default models
        self.load_default_models().await?;
        // Update status to running
        {
            let mut status = self.status.write().await;
            *status = EngineStatus::Running;
        }
        Ok(())
    }
    /// Stop the AI Ops engine
    ///
    /// # Returns
    ///
    /// Returns `Result<()>` indicating success or failure
    pub async fn stop(&self) -> Result<()> {
        {
            let mut status = self.status.write().await;
            *status = EngineStatus::Stopping;
        }
        // TODO: Stop all background tasks
        // TODO: Save model state
        // TODO: Cleanup resources
        // Update status to stopped
        {
            let mut status = self.status.write().await;
            *status = EngineStatus::Stopped;
        }
        Ok(())
    }
    /// Get engine status
    ///
    /// # Returns
    ///
    /// Returns `EngineStatus` indicating current engine state
    pub async fn get_status(&self) -> EngineStatus {
        let status: _ = self.status.read().await;
        status.clone()
    }
    /// Get latest performance metrics
    ///
    /// # Returns
    ///
    /// Returns `Vec<Metric>` containing latest metrics
    pub async fn get_latest_metrics(&self) -> Vec<Metric> {
        self.data_collector.get_latest_metrics().await
    }
    /// Get performance history
    ///
    /// # Arguments
    ///
    /// * `count` - Number of snapshots to retrieve
    ///
    /// # Returns
    ///
    /// Returns `Vec<PerformanceSnapshot>` containing historical data
    pub async fn get_performance_history(&self, count: usize) -> Vec<PerformanceSnapshot> {
        self.data_collector.get_history(count).await
    }
    /// Predict potential failures
    ///
    /// # Returns
    ///
    /// Returns `AIOpsResult` containing prediction results
    pub async fn predict_failures(&self) -> Result<AIOpsResult> {
        if !self.config.enable_failure_prediction {
            return Ok(AIOpsResult {
                success: true,
                message: "Failure prediction disabled".to_string(),
                timestamp: std::time::Duration::from_secs(0),
                data: None,
            });
        }
        // TODO: Implement actual failure prediction logic
        // This would use the loaded models to analyze metrics and predict failures
        Ok(AIOpsResult {
            success: true,
            message: "Failure prediction completed".to_string(),
            timestamp: std::time::Duration::from_secs(0),
            data: Some(serde_json::json!({
                "predictions": [],
                "confidence": 0.0,
            })),
        })
    }
    /// Optimize performance
    ///
    /// # Returns
    ///
    /// Returns `AIOpsResult` containing optimization results
    pub async fn optimize_performance(&self) -> Result<AIOpsResult> {
        if !self.config.enable_performance_optimization {
            return Ok(AIOpsResult {
                success: true,
                message: "Performance optimization disabled".to_string(),
                timestamp: std::time::Duration::from_secs(0),
                data: None,
            });
        }
        // TODO: Implement actual performance optimization logic
        // This would analyze performance metrics and suggest optimizations
        Ok(AIOpsResult {
            success: true,
            message: "Performance optimization completed".to_string(),
            timestamp: std::time::Duration::from_secs(0),
            data: Some(serde_json::json!({
                "optimizations": [],
                "improvement": 0.0,
            })),
        })
    }
    /// Allocate resources intelligently
    ///
    /// # Returns
    ///
    /// Returns `AIOpsResult` containing allocation results
    pub async fn allocate_resources(&self) -> Result<AIOpsResult> {
        if !self.config.enable_resource_allocation {
            return Ok(AIOpsResult {
                success: true,
                message: "Resource allocation disabled".to_string(),
                timestamp: std::time::Duration::from_secs(0),
                data: None,
            });
        }
        // TODO: Implement actual resource allocation logic
        // This would analyze resource usage and suggest optimal allocation
        Ok(AIOpsResult {
            success: true,
            message: "Resource allocation completed".to_string(),
            timestamp: std::time::Duration::from_secs(0),
            data: Some(serde_json::json!({
                "allocations": [],
                "efficiency": 0.0,
            })),
        })
    }
    /// Adapt architecture
    ///
    /// # Returns
    ///
    /// Returns `AIOpsResult` containing adaptation results
    pub async fn adapt_architecture(&self) -> Result<AIOpsResult> {
        if !self.config.enable_architecture_adaptation {
            return Ok(AIOpsResult {
                success: true,
                message: "Architecture adaptation disabled".to_string(),
                timestamp: std::time::Duration::from_secs(0),
                data: None,
            });
        }
        // TODO: Implement actual architecture adaptation logic
        // This would analyze system architecture and suggest improvements
        Ok(AIOpsResult {
            success: true,
            message: "Architecture adaptation completed".to_string(),
            timestamp: std::time::Duration::from_secs(0),
            data: Some(serde_json::json!({
                "adaptations": [],
                "performance_gain": 0.0,
            })),
        })
    }
    /// Load default AI models
    ///
    /// # Returns
    ///
    /// Returns `Result<()>` indicating success or failure
    async fn load_default_models(&self) -> Result<()> {
        // Load anomaly detection model
        self.model_manager
            .load_model(
                "anomaly_detection_v1",
                ModelType::AnomalyDetection,
                vec![], // Empty model data for now
            )
            .await?;
        // Load trend prediction model
        self.model_manager
            .load_model(
                "trend_prediction_v1",
                ModelType::TrendPrediction,
                vec![], // Empty model data for now
            )
            .await?;
        // Load failure prediction model
        self.model_manager
            .load_model(
                "failure_prediction_v1",
                ModelType::FailurePrediction,
                vec![], // Empty model data for now
            )
            .await?;
        Ok(())
    }
}
impl Default for AIOpsEngine {
    fn default() -> Self {
        Self::new(AIOpsConfig::default())
    }
}