kandil_code 0.1.0

//! Deployment agent
//! 
//! Specialized agent for managing deployments and CI/CD pipelines

use anyhow::Result;
use async_trait::async_trait;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use crate::core::agents::base::{Agent, AgentState};
use crate::core::adapters::ai::KandilAI;
use std::sync::Arc;

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeploymentPlan {
    pub environment: String,
    pub steps: Vec<DeploymentStep>,
    pub dependencies: Vec<String>,
    pub rollback_plan: RollbackPlan,
    pub estimated_duration: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeploymentStep {
    pub id: String,
    pub name: String,
    pub description: String,
    pub command: String,
    pub dependencies: Vec<String>,
    pub timeout: u64, // in seconds
    pub success_conditions: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RollbackPlan {
    pub steps: Vec<RollbackStep>,
    pub conditions: Vec<String>,
    pub notification_targets: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RollbackStep {
    pub id: String,
    pub action: String,
    pub command: String,
    pub description: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeploymentResult {
    pub success: bool,
    pub steps_executed: u32,
    pub steps_failed: u32,
    pub duration_seconds: u64,
    pub logs: Vec<String>,
    pub artifacts: Vec<String>,
}

pub struct DeploymentAgent {
    ai: Arc<KandilAI>,
    pub environment_configs: HashMap<String, EnvironmentConfig>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EnvironmentConfig {
    pub name: String,
    pub url: String,
    pub credentials: String, // This would be handled securely in practice
    pub allowed_deploy_times: Vec<String>,
    pub health_check_url: String,
    pub backup_locations: Vec<String>,
}

impl DeploymentAgent {
    pub fn new(ai: Arc<KandilAI>) -> Result<Self> {
        let mut env_configs = HashMap::new();
        
        // Add default environment configurations
        env_configs.insert("dev".to_string(), EnvironmentConfig {
            name: "Development".to_string(),
            url: "https://dev.example.com".to_string(),
            credentials: "dev-credentials".to_string(),
            allowed_deploy_times: vec!["any".to_string()],
            health_check_url: "https://dev.example.com/health".to_string(),
            backup_locations: vec!["s3://dev-backups".to_string()],
        });
        
        env_configs.insert("staging".to_string(), EnvironmentConfig {
            name: "Staging".to_string(),
            url: "https://staging.example.com".to_string(),
            credentials: "staging-credentials".to_string(),
            allowed_deploy_times: vec!["09:00-17:00".to_string()],
            health_check_url: "https://staging.example.com/health".to_string(),
            backup_locations: vec!["s3://staging-backups".to_string()],
        });
        
        env_configs.insert("prod".to_string(), EnvironmentConfig {
            name: "Production".to_string(),
            url: "https://example.com".to_string(),
            credentials: "prod-credentials".to_string(),
            allowed_deploy_times: vec!["01:00-05:00".to_string()],
            health_check_url: "https://example.com/health".to_string(),
            backup_locations: vec!["s3://prod-backups".to_string(), "gcs://prod-backups".to_string()],
        });

        Ok(Self {
            ai,
            environment_configs: env_configs,
        })
    }

    pub async fn create_deployment_plan(&self, environment: &str, app_name: &str) -> Result<DeploymentPlan> {
        let config = self.environment_configs.get(environment)
            .ok_or_else(|| anyhow::anyhow!("Environment {} not found", environment))?;
            
        let prompt = format!(
            r#"Create a deployment plan for {} to {} environment.
            
            Environment details:
            - URL: {}
            - Health check: {}
            - Backups: {:?}
            
            Include steps for:
            - Pre-deployment checks
            - Backup procedures
            - Actual deployment
            - Post-deployment validation
            - Rollback procedures if needed
            "#,
            app_name, config.name, config.url, config.health_check_url, config.backup_locations
        );

        let result = self.ai.chat(&prompt).await?;
        
        // In a real implementation, this would parse the structured response
        // For now, we'll return a basic plan
        Ok(DeploymentPlan {
            environment: environment.to_string(),
            steps: vec![
                DeploymentStep {
                    id: "backup".to_string(),
                    name: "Create backup".to_string(),
                    description: "Backup current deployment".to_string(),
                    command: "kubectl create backup".to_string(),
                    dependencies: vec![],
                    timeout: 300,
                    success_conditions: vec!["Backup completed successfully".to_string()],
                },
                DeploymentStep {
                    id: "deploy".to_string(),
                    name: "Deploy application".to_string(),
                    description: "Deploy new version".to_string(),
                    command: "kubectl apply -f deployment.yaml".to_string(),
                    dependencies: vec!["backup".to_string()],
                    timeout: 600,
                    success_conditions: vec!["Pods are running".to_string()],
                }
            ],
            dependencies: vec![],
            rollback_plan: RollbackPlan {
                steps: vec![
                    RollbackStep {
                        id: "rollback".to_string(),
                        action: "Rollback deployment".to_string(),
                        command: "kubectl rollout undo".to_string(),
                        description: "Rollback to previous version".to_string(),
                    }
                ],
                conditions: vec!["Health check fails".to_string(), "Manual trigger".to_string()],
                notification_targets: vec!["dev-team@example.com".to_string()],
            },
            estimated_duration: "10-15 minutes".to_string(),
        })
    }

    pub async fn execute_deployment(&self, plan: &DeploymentPlan) -> Result<DeploymentResult> {
        println!("Starting deployment to {} environment...", plan.environment);
        
        // In a real implementation, this would execute the actual deployment steps
        // For simulation, we'll return mock results
        Ok(DeploymentResult {
            success: true,
            steps_executed: 2,
            steps_failed: 0,
            duration_seconds: 320,
            logs: vec![
                "Backup completed successfully".to_string(),
                "Deploying application...".to_string(),
                "Application started successfully".to_string(),
                "Health checks passed".to_string(),
            ],
            artifacts: vec!["deployment-artifact-v1.2.3".to_string()],
        })
    }
}

#[async_trait]
impl Agent for DeploymentAgent {
    async fn plan(&self, state: &AgentState) -> Result<String> {
        let prompt = format!(
            "Given this deployment task: {}\n\nPlan the next deployment step. Consider environment constraints, dependencies, and risk mitigation.",
            state.task
        );
        
        self.ai.chat(&prompt).await
    }

    async fn act(&self, plan: &str) -> Result<String> {
        // Execute the planned deployment action
        let prompt = format!(
            "Execute this deployment plan: {}\n\nSimulate the deployment process and report outcomes.",
            plan
        );
        
        self.ai.chat(&prompt).await
    }

    async fn observe(&self, result: &str) -> Result<String> {
        // Analyze the deployment results
        let prompt = format!(
            "Analyze these deployment results: {}\n\nWhat does this tell us about the deployment success and any issues encountered?",
            result
        );
        
        self.ai.chat(&prompt).await
    }
}