AI Service Lock File Enhancement

pub struct AIService {
    config: AIServiceConfig,
    _cache: Arc<TimedCache<String, AIResponse>>,
    models: Arc<RwLock<HashMap<String, AIModel>>>,
    metrics: Arc<RwLock<AIServiceMetrics>>,
    client: reqwest::Client,
    // Lock file awareness components
    lock_file_integration: Option<Arc<LockFileAIIntegration>>,
    context_injector: Option<Arc<EnhancedContextInjector>>,
    lock_file_cache: Arc<RwLock<Option<LockFileAIContext>>>,
    dependency_version_cache: Arc<RwLock<HashMap<String, String>>>,
}

pub struct AIServiceConfig {
    // ... existing fields ...
    
    // Lock file awareness configuration
    pub enable_lock_file_awareness: bool,
    pub lock_file_path: Option<PathBuf>,
    pub auto_validate_lock_file: bool,
    pub conflict_prevention_enabled: bool,
    pub dependency_version_consistency: bool,
}

pub struct AIRequest {
    // ... existing fields ...
    
    // Lock file context
    pub lock_file_context: Option<LockFileRequestContext>,
    pub task_type: Option<TaskType>,
    pub scope_path: Option<String>,
}
 
pub struct LockFileRequestContext {
    pub include_dependency_versions: bool,
    pub include_conflict_prevention: bool,
    pub include_health_info: bool,
    pub include_recommendations: bool,
    pub target_scopes: Option<Vec<String>>,
    pub include_transitive_deps: bool,
    pub validate_before_processing: bool,
    pub conflict_resolution_mode: ConflictResolutionMode,
}

pub struct AIResponse {
    // ... existing fields ...
    
    // Lock file awareness
    pub lock_file_validation: Option<LockFileValidationResult>,
    pub dependency_consistency_check: Option<DependencyConsistencyResult>,
    pub conflict_analysis: Option<ConflictAnalysisResult>,
    pub recommendations: Option<Vec<AIRecommendation>>,
}

use rhema_ai::ai_service::{AIService, AIServiceConfig, AIRequest, LockFileRequestContext, ConflictResolutionMode};
use rhema_ai::context_injection::TaskType;
 
// Configure AI service with lock file awareness
let config = AIServiceConfig {
    // ... basic configuration ...
    enable_lock_file_awareness: true,
    lock_file_path: Some(PathBuf::from(".")),
    auto_validate_lock_file: true,
    conflict_prevention_enabled: true,
    dependency_version_consistency: true,
};
 
let ai_service = Arc::new(AIService::new(config).await?);
 
// Create request with lock file context
let lock_context = LockFileRequestContext {
    include_dependency_versions: true,
    include_conflict_prevention: true,
    include_health_info: true,
    include_recommendations: true,
    target_scopes: Some(vec!["crates/rhema-core".to_string()]),
    include_transitive_deps: true,
    validate_before_processing: true,
    conflict_resolution_mode: ConflictResolutionMode::Automatic,
};
 
let request = AIRequest {
    id: uuid::Uuid::new_v4().to_string(),
    prompt: "Analyze the current dependency structure and provide recommendations.".to_string(),
    model: "gpt-4".to_string(),
    temperature: 0.3,
    max_tokens: 1000,
    user_id: Some("example-user".to_string()),
    session_id: Some("example-session".to_string()),
    created_at: chrono::Utc::now(),
    lock_file_context: Some(lock_context),
    task_type: Some(TaskType::DependencyUpdate),
    scope_path: Some("crates/rhema-core".to_string()),
};
 
let response = ai_service.process_request(request).await?;
 
// Access lock file awareness results
if let Some(validation) = &response.lock_file_validation {
    println!("Lock file validation: {} (score: {:.1}/100)", 
        if validation.is_valid { "PASSED" } else { "FAILED" }, 
        validation.validation_score);
}
 
if let Some(consistency) = &response.dependency_consistency_check {
    println!("Dependency consistency: {}", 
        if consistency.is_consistent { "CONSISTENT" } else { "INCONSISTENT" });
}
 
if let Some(recommendations) = &response.recommendations {
    for rec in recommendations {
        println!("Recommendation: {} ({:?} priority)", rec.title, rec.priority);
    }
}

// Simulate multiple AI agents working on the same project
let agents = vec!["agent-1", "agent-2", "agent-3"];
 
for agent_id in agents {
    let lock_context = LockFileRequestContext {
        include_dependency_versions: true,
        include_conflict_prevention: true,
        include_health_info: false,
        include_recommendations: false,
        target_scopes: None,
        include_transitive_deps: true,
        validate_before_processing: true,
        conflict_resolution_mode: ConflictResolutionMode::Automatic,
    };
 
    let request = AIRequest {
        // ... request configuration ...
        lock_file_context: Some(lock_context),
        task_type: Some(TaskType::DependencyUpdate),
        scope_path: None,
    };
 
    let response = ai_service.process_request(request).await?;
 
    if let Some(consistency) = &response.dependency_consistency_check {
        if consistency.is_consistent {
            println!("Agent {}: Dependencies are consistent", agent_id);
        } else {
            println!("Agent {}: Found {} version conflicts", 
                agent_id, consistency.version_conflicts.len());
        }
    }
}

use rhema_ai::agent::workflow_manager::{AIWorkflowManager, WorkflowConfig};
 
let workflow_manager = Arc::new(AIWorkflowManager::new(ai_service.clone(), PathBuf::from(".")).await?);
 
let workflow_config = WorkflowConfig {
    auto_validate: true,
    auto_resolve_conflicts: true,
    require_confirmation: false,
    rollback_on_failure: true,
    max_retry_attempts: 3,
    timeout_seconds: 300,
    include_security_checks: true,
    include_performance_checks: true,
};
 
// Start a workflow for dependency updates
let workflow_id = workflow_manager.start_workflow(
    "conflict-prevention-agent",
    TaskType::DependencyUpdate,
    Some("crates/rhema-core".to_string()),
    workflow_config.clone(),
).await?;
 
// Execute the workflow with conflict prevention
let prompt = "Update dependencies while preventing conflicts. Check for version compatibility and resolve any issues automatically.";
 
let result = workflow_manager.execute_workflow(&workflow_id, prompt, workflow_config).await?;
 
println!("Workflow completed: {}", if result.success { "SUCCESS" } else { "FAILED" });
println!("Actions taken: {}", result.actions_taken.len());
println!("Recommendations: {}", result.recommendations.len());

// Validate lock file before processing
let validation_result = ai_service.validate_lock_file_consistency().await?;
 
println!("Lock file validation result:");
println!("  Consistent: {}", validation_result.is_consistent);
println!("  Version conflicts: {}", validation_result.version_conflicts.len());
println!("  Circular dependencies: {}", validation_result.circular_dependencies.len());
println!("  Outdated dependencies: {}", validation_result.outdated_dependencies.len());
println!("  Security concerns: {}", validation_result.security_concerns.len());
 
if !validation_result.is_consistent {
    println!("⚠️  Lock file has issues that need attention");
    
    // Use AI to generate recommendations for fixing issues
    let lock_context = LockFileRequestContext {
        include_dependency_versions: true,
        include_conflict_prevention: true,
        include_health_info: true,
        include_recommendations: true,
        target_scopes: None,
        include_transitive_deps: true,
        validate_before_processing: false,
        conflict_resolution_mode: ConflictResolutionMode::Manual,
    };
 
    let request = AIRequest {
        prompt: "The lock file has validation issues. Provide specific steps to fix these problems.".to_string(),
        lock_file_context: Some(lock_context),
        task_type: Some(TaskType::LockFileManagement),
        // ... other fields ...
    };
 
    let response = ai_service.process_request(request).await?;
    println!("AI recommendations for fixing validation issues:");
    println!("{}", response.content);
}

use rhema_ai::agent::workflow_manager::AIAgentTools;
 
let agent_tools = AIAgentTools::new(workflow_manager.clone()).await;
 
// Use AI agent tools for conflict resolution
let conflict_result = agent_tools.resolve_dependency_conflicts(
    "conflict-resolution-agent",
    Some("crates/rhema-core".to_string())
).await?;
 
println!("Conflict resolution workflow: {}", conflict_result.workflow_id);
println!("Conflicts resolved: {}", conflict_result.conflicts_resolved);
println!("Actions taken: {}", conflict_result.actions_taken.len());
println!("Recommendations: {}", conflict_result.recommendations.len());
 
// Show detailed conflict resolution steps
for action in &conflict_result.actions_taken {
    println!("Action: {} ({})", 
        action.description,
        if action.success { "SUCCESS" } else { "FAILED" });
    
    if let Some(details) = &action.details {
        println!("  Details: {}", details);
    }
}

pub struct AIServiceMetrics {
    // ... existing metrics ...
    
    // Lock file awareness metrics
    pub lock_file_validation_requests: u64,
    pub conflict_resolution_requests: u64,
    pub dependency_consistency_checks: u64,
    pub ai_assisted_resolutions: u64,
    pub validation_failures: u64,
    pub conflict_detections: u64,
}

pub struct WorkflowStatistics {
    pub total_workflows: usize,
    pub active_workflows: usize,
    pub completed_workflows: usize,
    pub failed_workflows: usize,
    pub active_agents: usize,
    pub total_recommendations: usize,
    pub total_actions: usize,
    pub average_workflow_duration_ms: u64,
}

match ai_service.process_request(request).await {
    Ok(response) => {
        // Process successful response
        if let Some(validation) = &response.lock_file_validation {
            if !validation.is_valid {
                println!("Warning: Lock file validation failed");
            }
        }
    }
    Err(e) => {
        match e {
            RhemaError::ConfigError(msg) => {
                println!("Configuration error: {}", msg);
            }
            RhemaError::ValidationError(msg) => {
                println!("Validation error: {}", msg);
            }
            _ => {
                println!("Unexpected error: {}", e);
            }
        }
    }
}