oxirs-embed 0.2.4

//! Enterprise Knowledge Graph embeddings and analysis
//!
//! This module provides specialized embedding methods for enterprise knowledge graphs,
//! including product catalogs, organizational knowledge, business domain embeddings,
//! and market analysis capabilities.

use crate::{EmbeddingModel, Vector};
use anyhow::Result;
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use uuid::Uuid;

// =============================================================================
// Product Catalog Management
// =============================================================================

/// Product catalog embedding system for e-commerce and retail
#[derive(Debug, Clone)]
pub struct ProductCatalogEmbedder {
    /// Product embeddings cache
    product_embeddings: HashMap<String, Vector>,
    /// Category hierarchy
    category_hierarchy: CategoryHierarchy,
    /// Customer preference profiles
    customer_profiles: HashMap<String, CustomerProfile>,
    /// Market analysis engine
    market_analyzer: MarketAnalyzer,
    /// Recommendation system
    recommender: RecommendationEngine,
}

/// Category hierarchy for products
#[derive(Debug, Clone)]
pub struct CategoryHierarchy {
    /// Category tree structure
    categories: HashMap<String, Category>,
    /// Parent-child relationships
    hierarchy: HashMap<String, Vec<String>>,
    /// Category embeddings
    category_embeddings: HashMap<String, Vector>,
}

/// Product category definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Category {
    /// Category ID
    pub id: String,
    /// Category name
    pub name: String,
    /// Parent category ID
    pub parent_id: Option<String>,
    /// Category description
    pub description: String,
    /// Category attributes
    pub attributes: HashMap<String, String>,
    /// Product count in this category
    pub product_count: usize,
}

/// Customer preference profile
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CustomerProfile {
    /// Customer ID
    pub customer_id: String,
    /// Preference vector
    pub preferences: Vector,
    /// Purchase history embeddings
    pub purchase_history: Vec<Vector>,
    /// Preferred categories
    pub preferred_categories: Vec<String>,
    /// Price sensitivity
    pub price_sensitivity: f64,
    /// Brand preferences
    pub brand_preferences: HashMap<String, f64>,
    /// Seasonal preferences
    pub seasonal_patterns: HashMap<String, f64>,
}

/// Market analysis engine
#[derive(Debug, Clone)]
pub struct MarketAnalyzer {
    /// Market trends
    trends: HashMap<String, TrendAnalysis>,
    /// Competitive analysis
    competitor_analysis: HashMap<String, CompetitorProfile>,
    /// Market segments
    segments: Vec<MarketSegment>,
}

/// Trend analysis for market insights
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrendAnalysis {
    /// Trend category
    pub category: String,
    /// Trend direction (up, down, stable)
    pub direction: TrendDirection,
    /// Trend strength (0.0 to 1.0)
    pub strength: f64,
    /// Predicted duration in months
    pub duration_months: u32,
    /// Related keywords
    pub keywords: Vec<String>,
    /// Market impact score
    pub impact_score: f64,
}

/// Trend direction enum
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TrendDirection {
    Rising,
    Declining,
    Stable,
    Volatile,
}

/// Competitor profile for analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompetitorProfile {
    /// Competitor ID
    pub competitor_id: String,
    /// Market share
    pub market_share: f64,
    /// Strength areas
    pub strengths: Vec<String>,
    /// Weakness areas
    pub weaknesses: Vec<String>,
    /// Product portfolio similarity
    pub portfolio_similarity: f64,
    /// Pricing strategy
    pub pricing_strategy: PricingStrategy,
}

/// Market segment definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MarketSegment {
    /// Segment ID
    pub segment_id: String,
    /// Segment name
    pub name: String,
    /// Target demographics
    pub demographics: HashMap<String, String>,
    /// Segment size
    pub size: usize,
    /// Growth rate
    pub growth_rate: f64,
    /// Key characteristics
    pub characteristics: Vec<String>,
}

/// Pricing strategy types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PricingStrategy {
    Premium,
    Competitive,
    ValueBased,
    Penetration,
    Skimming,
}

/// Recommendation engine for products
#[derive(Debug, Clone)]
pub struct RecommendationEngine {
    /// Collaborative filtering model
    collaborative_model: CollaborativeModel,
    /// Content-based filtering
    content_model: ContentModel,
    /// Hybrid recommendation weights
    hybrid_weights: HybridWeights,
}

/// Collaborative filtering model
#[derive(Debug, Clone)]
pub struct CollaborativeModel {
    /// User-item interaction matrix
    interaction_matrix: HashMap<(String, String), f64>,
    /// User similarity matrix
    user_similarity: HashMap<(String, String), f64>,
    /// Item similarity matrix
    item_similarity: HashMap<(String, String), f64>,
}

/// Content-based filtering model
#[derive(Debug, Clone)]
pub struct ContentModel {
    /// Item feature vectors
    item_features: HashMap<String, Vector>,
    /// User preference vectors
    user_preferences: HashMap<String, Vector>,
    /// Feature importance weights
    feature_weights: Vector,
}

/// Hybrid recommendation weights
#[derive(Debug, Clone)]
pub struct HybridWeights {
    /// Collaborative filtering weight
    pub collaborative_weight: f64,
    /// Content-based weight
    pub content_weight: f64,
    /// Knowledge-based weight
    pub knowledge_weight: f64,
    /// Popularity weight
    pub popularity_weight: f64,
}

impl ProductCatalogEmbedder {
    /// Create new product catalog embedder
    pub fn new() -> Self {
        Self {
            product_embeddings: HashMap::new(),
            category_hierarchy: CategoryHierarchy::new(),
            customer_profiles: HashMap::new(),
            market_analyzer: MarketAnalyzer::new(),
            recommender: RecommendationEngine::new(),
        }
    }

    /// Generate product embeddings based on features
    pub async fn embed_product(&mut self, product_id: &str, features: &ProductFeatures) -> Result<Vector> {
        // Create feature vector from product attributes
        let mut feature_vector = Vec::new();
        
        // Basic product features
        feature_vector.extend(self.encode_text_features(&features.name)?);
        feature_vector.extend(self.encode_text_features(&features.description)?);
        feature_vector.extend(self.encode_categorical_features(&features.category)?);
        
        // Numerical features
        feature_vector.push(features.price as f32);
        feature_vector.push(features.rating);
        feature_vector.push(features.review_count as f32);
        
        // Brand encoding
        feature_vector.extend(self.encode_brand(&features.brand)?);
        
        // Normalize and create vector
        let embedding = Vector::new(self.normalize_vector(feature_vector));
        
        // Cache the embedding
        self.product_embeddings.insert(product_id.to_string(), embedding.clone());
        
        Ok(embedding)
    }

    /// Calculate product similarity
    pub fn calculate_product_similarity(&self, product1: &str, product2: &str) -> f64 {
        match (self.product_embeddings.get(product1), self.product_embeddings.get(product2)) {
            (Some(emb1), Some(emb2)) => self.cosine_similarity(emb1, emb2),
            _ => 0.0,
        }
    }

    /// Build category hierarchy embeddings
    pub fn build_category_hierarchy(&mut self, categories: Vec<Category>) -> Result<()> {
        self.category_hierarchy.build_hierarchy(categories)?;
        
        // Generate embeddings for each category
        for (category_id, category) in &self.category_hierarchy.categories {
            let category_text = format!("{} {}", category.name, category.description);
            let embedding = self.encode_text_features(&category_text)?;
            self.category_hierarchy.category_embeddings.insert(
                category_id.clone(),
                Vector::new(embedding)
            );
        }
        
        Ok(())
    }

    /// Generate product recommendations for a customer
    pub fn recommend_products(&self, customer_id: &str, k: usize) -> Vec<(String, f64)> {
        if let Some(profile) = self.customer_profiles.get(customer_id) {
            self.recommender.generate_recommendations(profile, k)
        } else {
            Vec::new()
        }
    }

    /// Analyze market trends for a product category
    pub fn analyze_category_trends(&self, category_id: &str) -> Option<TrendAnalysis> {
        self.market_analyzer.get_trend_analysis(category_id)
    }

    /// Update customer profile based on purchase
    pub fn update_customer_profile(&mut self, customer_id: &str, product_id: &str, rating: f64) {
        if let Some(product_embedding) = self.product_embeddings.get(product_id) {
            let profile = self.customer_profiles
                .entry(customer_id.to_string())
                .or_insert_with(|| CustomerProfile::new(customer_id));
            
            profile.update_from_purchase(product_embedding.clone(), rating);
        }
    }

    // Helper methods
    fn encode_text_features(&self, text: &str) -> Result<Vec<f32>> {
        // Simple text encoding - in practice would use transformer models
        let words: Vec<&str> = text.split_whitespace().collect();
        let mut features = vec![0.0; 100]; // Fixed size feature vector
        
        for (i, word) in words.iter().take(10).enumerate() {
            let hash = word.len() as f32 * 0.1; // Simple hash
            features[i * 10] = hash;
        }
        
        Ok(features)
    }

    fn encode_categorical_features(&self, category: &str) -> Result<Vec<f32>> {
        // One-hot encoding simulation
        let mut features = vec![0.0; 20];
        let category_hash = category.len() % 20;
        features[category_hash] = 1.0;
        Ok(features)
    }

    fn encode_brand(&self, brand: &str) -> Result<Vec<f32>> {
        // Brand embedding simulation
        let mut features = vec![0.0; 30];
        let brand_hash = brand.len() % 30;
        features[brand_hash] = 1.0;
        Ok(features)
    }

    fn normalize_vector(&self, mut vector: Vec<f32>) -> Vec<f32> {
        let magnitude: f32 = vector.iter().map(|x| x * x).sum::<f32>().sqrt();
        if magnitude > 0.0 {
            for value in &mut vector {
                *value /= magnitude;
            }
        }
        vector
    }

    /// Find similar products based on embeddings
    pub async fn find_similar_products(&self, product_id: &str, k: usize) -> Result<Vec<(String, f64)>> {
        if let Some(target_embedding) = self.product_embeddings.get(product_id) {
            let mut similarities = Vec::new();
            
            for (other_id, other_embedding) in &self.product_embeddings {
                if other_id != product_id {
                    let similarity = self.cosine_similarity(target_embedding, other_embedding);
                    similarities.push((other_id.clone(), similarity));
                }
            }
            
            // Sort by similarity and take top k
            similarities.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
            similarities.truncate(k);
            
            Ok(similarities)
        } else {
            Ok(Vec::new())
        }
    }

    /// Generate product recommendations for a customer
    pub async fn recommend_products(&self, customer_id: &str, k: usize) -> Result<Vec<(String, f64)>> {
        // Get customer profile
        if let Some(customer_profile) = self.customer_profiles.get(customer_id) {
            let recommendations = self.recommender.generate_recommendations(
                customer_id,
                &customer_profile.preferences,
                k
            ).await?;
            
            Ok(recommendations)
        } else {
            // Generate recommendations based on popularity for new customers
            self.generate_popular_recommendations(k).await
        }
    }

    /// Analyze market trends for a product category
    pub async fn analyze_market_trends(&self, category: &str) -> Result<MarketTrends> {
        self.market_analyzer.analyze_category_trends(category).await
    }

    /// Generate popular product recommendations
    async fn generate_popular_recommendations(&self, k: usize) -> Result<Vec<(String, f64)>> {
        // Simplified: return random products with mock scores
        let popular_products: Vec<(String, f64)> = self.product_embeddings.keys()
            .take(k)
            .map(|id| (id.clone(), 0.7 + (id.len() % 3) as f64 * 0.1))
            .collect();
        
        Ok(popular_products)
    }

    fn cosine_similarity(&self, v1: &Vector, v2: &Vector) -> f64 {
        let dot_product: f32 = v1.values.iter().zip(v2.values.iter()).map(|(a, b)| a * b).sum();
        let norm1: f32 = v1.values.iter().map(|x| x * x).sum::<f32>().sqrt();
        let norm2: f32 = v2.values.iter().map(|x| x * x).sum::<f32>().sqrt();
        
        if norm1 > 0.0 && norm2 > 0.0 {
            (dot_product / (norm1 * norm2)) as f64
        } else {
            0.0
        }
    }
}

/// Product features for embedding generation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProductFeatures {
    /// Product name
    pub name: String,
    /// Product description
    pub description: String,
    /// Product category
    pub category: String,
    /// Product price
    pub price: f64,
    /// Average rating
    pub rating: f32,
    /// Number of reviews
    pub review_count: usize,
    /// Brand name
    pub brand: String,
    /// Additional attributes
    pub attributes: HashMap<String, String>,
}

/// Product interaction types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum InteractionType {
    View,
    Purchase,
    AddToCart,
    Wishlist,
    Rating,
    Review,
}

/// Product interaction data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProductInteraction {
    pub product_id: String,
    pub interaction_type: InteractionType,
    pub rating: f64,
    pub timestamp: DateTime<Utc>,
}

/// Market trends analysis result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MarketTrends {
    pub category: String,
    pub trend_direction: TrendDirection,
    pub growth_rate: f64,
    pub seasonal_patterns: Vec<SeasonalPattern>,
    pub competitor_analysis: Vec<CompetitorData>,
    pub price_trends: PriceTrends,
    pub demand_forecast: Vec<DemandForecast>,
}

/// Trend direction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TrendDirection {
    Growing,
    Declining,
    Stable,
    Volatile,
}

/// Seasonal pattern data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SeasonalPattern {
    pub month: u32,
    pub multiplier: f64,
    pub confidence: f64,
}

/// Competitor analysis data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompetitorData {
    pub competitor_id: String,
    pub market_share: f64,
    pub average_price: f64,
    pub product_count: usize,
}

/// Price trends
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PriceTrends {
    pub average_price: f64,
    pub price_change_rate: f64,
    pub price_volatility: f64,
}

/// Demand forecast
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DemandForecast {
    pub period: String,
    pub demand_score: f64,
    pub confidence: f64,
}

// =============================================================================
// Organizational Knowledge Management
// =============================================================================

/// Organizational knowledge graph embedder
#[derive(Debug, Clone)]
pub struct OrganizationalKGEmbedder {
    /// Employee skill embeddings
    employee_skills: HashMap<String, EmployeeProfile>,
    /// Project relationship graph
    project_graph: ProjectGraph,
    /// Department structure
    department_structure: DepartmentStructure,
    /// Process optimization engine
    process_optimizer: ProcessOptimizer,
    /// Resource allocation system
    resource_allocator: ResourceAllocator,
    /// Performance predictor
    performance_predictor: PerformancePredictor,
}

/// Employee profile with skills and embeddings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmployeeProfile {
    /// Employee ID
    pub employee_id: String,
    /// Skill embeddings
    pub skill_embeddings: HashMap<String, Vector>,
    /// Experience levels
    pub experience_levels: HashMap<String, f64>,
    /// Project history
    pub project_history: Vec<String>,
    /// Performance metrics
    pub performance_metrics: PerformanceMetrics,
    /// Collaboration network
    pub collaboration_network: Vec<String>,
    /// Learning preferences
    pub learning_preferences: Vector,
}

/// Performance metrics for employees
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    /// Overall performance score
    pub overall_score: f64,
    /// Productivity metrics
    pub productivity: f64,
    /// Quality metrics
    pub quality: f64,
    /// Innovation score
    pub innovation: f64,
    /// Collaboration score
    pub collaboration: f64,
    /// Leadership potential
    pub leadership: f64,
}

/// Project relationship graph
#[derive(Debug, Clone)]
pub struct ProjectGraph {
    /// Projects and their relationships
    projects: HashMap<String, Project>,
    /// Project dependencies
    dependencies: HashMap<String, Vec<String>>,
    /// Resource requirements
    resource_requirements: HashMap<String, ResourceRequirements>,
}

/// Project definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Project {
    /// Project ID
    pub project_id: String,
    /// Project name
    pub name: String,
    /// Project description
    pub description: String,
    /// Required skills
    pub required_skills: HashMap<String, f64>,
    /// Team members
    pub team_members: Vec<String>,
    /// Project status
    pub status: ProjectStatus,
    /// Start date
    pub start_date: DateTime<Utc>,
    /// End date
    pub end_date: Option<DateTime<Utc>>,
    /// Budget allocated
    pub budget: f64,
}

/// Project status enum
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ProjectStatus {
    Planning,
    InProgress,
    OnHold,
    Completed,
    Cancelled,
}

/// Resource requirements for projects
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceRequirements {
    /// Human resources needed
    pub human_resources: HashMap<String, usize>,
    /// Equipment needed
    pub equipment: Vec<String>,
    /// Budget requirements
    pub budget_requirements: f64,
    /// Timeline requirements
    pub timeline_weeks: usize,
}

/// Department structure management
#[derive(Debug, Clone)]
pub struct DepartmentStructure {
    /// Departments and their hierarchies
    departments: HashMap<String, Department>,
    /// Reporting relationships
    reporting_structure: HashMap<String, String>,
    /// Cross-department collaboration
    collaboration_matrix: HashMap<(String, String), f64>,
}

/// Department definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Department {
    /// Department ID
    pub dept_id: String,
    /// Department name
    pub name: String,
    /// Parent department
    pub parent_dept: Option<String>,
    /// Department head
    pub head: String,
    /// Department members
    pub members: Vec<String>,
    /// Department goals
    pub goals: Vec<String>,
    /// Budget allocation
    pub budget: f64,
}

/// Process optimization engine
#[derive(Debug, Clone)]
pub struct ProcessOptimizer {
    /// Business processes
    processes: HashMap<String, BusinessProcess>,
    /// Process efficiency metrics
    efficiency_metrics: HashMap<String, ProcessMetrics>,
    /// Optimization recommendations
    recommendations: HashMap<String, Vec<OptimizationRecommendation>>,
}

/// Business process definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BusinessProcess {
    /// Process ID
    pub process_id: String,
    /// Process name
    pub name: String,
    /// Process steps
    pub steps: Vec<ProcessStep>,
    /// Input requirements
    pub inputs: Vec<String>,
    /// Output products
    pub outputs: Vec<String>,
    /// Stakeholders involved
    pub stakeholders: Vec<String>,
}

/// Process step definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProcessStep {
    /// Step ID
    pub step_id: String,
    /// Step description
    pub description: String,
    /// Required skills
    pub required_skills: Vec<String>,
    /// Estimated duration
    pub duration_hours: f64,
    /// Dependencies
    pub dependencies: Vec<String>,
}

/// Process efficiency metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProcessMetrics {
    /// Cycle time
    pub cycle_time: f64,
    /// Throughput
    pub throughput: f64,
    /// Error rate
    pub error_rate: f64,
    /// Cost per execution
    pub cost_per_execution: f64,
    /// Customer satisfaction
    pub customer_satisfaction: f64,
}

/// Optimization recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationRecommendation {
    /// Recommendation type
    pub recommendation_type: RecommendationType,
    /// Description
    pub description: String,
    /// Expected improvement
    pub expected_improvement: f64,
    /// Implementation cost
    pub implementation_cost: f64,
    /// Priority level
    pub priority: Priority,
}

/// Recommendation types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RecommendationType {
    Automation,
    Restructuring,
    SkillTraining,
    ResourceReallocation,
    ProcessElimination,
    TechnologyUpgrade,
}

/// Priority levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Priority {
    Low,
    Medium,
    High,
    Critical,
}

/// Resource allocation system
#[derive(Debug, Clone)]
pub struct ResourceAllocator {
    /// Available resources
    available_resources: HashMap<String, Resource>,
    /// Allocation strategies
    allocation_strategies: Vec<AllocationStrategy>,
    /// Current allocations
    current_allocations: HashMap<String, Vec<ResourceAllocation>>,
}

/// Resource definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Resource {
    /// Resource ID
    pub resource_id: String,
    /// Resource type
    pub resource_type: ResourceType,
    /// Availability
    pub availability: f64,
    /// Cost per unit
    pub cost_per_unit: f64,
    /// Quality rating
    pub quality_rating: f64,
}

/// Resource types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ResourceType {
    Human,
    Equipment,
    Financial,
    Space,
    Technology,
}

/// Allocation strategy
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AllocationStrategy {
    /// Strategy name
    pub name: String,
    /// Optimization criteria
    pub criteria: Vec<OptimizationCriteria>,
    /// Constraints
    pub constraints: Vec<AllocationConstraint>,
}

/// Optimization criteria for allocation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationCriteria {
    MinimizeCost,
    MaximizeEfficiency,
    BalanceWorkload,
    MaximizeQuality,
    MinimizeTime,
}

/// Allocation constraints
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AllocationConstraint {
    BudgetLimit(f64),
    TimeLimit(f64),
    SkillRequirement(String),
    AvailabilityRequirement(f64),
}

/// Resource allocation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceAllocation {
    /// Resource ID
    pub resource_id: String,
    /// Project ID
    pub project_id: String,
    /// Allocation percentage
    pub allocation_percentage: f64,
    /// Start date
    pub start_date: DateTime<Utc>,
    /// End date
    pub end_date: DateTime<Utc>,
}

/// Process analysis result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProcessAnalysis {
    pub process_id: String,
    pub efficiency_score: f64,
    pub bottlenecks: Vec<String>,
    pub improvement_recommendations: Vec<ProcessImprovement>,
    pub estimated_cost_reduction: f64,
    pub implementation_timeline: u32, // days
}

/// Process improvement recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProcessImprovement {
    pub improvement_type: String,
    pub description: String,
    pub estimated_impact: f64,
    pub implementation_cost: f64,
    pub priority: Priority,
}

/// Performance prediction system
#[derive(Debug, Clone)]
pub struct PerformancePredictor {
    /// Historical performance data
    historical_data: HashMap<String, Vec<PerformanceDataPoint>>,
    /// Prediction models
    models: HashMap<String, PredictionModel>,
    /// Performance trends
    trends: HashMap<String, PerformanceTrend>,
}

/// Performance data point
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceDataPoint {
    /// Timestamp
    pub timestamp: DateTime<Utc>,
    /// Performance value
    pub value: f64,
    /// Context factors
    pub context: HashMap<String, String>,
}

/// Prediction model
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictionModel {
    /// Model type
    pub model_type: ModelType,
    /// Model parameters
    pub parameters: HashMap<String, f64>,
    /// Accuracy metrics
    pub accuracy: f64,
}

/// Model types for prediction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ModelType {
    LinearRegression,
    RandomForest,
    NeuralNetwork,
    TimeSeriesARIMA,
    GradientBoosting,
}

/// Performance trend
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceTrend {
    /// Trend direction
    pub direction: TrendDirection,
    /// Trend strength
    pub strength: f64,
    /// Confidence level
    pub confidence: f64,
    /// Predicted future values
    pub predictions: Vec<f64>,
}

// Implementation methods for organizational KG embedder
impl OrganizationalKGEmbedder {
    /// Create new organizational KG embedder
    pub fn new() -> Self {
        Self {
            employee_skills: HashMap::new(),
            project_graph: ProjectGraph::new(),
            department_structure: DepartmentStructure::new(),
            process_optimizer: ProcessOptimizer::new(),
            resource_allocator: ResourceAllocator::new(),
            performance_predictor: PerformancePredictor::new(),
        }
    }

    /// Generate employee skill embeddings
    pub async fn embed_employee_skills(&mut self, employee_id: &str, skills: &[String]) -> Result<HashMap<String, Vector>> {
        let mut skill_embeddings = HashMap::new();
        
        for skill in skills {
            // Generate embedding for each skill
            let skill_vector = self.generate_skill_embedding(skill)?;
            skill_embeddings.insert(skill.clone(), skill_vector);
        }
        
        // Update employee profile
        let profile = self.employee_skills
            .entry(employee_id.to_string())
            .or_insert_with(|| EmployeeProfile::new(employee_id));
        
        profile.skill_embeddings.extend(skill_embeddings.clone());
        
        Ok(skill_embeddings)
    }

    /// Find similar employees based on skills
    pub fn find_similar_employees(&self, employee_id: &str, k: usize) -> Vec<(String, f64)> {
        if let Some(target_profile) = self.employee_skills.get(employee_id) {
            let mut similarities = Vec::new();
            
            for (other_id, other_profile) in &self.employee_skills {
                if other_id != employee_id {
                    let similarity = self.calculate_skill_similarity(target_profile, other_profile);
                    similarities.push((other_id.clone(), similarity));
                }
            }
            
            similarities.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
            similarities.into_iter().take(k).collect()
        } else {
            Vec::new()
        }
    }

    /// Recommend optimal team composition for a project
    pub fn recommend_team_composition(&self, project_id: &str) -> Vec<String> {
        if let Some(project) = self.project_graph.projects.get(project_id) {
            let mut team_recommendations = Vec::new();
            
            // For each required skill, find best matching employees
            for (skill, required_level) in &project.required_skills {
                let best_match = self.find_best_skill_match(skill, *required_level);
                if let Some(employee_id) = best_match {
                    if !team_recommendations.contains(&employee_id) {
                        team_recommendations.push(employee_id);
                    }
                }
            }
            
            team_recommendations
        } else {
            Vec::new()
        }
    }

    /// Predict project success probability
    pub fn predict_project_success(&self, project_id: &str) -> f64 {
        // Simplified prediction based on team skills and historical data
        if let Some(project) = self.project_graph.projects.get(project_id) {
            let team_skill_coverage = self.calculate_team_skill_coverage(project);
            let resource_adequacy = self.calculate_resource_adequacy(project_id);
            let timeline_feasibility = self.calculate_timeline_feasibility(project);
            
            // Weighted average
            (team_skill_coverage * 0.4 + resource_adequacy * 0.3 + timeline_feasibility * 0.3)
        } else {
            0.0
        }
    }

    /// Optimize resource allocation across projects
    pub fn optimize_resource_allocation(&mut self) -> HashMap<String, Vec<ResourceAllocation>> {
        // Simplified optimization algorithm
        let mut optimized_allocations = HashMap::new();
        
        for (project_id, project) in &self.project_graph.projects {
            if project.status == ProjectStatus::InProgress || project.status == ProjectStatus::Planning {
                let allocations = self.resource_allocator.allocate_for_project(project_id, project);
                optimized_allocations.insert(project_id.clone(), allocations);
            }
        }
        
        optimized_allocations
    }

    // Helper methods
    fn generate_skill_embedding(&self, skill: &str) -> Result<Vector> {
        // Simplified skill embedding generation
        let mut embedding = vec![0.0; 128];
        let skill_hash = skill.len() % 128;
        embedding[skill_hash] = 1.0;
        
        // Add some domain-specific features
        if skill.contains("programming") || skill.contains("coding") {
            embedding[0] = 0.8;
        }
        if skill.contains("management") || skill.contains("leadership") {
            embedding[1] = 0.8;
        }
        if skill.contains("design") || skill.contains("creative") {
            embedding[2] = 0.8;
        }
        
        Ok(Vector::new(embedding))
    }

    fn calculate_skill_similarity(&self, profile1: &EmployeeProfile, profile2: &EmployeeProfile) -> f64 {
        let mut total_similarity = 0.0;
        let mut common_skills = 0;
        
        for (skill, emb1) in &profile1.skill_embeddings {
            if let Some(emb2) = profile2.skill_embeddings.get(skill) {
                total_similarity += self.cosine_similarity(emb1, emb2);
                common_skills += 1;
            }
        }
        
        if common_skills > 0 {
            total_similarity / common_skills as f64
        } else {
            0.0
        }
    }

    fn find_best_skill_match(&self, skill: &str, required_level: f64) -> Option<String> {
        let mut best_match = None;
        let mut best_score = 0.0;
        
        for (employee_id, profile) in &self.employee_skills {
            if let Some(level) = profile.experience_levels.get(skill) {
                if *level >= required_level && *level > best_score {
                    best_score = *level;
                    best_match = Some(employee_id.clone());
                }
            }
        }
        
        best_match
    }

    fn calculate_team_skill_coverage(&self, project: &Project) -> f64 {
        let mut covered_skills = 0;
        let total_skills = project.required_skills.len();
        
        for team_member in &project.team_members {
            if let Some(profile) = self.employee_skills.get(team_member) {
                for skill in project.required_skills.keys() {
                    if profile.skill_embeddings.contains_key(skill) {
                        covered_skills += 1;
                        break;
                    }
                }
            }
        }
        
        if total_skills > 0 {
            covered_skills as f64 / total_skills as f64
        } else {
            1.0
        }
    }

    fn calculate_resource_adequacy(&self, project_id: &str) -> f64 {
        // Simplified resource adequacy calculation
        0.75 // Placeholder
    }

    fn calculate_timeline_feasibility(&self, project: &Project) -> f64 {
        // Simplified timeline feasibility calculation
        0.80 // Placeholder
    }

    /// Analyze department collaboration patterns
    pub async fn analyze_department_collaboration(&self) -> Result<HashMap<String, f64>> {
        let mut collaboration_scores = HashMap::new();
        
        for (dept_id, _department) in &self.department_structure.departments {
            let mut total_collaboration = 0.0;
            let mut collaboration_count = 0;
            
            // Calculate collaboration with other departments
            for other_dept_id in self.department_structure.departments.keys() {
                if dept_id != other_dept_id {
                    if let Some(collaboration_score) = self.department_structure.collaboration_matrix
                        .get(&format!("{}_{}", dept_id, other_dept_id)) {
                        total_collaboration += collaboration_score;
                        collaboration_count += 1;
                    }
                }
            }
            
            let avg_collaboration = if collaboration_count > 0 {
                total_collaboration / collaboration_count as f64
            } else {
                0.0
            };
            
            collaboration_scores.insert(dept_id.clone(), avg_collaboration);
        }
        
        Ok(collaboration_scores)
    }

    /// Predict employee performance for a specific role
    pub async fn predict_employee_performance(&self, employee_id: &str, role: &str) -> Result<f64> {
        if let Some(employee_profile) = self.employee_skills.get(employee_id) {
            // Calculate performance based on skill match and experience
            let mut performance_score = 0.0;
            let mut skill_count = 0;
            
            // Role-specific skill requirements (simplified)
            let required_skills = self.get_role_requirements(role);
            
            for required_skill in &required_skills {
                if let Some(experience) = employee_profile.experience_levels.get(required_skill) {
                    performance_score += experience;
                    skill_count += 1;
                }
            }
            
            let avg_performance = if skill_count > 0 {
                performance_score / skill_count as f64
            } else {
                0.5 // Default moderate performance
            };
            
            Ok(avg_performance.min(1.0).max(0.0))
        } else {
            Ok(0.5) // Default for unknown employees
        }
    }

    /// Get role-specific skill requirements
    fn get_role_requirements(&self, role: &str) -> Vec<String> {
        // Simplified role requirements mapping
        match role.to_lowercase().as_str() {
            "software_engineer" => vec!["programming".to_string(), "problem_solving".to_string(), "testing".to_string()],
            "project_manager" => vec!["management".to_string(), "communication".to_string(), "planning".to_string()],
            "designer" => vec!["design".to_string(), "creativity".to_string(), "prototyping".to_string()],
            "data_scientist" => vec!["programming".to_string(), "statistics".to_string(), "machine_learning".to_string()],
            _ => vec!["communication".to_string(), "teamwork".to_string()],
        }
    }

    fn cosine_similarity(&self, v1: &Vector, v2: &Vector) -> f64 {
        let dot_product: f32 = v1.values.iter().zip(v2.values.iter()).map(|(a, b)| a * b).sum();
        let norm1: f32 = v1.values.iter().map(|x| x * x).sum::<f32>().sqrt();
        let norm2: f32 = v2.values.iter().map(|x| x * x).sum::<f32>().sqrt();
        
        if norm1 > 0.0 && norm2 > 0.0 {
            (dot_product / (norm1 * norm2)) as f64
        } else {
            0.0
        }
    }
}

// Default implementations and additional helper structs
impl Default for ProductCatalogEmbedder {
    fn default() -> Self {
        Self::new()
    }
}

impl Default for OrganizationalKGEmbedder {
    fn default() -> Self {
        Self::new()
    }
}

impl CategoryHierarchy {
    fn new() -> Self {
        Self {
            categories: HashMap::new(),
            hierarchy: HashMap::new(),
            category_embeddings: HashMap::new(),
        }
    }

    fn build_hierarchy(&mut self, categories: Vec<Category>) -> Result<()> {
        for category in categories {
            let category_id = category.id.clone();
            if let Some(parent_id) = &category.parent_id {
                self.hierarchy.entry(parent_id.clone()).or_default().push(category_id.clone());
            }
            self.categories.insert(category_id, category);
        }
        Ok(())
    }
}

impl CustomerProfile {
    fn new(customer_id: &str) -> Self {
        Self {
            customer_id: customer_id.to_string(),
            preferences: Vector::new(vec![0.0; 128]),
            purchase_history: Vec::new(),
            preferred_categories: Vec::new(),
            price_sensitivity: 0.5,
            brand_preferences: HashMap::new(),
            seasonal_patterns: HashMap::new(),
        }
    }

    fn update_from_purchase(&mut self, product_embedding: Vector, rating: f64) {
        self.purchase_history.push(product_embedding);
        // Update preferences based on purchase and rating
        // Simplified implementation
    }
}

impl MarketAnalyzer {
    fn new() -> Self {
        Self {
            trends: HashMap::new(),
            competitor_analysis: HashMap::new(),
            segments: Vec::new(),
        }
    }

    fn get_trend_analysis(&self, category_id: &str) -> Option<TrendAnalysis> {
        self.trends.get(category_id).cloned()
    }
}

impl RecommendationEngine {
    fn new() -> Self {
        Self {
            collaborative_model: CollaborativeModel::new(),
            content_model: ContentModel::new(),
            hybrid_weights: HybridWeights {
                collaborative_weight: 0.4,
                content_weight: 0.3,
                knowledge_weight: 0.2,
                popularity_weight: 0.1,
            },
        }
    }

    fn generate_recommendations(&self, profile: &CustomerProfile, k: usize) -> Vec<(String, f64)> {
        // Simplified recommendation generation
        vec![
            ("product_1".to_string(), 0.9),
            ("product_2".to_string(), 0.8),
            ("product_3".to_string(), 0.7),
        ].into_iter().take(k).collect()
    }
}

impl CollaborativeModel {
    fn new() -> Self {
        Self {
            interaction_matrix: HashMap::new(),
            user_similarity: HashMap::new(),
            item_similarity: HashMap::new(),
        }
    }
}

impl ContentModel {
    fn new() -> Self {
        Self {
            item_features: HashMap::new(),
            user_preferences: HashMap::new(),
            feature_weights: Vector::new(vec![0.0; 100]),
        }
    }
}

impl EmployeeProfile {
    fn new(employee_id: &str) -> Self {
        Self {
            employee_id: employee_id.to_string(),
            skill_embeddings: HashMap::new(),
            experience_levels: HashMap::new(),
            project_history: Vec::new(),
            performance_metrics: PerformanceMetrics {
                overall_score: 0.0,
                productivity: 0.0,
                quality: 0.0,
                innovation: 0.0,
                collaboration: 0.0,
                leadership: 0.0,
            },
            collaboration_network: Vec::new(),
            learning_preferences: Vector::new(vec![0.0; 64]),
        }
    }
}

impl ProjectGraph {
    fn new() -> Self {
        Self {
            projects: HashMap::new(),
            dependencies: HashMap::new(),
            resource_requirements: HashMap::new(),
        }
    }
}

impl DepartmentStructure {
    fn new() -> Self {
        Self {
            departments: HashMap::new(),
            reporting_structure: HashMap::new(),
            collaboration_matrix: HashMap::new(),
        }
    }
}

impl ProcessOptimizer {
    fn new() -> Self {
        Self {
            processes: HashMap::new(),
            efficiency_metrics: HashMap::new(),
            recommendations: HashMap::new(),
        }
    }
}

impl ResourceAllocator {
    fn new() -> Self {
        Self {
            available_resources: HashMap::new(),
            allocation_strategies: Vec::new(),
            current_allocations: HashMap::new(),
        }
    }

    fn allocate_for_project(&self, project_id: &str, _project: &Project) -> Vec<ResourceAllocation> {
        // Simplified allocation
        vec![
            ResourceAllocation {
                resource_id: "resource_1".to_string(),
                project_id: project_id.to_string(),
                allocation_percentage: 0.8,
                start_date: Utc::now(),
                end_date: Utc::now(),
            }
        ]
    }
}

impl PerformancePredictor {
    fn new() -> Self {
        Self {
            historical_data: HashMap::new(),
            models: HashMap::new(),
            trends: HashMap::new(),
        }
    }
}

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

    #[tokio::test]
    async fn test_product_catalog_embedder() {
        let mut embedder = ProductCatalogEmbedder::new();
        
        let features = ProductFeatures {
            name: "Wireless Headphones".to_string(),
            description: "High-quality wireless headphones with noise cancellation".to_string(),
            category: "Electronics".to_string(),
            price: 199.99,
            rating: 4.5,
            review_count: 1250,
            brand: "TechBrand".to_string(),
            attributes: HashMap::new(),
        };
        
        let embedding = embedder.embed_product("product_1", &features).await.expect("should succeed");
        assert_eq!(embedding.values.len(), 150); // Total feature vector size
        assert!(embedder.product_embeddings.contains_key("product_1"));
    }

    #[tokio::test]
    async fn test_organizational_kg_embedder() {
        let mut embedder = OrganizationalKGEmbedder::new();
        
        let skills = vec![
            "programming".to_string(),
            "management".to_string(),
            "design".to_string(),
        ];
        
        let skill_embeddings = embedder.embed_employee_skills("emp_1", &skills).await.expect("should succeed");
        assert_eq!(skill_embeddings.len(), 3);
        assert!(embedder.employee_skills.contains_key("emp_1"));
    }

    #[test]
    fn test_product_similarity() {
        let mut embedder = ProductCatalogEmbedder::new();
        
        let emb1 = Vector::new(vec![1.0, 0.0, 0.0]);
        let emb2 = Vector::new(vec![0.0, 1.0, 0.0]);
        
        embedder.product_embeddings.insert("prod1".to_string(), emb1);
        embedder.product_embeddings.insert("prod2".to_string(), emb2);
        
        let similarity = embedder.calculate_product_similarity("prod1", "prod2");
        assert_eq!(similarity, 0.0); // Orthogonal vectors
    }

    #[test]
    fn test_employee_similarity() {
        let embedder = OrganizationalKGEmbedder::new();
        
        let mut profile1 = EmployeeProfile::new("emp1");
        let mut profile2 = EmployeeProfile::new("emp2");
        
        profile1.skill_embeddings.insert("programming".to_string(), Vector::new(vec![1.0, 0.0]));
        profile2.skill_embeddings.insert("programming".to_string(), Vector::new(vec![1.0, 0.0]));
        
        let similarity = embedder.calculate_skill_similarity(&profile1, &profile2);
        assert_eq!(similarity, 1.0); // Identical skill vectors
    }
}