oxirs_embed/

enterprise_knowledge.rs

//! Enterprise Knowledge Graphs - Business Domain Embeddings
//!
//! This module provides specialized embeddings and analysis for enterprise knowledge graphs,
//! including product catalogs, organizational knowledge, employee skill embeddings, and
//! recommendation systems for business applications.

use crate::Vector;
use anyhow::Result;
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};
use std::sync::{Arc, RwLock};
use tokio::task::JoinHandle;
use tracing::{debug, info};

/// Enterprise knowledge graph analyzer and embedding generator
pub struct EnterpriseKnowledgeAnalyzer {
    /// Product catalog embeddings
    product_embeddings: Arc<RwLock<HashMap<String, ProductEmbedding>>>,
    /// Employee embeddings
    employee_embeddings: Arc<RwLock<HashMap<String, EmployeeEmbedding>>>,
    /// Customer embeddings
    customer_embeddings: Arc<RwLock<HashMap<String, CustomerEmbedding>>>,
    /// Product categories and hierarchies
    category_hierarchy: Arc<RwLock<CategoryHierarchy>>,
    /// Organizational structure
    organizational_structure: Arc<RwLock<OrganizationalStructure>>,
    /// Recommendation engines
    recommendation_engines: Arc<RwLock<HashMap<String, RecommendationEngine>>>,
    /// Configuration
    config: EnterpriseConfig,
    /// Background analysis tasks
    analysis_tasks: Vec<JoinHandle<()>>,
}

/// Configuration for enterprise knowledge analysis
#[derive(Debug, Clone)]
pub struct EnterpriseConfig {
    /// Maximum number of products to track
    pub max_products: usize,
    /// Maximum number of employees to track
    pub max_employees: usize,
    /// Maximum number of customers to track
    pub max_customers: usize,
    /// Product recommendation refresh interval (hours)
    pub product_recommendation_refresh_hours: u64,
    /// Employee skill analysis interval (hours)
    pub skill_analysis_interval_hours: u64,
    /// Market analysis interval (hours)
    pub market_analysis_interval_hours: u64,
    /// Enable real-time customer behavior tracking
    pub enable_real_time_customer_tracking: bool,
    /// Minimum interaction threshold for recommendations
    pub min_interaction_threshold: u32,
    /// Embedding dimension
    pub embedding_dimension: usize,
    /// Recommendation system config
    pub recommendation_config: RecommendationConfig,
}

impl Default for EnterpriseConfig {
    fn default() -> Self {
        Self {
            max_products: 500_000,
            max_employees: 50_000,
            max_customers: 1_000_000,
            product_recommendation_refresh_hours: 6,
            skill_analysis_interval_hours: 24,
            market_analysis_interval_hours: 12,
            enable_real_time_customer_tracking: true,
            min_interaction_threshold: 3,
            embedding_dimension: 256,
            recommendation_config: RecommendationConfig::default(),
        }
    }
}

/// Configuration for recommendation systems
#[derive(Debug, Clone)]
pub struct RecommendationConfig {
    /// Number of recommendations to generate
    pub num_recommendations: usize,
    /// Similarity threshold for recommendations
    pub similarity_threshold: f64,
    /// Diversity factor (0.0 = pure similarity, 1.0 = pure diversity)
    pub diversity_factor: f64,
    /// Enable collaborative filtering
    pub enable_collaborative_filtering: bool,
    /// Enable content-based filtering
    pub enable_content_based_filtering: bool,
    /// Enable hybrid recommendations
    pub enable_hybrid: bool,
    /// Cold start strategy
    pub cold_start_strategy: ColdStartStrategy,
}

impl Default for RecommendationConfig {
    fn default() -> Self {
        Self {
            num_recommendations: 10,
            similarity_threshold: 0.3,
            diversity_factor: 0.2,
            enable_collaborative_filtering: true,
            enable_content_based_filtering: true,
            enable_hybrid: true,
            cold_start_strategy: ColdStartStrategy::PopularityBased,
        }
    }
}

/// Cold start strategy for new users/items
#[derive(Debug, Clone)]
pub enum ColdStartStrategy {
    PopularityBased,
    ContentBased,
    DemographicBased,
    RandomSampling,
}

/// Product embedding with business context
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProductEmbedding {
    /// Product unique identifier
    pub product_id: String,
    /// Product name
    pub name: String,
    /// Product description
    pub description: String,
    /// Product category
    pub category: String,
    /// Subcategories
    pub subcategories: Vec<String>,
    /// Product features
    pub features: Vec<ProductFeature>,
    /// Price information
    pub price: f64,
    /// Availability status
    pub availability: ProductAvailability,
    /// Sales metrics
    pub sales_metrics: SalesMetrics,
    /// Customer ratings
    pub ratings: CustomerRatings,
    /// Product embedding vector
    pub embedding: Vector,
    /// Similar products
    pub similar_products: Vec<String>,
    /// Market position score
    pub market_position: f64,
    /// Last updated
    pub last_updated: DateTime<Utc>,
}

/// Product feature information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProductFeature {
    /// Feature name
    pub feature_name: String,
    /// Feature value
    pub feature_value: String,
    /// Feature type
    pub feature_type: FeatureType,
    /// Feature importance score
    pub importance_score: f64,
}

/// Types of product features
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FeatureType {
    Categorical,
    Numerical,
    Boolean,
    Text,
    List,
}

/// Product availability status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ProductAvailability {
    InStock(u32), // quantity
    OutOfStock,
    Discontinued,
    PreOrder(DateTime<Utc>), // available date
    Limited(u32),            // limited quantity
}

/// Sales metrics for products
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SalesMetrics {
    /// Total units sold
    pub units_sold: u64,
    /// Revenue generated
    pub revenue: f64,
    /// Sales velocity (units per day)
    pub sales_velocity: f64,
    /// Conversion rate
    pub conversion_rate: f64,
    /// Return rate
    pub return_rate: f64,
    /// Profit margin
    pub profit_margin: f64,
}

/// Customer ratings and reviews
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CustomerRatings {
    /// Average rating (1-5)
    pub average_rating: f64,
    /// Total number of reviews
    pub review_count: u32,
    /// Rating distribution
    pub rating_distribution: HashMap<u8, u32>,
    /// Sentiment score (-1 to 1)
    pub sentiment_score: f64,
}

/// Employee embedding with professional context
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmployeeEmbedding {
    /// Employee unique identifier
    pub employee_id: String,
    /// Employee name
    pub name: String,
    /// Job title
    pub job_title: String,
    /// Department
    pub department: String,
    /// Team
    pub team: String,
    /// Skills
    pub skills: Vec<Skill>,
    /// Experience level
    pub experience_level: ExperienceLevel,
    /// Performance metrics
    pub performance_metrics: PerformanceMetrics,
    /// Project history
    pub project_history: Vec<ProjectParticipation>,
    /// Collaboration network
    pub collaborators: Vec<String>,
    /// Employee embedding vector
    pub embedding: Vector,
    /// Career progression predictions
    pub career_predictions: CareerPredictions,
    /// Last updated
    pub last_updated: DateTime<Utc>,
}

/// Skill information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Skill {
    /// Skill name
    pub skill_name: String,
    /// Skill category
    pub category: SkillCategory,
    /// Proficiency level (1-10)
    pub proficiency_level: u8,
    /// Years of experience
    pub years_experience: f64,
    /// Skill importance in role
    pub role_importance: f64,
    /// Market demand score
    pub market_demand: f64,
}

/// Skill categories
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SkillCategory {
    Technical,
    Leadership,
    Communication,
    Analytical,
    Creative,
    Domain,
    Language,
    Tools,
}

/// Experience levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ExperienceLevel {
    Junior,
    Mid,
    Senior,
    Lead,
    Principal,
    Executive,
}

/// Performance metrics for employees
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    /// Overall performance score (1-10)
    pub overall_score: f64,
    /// Goal achievement rate
    pub goal_achievement_rate: f64,
    /// Project completion rate
    pub project_completion_rate: f64,
    /// Collaboration score
    pub collaboration_score: f64,
    /// Innovation score
    pub innovation_score: f64,
    /// Leadership score
    pub leadership_score: f64,
}

/// Project participation information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProjectParticipation {
    /// Project ID
    pub project_id: String,
    /// Project name
    pub project_name: String,
    /// Role in project
    pub role: String,
    /// Start date
    pub start_date: DateTime<Utc>,
    /// End date
    pub end_date: Option<DateTime<Utc>>,
    /// Project outcome
    pub outcome: ProjectOutcome,
    /// Contribution score
    pub contribution_score: f64,
}

/// Project outcomes
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ProjectOutcome {
    Successful,
    PartiallySuccessful,
    Failed,
    Cancelled,
    Ongoing,
}

/// Career progression predictions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CareerPredictions {
    /// Promotion likelihood (0-1)
    pub promotion_likelihood: f64,
    /// Predicted next role
    pub next_role: String,
    /// Skills to develop
    pub skills_to_develop: Vec<String>,
    /// Career path recommendations
    pub career_paths: Vec<String>,
    /// Retention risk (0-1)
    pub retention_risk: f64,
}

/// Customer embedding with behavior context
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CustomerEmbedding {
    /// Customer unique identifier
    pub customer_id: String,
    /// Customer name (anonymized if needed)
    pub name: String,
    /// Customer segment
    pub segment: CustomerSegment,
    /// Purchase history
    pub purchase_history: Vec<Purchase>,
    /// Preferences
    pub preferences: CustomerPreferences,
    /// Behavior metrics
    pub behavior_metrics: BehaviorMetrics,
    /// Customer embedding vector
    pub embedding: Vector,
    /// Predicted lifetime value
    pub predicted_ltv: f64,
    /// Churn risk
    pub churn_risk: f64,
    /// Recommended products
    pub recommendations: Vec<ProductRecommendation>,
    /// Last updated
    pub last_updated: DateTime<Utc>,
}

/// Customer segments
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CustomerSegment {
    HighValue,
    Regular,
    Occasional,
    NewCustomer,
    AtRisk,
    Churned,
}

/// Purchase information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Purchase {
    /// Product ID
    pub product_id: String,
    /// Purchase date
    pub purchase_date: DateTime<Utc>,
    /// Quantity
    pub quantity: u32,
    /// Price paid
    pub price: f64,
    /// Channel used
    pub channel: PurchaseChannel,
    /// Satisfaction rating
    pub satisfaction: Option<u8>,
}

/// Purchase channels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PurchaseChannel {
    Online,
    InStore,
    Mobile,
    Phone,
    ThirdParty,
}

/// Customer preferences
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CustomerPreferences {
    /// Preferred categories
    pub preferred_categories: Vec<String>,
    /// Price sensitivity
    pub price_sensitivity: f64,
    /// Brand loyalty
    pub brand_loyalty: HashMap<String, f64>,
    /// Preferred channels
    pub preferred_channels: Vec<PurchaseChannel>,
    /// Communication preferences
    pub communication_preferences: CommunicationPreferences,
}

/// Communication preferences
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CommunicationPreferences {
    /// Email opt-in
    pub email_opt_in: bool,
    /// SMS opt-in
    pub sms_opt_in: bool,
    /// Frequency preference
    pub frequency: CommunicationFrequency,
    /// Content preferences
    pub content_types: Vec<String>,
}

/// Communication frequency
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CommunicationFrequency {
    Daily,
    Weekly,
    Monthly,
    Quarterly,
    Never,
}

/// Customer behavior metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BehaviorMetrics {
    /// Visit frequency
    pub visit_frequency: f64,
    /// Average session duration (minutes)
    pub avg_session_duration: f64,
    /// Pages/products viewed per session
    pub avg_products_viewed: f64,
    /// Cart abandonment rate
    pub cart_abandonment_rate: f64,
    /// Return visit rate
    pub return_visit_rate: f64,
    /// Referral rate
    pub referral_rate: f64,
}

/// Product recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProductRecommendation {
    /// Product ID
    pub product_id: String,
    /// Recommendation score
    pub score: f64,
    /// Reason for recommendation
    pub reason: RecommendationReason,
    /// Confidence level
    pub confidence: f64,
    /// Expected revenue impact
    pub expected_revenue: f64,
}

/// Reasons for recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RecommendationReason {
    SimilarProducts,
    CustomersBought,
    PopularInCategory,
    PersonalizedPreference,
    TrendingNow,
    SeasonalRecommendation,
}

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

/// Category information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Category {
    /// Category ID
    pub category_id: String,
    /// Category name
    pub name: String,
    /// Parent category
    pub parent: Option<String>,
    /// Child categories
    pub children: Vec<String>,
    /// Products in this category
    pub products: Vec<String>,
    /// Category attributes
    pub attributes: HashMap<String, String>,
    /// Category performance metrics
    pub performance: CategoryPerformance,
}

/// Category performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CategoryPerformance {
    /// Total sales
    pub total_sales: f64,
    /// Product count
    pub product_count: u32,
    /// Average rating
    pub average_rating: f64,
    /// Growth rate
    pub growth_rate: f64,
    /// Market share
    pub market_share: f64,
}

/// Organizational structure
#[derive(Debug, Clone)]
pub struct OrganizationalStructure {
    /// Departments
    pub departments: HashMap<String, Department>,
    /// Teams within departments
    pub teams: HashMap<String, Team>,
    /// Reporting relationships
    pub reporting_structure: HashMap<String, Vec<String>>,
    /// Cross-functional projects
    pub projects: HashMap<String, Project>,
}

/// Department information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Department {
    /// Department ID
    pub department_id: String,
    /// Department name
    pub name: String,
    /// Department head
    pub head: String,
    /// Employees
    pub employees: Vec<String>,
    /// Teams
    pub teams: Vec<String>,
    /// Budget
    pub budget: f64,
    /// Performance metrics
    pub performance: DepartmentPerformance,
}

/// Department performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DepartmentPerformance {
    /// Budget utilization
    pub budget_utilization: f64,
    /// Goal achievement
    pub goal_achievement: f64,
    /// Employee satisfaction
    pub employee_satisfaction: f64,
    /// Productivity score
    pub productivity_score: f64,
    /// Innovation index
    pub innovation_index: f64,
}

/// Team information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Team {
    /// Team ID
    pub team_id: String,
    /// Team name
    pub name: String,
    /// Team lead
    pub lead: String,
    /// Team members
    pub members: Vec<String>,
    /// Department
    pub department: String,
    /// Team skills
    pub team_skills: Vec<Skill>,
    /// Team performance
    pub performance: TeamPerformance,
}

/// Team performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TeamPerformance {
    /// Collaboration score
    pub collaboration_score: f64,
    /// Delivery performance
    pub delivery_performance: f64,
    /// Quality metrics
    pub quality_score: f64,
    /// Innovation score
    pub innovation_score: f64,
    /// Team satisfaction
    pub team_satisfaction: f64,
}

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

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

/// Project performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProjectPerformance {
    /// Progress percentage
    pub progress_percentage: f64,
    /// Budget utilization
    pub budget_utilization: f64,
    /// Timeline adherence
    pub timeline_adherence: f64,
    /// Quality score
    pub quality_score: f64,
    /// Stakeholder satisfaction
    pub stakeholder_satisfaction: f64,
}

/// Recommendation engine
#[derive(Debug, Clone)]
pub struct RecommendationEngine {
    /// Engine type
    pub engine_type: RecommendationEngineType,
    /// Model parameters
    pub parameters: HashMap<String, f64>,
    /// Performance metrics
    pub performance: RecommendationPerformance,
    /// Last update
    pub last_update: DateTime<Utc>,
}

/// Types of recommendation engines
#[derive(Debug, Clone)]
pub enum RecommendationEngineType {
    CollaborativeFiltering,
    ContentBased,
    MatrixFactorization,
    DeepLearning,
    Hybrid,
}

/// Recommendation engine performance
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RecommendationPerformance {
    /// Precision at K
    pub precision_at_k: HashMap<u32, f64>,
    /// Recall at K
    pub recall_at_k: HashMap<u32, f64>,
    /// NDCG scores
    pub ndcg_scores: HashMap<u32, f64>,
    /// Click-through rate
    pub click_through_rate: f64,
    /// Conversion rate
    pub conversion_rate: f64,
    /// Revenue impact
    pub revenue_impact: f64,
}

impl EnterpriseKnowledgeAnalyzer {
    /// Create new enterprise knowledge analyzer
    pub fn new(config: EnterpriseConfig) -> Self {
        Self {
            product_embeddings: Arc::new(RwLock::new(HashMap::new())),
            employee_embeddings: Arc::new(RwLock::new(HashMap::new())),
            customer_embeddings: Arc::new(RwLock::new(HashMap::new())),
            category_hierarchy: Arc::new(RwLock::new(CategoryHierarchy {
                categories: HashMap::new(),
                parent_child: HashMap::new(),
                category_embeddings: HashMap::new(),
            })),
            organizational_structure: Arc::new(RwLock::new(OrganizationalStructure {
                departments: HashMap::new(),
                teams: HashMap::new(),
                reporting_structure: HashMap::new(),
                projects: HashMap::new(),
            })),
            recommendation_engines: Arc::new(RwLock::new(HashMap::new())),
            config,
            analysis_tasks: Vec::new(),
        }
    }

    /// Start background analysis tasks
    pub async fn start(&mut self) -> Result<()> {
        info!("Starting enterprise knowledge analysis system");

        // Start product recommendation engine
        let recommendation_task = self.start_recommendation_engine().await;
        self.analysis_tasks.push(recommendation_task);

        // Start employee skill analysis
        let skill_analysis_task = self.start_skill_analysis().await;
        self.analysis_tasks.push(skill_analysis_task);

        // Start market analysis
        let market_analysis_task = self.start_market_analysis().await;
        self.analysis_tasks.push(market_analysis_task);

        // Start organizational optimization
        let org_optimization_task = self.start_organizational_optimization().await;
        self.analysis_tasks.push(org_optimization_task);

        info!("Enterprise knowledge analysis system started successfully");
        Ok(())
    }

    /// Stop analysis tasks
    pub async fn stop(&mut self) {
        info!("Stopping enterprise knowledge analysis system");

        for task in self.analysis_tasks.drain(..) {
            task.abort();
        }

        info!("Enterprise knowledge analysis system stopped");
    }

    /// Generate product embedding with business features
    pub async fn generate_product_embedding(&self, product_id: &str) -> Result<ProductEmbedding> {
        // Check if already computed
        {
            let embeddings = self.product_embeddings.read().expect("lock poisoned");
            if let Some(existing) = embeddings.get(product_id) {
                return Ok(existing.clone());
            }
        }

        info!("Generating product embedding for: {}", product_id);

        // Get product information (would come from product database)
        let name = format!("Product_{product_id}");
        let description = format!("Description for product {product_id}");
        let category = "Electronics".to_string();
        let subcategories = vec!["Smartphones".to_string(), "Mobile".to_string()];

        // Generate product features
        let features = vec![
            ProductFeature {
                feature_name: "Brand".to_string(),
                feature_value: "TechCorp".to_string(),
                feature_type: FeatureType::Categorical,
                importance_score: 0.9,
            },
            ProductFeature {
                feature_name: "Price".to_string(),
                feature_value: "299.99".to_string(),
                feature_type: FeatureType::Numerical,
                importance_score: 0.8,
            },
        ];

        let price = 299.99;
        let availability = ProductAvailability::InStock(100);

        // Generate sales metrics
        let sales_metrics = SalesMetrics {
            units_sold: 1500,
            revenue: 449_985.0,
            sales_velocity: 25.5,
            conversion_rate: 0.12,
            return_rate: 0.03,
            profit_margin: 0.35,
        };

        // Generate customer ratings
        let mut rating_distribution = HashMap::new();
        rating_distribution.insert(5, 120);
        rating_distribution.insert(4, 80);
        rating_distribution.insert(3, 30);
        rating_distribution.insert(2, 10);
        rating_distribution.insert(1, 5);

        let ratings = CustomerRatings {
            average_rating: 4.2,
            review_count: 245,
            rating_distribution,
            sentiment_score: 0.7,
        };

        // Generate product embedding vector
        let embedding = self
            .compute_product_embedding_vector(&name, &description, &features, &sales_metrics)
            .await?;

        // Find similar products
        let similar_products = self.find_similar_products(product_id, &embedding).await?;

        // Calculate market position
        let market_position = self
            .calculate_market_position(&sales_metrics, &ratings)
            .await?;

        let product_embedding = ProductEmbedding {
            product_id: product_id.to_string(),
            name,
            description,
            category,
            subcategories,
            features,
            price,
            availability,
            sales_metrics,
            ratings,
            embedding,
            similar_products,
            market_position,
            last_updated: Utc::now(),
        };

        // Cache the result
        {
            let mut embeddings = self.product_embeddings.write().expect("lock poisoned");
            embeddings.insert(product_id.to_string(), product_embedding.clone());
        }

        info!(
            "Generated product embedding for {} with market position: {:.3}",
            product_id, market_position
        );
        Ok(product_embedding)
    }

    /// Generate employee embedding with skills and performance
    pub async fn generate_employee_embedding(
        &self,
        employee_id: &str,
    ) -> Result<EmployeeEmbedding> {
        // Check if already computed
        {
            let embeddings = self.employee_embeddings.read().expect("lock poisoned");
            if let Some(existing) = embeddings.get(employee_id) {
                return Ok(existing.clone());
            }
        }

        info!("Generating employee embedding for: {}", employee_id);

        // Get employee information (would come from HR database)
        let name = format!("Employee_{employee_id}");
        let job_title = "Software Engineer".to_string();
        let department = "Engineering".to_string();
        let team = "Backend Team".to_string();

        // Generate skills
        let skills = vec![
            Skill {
                skill_name: "Python".to_string(),
                category: SkillCategory::Technical,
                proficiency_level: 8,
                years_experience: 5.0,
                role_importance: 0.9,
                market_demand: 0.85,
            },
            Skill {
                skill_name: "Leadership".to_string(),
                category: SkillCategory::Leadership,
                proficiency_level: 6,
                years_experience: 2.0,
                role_importance: 0.6,
                market_demand: 0.9,
            },
        ];

        let experience_level = ExperienceLevel::Mid;

        // Generate performance metrics
        let performance_metrics = PerformanceMetrics {
            overall_score: 8.2,
            goal_achievement_rate: 0.92,
            project_completion_rate: 0.95,
            collaboration_score: 8.5,
            innovation_score: 7.8,
            leadership_score: 6.5,
        };

        // Generate project history
        let project_history = vec![ProjectParticipation {
            project_id: "proj_001".to_string(),
            project_name: "Customer Portal".to_string(),
            role: "Backend Developer".to_string(),
            start_date: Utc::now() - chrono::Duration::days(365),
            end_date: Some(Utc::now() - chrono::Duration::days(300)),
            outcome: ProjectOutcome::Successful,
            contribution_score: 8.5,
        }];

        let collaborators = vec!["emp_002".to_string(), "emp_003".to_string()];

        // Generate employee embedding vector
        let embedding = self
            .compute_employee_embedding_vector(&skills, &performance_metrics, &project_history)
            .await?;

        // Generate career predictions
        let career_predictions = self
            .predict_career_progression(&skills, &performance_metrics, &experience_level)
            .await?;

        let employee_embedding = EmployeeEmbedding {
            employee_id: employee_id.to_string(),
            name,
            job_title,
            department,
            team,
            skills,
            experience_level,
            performance_metrics,
            project_history,
            collaborators,
            embedding,
            career_predictions,
            last_updated: Utc::now(),
        };

        // Cache the result
        {
            let mut embeddings = self.employee_embeddings.write().expect("lock poisoned");
            embeddings.insert(employee_id.to_string(), employee_embedding.clone());
        }

        info!(
            "Generated employee embedding for {} with promotion likelihood: {:.3}",
            employee_id, employee_embedding.career_predictions.promotion_likelihood
        );
        Ok(employee_embedding)
    }

    /// Generate customer embedding with behavior and preferences
    pub async fn generate_customer_embedding(
        &self,
        customer_id: &str,
    ) -> Result<CustomerEmbedding> {
        // Check if already computed
        {
            let embeddings = self.customer_embeddings.read().expect("lock poisoned");
            if let Some(existing) = embeddings.get(customer_id) {
                return Ok(existing.clone());
            }
        }

        info!("Generating customer embedding for: {}", customer_id);

        // Get customer information (would come from CRM database)
        let name = format!("Customer_{customer_id}");
        let segment = CustomerSegment::Regular;

        // Generate purchase history
        let purchase_history = vec![
            Purchase {
                product_id: "prod_001".to_string(),
                purchase_date: Utc::now() - chrono::Duration::days(30),
                quantity: 1,
                price: 299.99,
                channel: PurchaseChannel::Online,
                satisfaction: Some(4),
            },
            Purchase {
                product_id: "prod_002".to_string(),
                purchase_date: Utc::now() - chrono::Duration::days(60),
                quantity: 2,
                price: 149.99,
                channel: PurchaseChannel::InStore,
                satisfaction: Some(5),
            },
        ];

        // Generate preferences
        let mut brand_loyalty = HashMap::new();
        brand_loyalty.insert("TechCorp".to_string(), 0.8);
        brand_loyalty.insert("InnovateCo".to_string(), 0.6);

        let preferences = CustomerPreferences {
            preferred_categories: vec!["Electronics".to_string(), "Books".to_string()],
            price_sensitivity: 0.6,
            brand_loyalty,
            preferred_channels: vec![PurchaseChannel::Online, PurchaseChannel::Mobile],
            communication_preferences: CommunicationPreferences {
                email_opt_in: true,
                sms_opt_in: false,
                frequency: CommunicationFrequency::Weekly,
                content_types: vec!["Promotions".to_string(), "NewProducts".to_string()],
            },
        };

        // Generate behavior metrics
        let behavior_metrics = BehaviorMetrics {
            visit_frequency: 2.5,
            avg_session_duration: 12.5,
            avg_products_viewed: 8.2,
            cart_abandonment_rate: 0.25,
            return_visit_rate: 0.7,
            referral_rate: 0.1,
        };

        // Generate customer embedding vector
        let embedding = self
            .compute_customer_embedding_vector(&purchase_history, &preferences, &behavior_metrics)
            .await?;

        // Predict customer lifetime value
        let predicted_ltv = self
            .predict_customer_ltv(&purchase_history, &behavior_metrics)
            .await?;

        // Calculate churn risk
        let churn_risk = self
            .calculate_churn_risk(&behavior_metrics, &purchase_history)
            .await?;

        // Generate recommendations
        let recommendations = self
            .generate_customer_recommendations(customer_id, &embedding)
            .await?;

        let customer_embedding = CustomerEmbedding {
            customer_id: customer_id.to_string(),
            name,
            segment,
            purchase_history,
            preferences,
            behavior_metrics,
            embedding,
            predicted_ltv,
            churn_risk,
            recommendations,
            last_updated: Utc::now(),
        };

        // Cache the result
        {
            let mut embeddings = self.customer_embeddings.write().expect("lock poisoned");
            embeddings.insert(customer_id.to_string(), customer_embedding.clone());
        }

        info!(
            "Generated customer embedding for {} with LTV: ${:.2} and churn risk: {:.3}",
            customer_id, predicted_ltv, churn_risk
        );
        Ok(customer_embedding)
    }

    /// Get product recommendations for a customer
    pub async fn recommend_products(
        &self,
        customer_id: &str,
        num_recommendations: usize,
    ) -> Result<Vec<ProductRecommendation>> {
        let customer_embedding = self.generate_customer_embedding(customer_id).await?;

        // Use existing recommendations if available and fresh
        if !customer_embedding.recommendations.is_empty()
            && customer_embedding.last_updated > Utc::now() - chrono::Duration::hours(6)
        {
            return Ok(customer_embedding
                .recommendations
                .into_iter()
                .take(num_recommendations)
                .collect());
        }

        // Generate new recommendations
        self.generate_customer_recommendations(customer_id, &customer_embedding.embedding)
            .await
    }

    /// Find similar employees based on skills and experience
    pub async fn find_similar_employees(
        &self,
        employee_id: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let target_embedding = self.generate_employee_embedding(employee_id).await?;
        let embeddings = {
            let guard = self.employee_embeddings.read().expect("lock poisoned");
            guard.clone()
        };

        let mut similarities = Vec::new();

        for (other_id, other_embedding) in embeddings.iter() {
            if other_id != employee_id {
                let similarity = self
                    .calculate_employee_similarity(&target_embedding, other_embedding)
                    .await?;
                similarities.push((other_id.clone(), similarity));
            }
        }

        // Sort by similarity and take top k
        similarities.sort_by(|a, b| {
            b.1.partial_cmp(&a.1)
                .expect("similarity scores should be finite")
        });
        similarities.truncate(k);

        Ok(similarities)
    }

    /// Optimize team composition for a project
    pub async fn optimize_team_composition(
        &self,
        _project_id: &str,
        required_skills: &[String],
    ) -> Result<Vec<String>> {
        let employees = {
            let guard = self.employee_embeddings.read().expect("lock poisoned");
            guard.clone()
        };
        let mut candidates = Vec::new();

        // Score employees based on skill match
        for (employee_id, employee) in employees.iter() {
            let skill_match_score = self
                .calculate_skill_match_score(&employee.skills, required_skills)
                .await?;
            candidates.push((employee_id.clone(), skill_match_score));
        }

        // Sort by skill match and take top candidates
        candidates.sort_by(|a, b| {
            b.1.partial_cmp(&a.1)
                .expect("candidate scores should be finite")
        });

        // Select optimal team (considering diversity, collaboration history, etc.)
        let optimal_team = self.select_optimal_team(candidates, 5).await?;

        Ok(optimal_team)
    }

    /// Analyze market trends and opportunities
    pub async fn analyze_market_trends(&self) -> Result<MarketAnalysis> {
        let products = {
            let guard = self.product_embeddings.read().expect("lock poisoned");
            guard.clone()
        };
        let customers = {
            let guard = self.customer_embeddings.read().expect("lock poisoned");
            guard.clone()
        };

        // Analyze product performance trends
        let mut category_performance = HashMap::new();
        let mut trending_products = Vec::new();

        for (product_id, product) in products.iter() {
            // Track category performance
            let performance = category_performance
                .entry(product.category.clone())
                .or_insert(CategoryPerformance {
                    total_sales: 0.0,
                    product_count: 0,
                    average_rating: 0.0,
                    growth_rate: 0.0,
                    market_share: 0.0,
                });

            performance.total_sales += product.sales_metrics.revenue;
            performance.product_count += 1;
            performance.average_rating += product.ratings.average_rating;

            // Identify trending products
            if product.sales_metrics.sales_velocity > 20.0 {
                trending_products.push(product_id.clone());
            }
        }

        // Normalize category averages
        for performance in category_performance.values_mut() {
            if performance.product_count > 0 {
                performance.average_rating /= performance.product_count as f64;
            }
        }

        // Analyze customer segments
        let mut segment_analysis = HashMap::new();
        for customer in customers.values() {
            let segment_name = format!("{:?}", customer.segment);
            let count = segment_analysis.entry(segment_name).or_insert(0);
            *count += 1;
        }

        Ok(MarketAnalysis {
            category_performance,
            trending_products,
            segment_distribution: segment_analysis,
            market_opportunities: self.identify_market_opportunities().await?,
            competitive_landscape: self.analyze_competitive_landscape().await?,
            forecast: self.generate_market_forecast().await?,
        })
    }

    // ===== PRIVATE HELPER METHODS =====

    async fn compute_product_embedding_vector(
        &self,
        _name: &str,
        _description: &str,
        _features: &[ProductFeature],
        _sales_metrics: &SalesMetrics,
    ) -> Result<Vector> {
        // Placeholder - would compute actual embedding
        let values = {
            use scirs2_core::random::{Random, Rng};
            let mut random = Random::default();
            (0..self.config.embedding_dimension)
                .map(|_| random.random::<f32>())
                .collect()
        };
        Ok(Vector::new(values))
    }

    async fn find_similar_products(
        &self,
        _product_id: &str,
        _embedding: &Vector,
    ) -> Result<Vec<String>> {
        // Placeholder - would find similar products using embedding similarity
        Ok(vec!["prod_002".to_string(), "prod_003".to_string()])
    }

    async fn calculate_market_position(
        &self,
        sales_metrics: &SalesMetrics,
        ratings: &CustomerRatings,
    ) -> Result<f64> {
        // Combine sales performance and customer satisfaction
        let sales_score = (sales_metrics.sales_velocity / 100.0).min(1.0);
        let rating_score = ratings.average_rating / 5.0;
        let position = (sales_score * 0.6 + rating_score * 0.4).min(1.0);
        Ok(position)
    }

    async fn compute_employee_embedding_vector(
        &self,
        _skills: &[Skill],
        _performance: &PerformanceMetrics,
        _projects: &[ProjectParticipation],
    ) -> Result<Vector> {
        // Placeholder - would compute actual embedding
        let values = {
            use scirs2_core::random::{Random, Rng};
            let mut random = Random::default();
            (0..self.config.embedding_dimension)
                .map(|_| random.random::<f32>())
                .collect()
        };
        Ok(Vector::new(values))
    }

    async fn predict_career_progression(
        &self,
        skills: &[Skill],
        performance: &PerformanceMetrics,
        _experience_level: &ExperienceLevel,
    ) -> Result<CareerPredictions> {
        // Calculate promotion likelihood based on performance and skills
        let performance_factor = performance.overall_score / 10.0;
        let skill_factor = skills
            .iter()
            .map(|s| s.proficiency_level as f64 / 10.0)
            .sum::<f64>()
            / skills.len() as f64;
        let promotion_likelihood = (performance_factor * 0.7 + skill_factor * 0.3).min(1.0);

        Ok(CareerPredictions {
            promotion_likelihood,
            next_role: "Senior Software Engineer".to_string(),
            skills_to_develop: vec!["Team Leadership".to_string(), "System Design".to_string()],
            career_paths: vec![
                "Technical Lead".to_string(),
                "Engineering Manager".to_string(),
            ],
            retention_risk: 1.0 - promotion_likelihood * 0.8,
        })
    }

    async fn compute_customer_embedding_vector(
        &self,
        _purchases: &[Purchase],
        _preferences: &CustomerPreferences,
        _behavior: &BehaviorMetrics,
    ) -> Result<Vector> {
        // Placeholder - would compute actual embedding
        let values = {
            use scirs2_core::random::{Random, Rng};
            let mut random = Random::default();
            (0..self.config.embedding_dimension)
                .map(|_| random.random::<f32>())
                .collect()
        };
        Ok(Vector::new(values))
    }

    async fn predict_customer_ltv(
        &self,
        purchases: &[Purchase],
        behavior: &BehaviorMetrics,
    ) -> Result<f64> {
        if purchases.is_empty() {
            return Ok(0.0);
        }

        // Simple LTV calculation based on purchase history and behavior
        let total_spent: f64 = purchases.iter().map(|p| p.price * p.quantity as f64).sum();
        let avg_purchase = total_spent / purchases.len() as f64;
        let frequency_factor = behavior.visit_frequency;
        let ltv = avg_purchase * frequency_factor * 12.0; // Annualized

        Ok(ltv)
    }

    async fn calculate_churn_risk(
        &self,
        behavior: &BehaviorMetrics,
        purchases: &[Purchase],
    ) -> Result<f64> {
        // Calculate churn risk based on recent activity
        let recency_factor = if let Some(last_purchase) = purchases.last() {
            let days_since_last = (Utc::now() - last_purchase.purchase_date).num_days() as f64;
            (days_since_last / 90.0).min(1.0) // Higher risk if no purchase in 90 days
        } else {
            1.0
        };

        let engagement_factor = 1.0 - (behavior.visit_frequency / 10.0).min(1.0);
        let abandonment_factor = behavior.cart_abandonment_rate;

        let churn_risk =
            (recency_factor * 0.4 + engagement_factor * 0.3 + abandonment_factor * 0.3).min(1.0);
        Ok(churn_risk)
    }

    async fn generate_customer_recommendations(
        &self,
        _customer_id: &str,
        _embedding: &Vector,
    ) -> Result<Vec<ProductRecommendation>> {
        // Placeholder - would generate recommendations using collaborative filtering, content-based, etc.
        Ok(vec![
            ProductRecommendation {
                product_id: "prod_101".to_string(),
                score: 0.95,
                reason: RecommendationReason::SimilarProducts,
                confidence: 0.85,
                expected_revenue: 199.99,
            },
            ProductRecommendation {
                product_id: "prod_102".to_string(),
                score: 0.88,
                reason: RecommendationReason::CustomersBought,
                confidence: 0.78,
                expected_revenue: 149.99,
            },
        ])
    }

    async fn calculate_employee_similarity(
        &self,
        emp1: &EmployeeEmbedding,
        emp2: &EmployeeEmbedding,
    ) -> Result<f64> {
        // Calculate cosine similarity between embeddings
        let embedding1 = &emp1.embedding.values;
        let embedding2 = &emp2.embedding.values;

        let dot_product: f32 = embedding1
            .iter()
            .zip(embedding2.iter())
            .map(|(a, b)| a * b)
            .sum();
        let norm1: f32 = embedding1.iter().map(|x| x * x).sum::<f32>().sqrt();
        let norm2: f32 = embedding2.iter().map(|x| x * x).sum::<f32>().sqrt();

        let cosine_similarity = if norm1 > 0.0 && norm2 > 0.0 {
            dot_product / (norm1 * norm2)
        } else {
            0.0
        };

        // Combine with skill similarity
        let skill_similarity = self
            .calculate_skill_similarity(&emp1.skills, &emp2.skills)
            .await?;

        // Weighted combination
        let final_similarity = 0.6 * cosine_similarity as f64 + 0.4 * skill_similarity;

        Ok(final_similarity)
    }

    async fn calculate_skill_similarity(
        &self,
        skills1: &[Skill],
        skills2: &[Skill],
    ) -> Result<f64> {
        let skill_set1: HashSet<_> = skills1.iter().map(|s| &s.skill_name).collect();
        let skill_set2: HashSet<_> = skills2.iter().map(|s| &s.skill_name).collect();

        let intersection = skill_set1.intersection(&skill_set2).count();
        let union = skill_set1.union(&skill_set2).count();

        if union > 0 {
            Ok(intersection as f64 / union as f64)
        } else {
            Ok(0.0)
        }
    }

    async fn calculate_skill_match_score(
        &self,
        employee_skills: &[Skill],
        required_skills: &[String],
    ) -> Result<f64> {
        let employee_skill_names: HashSet<_> =
            employee_skills.iter().map(|s| &s.skill_name).collect();
        let required_skill_set: HashSet<_> = required_skills.iter().collect();

        let matches = required_skill_set
            .intersection(&employee_skill_names)
            .count();
        let score = matches as f64 / required_skills.len() as f64;

        Ok(score)
    }

    async fn select_optimal_team(
        &self,
        _candidates: Vec<(String, f64)>,
        team_size: usize,
    ) -> Result<Vec<String>> {
        // Placeholder - would use optimization algorithm to select diverse, high-performing team
        let team: Vec<String> = _candidates
            .into_iter()
            .take(team_size)
            .map(|(id, _score)| id)
            .collect();

        Ok(team)
    }

    async fn identify_market_opportunities(&self) -> Result<Vec<String>> {
        // Placeholder - would analyze market gaps and opportunities
        Ok(vec![
            "AI-powered fitness devices".to_string(),
            "Sustainable electronics".to_string(),
            "Remote work solutions".to_string(),
        ])
    }

    async fn analyze_competitive_landscape(&self) -> Result<HashMap<String, f64>> {
        // Placeholder - would analyze competitor market shares
        let mut landscape = HashMap::new();
        landscape.insert("TechCorp".to_string(), 0.35);
        landscape.insert("InnovateCo".to_string(), 0.28);
        landscape.insert("FutureTech".to_string(), 0.22);
        landscape.insert("Others".to_string(), 0.15);

        Ok(landscape)
    }

    async fn generate_market_forecast(&self) -> Result<HashMap<String, f64>> {
        // Placeholder - would generate growth forecasts
        let mut forecast = HashMap::new();
        forecast.insert("Q1_growth".to_string(), 0.12);
        forecast.insert("Q2_growth".to_string(), 0.15);
        forecast.insert("Q3_growth".to_string(), 0.18);
        forecast.insert("Q4_growth".to_string(), 0.10);

        Ok(forecast)
    }

    // ===== BACKGROUND ANALYSIS TASKS =====

    async fn start_recommendation_engine(&self) -> JoinHandle<()> {
        let interval =
            std::time::Duration::from_secs(self.config.product_recommendation_refresh_hours * 3600);

        tokio::spawn(async move {
            let mut interval_timer = tokio::time::interval(interval);

            loop {
                interval_timer.tick().await;

                // Refresh recommendation models
                info!("Refreshing product recommendation engines");

                // Placeholder for actual recommendation model training/updating
                // Would retrain collaborative filtering, content-based, and hybrid models

                debug!("Product recommendation engines refreshed");
            }
        })
    }

    async fn start_skill_analysis(&self) -> JoinHandle<()> {
        let interval =
            std::time::Duration::from_secs(self.config.skill_analysis_interval_hours * 3600);

        tokio::spawn(async move {
            let mut interval_timer = tokio::time::interval(interval);

            loop {
                interval_timer.tick().await;

                // Analyze employee skills and career progression
                info!("Performing employee skill analysis");

                // Placeholder for actual skill gap analysis, career path optimization, etc.

                debug!("Employee skill analysis completed");
            }
        })
    }

    async fn start_market_analysis(&self) -> JoinHandle<()> {
        let interval =
            std::time::Duration::from_secs(self.config.market_analysis_interval_hours * 3600);

        tokio::spawn(async move {
            let mut interval_timer = tokio::time::interval(interval);

            loop {
                interval_timer.tick().await;

                // Perform market trend analysis
                info!("Performing market trend analysis");

                // Placeholder for actual market analysis, competitive intelligence, etc.

                debug!("Market trend analysis completed");
            }
        })
    }

    async fn start_organizational_optimization(&self) -> JoinHandle<()> {
        let interval = std::time::Duration::from_secs(24 * 3600); // Daily

        tokio::spawn(async move {
            let mut interval_timer = tokio::time::interval(interval);

            loop {
                interval_timer.tick().await;

                // Optimize organizational structure and resource allocation
                info!("Performing organizational optimization");

                // Placeholder for actual org optimization, team formation, resource allocation

                debug!("Organizational optimization completed");
            }
        })
    }
}

/// Market analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MarketAnalysis {
    /// Performance by category
    pub category_performance: HashMap<String, CategoryPerformance>,
    /// Trending products
    pub trending_products: Vec<String>,
    /// Customer segment distribution
    pub segment_distribution: HashMap<String, u32>,
    /// Market opportunities
    pub market_opportunities: Vec<String>,
    /// Competitive landscape
    pub competitive_landscape: HashMap<String, f64>,
    /// Market forecast
    pub forecast: HashMap<String, f64>,
}

/// Enterprise analytics and metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EnterpriseMetrics {
    /// Total products
    pub total_products: usize,
    /// Total employees
    pub total_employees: usize,
    /// Total customers
    pub total_customers: usize,
    /// Total revenue
    pub total_revenue: f64,
    /// Average customer satisfaction
    pub avg_customer_satisfaction: f64,
    /// Employee engagement score
    pub employee_engagement: f64,
    /// Organizational efficiency
    pub organizational_efficiency: f64,
    /// Innovation index
    pub innovation_index: f64,
    /// Top performing products
    pub top_products: Vec<String>,
    /// Top performing employees
    pub top_employees: Vec<String>,
    /// High-value customers
    pub high_value_customers: Vec<String>,
}

impl EnterpriseKnowledgeAnalyzer {
    /// Get comprehensive enterprise metrics
    pub async fn get_enterprise_metrics(&self) -> Result<EnterpriseMetrics> {
        let product_embeddings = self.product_embeddings.read().expect("lock poisoned");
        let employee_embeddings = self.employee_embeddings.read().expect("lock poisoned");
        let customer_embeddings = self.customer_embeddings.read().expect("lock poisoned");

        let total_products = product_embeddings.len();
        let total_employees = employee_embeddings.len();
        let total_customers = customer_embeddings.len();

        // Calculate total revenue
        let total_revenue = product_embeddings
            .values()
            .map(|p| p.sales_metrics.revenue)
            .sum();

        // Calculate average customer satisfaction
        let avg_customer_satisfaction = product_embeddings
            .values()
            .map(|p| p.ratings.average_rating)
            .sum::<f64>()
            / total_products.max(1) as f64;

        // Calculate employee engagement
        let employee_engagement = employee_embeddings
            .values()
            .map(|e| e.performance_metrics.overall_score)
            .sum::<f64>()
            / total_employees.max(1) as f64;

        // Get top performers
        let mut product_scores: Vec<_> = product_embeddings
            .iter()
            .map(|(id, p)| (id.clone(), p.market_position))
            .collect();
        product_scores.sort_by(|a, b| {
            b.1.partial_cmp(&a.1)
                .expect("product scores should be finite")
        });
        let top_products: Vec<String> = product_scores
            .into_iter()
            .take(10)
            .map(|(id, _)| id)
            .collect();

        let mut employee_scores: Vec<_> = employee_embeddings
            .iter()
            .map(|(id, e)| (id.clone(), e.performance_metrics.overall_score))
            .collect();
        employee_scores.sort_by(|a, b| {
            b.1.partial_cmp(&a.1)
                .expect("employee scores should be finite")
        });
        let top_employees: Vec<String> = employee_scores
            .into_iter()
            .take(10)
            .map(|(id, _)| id)
            .collect();

        let mut customer_values: Vec<_> = customer_embeddings
            .iter()
            .map(|(id, c)| (id.clone(), c.predicted_ltv))
            .collect();
        customer_values.sort_by(|a, b| {
            b.1.partial_cmp(&a.1)
                .expect("customer values should be finite")
        });
        let high_value_customers: Vec<String> = customer_values
            .into_iter()
            .take(10)
            .map(|(id, _)| id)
            .collect();

        Ok(EnterpriseMetrics {
            total_products,
            total_employees,
            total_customers,
            total_revenue,
            avg_customer_satisfaction,
            employee_engagement,
            organizational_efficiency: 0.75, // Placeholder
            innovation_index: 0.68,          // Placeholder
            top_products,
            top_employees,
            high_value_customers,
        })
    }
}

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

    #[tokio::test]
    async fn test_enterprise_analyzer_creation() {
        let config = EnterpriseConfig::default();
        let analyzer = EnterpriseKnowledgeAnalyzer::new(config);

        // Test that analyzer is created successfully
        assert_eq!(analyzer.product_embeddings.read().unwrap().len(), 0);
        assert_eq!(analyzer.employee_embeddings.read().unwrap().len(), 0);
        assert_eq!(analyzer.customer_embeddings.read().unwrap().len(), 0);
    }

    #[tokio::test]
    async fn test_product_embedding_generation() {
        let config = EnterpriseConfig::default();
        let analyzer = EnterpriseKnowledgeAnalyzer::new(config);

        let result = analyzer.generate_product_embedding("test_product").await;
        assert!(result.is_ok());

        let embedding = result.unwrap();
        assert_eq!(embedding.product_id, "test_product");
        assert!(embedding.market_position >= 0.0);
        assert!(embedding.market_position <= 1.0);
        assert_eq!(embedding.embedding.values.len(), 256); // Default dimension
    }

    #[tokio::test]
    async fn test_employee_embedding_generation() {
        let config = EnterpriseConfig::default();
        let analyzer = EnterpriseKnowledgeAnalyzer::new(config);

        let result = analyzer.generate_employee_embedding("test_employee").await;
        assert!(result.is_ok());

        let embedding = result.unwrap();
        assert_eq!(embedding.employee_id, "test_employee");
        assert!(embedding.career_predictions.promotion_likelihood >= 0.0);
        assert!(embedding.career_predictions.promotion_likelihood <= 1.0);
    }

    #[tokio::test]
    async fn test_customer_embedding_generation() {
        let config = EnterpriseConfig::default();
        let analyzer = EnterpriseKnowledgeAnalyzer::new(config);

        let result = analyzer.generate_customer_embedding("test_customer").await;
        assert!(result.is_ok());

        let embedding = result.unwrap();
        assert_eq!(embedding.customer_id, "test_customer");
        assert!(embedding.predicted_ltv >= 0.0);
        assert!(embedding.churn_risk >= 0.0);
        assert!(embedding.churn_risk <= 1.0);
    }

    #[tokio::test]
    async fn test_product_recommendations() {
        let config = EnterpriseConfig::default();
        let analyzer = EnterpriseKnowledgeAnalyzer::new(config);

        // First generate customer embedding
        let _customer = analyzer
            .generate_customer_embedding("test_customer")
            .await
            .unwrap();

        let recommendations = analyzer.recommend_products("test_customer", 5).await;
        assert!(recommendations.is_ok());

        let recs = recommendations.unwrap();
        assert!(!recs.is_empty());
        assert!(recs.len() <= 5);

        for rec in &recs {
            assert!(rec.score >= 0.0);
            assert!(rec.score <= 1.0);
            assert!(rec.confidence >= 0.0);
            assert!(rec.confidence <= 1.0);
        }
    }

    #[tokio::test]
    async fn test_market_analysis() {
        let config = EnterpriseConfig::default();
        let analyzer = EnterpriseKnowledgeAnalyzer::new(config);

        // Add some test data
        let _product = analyzer
            .generate_product_embedding("test_product")
            .await
            .unwrap();
        let _customer = analyzer
            .generate_customer_embedding("test_customer")
            .await
            .unwrap();

        let analysis = analyzer.analyze_market_trends().await;
        assert!(analysis.is_ok());

        let market_analysis = analysis.unwrap();
        assert!(!market_analysis.competitive_landscape.is_empty());
        assert!(!market_analysis.forecast.is_empty());
    }

    #[tokio::test]
    async fn test_enterprise_metrics() {
        let config = EnterpriseConfig::default();
        let analyzer = EnterpriseKnowledgeAnalyzer::new(config);

        // Add some test data
        let _product = analyzer
            .generate_product_embedding("test_product")
            .await
            .unwrap();
        let _employee = analyzer
            .generate_employee_embedding("test_employee")
            .await
            .unwrap();
        let _customer = analyzer
            .generate_customer_embedding("test_customer")
            .await
            .unwrap();

        let metrics = analyzer.get_enterprise_metrics().await;
        assert!(metrics.is_ok());

        let enterprise_metrics = metrics.unwrap();
        assert_eq!(enterprise_metrics.total_products, 1);
        assert_eq!(enterprise_metrics.total_employees, 1);
        assert_eq!(enterprise_metrics.total_customers, 1);
        assert!(enterprise_metrics.total_revenue >= 0.0);
    }
}