use super::enterprise_knowledge_types::*;
use crate::Vector;
use anyhow::Result;
use chrono::Utc;
use std::collections::HashMap;
use std::sync::{Arc, RwLock};
use tokio::task::JoinHandle;
use tracing::{debug, info};
pub struct EnterpriseKnowledgeAnalyzer {
pub(crate) product_embeddings: Arc<RwLock<HashMap<String, ProductEmbedding>>>,
pub(crate) employee_embeddings: Arc<RwLock<HashMap<String, EmployeeEmbedding>>>,
pub(crate) customer_embeddings: Arc<RwLock<HashMap<String, CustomerEmbedding>>>,
pub(crate) category_hierarchy: Arc<RwLock<CategoryHierarchy>>,
pub(crate) organizational_structure: Arc<RwLock<OrganizationalStructure>>,
pub(crate) recommendation_engines: Arc<RwLock<HashMap<String, RecommendationEngine>>>,
pub(crate) config: EnterpriseConfig,
pub(crate) analysis_tasks: Vec<JoinHandle<()>>,
}
impl EnterpriseKnowledgeAnalyzer {
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(),
}
}
pub async fn start(&mut self) -> Result<()> {
info!("Starting enterprise knowledge analysis system");
let recommendation_task = self.start_recommendation_engine().await;
self.analysis_tasks.push(recommendation_task);
let skill_analysis_task = self.start_skill_analysis().await;
self.analysis_tasks.push(skill_analysis_task);
let market_analysis_task = self.start_market_analysis().await;
self.analysis_tasks.push(market_analysis_task);
let org_optimization_task = self.start_organizational_optimization().await;
self.analysis_tasks.push(org_optimization_task);
info!("Enterprise knowledge analysis system started successfully");
Ok(())
}
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");
}
pub async fn generate_product_embedding(&self, product_id: &str) -> Result<ProductEmbedding> {
{
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);
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()];
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);
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,
};
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,
};
let embedding = self
.compute_product_embedding_vector(&name, &description, &features, &sales_metrics)
.await?;
let similar_products = self.find_similar_products(product_id, &embedding).await?;
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(),
};
{
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)
}
pub async fn generate_employee_embedding(
&self,
employee_id: &str,
) -> Result<EmployeeEmbedding> {
{
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);
let name = format!("Employee_{employee_id}");
let job_title = "Software Engineer".to_string();
let department = "Engineering".to_string();
let team = "Backend Team".to_string();
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;
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,
};
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()];
let embedding = self
.compute_employee_embedding_vector(&skills, &performance_metrics, &project_history)
.await?;
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(),
};
{
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)
}
pub async fn generate_customer_embedding(
&self,
customer_id: &str,
) -> Result<CustomerEmbedding> {
{
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);
let name = format!("Customer_{customer_id}");
let segment = CustomerSegment::Regular;
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),
},
];
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()],
},
};
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,
};
let embedding = self
.compute_customer_embedding_vector(&purchase_history, &preferences, &behavior_metrics)
.await?;
let predicted_ltv = self
.predict_customer_ltv(&purchase_history, &behavior_metrics)
.await?;
let churn_risk = self
.calculate_churn_risk(&behavior_metrics, &purchase_history)
.await?;
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(),
};
{
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)
}
pub(crate) async fn compute_product_embedding_vector(
&self,
_name: &str,
_description: &str,
_features: &[ProductFeature],
_sales_metrics: &SalesMetrics,
) -> Result<Vector> {
let values = {
use scirs2_core::random::{Random, RngExt};
let mut random = Random::default();
(0..self.config.embedding_dimension)
.map(|_| random.random::<f32>())
.collect()
};
Ok(Vector::new(values))
}
pub(crate) async fn find_similar_products(
&self,
_product_id: &str,
_embedding: &Vector,
) -> Result<Vec<String>> {
Ok(vec!["prod_002".to_string(), "prod_003".to_string()])
}
pub(crate) async fn calculate_market_position(
&self,
sales_metrics: &SalesMetrics,
ratings: &CustomerRatings,
) -> Result<f64> {
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)
}
pub(crate) async fn compute_employee_embedding_vector(
&self,
_skills: &[Skill],
_performance: &PerformanceMetrics,
_projects: &[ProjectParticipation],
) -> Result<Vector> {
let values = {
use scirs2_core::random::{Random, RngExt};
let mut random = Random::default();
(0..self.config.embedding_dimension)
.map(|_| random.random::<f32>())
.collect()
};
Ok(Vector::new(values))
}
pub(crate) async fn predict_career_progression(
&self,
skills: &[Skill],
performance: &PerformanceMetrics,
_experience_level: &ExperienceLevel,
) -> Result<CareerPredictions> {
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,
})
}
pub(crate) async fn compute_customer_embedding_vector(
&self,
_purchases: &[Purchase],
_preferences: &CustomerPreferences,
_behavior: &BehaviorMetrics,
) -> Result<Vector> {
let values = {
use scirs2_core::random::{Random, RngExt};
let mut random = Random::default();
(0..self.config.embedding_dimension)
.map(|_| random.random::<f32>())
.collect()
};
Ok(Vector::new(values))
}
pub(crate) async fn predict_customer_ltv(
&self,
purchases: &[Purchase],
behavior: &BehaviorMetrics,
) -> Result<f64> {
if purchases.is_empty() {
return Ok(0.0);
}
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;
Ok(ltv)
}
pub(crate) async fn calculate_churn_risk(
&self,
behavior: &BehaviorMetrics,
purchases: &[Purchase],
) -> Result<f64> {
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)
} 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)
}
pub(crate) async fn generate_customer_recommendations(
&self,
_customer_id: &str,
_embedding: &Vector,
) -> Result<Vec<ProductRecommendation>> {
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,
},
])
}
pub(crate) async fn identify_market_opportunities(&self) -> Result<Vec<String>> {
Ok(vec![
"AI-powered fitness devices".to_string(),
"Sustainable electronics".to_string(),
"Remote work solutions".to_string(),
])
}
pub(crate) async fn analyze_competitive_landscape(&self) -> Result<HashMap<String, f64>> {
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)
}
pub(crate) async fn generate_market_forecast(&self) -> Result<HashMap<String, f64>> {
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)
}
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;
info!("Refreshing product recommendation engines");
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;
info!("Performing employee skill analysis");
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;
info!("Performing market trend analysis");
debug!("Market trend analysis completed");
}
})
}
async fn start_organizational_optimization(&self) -> JoinHandle<()> {
let interval = std::time::Duration::from_secs(24 * 3600);
tokio::spawn(async move {
let mut interval_timer = tokio::time::interval(interval);
loop {
interval_timer.tick().await;
info!("Performing organizational optimization");
debug!("Organizational optimization completed");
}
})
}
}