use crate::enterprise_knowledge::EnterpriseKnowledgeAnalyzer;
use crate::enterprise_knowledge_customer::{
BehaviorMetrics, CommunicationFrequency, CommunicationPreferences, CustomerEmbedding,
CustomerPreferences, CustomerSegment, ProductRecommendation, Purchase, PurchaseChannel,
};
use crate::enterprise_knowledge_employee::{
EmployeeEmbedding, ExperienceLevel, PerformanceMetrics, ProjectOutcome, ProjectParticipation,
Skill, SkillCategory,
};
use crate::enterprise_knowledge_engine::MarketAnalysis;
use crate::enterprise_knowledge_product::{
CategoryPerformance, CustomerRatings, FeatureType, ProductAvailability, ProductEmbedding,
ProductFeature, SalesMetrics,
};
use anyhow::Result;
use chrono::Utc;
use std::collections::HashMap;
use tracing::info;
impl EnterpriseKnowledgeAnalyzer {
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 async fn recommend_products(
&self,
customer_id: &str,
num_recommendations: usize,
) -> Result<Vec<ProductRecommendation>> {
let customer_embedding = self.generate_customer_embedding(customer_id).await?;
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());
}
self.generate_customer_recommendations(customer_id, &customer_embedding.embedding)
.await
}
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));
}
}
similarities.sort_by(|a, b| {
b.1.partial_cmp(&a.1)
.expect("similarity scores should be finite")
});
similarities.truncate(k);
Ok(similarities)
}
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();
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));
}
candidates.sort_by(|a, b| {
b.1.partial_cmp(&a.1)
.expect("candidate scores should be finite")
});
let optimal_team = self.select_optimal_team(candidates, 5).await?;
Ok(optimal_team)
}
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()
};
let mut category_performance = HashMap::new();
let mut trending_products = Vec::new();
for (product_id, product) in products.iter() {
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;
if product.sales_metrics.sales_velocity > 20.0 {
trending_products.push(product_id.clone());
}
}
for performance in category_performance.values_mut() {
if performance.product_count > 0 {
performance.average_rating /= performance.product_count as f64;
}
}
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?,
})
}
}