use crate::enterprise_knowledge::EnterpriseKnowledgeAnalyzer;
use crate::enterprise_knowledge_customer::{
BehaviorMetrics, CustomerPreferences, ProductRecommendation, Purchase, RecommendationReason,
};
use crate::enterprise_knowledge_employee::{
CareerPredictions, EmployeeEmbedding, ExperienceLevel, PerformanceMetrics,
ProjectParticipation, Skill,
};
use crate::enterprise_knowledge_engine::EnterpriseMetrics;
use crate::enterprise_knowledge_product::{CustomerRatings, ProductFeature, SalesMetrics};
use crate::Vector;
use anyhow::Result;
use chrono::Utc;
use std::collections::{HashMap, HashSet};
use tokio::task::JoinHandle;
use tracing::{debug, info};
impl EnterpriseKnowledgeAnalyzer {
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 calculate_employee_similarity(
&self,
emp1: &EmployeeEmbedding,
emp2: &EmployeeEmbedding,
) -> Result<f64> {
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
};
let skill_similarity = self
.calculate_skill_similarity(&emp1.skills, &emp2.skills)
.await?;
let final_similarity = 0.6 * cosine_similarity as f64 + 0.4 * skill_similarity;
Ok(final_similarity)
}
pub(crate) 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)
}
}
pub(crate) 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)
}
pub(crate) async fn select_optimal_team(
&self,
_candidates: Vec<(String, f64)>,
team_size: usize,
) -> Result<Vec<String>> {
let team: Vec<String> = _candidates
.into_iter()
.take(team_size)
.map(|(id, _score)| id)
.collect();
Ok(team)
}
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)
}
pub(crate) 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");
}
})
}
pub(crate) 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");
}
})
}
pub(crate) 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");
}
})
}
pub(crate) 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");
}
})
}
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();
let total_revenue = product_embeddings
.values()
.map(|p| p.sales_metrics.revenue)
.sum();
let avg_customer_satisfaction = product_embeddings
.values()
.map(|p| p.ratings.average_rating)
.sum::<f64>()
/ total_products.max(1) as f64;
let employee_engagement = employee_embeddings
.values()
.map(|e| e.performance_metrics.overall_score)
.sum::<f64>()
/ total_employees.max(1) as f64;
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,
innovation_index: 0.68,
top_products,
top_employees,
high_value_customers,
})
}
}