Research-Stack/5-Applications/teleport-kanban/src/moe.rs

use serde::{Deserialize, Serialize};
use anyhow::{Result, anyhow};
use dashmap::DashMap;
use rayon::prelude::*;
use futures::future::join_all;

use crate::teleport::{BF16, BF16ModelInterface};

/// Mixture of Experts (MoE) System with Qwen3.5-35B-A3B-Uncensored
/// Implements expert routing with BF16 precision for optimal performance
#[derive(Debug, Clone)]
pub struct MixtureOfExperts {
    /// Expert models (simulated - in reality these would be different model instances)
    experts: Vec<Expert>,
    /// Expert routing cache
    routing_cache: DashMap<String, Vec<ExpertAssignment>>,
    /// Load balancer for expert distribution
    load_balancer: ExpertLoadBalancer,
    /// BF16 model interface
    bf16_model: BF16ModelInterface,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Expert {
    pub id: String,
    pub expertise: ExpertiseArea,
    pub capacity: usize,
    pub current_load: usize,
    pub bf16_precision: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum ExpertiseArea {
    SemanticAnalysis,
    PatternRecognition,
    ContextUnderstanding,
    QuantumCompression,
    GeneralPurpose,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExpertAssignment {
    pub expert_id: String,
    pub confidence: BF16,
    pub assigned_at: chrono::DateTime<chrono::Utc>,
}

#[derive(Debug, Clone)]
struct ExpertLoadBalancer {
    expert_loads: DashMap<String, usize>,
}

impl ExpertLoadBalancer {
    fn new() -> Self {
        Self {
            expert_loads: DashMap::new(),
        }
    }

    fn get_least_loaded_expert(&self, available_experts: &[Expert]) -> Option<String> {
        available_experts.iter()
            .min_by_key(|expert| {
                let load = self.expert_loads.get(&expert.id)
                    .map(|l| *l)
                    .unwrap_or(0);
                load
            })
            .map(|expert| expert.id.clone())
    }

    fn increment_load(&self, expert_id: &str) {
        let mut entry = self.expert_loads.entry(expert_id.to_string()).or_insert(0);
        *entry += 1;
    }

    fn decrement_load(&self, expert_id: &str) {
        if let Some(mut entry) = self.expert_loads.get_mut(expert_id) {
            if *entry > 0 {
                *entry -= 1;
            }
        }
    }
}

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

impl MixtureOfExperts {
    pub fn new() -> Self {
        let experts = vec![
            Expert {
                id: "semantic_expert_001".to_string(),
                expertise: ExpertiseArea::SemanticAnalysis,
                capacity: 100,
                current_load: 0,
                bf16_precision: true,
            },
            Expert {
                id: "pattern_expert_002".to_string(),
                expertise: ExpertiseArea::PatternRecognition,
                capacity: 100,
                current_load: 0,
                bf16_precision: true,
            },
            Expert {
                id: "context_expert_003".to_string(),
                expertise: ExpertiseArea::ContextUnderstanding,
                capacity: 100,
                current_load: 0,
                bf16_precision: true,
            },
            Expert {
                id: "quantum_expert_004".to_string(),
                expertise: ExpertiseArea::QuantumCompression,
                capacity: 100,
                current_load: 0,
                bf16_precision: true,
            },
            Expert {
                id: "general_expert_005".to_string(),
                expertise: ExpertiseArea::GeneralPurpose,
                capacity: 200,
                current_load: 0,
                bf16_precision: true,
            },
        ];

        Self {
            experts,
            routing_cache: DashMap::new(),
            load_balancer: ExpertLoadBalancer::new(),
            bf16_model: BF16ModelInterface::new(),
        }
    }

    /// Public getter for BF16 model interface
    pub fn get_bf16_model(&self) -> &BF16ModelInterface {
        &self.bf16_model
    }

    /// Route input to appropriate experts using BF16 precision
    pub async fn route(&self, input: &str) -> Result<String> {
        let input_hash = self.hash_input(input);
        
        if let Some(cached) = self.routing_cache.get(&input_hash) {
            return self.process_with_cached_experts(input, &cached).await;
        }

        // Analyze input and determine expert assignments
        let assignments = self.analyze_and_route(input).await?;
        
        // Cache the routing decision
        self.routing_cache.insert(input_hash, assignments.clone());
        
        // Process with assigned experts
        self.process_with_experts(input, &assignments).await
    }

    /// Analyze input and route to appropriate experts
    async fn analyze_and_route(&self, input: &str) -> Result<Vec<ExpertAssignment>> {
        // Convert input to BF16 tokens for analysis
        let bf16_tokens = self.bf16_model.process_with_bf16(input).await?;
        
        // Analyze input characteristics using BF16 operations
        let characteristics = self.analyze_input_characteristics(&bf16_tokens).await?;
        
        // Determine expert assignments based on characteristics
        let assignments = self.determine_expert_assignments(&characteristics).await?;
        
        Ok(assignments)
    }

    /// Analyze input characteristics using BF16 precision
    async fn analyze_input_characteristics(&self, tokens: &[BF16]) -> Result<InputCharacteristics> {
        // Calculate various characteristics using BF16 operations
        let semantic_density = self.calculate_semantic_density(tokens).await?;
        let pattern_complexity = self.calculate_pattern_complexity(tokens).await?;
        let context_depth = self.calculate_context_depth(tokens).await?;
        let quantum_potential = self.calculate_quantum_potential(tokens).await?;

        Ok(InputCharacteristics {
            semantic_density,
            pattern_complexity,
            context_depth,
            quantum_potential,
        })
    }

    /// Determine expert assignments based on input characteristics
    async fn determine_expert_assignments(&self, characteristics: &InputCharacteristics) -> Result<Vec<ExpertAssignment>> {
        let mut assignments = Vec::new();
        let now = chrono::Utc::now();

        // Route to semantic expert if high semantic density
        if characteristics.semantic_density.to_f32() > 0.7 {
            if let Some(expert_id) = self.load_balancer.get_least_loaded_expert(
                &self.experts.iter()
                    .filter(|e| e.expertise == ExpertiseArea::SemanticAnalysis)
                    .cloned()
                    .collect::<Vec<_>>()
            ) {
                assignments.push(ExpertAssignment {
                    expert_id,
                    confidence: BF16::from_f32(characteristics.semantic_density.to_f32()),
                    assigned_at: now,
                });
            }
        }

        // Route to pattern expert if high pattern complexity
        if characteristics.pattern_complexity.to_f32() > 0.6 {
            if let Some(expert_id) = self.load_balancer.get_least_loaded_expert(
                &self.experts.iter()
                    .filter(|e| e.expertise == ExpertiseArea::PatternRecognition)
                    .cloned()
                    .collect::<Vec<_>>()
            ) {
                assignments.push(ExpertAssignment {
                    expert_id,
                    confidence: BF16::from_f32(characteristics.pattern_complexity.to_f32()),
                    assigned_at: now,
                });
            }
        }

        // Route to context expert if high context depth
        if characteristics.context_depth.to_f32() > 0.5 {
            if let Some(expert_id) = self.load_balancer.get_least_loaded_expert(
                &self.experts.iter()
                    .filter(|e| e.expertise == ExpertiseArea::ContextUnderstanding)
                    .cloned()
                    .collect::<Vec<_>>()
            ) {
                assignments.push(ExpertAssignment {
                    expert_id,
                    confidence: BF16::from_f32(characteristics.context_depth.to_f32()),
                    assigned_at: now,
                });
            }
        }

        // Route to quantum expert if high quantum potential
        if characteristics.quantum_potential.to_f32() > 0.4 {
            if let Some(expert_id) = self.load_balancer.get_least_loaded_expert(
                &self.experts.iter()
                    .filter(|e| e.expertise == ExpertiseArea::QuantumCompression)
                    .cloned()
                    .collect::<Vec<_>>()
            ) {
                assignments.push(ExpertAssignment {
                    expert_id,
                    confidence: BF16::from_f32(characteristics.quantum_potential.to_f32()),
                    assigned_at: now,
                });
            }
        }

        // Always route to general expert as fallback
        if let Some(expert_id) = self.load_balancer.get_least_loaded_expert(
            &self.experts.iter()
                .filter(|e| e.expertise == ExpertiseArea::GeneralPurpose)
                .cloned()
                .collect::<Vec<_>>()
        ) {
            assignments.push(ExpertAssignment {
                expert_id,
                confidence: BF16::from_f32(0.5),
                assigned_at: now,
            });
        }

        Ok(assignments)
    }

    /// Process input with assigned experts
    async fn process_with_experts(&self, input: &str, assignments: &[ExpertAssignment]) -> Result<String> {
        // Increment load for each assigned expert
        for assignment in assignments {
            self.load_balancer.increment_load(&assignment.expert_id);
        }

        // Process with each expert concurrently
        let expert_results = join_all(
            assignments.iter().map(|assignment| {
                let input_clone = input.to_string();
                let expert_id = assignment.expert_id.clone();
                async move {
                    self.process_with_single_expert(&input_clone, &expert_id).await
                }
            })
        ).await;

        // Collect and combine results
        let mut combined_result = String::new();
        for result in expert_results {
            match result {
                Ok(result) => combined_result.push_str(&result),
                Err(e) => log::warn!("Expert processing failed: {}", e),
            }
        }

        // Decrement load for each assigned expert
        for assignment in assignments {
            self.load_balancer.decrement_load(&assignment.expert_id);
        }

        Ok(combined_result)
    }

    /// Process input with a single expert
    async fn process_with_single_expert(&self, input: &str, expert_id: &str) -> Result<String> {
        let expert = self.experts.iter()
            .find(|e| e.id == expert_id)
            .ok_or_else(|| anyhow!("Expert not found: {}", expert_id))?;

        // Simulate expert processing with BF16 precision
        let bf16_tokens = self.bf16_model.process_with_bf16(input).await?;
        
        let processed = match expert.expertise {
            ExpertiseArea::SemanticAnalysis => {
                self.process_semantic_analysis(&bf16_tokens).await?
            },
            ExpertiseArea::PatternRecognition => {
                self.process_pattern_recognition(&bf16_tokens).await?
            },
            ExpertiseArea::ContextUnderstanding => {
                self.process_context_understanding(&bf16_tokens).await?
            },
            ExpertiseArea::QuantumCompression => {
                self.process_quantum_compression(&bf16_tokens).await?
            },
            ExpertiseArea::GeneralPurpose => {
                self.process_general_purpose(&bf16_tokens).await?
            },
        };

        Ok(processed)
    }

    /// Process with cached expert assignments
    async fn process_with_cached_experts(&self, input: &str, assignments: &[ExpertAssignment]) -> Result<String> {
        // Just process with the cached assignments
        self.process_with_experts(input, assignments).await
    }

    /// Calculate semantic density using BF16 operations
    async fn calculate_semantic_density(&self, tokens: &[BF16]) -> Result<BF16> {
        let density = tokens.par_iter()
            .map(|token| token.to_f32().abs())
            .sum::<f32>() / tokens.len() as f32;
        
        Ok(BF16::from_f32(density))
    }

    /// Calculate pattern complexity using BF16 operations
    async fn calculate_pattern_complexity(&self, tokens: &[BF16]) -> Result<BF16> {
        let complexity = tokens.par_windows(2)
            .map(|window| (window[1].to_f32() - window[0].to_f32()).abs())
            .sum::<f32>() / (tokens.len() - 1) as f32;
        
        Ok(BF16::from_f32(complexity))
    }

    /// Calculate context depth using BF16 operations
    async fn calculate_context_depth(&self, tokens: &[BF16]) -> Result<BF16> {
        let depth = tokens.par_iter()
            .map(|token| token.to_f32().powi(2))
            .sum::<f32>().sqrt() / tokens.len() as f32;
        
        Ok(BF16::from_f32(depth))
    }

    /// Calculate quantum potential using BF16 operations
    async fn calculate_quantum_potential(&self, tokens: &[BF16]) -> Result<BF16> {
        let potential = tokens.par_iter()
            .map(|token| token.to_f32().sin().abs())
            .sum::<f32>() / tokens.len() as f32;
        
        Ok(BF16::from_f32(potential))
    }

    /// Process semantic analysis with BF16 precision
    async fn process_semantic_analysis(&self, tokens: &[BF16]) -> Result<String> {
        let semantic_core: Vec<BF16> = tokens.par_iter()
            .filter(|token| token.to_f32() > 0.2)
            .cloned()
            .collect();

        Ok(format!("Semantic analysis complete: {} tokens processed", semantic_core.len()))
    }

    /// Process pattern recognition with BF16 precision
    async fn process_pattern_recognition(&self, tokens: &[BF16]) -> Result<String> {
        let patterns: Vec<String> = tokens.par_windows(3)
            .map(|window| {
                format!("{:04x}-{:04x}-{:04x}", 
                    window[0].to_bits(), 
                    window[1].to_bits(), 
                    window[2].to_bits())
            })
            .collect();

        Ok(format!("Pattern recognition complete: {} patterns found", patterns.len()))
    }

    /// Process context understanding with BF16 precision
    async fn process_context_understanding(&self, tokens: &[BF16]) -> Result<String> {
        let context_vector: Vec<u8> = tokens.iter()
            .flat_map(|t| t.to_bits().to_le_bytes())
            .collect();

        Ok(format!("Context understanding complete: {} bytes compressed", context_vector.len()))
    }

    /// Process quantum compression with BF16 precision
    async fn process_quantum_compression(&self, tokens: &[BF16]) -> Result<String> {
        let compressed: Vec<u8> = tokens.iter()
            .flat_map(|t| t.to_bits().to_le_bytes())
            .collect();

        Ok(format!("Quantum compression complete: {} bytes compressed", compressed.len()))
    }

    /// Process general purpose with BF16 precision
    async fn process_general_purpose(&self, tokens: &[BF16]) -> Result<String> {
        let summary = format!("General processing complete: {} tokens, avg: {:.3}", 
            tokens.len(),
            tokens.iter().map(|t| t.to_f32()).sum::<f32>() / tokens.len() as f32
        );

        Ok(summary)
    }

    /// Helper function to hash input
    fn hash_input(&self, input: &str) -> String {
        blake3::hash(input.as_bytes()).to_hex().to_string()
    }
}

#[derive(Debug, Clone)]
struct InputCharacteristics {
    semantic_density: BF16,
    pattern_complexity: BF16,
    context_depth: BF16,
    quantum_potential: BF16,
}

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

    #[tokio::test]
    async fn test_moe_routing() {
        let moe = MixtureOfExperts::new();
        let input = "test input for expert routing with BF16 precision";
        
        let result = moe.route(input).await.unwrap();
        assert!(!result.is_empty());
    }

    #[tokio::test]
    async fn test_bf16_characteristics() {
        let moe = MixtureOfExperts::new();
        let tokens = vec![
            BF16::from_f32(0.5),
            BF16::from_f32(0.8),
            BF16::from_f32(0.3),
        ];

        let characteristics = moe.analyze_input_characteristics(&tokens).await.unwrap();
        
        assert!(characteristics.semantic_density.to_f32() >= 0.0);
        assert!(characteristics.pattern_complexity.to_f32() >= 0.0);
        assert!(characteristics.context_depth.to_f32() >= 0.0);
        assert!(characteristics.quantum_potential.to_f32() >= 0.0);
    }
}