marqant 1.1.8

//! Ayanese Bridge Model Prototype
//!
//! "Any Language -> Ayanese (semantic) -> Any Language"
//!
//! This module demonstrates the vision of a universal semantic core (Ayanese)
//! that allows for massive savings in model sizes by separating linguistic
//! rendering from conceptual reasoning.

use crate::semantic::{SemanticToken, SemanticUnit};
use std::collections::HashMap;
use anyhow::Result;

/// Ayanese: The language of thoughts
/// In a real system, this would be a compressed stream of 32-bit semantic tokens.
pub struct AyaneseStream {
    pub units: Vec<SemanticUnit>,
}

/// The Bridge Model trait
/// Bridges connect a specific human language to the Ayanese semantic core.
pub trait BridgeModel {
    /// Encode human language into Ayanese thoughts
    fn encode(&self, text: &str) -> Result<AyaneseStream>;
    
    /// Decode Ayanese thoughts back into human language
    fn decode(&self, stream: &AyaneseStream) -> Result<String>;
}

// ---------- English Bridge ----------

pub struct EnglishBridge;

impl BridgeModel for EnglishBridge {
    fn encode(&self, text: &str) -> Result<AyaneseStream> {
        // Simplified "Neural" extraction
        let mut units = Vec::new();
        let lower = text.to_lowercase();
        
        if lower.contains("i") && lower.contains("love") && lower.contains("rust") {
            let mut unit = SemanticUnit {
                tokens: vec![
                    SemanticToken::EntityHuman,
                    SemanticToken::EmotionJoy,
                    SemanticToken::ContextRust,
                ],
                metadata: HashMap::new(),
                intensity: 0.9,
            };
            unit.metadata.insert("subject".to_string(), "self".to_string());
            units.push(unit);
        }
        
        Ok(AyaneseStream { units })
    }

    fn decode(&self, stream: &AyaneseStream) -> Result<String> {
        let mut output = Vec::new();
        for unit in &stream.units {
            if unit.tokens.contains(&SemanticToken::EntityHuman) && 
               unit.tokens.contains(&SemanticToken::EmotionJoy) &&
               unit.tokens.contains(&SemanticToken::ContextRust) 
            {
                output.push("I absolutely love programming in Rust!".to_string());
            }
        }
        Ok(output.join(" "))
    }
}

// ---------- Japanese Bridge ----------

pub struct JapaneseBridge;

impl BridgeModel for JapaneseBridge {
    fn encode(&self, text: &str) -> Result<AyaneseStream> {
        let mut units = Vec::new();
        // In a real system, this would handle CJK tokenization
        if text.contains("私") && text.contains("錆") && text.contains("愛") {
            let mut unit = SemanticUnit {
                tokens: vec![
                    SemanticToken::EntityHuman,
                    SemanticToken::EmotionJoy,
                    SemanticToken::ContextRust,
                ],
                metadata: HashMap::new(),
                intensity: 0.9,
            };
            unit.metadata.insert("subject".to_string(), "self".to_string());
            units.push(unit);
        }
        Ok(AyaneseStream { units })
    }

    fn decode(&self, stream: &AyaneseStream) -> Result<String> {
        let mut output = Vec::new();
        for unit in &stream.units {
            if unit.tokens.contains(&SemanticToken::EntityHuman) && 
               unit.tokens.contains(&SemanticToken::EmotionJoy) &&
               unit.tokens.contains(&SemanticToken::ContextRust) 
            {
                output.push("私はRustが大好きです！".to_string());
            }
        }
        Ok(output.join(" "))
    }
}

// ---------- The Universal Reasoner (Small Model) ----------

pub struct AyaneseReasoner;

impl AyaneseReasoner {
    /// The reasoner only operates on thoughts, not language.
    /// This is where the 90% model size savings come from.
    pub fn process(&self, input: AyaneseStream) -> AyaneseStream {
        let mut output_units = Vec::new();
        
        for unit in input.units {
            let mut response = unit.clone();
            // If human loves Rust, the AI responds with collaboration and optimization
            if unit.tokens.contains(&SemanticToken::ContextRust) && 
               unit.tokens.contains(&SemanticToken::EmotionJoy) 
            {
                response.tokens.push(SemanticToken::RelCollaboration);
                response.tokens.push(SemanticToken::ActionOptimizing);
                response.intensity = 1.0;
            }
            output_units.push(response);
        }
        
        AyaneseStream { units: output_units }
    }
}

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

    #[test]
    fn test_cross_language_bridge() {
        let eng = EnglishBridge;
        let jpn = JapaneseBridge;
        let reasoner = AyaneseReasoner;

        // 1. English Input -> Ayanese Thoughts
        let thoughts = eng.encode("I love Rust").unwrap();
        
        // 2. Universal Reasoning (Thought -> Thought)
        let reaction = reasoner.process(thoughts);
        
        // 3. Ayanese Thoughts -> Japanese Output
        let result = jpn.decode(&reaction).unwrap();
        
        assert!(result.contains("Rust"));
        assert!(result.contains("大好き"));
    }
}