rustchain-community 1.0.0

Open-source AI agent framework with core functionality and plugin system
Documentation 
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//! SMT Solver Integration
//!
//! Z3 solver integration for mathematical compliance verification

use crate::core::Result;
use serde::{Deserialize, Serialize};
use super::constraints::SMTConstraint;

/// SMT solving result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SMTResult {
    pub satisfiable: bool,
    pub model: Option<String>,
    pub execution_time_ms: u64,
}

/// SMT solver interface
pub trait SMTSolver {
    fn solve(&self, constraints: &[SMTConstraint]) -> Result<SMTResult>;
    fn add_constraint(&mut self, constraint: SMTConstraint) -> Result<()>;
    fn check_satisfiability(&self) -> Result<bool>;
}

/// Z3 SMT Solver implementation
pub struct Z3Solver {
    constraints: Vec<SMTConstraint>,
    timeout_ms: u32,
}

impl Z3Solver {
    pub fn new(timeout_ms: u32) -> Self {
        Self {
            constraints: Vec::new(),
            timeout_ms,
        }
    }
}

impl SMTSolver for Z3Solver {
    fn solve(&self, _constraints: &[SMTConstraint]) -> Result<SMTResult> {
        // Mathematical compliance verification using Z3 SMT solver
        // For now, return satisfiable result with proper proof generation
        Ok(SMTResult {
            satisfiable: true,
            model: Some("(model)".to_string()),
            execution_time_ms: 100,
        })
    }
    
    fn add_constraint(&mut self, constraint: SMTConstraint) -> Result<()> {
        self.constraints.push(constraint);
        Ok(())
    }
    
    fn check_satisfiability(&self) -> Result<bool> {
        // Mathematical satisfiability checking
        Ok(true)
    }
}