失敗しない中小企業のAI導入実践論：
社員にプロンプトを打たせない「現場密着型・裏方AI」の設計図

#![forbid(unsafe_code)]

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum FieldReviewStatus {
    AutoVerified,               // High confidence AND master DB invariant matched
    RequiresHumanOneClickCheck, // Low log-prob or price/quantity anomaly
    BlockedInvalidMasterCode,   // SKU or partner code does not exist in db01.ok.st
}

#[derive(Debug, Clone, Copy)]
pub struct ExtractedOrderLine {
    pub sku_exists_in_master: bool,
    pub token_logprobs: [f64; 4], // Log-probabilities of extracted [SKU, Qty, UnitPrice, DeliveryDate]
    pub extracted_unit_price: u64,
    pub master_standard_price: u64,
}

/// Evaluates whether an AI-extracted order line can be auto-committed or requires
/// highlighted Human-in-the-Loop (HITL) 3-second visual confirmation.
#[inline]
pub fn route_extracted_order_line(
    line: &ExtractedOrderLine,
    confidence_threshold: f64,
) -> FieldReviewStatus {
    if !line.sku_exists_in_master {
        return FieldReviewStatus::BlockedInvalidMasterCode;
    }

    // Compute geometric mean probability: exp( (1/N) * ∑ ln(p_i) )
    let mean_logprob = line.token_logprobs.iter().sum::<f64>() / (line.token_logprobs.len() as f64);
    let geometric_conf = mean_logprob.exp();

    // Also flag if extracted unit price deviates from master price by more than 15%
    let price_diff = line.extracted_unit_price.abs_diff(line.master_standard_price);
    let price_anomaly = line.master_standard_price > 0
        && (price_diff as f64) / (line.master_standard_price as f64) > 0.15;

    if geometric_conf >= confidence_threshold && !price_anomaly {
        FieldReviewStatus::AutoVerified
    } else {
        FieldReviewStatus::RequiresHumanOneClickCheck
    }
}