Upload hallucination_detector.py with huggingface_hub

hallucination_detector.py

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+import numpy as np
+
+def _classify_risk_level(score: float) -> str:
+    """リスクスコアをカテゴリに分類する。"""
+    if score < 0.1: return "very_low"
+    if score < 0.3: return "low"
+    if score < 0.6: return "moderate"
+    if score < 0.8: return "high"
+    return "very_high"
+
+def calculate_hallucination_risk_score(alpha_response: dict, validation_result: dict) -> dict:
+    """
+    α-Lobeの回答とβ-Lobeの検証結果から、ハルシネーションのリスクを計算します。
+
+    Args:
+        alpha_response (dict): α-Lobeからの構造化レスポンス。
+        validation_result (dict): β-Lobeによる検証結果。
+
+    Returns:
+        dict: ハルシネーションリスクスコアと関連情報。
+    """
+    risk_score = 0.0
+    
+    # --- 検証結果に基づくリスク加算 ---
+    anchor_passed = validation_result["checks"]["anchor_facts"]["passed"]
+    logic_passed = validation_result["checks"].get("logic", {"passed": True})["passed"]
+    context_passed = validation_result["checks"].get("context", {"passed": True})["passed"]
+    
+    # Anchor事実との矛盾は最大のリスク
+    risk_score += (1 - (1 if anchor_passed else 0)) * 0.5
+    # 論理矛盾も高いリスク
+    risk_score += (1 - (1 if logic_passed else 0)) * 0.3
+    # 医学的文脈の矛盾
+    risk_score += (1 - (1 if context_passed else 0)) * 0.2
+    
+    # --- 回答内容に基づくリスク加算 ---
+    
+    # α-Lobe自体の自信度が低い場合
+    alpha_confidence = alpha_response.get("confidence", 0.7)
+    if alpha_confidence < 0.5:
+        risk_score += 0.1
+        
+    # 不確実性に関する言及がない場合、過信しているリスク
+    uncertainties = alpha_response.get("uncertainties", [])
+    if not uncertainties:
+        risk_score += 0.05
+        
+    # 引用元が全くない場合
+    sources_cited = alpha_response.get("sources_cited", [])
+    if not sources_cited:
+        risk_score += 0.1
+
+    final_risk_score = min(1.0, risk_score)
+    
+    return {
+        "hallucination_risk_score": final_risk_score,
+        "risk_level": _classify_risk_level(final_risk_score),
+        "action_required": final_risk_score >= 0.3
+    }
+
+if __name__ == '__main__':
+    # --- ダミーデータによる使用例 ---
+    
+    # ケース1: 安全な回答
+    safe_alpha_res = {
+        "confidence": 0.9, "uncertainties": [], "sources_cited": ["JCS 2023 Guideline"]
+    }
+    safe_validation_res = {
+        "checks": {"anchor_facts": {"passed": True}, "logic": {"passed": True}, "context": {"passed": True}}
+    }
+    risk_1 = calculate_hallucination_risk_score(safe_alpha_res, safe_validation_res)
+    print(f"--- Case 1: Safe Response ---")
+    print(f"  Risk Score: {risk_1['hallucination_risk_score']:.2f} ({risk_1['risk_level']})")
+    print(f"  Action Required: {risk_1['action_required']}")
+
+    # ケース2: リスクのある回答 (事実誤認、引用なし)
+    risky_alpha_res = {
+        "confidence": 0.95, "uncertainties": [], "sources_cited": []
+    }
+    risky_validation_res = {
+        "checks": {"anchor_facts": {"passed": False}, "logic": {"passed": True}, "context": {"passed": True}}
+    }
+    risk_2 = calculate_hallucination_risk_score(risky_alpha_res, risky_validation_res)
+    print(f"\n--- Case 2: Risky Response ---")
+    print(f"  Risk Score: {risk_2['hallucination_risk_score']:.2f} ({risk_2['risk_level']})")
+    print(f"  Action Required: {risk_2['action_required']}")
+    
+    # ケース3: リスク中程度の回答 (論理エラー、自信度低い)
+    medium_alpha_res = {
+        "confidence": 0.4, "uncertainties": ["かもしれない"], "sources_cited": ["Some Journal"]
+    }
+    medium_validation_res = {
+        "checks": {"anchor_facts": {"passed": True}, "logic": {"passed": False}, "context": {"passed": True}}
+    }
+    risk_3 = calculate_hallucination_risk_score(medium_alpha_res, medium_validation_res)
+    print(f"\n--- Case 3: Medium Risk Response ---")
+    print(f"  Risk Score: {risk_3['hallucination_risk_score']:.2f} ({risk_3['risk_level']})")
+    print(f"  Action Required: {risk_3['action_required']}")