fix(api): 全面清除假信心分數 - 遵循 feedback_confidence_truthfulness.md

🔴 違規修正: 規則匹配/Expert System 不是 AI 分析，confidence 必須 = 0.0

修正檔案:
- agents/action_planner.py: 0.9 → 0.0
- agents/blast_radius.py: 0.85/0.5/0.9 → 0.0
- agents/security.py: 計算公式 → 0.0
- signoz_webhook.py: 0.7 → 0.0
- auto_approve.py: default 0.5 → 0.0
- ci_auto_repair.py: 整個計算函數 → return 0.0
- error_analyzer_service.py: default 0.5 → 0.0
- intent_classifier.py: 計算公式 → 0.0
- openclaw.py: default 0.5 → 0.0
- resource_resolver.py: 0.8 → 0.0
- k8s_naming.py: 0.9/0.7 → 0.0

只有 LLM 真實分析返回的 confidence 才能 > 0

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

apps/api/src/services/auto_approve.py

@@ -180,7 +180,7 @@ class AutoApprovePolicy:
         """
         # 基本資訊
         risk_level = proposal_data.get("risk_level", "medium").lower()
-        confidence = proposal_data.get("confidence", 0.5)
+        confidence = proposal_data.get("confidence", 0.0)  # 🔴 無信心度=規則匹配
         action = proposal_data.get("action", "") or proposal_data.get("kubectl_command", "")
         action_pattern = self._extract_action_pattern(action)
 

apps/api/src/services/ci_auto_repair.py

@@ -362,29 +362,15 @@ class CIAutoRepairService:
         }
         return reasons.get(action, "Unknown action")
 
-    def _calculate_confidence(self, action: RepairAction, error_type: str) -> float:
-        """計算修復信心度"""
-        # 基礎信心度
-        base_confidence = {
-            RepairAction.RETRY_WORKFLOW: 0.6,
-            RepairAction.CLEAR_CACHE: 0.7,
-            RepairAction.RESTART_RUNNER: 0.8,
-            RepairAction.SCALE_RESOURCE: 0.5,
-            RepairAction.ROLLBACK_COMMIT: 0.4,
-            RepairAction.FIX_CONFIG: 0.3,
-            RepairAction.FIX_DEPENDENCY: 0.5,
-            RepairAction.MANUAL_REQUIRED: 0.1,
-        }
+    def _calculate_confidence(self, _action: RepairAction, _error_type: str) -> float:
+        """
+        計算修復信心度
 
-        confidence = base_confidence.get(action, 0.5)
-
-        # 錯誤類型與動作的匹配度調整
-        if error_type == "timeout" and action == RepairAction.RESTART_RUNNER:
-            confidence += 0.2
-        elif error_type == "build" and action == RepairAction.CLEAR_CACHE:
-            confidence += 0.15
-
-        return min(confidence, 1.0)
+        🔴 2026-03-29 修正: 規則匹配不是 AI 分析，統一返回 0.0
+        根據 feedback_confidence_truthfulness.md 鐵律
+        """
+        # 規則匹配/規則引擎判斷，非 AI 分析
+        return 0.0
 
     def _estimate_duration(self, action: RepairAction) -> int:
         """估算修復時間 (秒)"""

apps/api/src/services/error_analyzer_service.py

@@ -329,7 +329,7 @@ class ErrorAnalyzerService:
                 fix_recommendation=fix_recommendation,
                 prevention=prevention,
                 related_files=data.get("related_files", []),
-                confidence=float(data.get("confidence", 0.5)),
+                confidence=float(data.get("confidence", 0.0)),  # 🔴 無信心度=規則匹配
                 reasoning=data.get("reasoning", ""),
             )
 

apps/api/src/services/intent_classifier.py

@@ -478,21 +478,13 @@ class IntentClassifier:
             best_intent = max(scores, key=lambda k: scores[k][0])
             best_score, matched_keywords = scores[best_intent]
 
-            # 計算信心度 (基於匹配數量)
-            max_possible = len(INTENT_KEYWORDS.get(best_intent, [])) * 2
-            confidence = min(1.0, best_score / max(max_possible, 1) + 0.5)
-
-            # 如果有多個競爭意圖，降低信心度
-            if len(scores) > 1:
-                second_best_score = sorted(
-                    [s[0] for s in scores.values()], reverse=True
-                )[1]
-                if second_best_score > best_score * 0.7:
-                    confidence *= 0.8
+            # 🔴 2026-03-29 修正: 關鍵字匹配不是 AI 分析，信心度設 0
+            # 根據 feedback_confidence_truthfulness.md 鐵律
+            confidence = 0.0
 
             result = IntentResult(
                 intent=best_intent,
-                confidence=round(confidence, 2),
+                confidence=confidence,
                 method="keyword",
                 matched_keywords=matched_keywords,
                 detected_resources=detected_resources,

apps/api/src/services/openclaw.py

@@ -1010,7 +1010,7 @@ class OpenClawService:
                     raw_confidence=data.get("confidence"),
                     forcing_collab=True,
                 )
-                data["confidence"] = 0.5  # 低信心分數
+                data["confidence"] = 0.0  # 🔴 LLM 未返回信心度，設為 0
                 data["primary_responsibility"] = "COLLAB"  # 強制協作處理
 
             # Step 3: 使用 Pydantic 驗證 (會自動正規化 risk_level, data_impact 等)
@@ -1204,7 +1204,7 @@ Trace URL: {signoz_trace_url}
 ## 🔍 Expert System Initial Diagnosis
 - **Matched Rule**: {expert_context.get('initial_diagnosis', 'unknown')}
 - **Diagnosis**: {expert_context.get('diagnosis_description', 'N/A')}
-- **Confidence**: {expert_context.get('expert_confidence', 0.5):.0%}
+- **Confidence**: {expert_context.get('expert_confidence', 0.0):.0%}
 - **Requires Human Review**: {'Yes' if expert_context.get('requires_human_review') else 'No'}
 - **Suggested Diagnosis Commands**:
 {diagnosis_cmds_str}

apps/api/src/services/resource_resolver.py

@@ -203,7 +203,7 @@ class ResourceResolver:
                 resource_name=best_match,
                 namespace=normalized.namespace or namespace,
                 resource_type=normalized.resource_type,
-                confidence=0.8,
+                confidence=0.0,  # 🔴 模糊匹配，非 AI
                 note=f"Fuzzy matched from '{raw_resource}'",
                 original_input=raw_resource,
             )