fix: restore main runtime sources and cache consistency

exam_pkg/generator.py

@@ -1032,6 +1032,7 @@ def analyze_document_for_exam(chunks: List[Dict], max_total: int = None) -> Dict
         )
         prompt += f"\n### {section_name[:30]}\n{content_preview[:300]}\n"
 
+    question_limit = min(total_knowledge_points * 2, max_total if max_total else 20)
     prompt += """
 ## 要求
 根据文档内容特点，决定：
@@ -1055,10 +1056,10 @@ def analyze_document_for_exam(chunks: List[Dict], max_total: int = None) -> Dict
 
 注意：
 - 不适合的题型数量设为 0
-- 所有数量之和不要超过 {min(total_knowledge_points * 2, 20)}
+- 所有数量之和不要超过 %d
 - 必须返回合法 JSON，不要有其他内容
 
-请直接输出 JSON："""
+请直接输出 JSON：""" % question_limit
 
     try:
         response = generator._call_llm(prompt)

exam_pkg/grader.py

@@ -219,6 +219,41 @@ def grade_fill_blank(answer: Dict) -> Dict:
     student_answers = answer.get('student_answer', [])
     max_score = answer.get('max_score', 4.0)
 
+    if not isinstance(student_answers, list):
+        logger.warning(
+            "填空题学生答案格式错误: 期望列表，实际为 %s",
+            type(student_answers).__name__,
+        )
+        return {
+            "question_id": answer.get('question_id'),
+            "score": 0,
+            "max_score": max_score,
+            "grading_status": "failed",
+            "details": {
+                "error": f"学生答案格式错误，期望列表，实际为 {type(student_answers).__name__}"
+            },
+        }
+
+    for index, student_answer in enumerate(student_answers):
+        if not isinstance(student_answer, str):
+            logger.warning(
+                "填空题学生答案第 %s 项格式错误: 期望字符串，实际为 %s",
+                index + 1,
+                type(student_answer).__name__,
+            )
+            return {
+                "question_id": answer.get('question_id'),
+                "score": 0,
+                "max_score": max_score,
+                "grading_status": "failed",
+                "details": {
+                    "error": (
+                        f"填空题学生答案第 {index + 1} 项格式错误，期望字符串，"
+                        f"实际为 {type(student_answer).__name__}"
+                    )
+                },
+            }
+
     # 归一化答案格式（修复 LLM 生成的扁平数组问题）
     blank_count = question_content.get('data', {}).get('blank_count', 0)
     correct_answers = _normalize_fill_blank_answer(correct_answers, blank_count)

exam_pkg/local_db.py

@@ -170,8 +170,9 @@ class ExamLocalDB:
                 VALUES (?, ?, ?, ?, ?, ?, ?, ?)
             ''', (exam_id, name, description, total_score, len(questions), duration, 'published', created_by))
 
-            # 关联题目
-            for order, qid in enumerate(question_ids):
+            # 仅关联实际存在的题目，避免无效 ID 触发外键约束。
+            valid_question_ids = [q['id'] for q in questions]
+            for order, qid in enumerate(valid_question_ids):
                 cursor.execute('''
                     INSERT INTO exam_questions (exam_id, question_id, question_order)
                     VALUES (?, ?, ?)
@@ -183,7 +184,7 @@ class ExamLocalDB:
             'total_score': total_score,
             'total_count': len(questions),
             'duration': duration,
-            'question_ids': question_ids
+            'question_ids': valid_question_ids
         }
 
     def get_exam(self, exam_id: str) -> Optional[Dict]: