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feat(exam): 出题批卷 API 增强 — 格式统一、校验、跨调用去重

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- grader: 多策略 JSON 提取 + 3 次重试 + prompt 优化，修复 LLM 输出不稳定问题
- grader: question_content → content 统一字段名，与出题接口一致
- api: 新增入参校验（题型枚举、difficulty、总题数上限 20、grade question_type）
- api/generator/manager: 新增 exclude_stems 参数，支持跨调用去重
This commit is contained in:
lacerate551
2026-06-05 14:06:11 +08:00
parent b6631c626b
commit 3f1980ba78
4 changed files with 204 additions and 57 deletions
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63
exam_pkg/api.py
View File

@@ -61,6 +61,22 @@ def validate_difficulty(difficulty) -> str:
return "difficulty 必须为 1-5 的整数"
return None
MAX_EXCLUDE_STEMS = 100
def validate_exclude_stems(exclude_stems) -> str:
"""校验 exclude_stems 参数（可选）"""
if exclude_stems is None:
return None
if not isinstance(exclude_stems, list):
return "exclude_stems 必须为字符串数组"
if len(exclude_stems) > MAX_EXCLUDE_STEMS:
return f"exclude_stems 数量不能超过 {MAX_EXCLUDE_STEMS}，当前 {len(exclude_stems)}"
for i, stem in enumerate(exclude_stems):
if not isinstance(stem, str) or not stem.strip():
return f"exclude_stems 第 {i+1} 项必须为非空字符串"
return None
# 创建蓝图
exam_bp = Blueprint('exam', __name__)
@@ -86,6 +102,7 @@ def api_generate_questions():
"subjective": 1
},
"difficulty": 3,
"exclude_stems": ["已有题目的题干1", "已有题目的题干2"], // 可选，排除已有题目避免重复
"options": {
"include_explanation": true,
"max_source_chunks": 50
@@ -135,6 +152,20 @@ def api_generate_questions():
if diff_error:
return error_response("INVALID_PARAMS", BAD_REQUEST, diff_error, http_status=400)
# 校验总题数上限
MAX_TOTAL_QUESTIONS = 20
total_requested = sum(question_types.values())
if total_requested > MAX_TOTAL_QUESTIONS:
return error_response("INVALID_PARAMS", BAD_REQUEST,
f"总题数不能超过 {MAX_TOTAL_QUESTIONS} 道，当前请求 {total_requested} 道",
http_status=400)
# 校验排除题干列表（可选）
exclude_stems = data.get('exclude_stems')
stems_error = validate_exclude_stems(exclude_stems)
if stems_error:
return error_response("INVALID_PARAMS", BAD_REQUEST, stems_error, http_status=400)
# 获取当前用户
user = get_current_user()
if not user:
@@ -156,7 +187,8 @@ def api_generate_questions():
question_types=question_types,
difficulty=data.get('difficulty', 3),
options=data.get('options', {}),
request_id=data.get('request_id')
request_id=data.get('request_id'),
exclude_stems=data.get('exclude_stems')
)
return success_response(data=result, status_code=EXAM_SUCCESS, message="出题成功")
@@ -199,6 +231,12 @@ def api_generate_smart():
if diff_error:
return error_response("INVALID_PARAMS", BAD_REQUEST, diff_error, http_status=400)
# 校验排除题干列表（可选）
exclude_stems = data.get('exclude_stems')
stems_error = validate_exclude_stems(exclude_stems)
if stems_error:
return error_response("INVALID_PARAMS", BAD_REQUEST, stems_error, http_status=400)
# 获取当前用户
user = get_current_user()
if not user:
@@ -231,7 +269,8 @@ def api_generate_smart():
question_types=question_types,
difficulty=data.get('difficulty', 3),
options=data.get('options', {}),
request_id=data.get('request_id')
request_id=data.get('request_id'),
exclude_stems=data.get('exclude_stems')
)
# 3. 在返回结果中添加 AI 分析信息
@@ -258,21 +297,21 @@ def api_grade_answers():
{
"question_id": "uuid",
"question_type": "single_choice",
"question_content": {"answer": "B"},
"content": {"answer": "B"},
"student_answer": "A",
"max_score": 2
},
{
"question_id": "uuid",
"question_type": "fill_blank",
"question_content": {"answer": [["答案1"], ["答案2"]]},
"content": {"answer": [["答案1", "同义词"], ["答案2"]]},
"student_answer": ["学生答案1", "学生答案2"],
"max_score": 4
},
{
"question_id": "uuid",
"question_type": "subjective",
"question_content": {
"content": {
"stem": "简述...",
"data": {"scoring_points": [...]},
"answer": "参考范文..."
@@ -283,6 +322,12 @@ def api_grade_answers():
]
}
说明：
- content 字段与出题接口返回的 content 结构一致，后端可直接透传
- 客观题：content 只需包含 answer
- 填空题：content.answer 格式为 [["答案1", "同义词"], ...] 每空一个列表
- 主观题：content 需包含 stem + answer + data.scoring_points
返回:
{
"success": true,
@@ -312,6 +357,14 @@ def api_grade_answers():
if not answers:
return error_response("MISSING_PARAMS", BAD_REQUEST, "缺少答案数据", http_status=400)
# 校验每道题的 question_type
for i, ans in enumerate(answers):
q_type = ans.get('question_type')
if q_type not in VALID_QUESTION_TYPES:
return error_response("INVALID_PARAMS", BAD_REQUEST,
f"第 {i+1} 题的 question_type 无效: {q_type}，合法值: {', '.join(sorted(VALID_QUESTION_TYPES))}",
http_status=400)
# 调用新版批题接口
result = grade_answers(
answers=answers,

23
exam_pkg/generator.py
View File

@@ -1114,7 +1114,8 @@ def generate_questions_structured_v2(
chunks: List[Dict],
document_name: str,
question_types: Dict[str, int],
difficulty: int = 3
difficulty: int = 3,
exclude_stems: List[str] = None
) -> List[Dict]:
"""
🔥 重构版：结构化出题 v2
@@ -1130,6 +1131,7 @@ def generate_questions_structured_v2(
document_name: 文档名称
question_types: 题型及数量 {"single_choice": 5, ...}
difficulty: 难度 1-5
exclude_stems: 需要排除的已有题目题干列表（跨调用去重）
Returns:
题目列表
@@ -1169,7 +1171,7 @@ def generate_questions_structured_v2(
logger.warning("[v2] 知识点提取失败，降级使用 chunks 出题")
fallback_questions = generator._generate_questions_fallback(chunks, document_name, question_types, difficulty)
# 降级路径也执行去重和数量平衡
deduped = _deduplicate_questions(fallback_questions)
deduped = _deduplicate_questions(fallback_questions, exclude_stems=exclude_stems)
final = _balance_question_types(deduped, question_types)
logger.info(f" [降级] 最终题目数: {len(final)}")
return final
@@ -1225,9 +1227,9 @@ def generate_questions_structured_v2(
# ========== Phase 4: 质量校验 ==========
logger.info("[v2] Phase 4: 质量校验")
# 4.1 去重
deduped = _deduplicate_questions(all_questions)
logger.info(f" 去重: {len(all_questions)} → {len(deduped)}")
# 4.1 去重（含跨调用排除已有题目）
deduped = _deduplicate_questions(all_questions, exclude_stems=exclude_stems)
logger.info(f" 去重: {len(all_questions)} → {len(deduped)}" + (f"（排除已有 {len(exclude_stems)} 道）" if exclude_stems else ""))
# 4.2 数量校正
final = _balance_question_types(deduped, question_types)
@@ -1381,22 +1383,31 @@ def _retrieve_kp_chunks_v2(
return result
def _deduplicate_questions(questions: List[Dict]) -> List[Dict]:
def _deduplicate_questions(questions: List[Dict], exclude_stems: List[str] = None) -> List[Dict]:
"""
题目去重
策略：
1. 相同题干去重（前 80 字）
2. 相同知识点 + 相同题型去重
3. 排除已有题目（跨调用去重）
Args:
questions: 原始题目列表
exclude_stems: 需要排除的已有题目题干列表（用于跨调用去重）
Returns:
去重后的题目列表
"""
seen_stems = set()
seen_kp_type = set()
# 预填已有题目的题干前缀，使新生成的题目与已有题目冲突时被过滤
if exclude_stems:
for stem in exclude_stems:
seen_stems.add(stem[:80])
seen_kp_type.add(f"{stem[:30]}_") # 通配题型匹配
deduped = []
for q in questions:

168
exam_pkg/grader.py
View File

@@ -15,6 +15,8 @@
"""
import json
import logging
import re
import threading
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
@@ -24,6 +26,8 @@ from typing import List, Dict, Any, Optional
# 导入 LLM 工具函数
from core.llm_utils import call_llm
logger = logging.getLogger(__name__)
# 导入 LLM 配置
try:
from config import API_KEY, BASE_URL, MODEL
@@ -77,7 +81,7 @@ def grade_objective(answer: Dict) -> Dict:
🔥 本地直接判断，无 LLM 调用
"""
q_type = answer['question_type']
question_content = answer.get('question_content', {})
question_content = answer.get('content', {})
correct_answer = question_content.get('answer')
student_answer = answer.get('student_answer')
max_score = answer.get('max_score', 2.0)
@@ -114,7 +118,7 @@ def grade_fill_blank(answer: Dict) -> Dict:
填空题答案格式：[["答案1", "同义词1", ...], ["答案2", ...], ...]
学生答案格式：["学生答案1", "学生答案2", ...]
"""
question_content = answer.get('question_content', {})
question_content = answer.get('content', {})
correct_answers = question_content.get('answer', []) # [[答案1, 同义词...], ...]
student_answers = answer.get('student_answer', [])
max_score = answer.get('max_score', 4.0)
@@ -284,33 +288,48 @@ class AnswerGrader:
"details": {"error": "批阅超时"}
}
@retry(times=2, delay=1)
@retry(times=3, delay=1)
def _grade_subjective(self, answer: Dict) -> Dict:
"""
批阅主观题 - 调用 LLM 评分
🔥 P1 改进：带重试
🔥 改进：3次重试 + 解析失败自动重试
"""
with grading_semaphore: # 限流
prompt = self._build_grading_prompt(answer)
response = self._call_llm(prompt)
return self._parse_grading_result(response, answer)
try:
return self._parse_grading_result(response, answer)
except (json.JSONDecodeError, ValueError, TypeError) as e:
# 解析失败抛异常 → 触发 @retry 重试
logger.warning(f"[主观题评分] 解析失败将重试: {e}, 原始响应前200字: {str(response)[:200]}")
raise
def _build_grading_prompt(self, answer: Dict) -> str:
"""构造评分 Prompt"""
question_content = answer.get('question_content', {})
question_content = answer.get('content', {})
scoring_points = question_content.get('data', {}).get('scoring_points', [])
stem = question_content.get('stem', '')
reference_answer = question_content.get('answer', '')
# 如果缺少评分标准，在 prompt 中补充提示
scoring_section = ""
if scoring_points:
scoring_section = json.dumps(scoring_points, ensure_ascii=False, indent=2)
else:
scoring_section = "（未提供评分标准，请根据参考答案自行判断要点）"
return f"""请批阅以下简答题。
## 题目
{question_content.get('stem', '')}
{stem if stem else '（未提供题目）'}
## 参考答案
{question_content.get('answer', '')}
{reference_answer if reference_answer else '（未提供参考答案）'}
## 评分标准
{json.dumps(scoring_points, ensure_ascii=False, indent=2)}
{scoring_section}
## 学生答案
{answer.get('student_answer', '')}
@@ -319,19 +338,20 @@ class AnswerGrader:
{answer.get('max_score', 10)} 分
## 输出约束
1. 必须输出合法 JSON
2. score 不能超过满分
3. achieved 为 0-1 之间的比例
1. 必须输出合法 JSON，不要包含任何占位符或中文说明
2. score 为数字，不能超过满分
3. achieved 为 0-1 之间的数字
4. 所有字段必须填入实际评分值
## 输出格式（JSON）
## 输出格式示例（JSON）
{{
"score": 得分,
"score": 7,
"scoring_breakdown": [
{{"point": "要点名称", "weight": 权重, "achieved": 实际得分比例, "comment": "评语"}}
{{"point": "核心概念正确", "weight": 0.5, "achieved": 0.8, "comment": "基本概念描述准确"}}
],
"highlights": ["亮点1", "亮点2"],
"shortcomings": ["不足1"],
"overall_feedback": "整体评价"
"highlights": ["回答条理清晰"],
"shortcomings": ["缺少具体应用场景"],
"overall_feedback": "整体回答较好，但不够全面"
}}
请直接输出 JSON："""
@@ -357,36 +377,96 @@ class AnswerGrader:
raise Exception("LLM 调用失败")
return result
def _extract_json(self, response: str) -> dict:
"""
多策略从 LLM 响应中提取 JSON 对象
策略优先级：
1. markdown 代码块提取 ```json ... ```
2. 直接 json.loads
3. 正则匹配第一个 {...} 块
"""
if not response:
raise ValueError("LLM 返回为空")
# 策略1：提取 markdown 代码块
json_match = re.search(r'```(?:json)?\s*([\s\S]*?)\s*```', response)
if json_match:
json_str = json_match.group(1).strip()
try:
result = json.loads(json_str)
if isinstance(result, dict):
return result
except json.JSONDecodeError:
pass # 继续下一策略
# 策略2：直接解析整个响应
try:
result = json.loads(response.strip())
if isinstance(result, dict):
return result
except json.JSONDecodeError:
pass # 继续下一策略
# 策略3：正则匹配最外层 JSON 对象
brace_match = re.search(r'\{[\s\S]*\}', response)
if brace_match:
try:
result = json.loads(brace_match.group(0))
if isinstance(result, dict):
return result
except json.JSONDecodeError:
pass
# 全部策略失败
raise json.JSONDecodeError(
f"无法从 LLM 响应中提取有效 JSON，响应前300字: {response[:300]}",
response, 0
)
def _parse_grading_result(self, response: str, answer: Dict) -> Dict:
"""解析评分结果"""
"""
解析评分结果
解析失败时抛出异常（由调用方的 @retry 处理重试）
"""
max_score = answer.get('max_score', 10)
try:
# 尝试解析 JSON
result = json.loads(response)
score = min(result.get('score', 0), max_score) # 不能超过满分
# 检查主观题内容完整性
question_content = answer.get('content', {})
warnings = []
if not question_content.get('stem'):
warnings.append("缺少题目(stem)")
if not question_content.get('answer'):
warnings.append("缺少参考答案(answer)")
if not question_content.get('data', {}).get('scoring_points'):
warnings.append("缺少评分标准(scoring_points)，评分结果仅供参考")
return {
"question_id": answer.get('question_id'),
"score": score,
"max_score": max_score,
"grading_status": "success",
"details": {
"scoring_breakdown": result.get('scoring_breakdown', []),
"highlights": result.get('highlights', []),
"shortcomings": result.get('shortcomings', []),
"overall_feedback": result.get('overall_feedback', '')
}
}
except (json.JSONDecodeError, KeyError, TypeError) as e:
# 解析失败，返回默认
return {
"question_id": answer.get('question_id'),
"score": 0,
"max_score": max_score,
"grading_status": "failed",
"details": {"error": "评分结果解析失败"}
}
# 多策略提取 JSON（失败抛异常 → 触发重试）
result = self._extract_json(response)
# 校验关键字段
score = result.get('score')
if score is None or not isinstance(score, (int, float)):
raise ValueError(f"score 字段缺失或类型错误: {score}")
score = min(float(score), max_score) # 不能超过满分
details = {
"scoring_breakdown": result.get('scoring_breakdown', []),
"highlights": result.get('highlights', []),
"shortcomings": result.get('shortcomings', []),
"overall_feedback": result.get('overall_feedback', '')
}
if warnings:
details["warnings"] = warnings
return {
"question_id": answer.get('question_id'),
"score": score,
"max_score": max_score,
"grading_status": "success",
"details": details
}
# ==================== 批题入口函数 ====================

7
exam_pkg/manager.py
View File

@@ -69,7 +69,8 @@ def generate_questions_from_file(
question_types: Dict[str, int],
difficulty: int = 3,
options: Dict = None,
request_id: str = None
request_id: str = None,
exclude_stems: List[str] = None
) -> Dict:
"""
从文件生成题目（结构化出题 v2）
@@ -88,6 +89,7 @@ def generate_questions_from_file(
options: 可选配置
- max_source_chunks: 最大切片数（默认 30）
request_id: 请求 ID（幂等性支持）
exclude_stems: 需要排除的已有题目题干列表（跨调用去重）
Returns:
{
@@ -115,7 +117,8 @@ def generate_questions_from_file(
chunks=chunks,
document_name=file_path,
question_types=question_types,
difficulty=difficulty
difficulty=difficulty,
exclude_stems=exclude_stems
)
# 3. 统计实际出题数量，与请求数量对比