init: RAG 知识库服务初始提交
- 后端 API（Flask + Gunicorn） - RAG 引擎（混合检索 + 云端 Reranker + 引用溯源） - 文档解析（MinerU + 多格式支持） - Docker 生产部署配置 - 排除前端项目、敏感配置、模型文件
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lacerate551
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exam_pkg/api.py
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274
exam_pkg/api.py
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"""
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出题与批题系统 API 蓝图
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提供 REST API 接口：
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- 出题：生成题目（返回 JSON 给后端）
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- 批题：批阅答案（返回结果给后端）
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职责边界：
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- RAG 服务负责：生成题目 + 批阅答案
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- 后端服务负责：审核入库 + 状态管理
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使用方式：
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from exam_pkg.api import exam_bp
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app.register_blueprint(exam_bp, url_prefix='/exam')
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"""
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from flask import Blueprint, request, jsonify
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import os
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# 导入考试管理模块
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from exam_pkg.manager import (
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generate_questions_from_file,
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grade_answers,
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)
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# 导入网关认证模块
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from auth.gateway import (
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require_gateway_auth, require_role, check_collection_permission, get_current_user
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)
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# 导入统一响应格式
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from core.status_codes import EXAM_SUCCESS, GRADE_SUCCESS, EXAM_ERROR, GRADE_ERROR, BAD_REQUEST, NO_CONTENT, LLM_ERROR
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from api.response_utils import success_response, error_response
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# 创建蓝图
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exam_bp = Blueprint('exam', __name__)
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# ==================== 出题 API ====================
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@exam_bp.route('/generate', methods=['POST'])
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@require_gateway_auth
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def api_generate_questions():
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"""
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🔥 新版出题接口
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请求体:
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{
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"request_id": "uuid-optional", // 幂等性支持
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"file_path": "public/产品手册.pdf",
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"collection": "public_kb",
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"question_types": {
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"single_choice": 3,
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"multiple_choice": 2,
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"true_false": 2,
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"fill_blank": 2,
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"subjective": 1
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},
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"difficulty": 3,
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"options": {
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"include_explanation": true,
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"max_source_chunks": 50
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}
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}
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返回:
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{
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"success": true,
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"request_id": "uuid",
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"questions": [
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{
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"metadata": {"question_id": "...", "question_type": "...", ...},
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"source_trace": {"document_name": "...", "sources": [...], ...},
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"content": {"stem": "...", "data": {...}, "answer": "...", ...}
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}
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],
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"total": 10,
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"source_chunks_used": 15
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}
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"""
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try:
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data = request.json
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file_path = data.get('file_path')
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collection = data.get('collection')
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question_types = data.get('question_types', {})
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if not file_path or not collection:
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return error_response("MISSING_PARAMS", BAD_REQUEST, "缺少 file_path 或 collection 参数", http_status=400)
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if not question_types:
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return error_response("MISSING_PARAMS", BAD_REQUEST, "缺少 question_types 参数", http_status=400)
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# 获取当前用户
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user = get_current_user()
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if not user:
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return error_response("UNAUTHORIZED", BAD_REQUEST, "未认证", http_status=401)
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# 检查向量库访问权限
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if not check_collection_permission(
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role=user['role'],
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department=user.get('department', ''),
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collection_name=collection,
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operation="read"
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):
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return error_response("FORBIDDEN", BAD_REQUEST, "权限不足", http_status=403)
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# 调用新版出题接口
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result = generate_questions_from_file(
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file_path=file_path,
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collection=collection,
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question_types=question_types,
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difficulty=data.get('difficulty', 3),
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options=data.get('options', {}),
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request_id=data.get('request_id')
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)
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return success_response(data=result, status_code=EXAM_SUCCESS, message="出题成功")
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except Exception as e:
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return error_response("EXAM_ERROR", EXAM_ERROR, str(e), http_status=500)
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@exam_bp.route('/generate-smart', methods=['POST'])
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@require_gateway_auth
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def api_generate_smart():
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"""
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AI 智能出题 - 自动分析文件并决定题型和数量
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与 /exam/generate 的区别：不需要传 question_types，AI 自动分析文档后决定
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请求体:
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{
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"file_path": "public/产品手册.pdf",
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"collection": "public_kb",
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"difficulty": 3, // 可选，默认 3
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"options": {} // 可选
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}
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返回:
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与 /exam/generate 相同格式，额外包含 ai_analysis 字段
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"""
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try:
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data = request.json
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file_path = data.get('file_path')
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collection = data.get('collection')
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if not file_path or not collection:
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return error_response("MISSING_PARAMS", BAD_REQUEST, "缺少 file_path 或 collection 参数", http_status=400)
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# 获取当前用户
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user = get_current_user()
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if not user:
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return error_response("UNAUTHORIZED", BAD_REQUEST, "未认证", http_status=401)
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# 检查向量库访问权限
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if not check_collection_permission(
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role=user['role'],
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department=user.get('department', ''),
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collection_name=collection,
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operation="read"
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):
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return error_response("FORBIDDEN", BAD_REQUEST, "权限不足", http_status=403)
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# 1. 调用 AI 分析文件，获取推荐的题型和数量
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from exam_pkg.manager import analyze_file_for_exam
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ai_analysis = analyze_file_for_exam(
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file_path=file_path,
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collection=collection
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)
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question_types = ai_analysis.get('question_types', {})
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if not question_types or sum(question_types.values()) == 0:
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return error_response("EXAM_ERROR", EXAM_ERROR, "AI 分析后未生成有效题型配置", http_status=500)
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# 2. 使用 AI 推荐的题型调用出题接口
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result = generate_questions_from_file(
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file_path=file_path,
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collection=collection,
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question_types=question_types,
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difficulty=data.get('difficulty', 3),
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options=data.get('options', {}),
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request_id=data.get('request_id')
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)
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# 3. 在返回结果中添加 AI 分析信息
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result['ai_analysis'] = ai_analysis
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return success_response(data=result, status_code=EXAM_SUCCESS, message="AI 智能出题成功")
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except Exception as e:
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return error_response("EXAM_ERROR", EXAM_ERROR, str(e), http_status=500)
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# ==================== 批题 API ====================
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@exam_bp.route('/grade', methods=['POST'])
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@require_gateway_auth
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def api_grade_answers():
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"""
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🔥 新版批题接口
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请求体:
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{
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"request_id": "uuid-optional",
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"answers": [
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{
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"question_id": "uuid",
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"question_type": "single_choice",
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"question_content": {"answer": "B"},
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"student_answer": "A",
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"max_score": 2
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},
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{
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"question_id": "uuid",
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"question_type": "fill_blank",
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"question_content": {"answer": [["答案1"], ["答案2"]]},
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"student_answer": ["学生答案1", "学生答案2"],
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"max_score": 4
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},
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{
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"question_id": "uuid",
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"question_type": "subjective",
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"question_content": {
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"stem": "简述...",
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"data": {"scoring_points": [...]},
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"answer": "参考范文..."
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},
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"student_answer": "学生作答内容...",
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"max_score": 10
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}
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]
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}
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返回:
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{
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"success": true,
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"request_id": "uuid",
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"results": [...],
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"total_score": 10.5,
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"total_max_score": 16.0,
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"score_rate": 65.6
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}
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"""
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try:
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data = request.json
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answers = data.get('answers', [])
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if not answers:
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return error_response("MISSING_PARAMS", BAD_REQUEST, "缺少答案数据", http_status=400)
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# 调用新版批题接口
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result = grade_answers(
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answers=answers,
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request_id=data.get('request_id')
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)
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return success_response(data=result, status_code=GRADE_SUCCESS, message="批阅完成")
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except Exception as e:
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return error_response("GRADE_ERROR", GRADE_ERROR, str(e), http_status=500)
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# ==================== 健康检查 ====================
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@exam_bp.route('/health', methods=['GET'])
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def api_health():
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"""健康检查"""
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return jsonify({
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"status": "ok",
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"service": "exam-api",
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"version": "2.0"
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})
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