feat(dc/tool-c): 完成AI代码生成服务（Day 3 MVP）
核心功能： - 新增AICodeService（550行）：AI代码生成核心服务 - 新增AIController（257行）：4个API端点 - 新增dc_tool_c_ai_history表：存储对话历史 - 实现自我修正机制：最多3次智能重试 - 集成LLMFactory：复用通用能力层 - 10个Few-shot示例：覆盖Level 1-4场景 技术优化： - 修复NaN序列化问题（Python端转None） - 修复数据传递问题（从Session获取真实数据） - 优化System Prompt（明确环境信息） - 调整Few-shot示例（移除import语句） 测试结果： - 通过率：9/11（81.8%） 达到MVP标准 - 成功场景：缺失值处理、编码、分箱、BMI、筛选、填补、统计、分类 - 待优化：数值清洗、智能去重（已记录技术债务TD-C-006） API端点： - POST /api/v1/dc/tool-c/ai/generate（生成代码） - POST /api/v1/dc/tool-c/ai/execute（执行代码） - POST /api/v1/dc/tool-c/ai/process（生成并执行，一步到位） - GET /api/v1/dc/tool-c/ai/history/:sessionId（对话历史） 文档更新： - 新增Day 3开发完成总结（770行） - 新增复杂场景优化技术债务（TD-C-006） - 更新工具C当前状态文档 - 更新技术债务清单 影响范围： - backend/src/modules/dc/tool-c/*（新增2个文件，更新1个文件） - backend/scripts/create-tool-c-ai-history-table.mjs（新增） - backend/prisma/schema.prisma（新增DcToolCAiHistory模型） - extraction_service/services/dc_executor.py（NaN序列化修复） - docs/03-业务模块/DC-数据清洗整理/*（5份文档更新） Breaking Changes: 无 总代码行数：+950行 Refs: #Tool-C-Day3
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extraction_service/test_dc_api.py
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extraction_service/test_dc_api.py
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"""
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DC工具C - API测试脚本
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测试项：
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1. 健康检查 (GET /api/health)
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2. AST安全检查 - 正常代码
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3. AST安全检查 - 危险代码
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4. Pandas代码执行 - 简单场景
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5. Pandas代码执行 - 医疗数据清洗场景
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"""
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import requests
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import json
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from typing import Dict, Any
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BASE_URL = "http://localhost:8000"
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def print_test_header(title: str):
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"""打印测试标题"""
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print("\n" + "=" * 70)
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print(f" {title}")
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print("=" * 70)
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def print_result(response: requests.Response):
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"""打印响应结果"""
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print(f"\n状态码: {response.status_code}")
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print(f"响应内容:")
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try:
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result = response.json()
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print(json.dumps(result, indent=2, ensure_ascii=False))
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except:
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print(response.text)
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def test_health_check():
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"""测试1: 健康检查"""
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print_test_header("测试1: 健康检查")
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try:
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response = requests.get(f"{BASE_URL}/api/health", timeout=5)
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print_result(response)
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if response.status_code == 200:
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print("\n✅ 健康检查通过")
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return True
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else:
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print("\n❌ 健康检查失败")
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return False
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except Exception as e:
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print(f"\n❌ 健康检查异常: {str(e)}")
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return False
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def test_validate_safe_code():
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"""测试2: AST安全检查 - 正常代码"""
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print_test_header("测试2: AST安全检查 - 正常代码")
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safe_code = """
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import pandas as pd
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df['age_group'] = df['age'].apply(lambda x: '老年' if x > 60 else '非老年')
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print(df['age_group'].value_counts())
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"""
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try:
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response = requests.post(
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f"{BASE_URL}/api/dc/validate",
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json={"code": safe_code},
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timeout=5
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)
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print_result(response)
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if response.status_code == 200:
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result = response.json()
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if result.get("valid"):
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print("\n✅ 正常代码验证通过（valid=True）")
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return True
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else:
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print("\n❌ 正常代码被误判为危险")
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return False
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else:
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print("\n❌ API调用失败")
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return False
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except Exception as e:
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print(f"\n❌ 测试异常: {str(e)}")
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return False
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def test_validate_dangerous_code():
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"""测试3: AST安全检查 - 危险代码"""
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print_test_header("测试3: AST安全检查 - 危险代码（应该被拦截）")
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dangerous_code = """
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import os
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import sys
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os.system('echo "危险操作"')
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eval('print("evil code")')
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"""
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try:
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response = requests.post(
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f"{BASE_URL}/api/dc/validate",
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json={"code": dangerous_code},
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timeout=5
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)
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print_result(response)
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if response.status_code == 200:
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result = response.json()
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if not result.get("valid") and len(result.get("errors", [])) > 0:
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print("\n✅ 危险代码成功拦截（valid=False, 有错误信息）")
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return True
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else:
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print("\n❌ 危险代码未被拦截！")
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return False
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else:
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print("\n❌ API调用失败")
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return False
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except Exception as e:
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print(f"\n❌ 测试异常: {str(e)}")
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return False
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def test_execute_simple_code():
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"""测试4: Pandas代码执行 - 简单场景"""
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print_test_header("测试4: Pandas代码执行 - 简单场景")
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test_data = [
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{"patient_id": "P001", "age": 25, "gender": "男"},
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{"patient_id": "P002", "age": 65, "gender": "女"},
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{"patient_id": "P003", "age": 45, "gender": "男"},
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{"patient_id": "P004", "age": 70, "gender": "女"},
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]
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simple_code = """
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df['age_group'] = df['age'].apply(lambda x: '老年' if x > 60 else '非老年')
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print(f"数据处理完成，共 {len(df)} 行")
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print(df['age_group'].value_counts())
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"""
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try:
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response = requests.post(
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f"{BASE_URL}/api/dc/execute",
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json={"data": test_data, "code": simple_code},
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timeout=10
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)
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print_result(response)
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if response.status_code == 200:
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result = response.json()
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if result.get("success"):
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result_data = result.get("result_data", [])
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print(f"\n结果数据行数: {len(result_data)}")
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print(f"执行时间: {result.get('execution_time', 0):.3f}秒")
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# 验证新列是否添加
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if len(result_data) > 0 and 'age_group' in result_data[0]:
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print("\n✅ 简单代码执行成功（新增列 age_group）")
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return True
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else:
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print("\n❌ 代码执行成功但结果不正确")
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return False
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else:
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print(f"\n❌ 代码执行失败: {result.get('error')}")
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return False
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else:
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print("\n❌ API调用失败")
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return False
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except Exception as e:
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print(f"\n❌ 测试异常: {str(e)}")
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return False
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def test_execute_medical_cleaning():
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"""测试5: Pandas代码执行 - 医疗数据清洗场景"""
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print_test_header("测试5: Pandas代码执行 - 医疗数据清洗场景")
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# 模拟医疗数据
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medical_data = [
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{"patient_id": "P001", "age": 25, "gender": "男", "sbp": 120, "dbp": 80},
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{"patient_id": "P002", "age": 65, "gender": "女", "sbp": 150, "dbp": 95},
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{"patient_id": "P003", "age": 45, "gender": "男", "sbp": 135, "dbp": 85},
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{"patient_id": "P004", "age": None, "gender": "女", "sbp": 160, "dbp": 100},
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{"patient_id": "P005", "age": 200, "gender": "男", "sbp": 110, "dbp": 70},
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]
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# 复杂的医疗数据清洗代码
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medical_code = """
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import numpy as np
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# 1. 清理异常年龄值（>120视为异常）
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df['age'] = df['age'].apply(lambda x: np.nan if x is None or x > 120 else x)
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# 2. 计算血压状态（收缩压 >= 140 或舒张压 >= 90 为高血压）
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df['hypertension'] = df.apply(
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lambda row: '高血压' if row['sbp'] >= 140 or row['dbp'] >= 90 else '正常',
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axis=1
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)
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# 3. 统计结果
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print(f"总样本数: {len(df)}")
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print(f"年龄缺失数: {df['age'].isna().sum()}")
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print(f"高血压人数: {(df['hypertension'] == '高血压').sum()}")
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"""
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try:
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response = requests.post(
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f"{BASE_URL}/api/dc/execute",
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json={"data": medical_data, "code": medical_code},
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timeout=10
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)
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print_result(response)
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if response.status_code == 200:
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result = response.json()
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if result.get("success"):
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result_data = result.get("result_data", [])
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print(f"\n结果数据行数: {len(result_data)}")
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print(f"执行时间: {result.get('execution_time', 0):.3f}秒")
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# 验证新列是否添加
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if len(result_data) > 0 and 'hypertension' in result_data[0]:
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# 验证数据清洗逻辑
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hypertension_count = sum(
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1 for row in result_data
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if row.get('hypertension') == '高血压'
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)
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print(f"高血压人数: {hypertension_count}")
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print("\n✅ 医疗数据清洗场景执行成功")
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return True
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else:
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print("\n❌ 代码执行成功但结果不正确")
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return False
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else:
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print(f"\n❌ 代码执行失败: {result.get('error')}")
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return False
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else:
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print("\n❌ API调用失败")
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return False
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except Exception as e:
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print(f"\n❌ 测试异常: {str(e)}")
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return False
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def main():
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"""主测试函数"""
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print("\n" + "🚀" * 35)
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print(" DC工具C - Python微服务API测试")
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print("🚀" * 35)
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# 运行所有测试
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results = {
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"健康检查": test_health_check(),
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"AST检查-正常代码": test_validate_safe_code(),
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"AST检查-危险代码": test_validate_dangerous_code(),
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"代码执行-简单场景": test_execute_simple_code(),
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"代码执行-医疗清洗": test_execute_medical_cleaning(),
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}
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# 汇总结果
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print("\n" + "=" * 70)
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print(" 测试结果汇总")
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print("=" * 70)
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for test_name, passed in results.items():
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status = "✅ 通过" if passed else "❌ 失败"
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print(f"{test_name:20s}: {status}")
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total = len(results)
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passed = sum(1 for r in results.values() if r)
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success_rate = (passed / total * 100) if total > 0 else 0
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print("\n" + "-" * 70)
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print(f"总计: {passed}/{total} 通过 ({success_rate:.1f}%)")
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print("-" * 70)
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if passed == total:
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print("\n🎉 所有测试通过！Day 1 Python服务开发完成！")
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else:
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print(f"\n⚠️ 有 {total - passed} 个测试失败，请检查")
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print("\n")
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if __name__ == "__main__":
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main()
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