HaHafeng
dfc0fe0b9a
Summary: - Migrate PostgreSQL to pgvector/pgvector:pg15 Docker image - Successfully install and verify pgvector 0.8.1 extension - Create comprehensive Dify-to-pgvector migration plan - Update PKB module documentation with pgvector status - Update system documentation with pgvector integration Key changes: - docker-compose.yml: Switch to pgvector/pgvector:pg15 image - Add EkbDocument and EkbChunk data model design - Design R-C-R-G hybrid retrieval architecture - Add clinical data JSONB fields (pico, studyDesign, regimen, safety, criteria, endpoints) - Create detailed 10-day implementation roadmap Documentation updates: - PKB module status: pgvector RAG infrastructure ready - System status: pgvector 0.8.1 integrated - New: Dify replacement development plan (01-Dify替换为pgvector开发计划.md) - New: Enterprise medical knowledge base solution V2 Tested: PostgreSQL with pgvector verified, frontend and backend functionality confirmed
87 lines
911 B
Python
87 lines
911 B
Python
"""测试dc_executor模块"""
|
|
print("测试dc_executor模块导入...")
|
|
try:
|
|
from services.dc_executor import validate_code, execute_pandas_code
|
|
print("✅ 模块导入成功")
|
|
|
|
# 测试验证功能
|
|
print("\n测试validate_code...")
|
|
result = validate_code("df['x'] = 1")
|
|
print(f"✅ validate_code成功: {result}")
|
|
|
|
# 测试执行功能
|
|
print("\n测试execute_pandas_code...")
|
|
test_data = [{"age": 25}, {"age": 65}]
|
|
result = execute_pandas_code(test_data, "df['old'] = df['age'] > 60")
|
|
print(f"✅ execute_pandas_code成功: success={result['success']}")
|
|
if result['success']:
|
|
print(f" 结果: {result['result_data']}")
|
|
|
|
print("\n🎉 所有模块测试通过！")
|
|
|
|
except Exception as e:
|
|
print(f"❌ 测试失败: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|