HaHafeng
dfc472810b
Completed features: - Created Dify dataset (Dify_test0102) with 2 processed documents - Linked test0102 project with Dify dataset ID - Extended intent detection to recognize query_protocol intent - Implemented queryDifyKnowledge method (semantic search Top 5) - Integrated hybrid retrieval (REDCap data + Dify documents) - Fixed AI hallucination bugs (intent detection + API field path) - Developed debugging scripts - Completed end-to-end testing (5 scenarios passed) - Generated comprehensive documentation (600+ lines) - Updated development plans and module status Technical highlights: - Single project single knowledge base architecture - Smart routing based on user intent - Prevent AI hallucination by injecting real data/documents - Session memory for multi-turn conversations - Reused LLMFactory for DeepSeek-V3 integration Bug fixes: - Fixed intent detection missing keywords - Fixed Dify API response field path error Testing: All scenarios verified in WeChat production environment Status: Fully tested and deployed
64 lines
888 B
Python
64 lines
888 B
Python
"""测试dc_executor模块"""
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print("测试dc_executor模块导入...")
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try:
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from services.dc_executor import validate_code, execute_pandas_code
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print("✅ 模块导入成功")
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# 测试验证功能
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print("\n测试validate_code...")
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result = validate_code("df['x'] = 1")
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print(f"✅ validate_code成功: {result}")
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# 测试执行功能
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print("\n测试execute_pandas_code...")
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test_data = [{"age": 25}, {"age": 65}]
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result = execute_pandas_code(test_data, "df['old'] = df['age'] > 60")
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print(f"✅ execute_pandas_code成功: success={result['success']}")
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if result['success']:
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print(f" 结果: {result['result_data']}")
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print("\n🎉 所有模块测试通过！")
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except Exception as e:
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print(f"❌ 测试失败: {e}")
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import traceback
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traceback.print_exc()
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