feat(iit-manager): Integrate Dify knowledge base for hybrid retrieval

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

extraction_service/.dockerignore

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extraction_service/operations/__init__.py

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extraction_service/operations/dropna.py

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extraction_service/operations/filter.py

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extraction_service/operations/unpivot.py

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extraction_service/test_dc_api.py

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extraction_service/test_execute_simple.py

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extraction_service/test_module.py

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