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7c3cc12b2e
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feat(iit): Complete CRA Agent V3.0 P1 - ChatOrchestrator with LLM Function Calling
P1 Architecture: Lightweight ReAct (Function Calling loop, max 3 rounds)
Core changes:
- Add ToolDefinition/ToolCall types to LLM adapters (DeepSeek + CloseAI + Claude)
- Replace 6 old tools with 4 semantic tools: read_report, look_up_data, check_quality, search_knowledge
- Create ChatOrchestrator (~160 lines) replacing ChatService (1,442 lines)
- Wire WechatCallbackController to ChatOrchestrator, deprecate ChatService
- Fix nullable content (string | null) across 12+ LLM consumer files
E2E test results: 8/8 scenarios passed (100%)
- QC report query, critical issues, patient data, trend, on-demand QC
- Knowledge base search, project overview, data modification refusal
Net code reduction: ~1,100 lines
Tested: E2E P1 chat test 8/8 passed with DeepSeek API
Made-with: Cursor
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2026-02-26 14:27:09 +08:00 |
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371e1c069c
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feat(ssa): Complete QPER architecture - Query, Planner, Execute, Reflection layers
Implement the full QPER intelligent analysis pipeline:
- Phase E+: Block-based standardization for all 7 R tools, DynamicReport renderer, Word export enhancement
- Phase Q: LLM intent parsing with dynamic Zod validation against real column names, ClarificationCard component, DataProfile is_id_like tagging
- Phase P: ConfigLoader with Zod schema validation and hot-reload API, DecisionTableService (4-dimension matching), FlowTemplateService with EPV protection, PlannedTrace audit output
- Phase R: ReflectionService with statistical slot injection, sensitivity analysis conflict rules, ConclusionReport with section reveal animation, conclusion caching API, graceful R error classification
End-to-end test: 40/40 passed across two complete analysis scenarios.
Co-authored-by: Cursor <cursoragent@cursor.com>
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2026-02-21 18:15:53 +08:00 |
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