7c3cc12b2e
feat(iit): Complete CRA Agent V3.0 P1 - ChatOrchestrator with LLM Function Calling
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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
2026-02-26 14:27:09 +08:00
88cc049fb3
feat(asl): Complete Day 5 - Fulltext Screening Backend API Development
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- Implement 5 core API endpoints (create task, get progress, get results, update decision, export Excel)
- Add FulltextScreeningController with Zod validation (652 lines)
- Implement ExcelExporter service with 4-sheet report generation (352 lines)
- Register routes under /api/v1/asl/fulltext-screening
- Create 31 REST Client test cases
- Add automated integration test script
- Fix PDF extraction fallback mechanism in LLM12FieldsService
- Update API design documentation to v3.0
- Update development plan to v1.2
- Create Day 5 development record
- Clean up temporary test files
2025-11-23 10:52:07 +08:00
beb7f7f559
feat(asl): Implement full-text screening core LLM service and validation system (Day 1-3)
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Core Components:
- PDFStorageService with Dify/OSS adapters
- LLM12FieldsService with Nougat-first + dual-model + 3-layer JSON parsing
- PromptBuilder for dynamic prompt assembly
- MedicalLogicValidator with 5 rules + fault tolerance
- EvidenceChainValidator for citation integrity
- ConflictDetectionService for dual-model comparison
Prompt Engineering:
- System Prompt (6601 chars, Section-Aware strategy)
- User Prompt template (PICOS context injection)
- JSON Schema (12 fields constraints)
- Cochrane standards (not loaded in MVP)
Key Innovations:
- 3-layer JSON parsing (JSON.parse + json-repair + code block extraction)
- Promise.allSettled for dual-model fault tolerance
- safeGetFieldValue for robust field extraction
- Mixed CN/EN token calculation
Integration Tests:
- integration-test.ts (full test)
- quick-test.ts (quick test)
- cached-result-test.ts (fault tolerance test)
Documentation Updates:
- Development record (Day 2-3 summary)
- Quality assurance strategy (full-text screening)
- Development plan (progress update)
- Module status (v1.1 update)
- Technical debt (10 new items)
Test Results:
- JSON parsing success rate: 100%
- Medical logic validation: 5/5 passed
- Dual-model parallel processing: OK
- Cost per PDF: CNY 0.10
Files: 238 changed, 14383 insertions(+), 32 deletions(-)
Docs: docs/03-涓氬姟妯″潡/ASL-AI鏅鸿兘鏂囩尞/05-寮€鍙戣褰?2025-11-22_Day2-Day3_LLM鏈嶅姟涓庨獙璇佺郴缁熷紑鍙?md
2025-11-22 22:21:12 +08:00
