lcbp3/specs/88-logs/session-2026-06-05-rag-pipeline-complete.md

Session 15 — 2026-06-05 (Feature 234 RAG Pipeline Enhancements — สมบูรณ์)

Summary

Implement RAG Pipeline Enhancements ตาม Spec 234 / ADR-035 ครบทุก Functional Requirement (FR-001 → FR-015), ผ่าน speckit-analyze → speckit-implement → speckit-tester → speckit-validate; ปิด Gap ทั้ง 2 รายการ

งานที่ทำในเซสชั่นนี้

งาน	ไฟล์หลัก	สถานะ
Sidecar /embed (BGE-M3 Dense+Sparse)	ocr-sidecar/app.py, requirements.txt	✅
Sidecar /rerank (BGE-Reranker-Large)	ocr-sidecar/app.py	✅
OcrService.embedViaSidecar() + rerankViaSidecar()	ocr.service.ts	✅
Hybrid Qdrant schema (1024 dims, drop+recreate)	qdrant.service.ts	✅
Payload indexes (project_public_id tenant, doc_public_id, status_code, doc_type)	qdrant.service.ts	✅
EmbeddingService.embedDocument() — Semantic Chunking + fallback	embedding.service.ts	✅
semanticChunkTextWithFallback() → parseChunkTags() → fixedSizeChunk()	embedding.service.ts	✅
AiBatchProcessor case rag-prepare → processRagPrepare()	ai-batch.processor.ts	✅
AiQueueService.enqueueRagPrepare() + jobId dedup	ai-queue.service.ts	✅
CorrespondenceWorkflowService.syncStatus() → triggerRagPrepare()	correspondence-workflow.service.ts	✅
AiRagService.processQuery() — Hybrid Search + Reranker	ai-rag.service.ts	✅
SQL delta rag_chunking prompt	deltas/2026-06-05-add-rag-chunking-prompt.sql	✅
Integration test (9 tests)	ai-rag-pipeline.integration.spec.ts	✅
Validation report	specs/200-fullstacks/234-rag-pipeline-enhancements/validation-report.md	✅

Verification

• npx jest (6 suites): 24/24 tests PASS
• npx tsc --noEmit: 0 errors
• speckit-validate: 15/15 FR covered, 6/6 SC verifiable, 0 Gaps remaining

Architecture Summary (ADR-035)

CorrespondenceWorkflowService.syncStatus()
  └── triggerRagPrepare() [fire-and-forget]
        └── AiQueueService.enqueueRagPrepare() [jobId dedup]
              └── BullMQ ai-batch queue
                    └── AiBatchProcessor.processRagPrepare()
                          ├── OcrService.detectAndExtract() [if no cached text]
                          └── EmbeddingService.embedDocument()
                                ├── semanticChunkTextWithFallback() → typhoon2.5 / fixed-size fallback
                                ├── OcrService.embedViaSidecar() → BGE-M3 [dense 1024 + sparse]
                                └── AiQdrantService.upsert() → lcbp3_vectors [delete-before-upsert]

AiRagService.processQuery()
  ├── OcrService.embedViaSidecar(question) → BGE-M3
  ├── AiQdrantService.searchByProject(dense, sparse, projectPublicId, 15) → RRF Fusion
  ├── OcrService.rerankViaSidecar(question, chunks) → BGE-Reranker top-5
  └── Ollama typhoon2.5-np-dms:latest → answer + citations