52 lines
3.1 KiB
Markdown
52 lines
3.1 KiB
Markdown
# Session 15 — 2026-06-05 (Feature 234 RAG Pipeline Enhancements — สมบูรณ์)
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## Summary
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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
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## งานที่ทำในเซสชั่นนี้
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| งาน | ไฟล์หลัก | สถานะ |
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| Sidecar `/embed` (BGE-M3 Dense+Sparse) | `ocr-sidecar/app.py`, `requirements.txt` | ✅ |
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| Sidecar `/rerank` (BGE-Reranker-Large) | `ocr-sidecar/app.py` | ✅ |
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| `OcrService.embedViaSidecar()` + `rerankViaSidecar()` | `ocr.service.ts` | ✅ |
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| Hybrid Qdrant schema (1024 dims, drop+recreate) | `qdrant.service.ts` | ✅ |
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| Payload indexes (project_public_id tenant, doc_public_id, status_code, doc_type) | `qdrant.service.ts` | ✅ |
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| `EmbeddingService.embedDocument()` — Semantic Chunking + fallback | `embedding.service.ts` | ✅ |
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| `semanticChunkTextWithFallback()` → `parseChunkTags()` → `fixedSizeChunk()` | `embedding.service.ts` | ✅ |
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| `AiBatchProcessor` case `rag-prepare` → `processRagPrepare()` | `ai-batch.processor.ts` | ✅ |
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| `AiQueueService.enqueueRagPrepare()` + jobId dedup | `ai-queue.service.ts` | ✅ |
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| `CorrespondenceWorkflowService.syncStatus()` → `triggerRagPrepare()` | `correspondence-workflow.service.ts` | ✅ |
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| `AiRagService.processQuery()` — Hybrid Search + Reranker | `ai-rag.service.ts` | ✅ |
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| SQL delta `rag_chunking` prompt | `deltas/2026-06-05-add-rag-chunking-prompt.sql` | ✅ |
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| Integration test (9 tests) | `ai-rag-pipeline.integration.spec.ts` | ✅ |
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| Validation report | `specs/200-fullstacks/234-rag-pipeline-enhancements/validation-report.md` | ✅ |
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## Verification
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- `npx jest` (6 suites): **24/24 tests PASS**
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- `npx tsc --noEmit`: **0 errors**
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- speckit-validate: **15/15 FR covered, 6/6 SC verifiable, 0 Gaps remaining**
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## Architecture Summary (ADR-035)
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```
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CorrespondenceWorkflowService.syncStatus()
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└── triggerRagPrepare() [fire-and-forget]
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└── AiQueueService.enqueueRagPrepare() [jobId dedup]
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└── BullMQ ai-batch queue
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└── AiBatchProcessor.processRagPrepare()
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├── OcrService.detectAndExtract() [if no cached text]
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└── EmbeddingService.embedDocument()
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├── semanticChunkTextWithFallback() → typhoon2.5 / fixed-size fallback
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├── OcrService.embedViaSidecar() → BGE-M3 [dense 1024 + sparse]
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└── AiQdrantService.upsert() → lcbp3_vectors [delete-before-upsert]
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AiRagService.processQuery()
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├── OcrService.embedViaSidecar(question) → BGE-M3
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├── AiQdrantService.searchByProject(dense, sparse, projectPublicId, 15) → RRF Fusion
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├── OcrService.rerankViaSidecar(question, chunks) → BGE-Reranker top-5
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└── Ollama typhoon2.5-np-dms:latest → answer + citations
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```
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