docs: update V2 arch doc to 2.2.0 — jina-v5, ST fallback, diagonal resize

.socraticodecontextartifacts.json

@@ -0,0 +1,44 @@
+{
+  "artifacts": [
+    {
+      "name": "architecture-v2",
+      "path": "./TIPITAKA_WEB_ARCHITECTURE_V2.md",
+      "description": "Full V2 architecture — React + FastAPI + Qdrant + ONNX Reranker + DeepSeek"
+    },
+    {
+      "name": "design-system",
+      "path": "./DESIGN.md",
+      "description": "Design system — colors, typography, components, themes, spacing"
+    },
+    {
+      "name": "product-vision",
+      "path": "./PRODUCT.md",
+      "description": "Product vision, target users (monks/scholars), brand personality, design principles"
+    },
+    {
+      "name": "api-schemas",
+      "path": "./webapp/tipitaka-api/app/schemas.py",
+      "description": "Pydantic API schemas — PageResponse, SearchResult, TOCItem, VolumeInfo"
+    },
+    {
+      "name": "app-config",
+      "path": "./webapp/tipitaka-api/app/config.py",
+      "description": "App settings — LLM provider, paths, CORS, Qdrant config"
+    },
+    {
+      "name": "rag-service",
+      "path": "./webapp/tipitaka-api/app/services/rag_service.py",
+      "description": "RAG Service — hybrid search (FTS5 + Qdrant vector + ONNX reranker), embedding with Ollama/sentence-transformers fallback"
+    },
+    {
+      "name": "docker-deployment",
+      "path": "./Dockerfile",
+      "description": "Multi-stage Docker build: React frontend + Python FastAPI backend, target HF Space port 7860"
+    },
+    {
+      "name": "download-assets",
+      "path": "./webapp/tipitaka-api/download_assets.py",
+      "description": "Downloads DB + Qdrant snapshots + ONNX reranker from HF dataset at startup"
+    }
+  ]
+}

TIPITAKA_WEB_ARCHITECTURE_V2.md

@@ -1,10 +1,10 @@
 # Tipitaka Web Application Architecture V2
 ## Vite + FastAPI + Qdrant — "Book Sanctuary + Floating AI Consultant"
 
-> **Version:** 2.1.0
-> **Date:** 2026-05-07
+> **Version:** 2.2.0
+> **Date:** 2026-05-15
 > **Status:** Production (HF Spaces)
-> **Based on:** TIPITAKA_WEB_ARCHITECTURE.md (V1), actual code base at commit `e04d706`
+> **Based on:** TIPITAKA_WEB_ARCHITECTURE_V2.md (2.1.0), actual code base at commit `abb8b56`
 
 ---
 
@@ -13,22 +13,14 @@
 ### 1.1 Vision
 Web Application สำหรับอ่านพระไตรปิฎก มจร. ที่ให้ประสบการณ์การอ่านแบบจิตวิเวก โดยมี AI เป็นผู้ช่วยลอยตัวที่ไม่รบกวนสมาธิ
 
-### 1.2 Key Changes from V1
-
-| Layer | V1 (Planning) | V2 (Actual) |
-|-------|---------------|-------------|
-| **Vector Store** | ChromaDB | **Qdrant** (local/server + snapshot restore) |
-| **Search** | FTS5 + ChromaDB | **Hybrid: FTS5 + Qdrant Vector + ONNX Reranker** |
-| **Pali Processing** | Basic | **Full pipeline:** dict correction → PyThaiNLP → similarity guard |
-| **Embedding Model** | Qwen3-Embedding-0.6B | **jina-embeddings-v3** (Q4_K_M GGUF via Ollama) |
-| **Reranker** | None | **jina-reranker-v2-base-multilingual** (ONNX, CPU) |
-| **Frontend CSS** | Tailwind v3 + shadcn/ui | **Tailwind CSS v4 + `@tailwindcss/typography`** |
-| **State Management** | Zustand + React Query | **Zustand only** (lightweight) |
-| **PWA / Offline** | Planned | **Not implemented** (server-dependant) |
-| **Continuous Scroll** | Planned | **Page-by-page** (prev/next/swipe) |
-| **Deployment** | Local server | **HF Spaces** (Docker multi-stage) |
-| **AI Provider** | DeepSeek / Local | **DeepSeek only** (provider-agnostic via OpenAI client) |
-| **Embedding Cache** | None | **LRU cache** (256 entries, OrderedDict) |
+### 1.2 Key Changes from V2.1.0
+
+| Layer | V2.1.0 | V2.2.0 |
+|-------|--------|--------|
+| **Embedding Model** | jina-embeddings-v3 (GGUF, Q4_K_M) | **jina-embeddings-v5-small-retrieval** (GGUF Q6_K / PyTorch) |
+| **Embedding Mode** | Ollama only | **Dual-mode:** Ollama (local) → sentence-transformers fallback (HF Space) |
+| **AI Popup Resize** | Right-edge only | **Vertical + Diagonal + Right-edge** resize, **persisted** panel size (localStorage) |
+| **Python Version** | 3.11 | 3.13 (ใหม่หลัง reinstall) |
 
 ### 1.3 Tech Stack
 
@@ -44,20 +36,25 @@ Web Application สำหรับอ่านพระไตรปิฎก ม
 └──────────────────────────┬───────────────────────────────────────┘
                            │ HTTP / SSE Streaming
 ┌──────────────────────────┴───────────────────────────────────────┐
-│                     SERVER (FastAPI Python 3.11)                 │
+│                     SERVER (FastAPI Python 3.13)                 │
 │                                                                  │
-│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌────────────────┐  │
-│  │ FastAPI  │  │ Local    │  │ Qdrant   │  │ Ollama Server  │  │
-│  │ Routes   │──│ Services │──│ (Vector) │──│ (jina-emb-v3)  │  │
-│  └──────────┘  └──────────┘  └──────────┘  └────────────────┘  │
-│       │              │                                           │
-│       │              ├── SQLite FTS5 (tipitaka_mcu.db)          │
-│       │              ├── ONNX Reranker (CPU, jina-reranker-v2)  │
-│       │              └── DeepSeek API (streaming)                │
+│  ┌──────────┐  ┌──────────┐  ┌──────────────┐  ┌────────────┐  │
+│  │ FastAPI  │  │ Local    │  │ Qdrant       │  │ Embedding  │  │
+│  │ Routes   │──│ Services │──│ (Vector)     │──│ Provider   │  │
+│  └──────────┘  └──────────┘  └──────────────┘  └────────────┘  │
+│       │              │                         ┌────────────┐  │
+│       │              ├── SQLite FTS5 (tipitaka_mcu.db)      │  │
+│       │              ├── ONNX Reranker (CPU, jina-reranker-v2)│  │
+│       │              └── DeepSeek API (streaming)              │  │
 │       └── Static files (Served Vite build in production)        │
 └──────────────────────────────────────────────────────────────────┘
 ```
 
+**Embedding Providers:**
+- **Ollama** (local dev): `jina-embeddings-v5-small-retrieval` GGUF Q6_K (596MB)
+- **sentence-transformers** (HF Space / fallback): `jinaai/jina-embeddings-v5-text-small-retrieval` PyTorch
+- ทั้งคู่ produce 1024-dims vectors ค่า `EMBED_DIMS = 1024`
+
 ---
 
 ## 2. System Architecture
@@ -73,14 +70,14 @@ graph TB
             ND[NavDrawer<br/>320px sidebar]
             RP[ReaderPanel<br/>Main reading area]
             RT[RightToolbar<br/>44px controls strip]
-            AIP[AIPopup<br/>Draggable chat]
+            AIP[AIPopup<br/>Draggable + resizable chat]
             SP[SelectionPopup<br/>AI from selection]
         end
         subgraph "State (Zustand)"
             RS[ReaderStore<br/>volume, page, content]
             TS[ThemeStore<br/>theme, fontSize]
             US[UIStore<br/>navOpen, activeTab]
-            AS[AIStore<br/>messages, mode, width, pos]
+            AS[AIStore<br/>messages, mode, width, pos, panelSize]
             SS[SearchStore<br/>query, results, breakdown]
         end
     end
@@ -107,7 +104,7 @@ graph TB
         end
         subgraph "Data"
             DB[(SQLite<br/>tipitaka_mcu.db)]
-            VDB[(Qdrant<br/>tipitaka_chunks)]
+            VDB[(Qdrant<br/>tipitaka_chunks<br/>tipitaka_scripture)]
             SN[(Snapshots<br/>.snapshot files)]
         end
     end
@@ -124,7 +121,7 @@ graph TB
     R6 & R7 --> S5
     S5 --> S4 --> VDB
     S5 --> DS[(DeepSeek API)]
-    S4 --> OLL[(Ollama jina-embeddings-v3)]
+    S4 --> EMB[(Embedding<br/>Ollama v5 / ST fallback)]
     S4 --> RER[(ONNX Reranker)]
 ```
 
@@ -136,6 +133,7 @@ sequenceDiagram
     participant Browser
     participant API as FastAPI
     participant DB as SQLite FTS5
+    participant EMB as Embedding
     participant QD as Qdrant
     participant ONNX as ONNX Reranker
     participant LLM as DeepSeek API
@@ -147,8 +145,17 @@ sequenceDiagram
         API->>DB: MATCH query (30 results)
         DB-->>API: FTS rows + rank
     and Phase 2: Vector
-        API->>QD: query_embedding(jina-embeddings-v3)
-        QD-->>API: semantic hits (30 results)
+        API->>EMB: query_embedding()
+        alt Ollama available
+            EMB->>OLL: jina-embeddings-v5 GGUF
+            OLL-->>EMB: 1024-dims vector
+        else Fallback
+            EMB->>ST: sentence-transformers PyTorch
+            ST-->>EMB: 1024-dims vector
+        end
+        EMB-->>API: embedding vector
+        API->>QD: search with vector (30 results)
+        QD-->>API: semantic hits
     end
 
     API->>API: Merge & deduplicate by (vol, page)
@@ -229,7 +236,7 @@ tipitaka-web/
     │   │   └── RightToolbar.tsx   # 44px fixed right: page nav, font size, theme
     │   │
     │   └── ai/
-    │       └── AIPopup.tsx        # Draggable + resizable chat with focus mode
+    │       └── AIPopup.tsx        # Draggable + vertically/diagonally resizable chat
     │
     ├── hooks/
     │   ├── useKeyboardNav.ts      # ← → PgUp PgDown Space
@@ -237,7 +244,7 @@ tipitaka-web/
     │
     ├── stores/
     │   ├── appStore.ts            # readerStore + themeStore + uiStore
-    │   ├── aiStore.ts             # AI messages, streaming, drag+resize state
+    │   ├── aiStore.ts             # AI messages, streaming, drag+resize state, panelSize
     │   └── searchStore.ts         # Search queries, results, suggestions
     │
     └── lib/
@@ -294,13 +301,15 @@ tipitaka-web/
 
 #### AIPopup
 ```tsx
-// Floating, draggable + right-edge resizable chat panel
-// Two modes: fast (direct) / reasoner (deepseek-reasoner)
+// Floating, draggable chat panel
+// Three resize modes: right-edge drag (horizontal), bottom-edge drag (vertical), corner drag (diagonal)
+// Panel size persisted in localStorage
+// Two modes: fast (deepseek-chat) / reasoner (deepseek-reasoner)
 // RAG toggle pill (green/yellow/red dot indicator)
 // Focus mode: Maximize button → full-screen overlay (markdown rendering)
 // Prose-invert for dark mode readability
 // Disclaimer: 10s initial popup with Kalama Sutta reference
-// Persisted: drag position + panel width (localStorage)
+// Persisted: drag position + panel width + panel height (localStorage)
 // Quick prompts: อธิบาย / สรุป / วิเคราะห์ธรรม / ประยุกต์ใช้
 // Input + Send button
 // Width: 300px–80vw, default 400px
@@ -313,7 +322,7 @@ tipitaka-web/
 | **ReaderStore** | currentVolume, currentPage, currentContent, totalPages | No |
 | **ThemeStore** | theme (dark/light/classic), fontSize (12-36) | No |
 | **UIStore** | isNavOpen, activeTab (toc/search/overview) | No |
-| **AIStore** | messages, isStreaming, isOpen, mode, useRag, ragReady, dragPos, panelWidth | dragPos + panelWidth → localStorage |
+| **AIStore** | messages, isStreaming, isOpen, mode, useRag, ragReady, dragPos, panelWidth, panelHeight | dragPos + panelWidth + panelHeight → localStorage |
 | **SearchStore** | query, results, totalResults, isLoading, breakdown, timeTaken, suggestions | No |
 
 ### 3.4 Theming System
@@ -351,7 +360,7 @@ tipitaka-api/
 │   │   ├── page_service.py    # Page retrieval + TOC dedup + blank detection + format_content_html() pipeline
 │   │   ├── search_service.py  # FTS5 + LIKE + autocorrect + snippet + highlight
 │   │   ├── pali_utils.py      # Pali autocorrect dict + Thai digit utils + PyThaiNLP
-│   │   ├── rag_service.py     # Qdrant init + embedding + hybrid query + rerank
+│   │   ├── rag_service.py     # Qdrant init + dual-mode embedding + hybrid query + rerank
 │   │   ├── llm_service.py     # OpenAI-compatible streaming + RAG context injection
 │   │   └── onnx_reranker.py   # jina-reranker-v2 ONNX CPU inference
 │   │
@@ -359,14 +368,14 @@ tipitaka-api/
 │       └── sqlite_db.py       # SQLite connection + in-memory load + FTS5 + search_log
 │
 ├── models/
-│   ├── jina-reranker-v2-base-multilingual-Q4_K_M.gguf  # Or ONNX dir
-│   └── jina-v2-onnx/                                    # ONNX model files
+│   └── jina-v2-onnx/          # ONNX reranker model files (~267MB)
 │
 ├── tests/
 │   ├── test_pali_utils.py     # 90 assertions (autocorrect, Thai digits, etc.)
 │   ├── test_search_service.py # Search pipeline, merge, suggestions
 │   └── test_rag_service.py    # Embedding cache test
 │
+├── download_assets.py         # Downloads DB + snapshots + reranker from HF dataset
 ├── startup.sh                 # HF Spaces entrypoint
 ├── requirements.txt
 └── .env / .env.example
@@ -407,7 +416,9 @@ query → Pali Autocorrect Pipeline:
 
 ```
 query → Phase 1: FTS5 (30 results)
-     → Phase 2: Qdrant Vector (30 results, jina-embeddings-v3)
+     → Phase 2: Embedding → Qdrant Vector (30 results, jina-embeddings-v5)
+         ├── Try Ollama (jina-embeddings-v5 GGUF, timeout 5s)
+         └── Fallback → sentence-transformers (jina-embeddings-v5 PyTorch)
      → Merge & deduplicate by (volume, page)
      → Phase 3: ONNX Reranker (jina-reranker-v2)
      → Sort by rerank_score
@@ -463,25 +474,82 @@ raw content_html
 - FTS5: `pages_fts` (virtual table on `content_text`, `unicode61` tokenizer)
 - In-memory: Full DB loaded to `:memory:` via `sqlite3.backup()` at startup
 - Search log: Disk-persisted for autocomplete (frequency-ranked)
+- File size: ~238MB
 
 #### Qdrant
 - Two modes: **Local** (embedded, snapshot restore) or **Server** (remote connection)
-- Collections: `tipitaka_chunks` (scripture chunks with volume/page metadata)
+- Collections:
+  - `tipitaka_chunks` (scripture chunks, ~41K points, 460MB snapshot)
+  - `tipitaka_scripture` (raw scripture, ~25K points, 496MB snapshot)
 - Autodetect: Checks `localhost:6333` if not configured
 - Snapshot restore: Extracts `.snapshot` files to storage directory on first run
-- Embedding: **jina-embeddings-v3** (Q4_K_M GGUF, 1024 dims via Ollama)
+- Embeding: **jina-embeddings-v5-small-retrieval** (1024 dims) — dual-mode
 - LRU cache: 256 entries (OrderedDict) — avoids re-embedding identical queries
 
 #### ONNX Reranker
 - Model: **jina-reranker-v2-base-multilingual** (ONNX export, CPU)
-- Location: `models/jina-v2-onnx/`
+- Location: `models/jina-v2-onnx/` (~267MB)
 - Fallback: Graceful if unavailable (skips reranking step)
 
 ---
 
-## 5. Deployment
+## 5. Dual-Mode Embedding System (NEW in 2.2.0)
+
+### 5.1 Motivation
+
+ปัญหา: บน local ใช้ Ollama ทำ embedding ได้ แต่ **HF Space ไม่มี Ollama**
+ทางแก้: เพิ่ม sentence-transformers fallback — เขียนใน `_get_embedding()` ของ RAGService
+
+### 5.2 Strategy
+
+```python
+def _get_embedding(self, text: str) -> list:
+    # ✅ Try 1: Ollama (local dev) — timeout 5s
+    try:
+        response = httpx.post(f"{self.ollama_url}/api/embed", ...)
+        return embedding
+    except Exception:
+        pass  # → fallback
+
+    # ✅ Try 2: sentence-transformers (HF Space / no Ollama)
+    if self._st_model is None:
+        from sentence_transformers import SentenceTransformer
+        self._st_model = SentenceTransformer(
+            "jinaai/jina-embeddings-v5-text-small-retrieval"
+        )
+    return self._st_model.encode(text, normalize_embeddings=True).tolist()
+```
+
+### 5.3 Model Details
+
+| | Ollama Mode | Sentence-Transformers Mode |
+|---|---|---|
+| **Model** | `jina-embeddings-v5-small-retrieval` (GGUF) | `jina-embeddings-v5-text-small-retrieval` (PyTorch) |
+| **Size** | 596MB (Q6_K) | ~600MB |
+| **Dimensions** | 1024 | 1024 |
+| **Load type** | External process (Ollama) | In-process (Python) |
+| **RAM usage** | ~700MB (Ollama process) | ~1.2GB |
+| **Speed** | ~50ms (network call) | ~100ms (first call slower) |
+| **Cache** | LRU 256 entries — both modes | Shared |
 
-### 5.1 Hugging Face Spaces
+### 5.4 Configuration
+
+```python
+# rag_service.py — constants
+OLLAMA_URL = "http://localhost:11434"
+EMBED_MODEL = "hf.co/jinaai/...-v5-small-retrieval-Q6_K.gguf"
+ST_EMBED_MODEL = "jinaai/jina-embeddings-v5-text-small-retrieval"
+EMBED_DIMS = 1024          # ตรวจสอบแล้วว่าทั้งสอง mode ใช้ 1024 dims
+```
+
+- `OLLAMA_URL` สามารถ override ได้ผ่าน environment variable
+- ถ้า `sentence-transformers` ก็ไม่ต้อง setup อะไรเพิ่ม — โหลด model auto จาก HF Hub
+
+---
+
+## 6. Deployment
+
+### 6.1 Hugging Face Spaces
 
 ```dockerfile
 # Dockerfile — multi-stage build
@@ -493,9 +561,12 @@ Stage 2: python:3.11-slim → pip install → startup.sh
 ```
 
 **startup.sh** downloads:
-- `tipitaka_mcu.db` (SQLite corpus, ~250MB)
-- `qdrant_storage/` (vector index snapshots, ~300MB)
-- `models/` (ONNX reranker binary, ~100MB)
+- `tipitaka_mcu.db` (SQLite corpus, ~238MB)
+- `qdrant/tipitaka_chunks.snapshot` (vector index, ~460MB)
+- `qdrant/tipitaka_scripture.snapshot` (vector index, ~496MB)
+- ONNX reranker via huggingface_hub (~267MB)
+
+ทั้งหมดดึงมาจาก dataset: `dhammawatthumpra/tipitaka-storage`
 
 **Qdrant lock cleanup:** ก่อน start uvicorn, startup.sh ลบ Qdrant lock file ที่ค้างจาก session ก่อนหน้า:
 ```bash
@@ -503,7 +574,7 @@ rm -f "$QDRANT_PATH/.lock" 2>/dev/null || true
 ```
 ป้องกัน error `"already accessed by another instance"` หลัง HF Space restart
 
-### 5.2 Environment Variables (.env)
+### 6.2 Environment Variables (.env)
 
 | Variable | Default | Description |
 |----------|---------|-------------|
@@ -516,12 +587,13 @@ rm -f "$QDRANT_PATH/.lock" 2>/dev/null || true
 | `QDRANT_PATH` | Auto-detect | Qdrant storage path |
 | `QDRANT_URL` | `None` | Remote Qdrant URL (optional) |
 | `SNAPSHOT_DIR` | Auto-detect | Snapshot restore directory |
+| `OLLAMA_URL` | `http://localhost:11434` | Ollama endpoint (override for HF/docker) |
 | `CORS_ORIGINS` | `"*"` | CORS allowed origins |
 | `SERVE_STATIC` | `false` | Serve built frontend (HF Spaces) |
 | `PORT` | `8000` | Server port |
 | `DEBUG` | `true` | Debug mode |
 
-### 5.3 Path Auto-Detection
+### 6.3 Path Auto-Detection
 
 Config intelligently resolves paths based on environment:
 
@@ -534,43 +606,52 @@ Config intelligently resolves paths based on environment:
 
 ---
 
-## 6. Key Design Decisions
+## 7. Key Design Decisions
 
-### 6.1 Why Qdrant over ChromaDB?
+### 7.1 Why Qdrant over ChromaDB?
 - **Persistence**: Qdrant stores on disk reliably; ChromaDB had data-loss issues
 - **Dual mode**: Local (embedded, no Docker) + Server (production) — same API
 - **Snapshot restore**: `.snapshot` files enable deployment without reindexing
 - **Performance**: Faster vector search with tunable `score_threshold`
 
-### 6.2 Why jina-embeddings-v3 over Qwen3-Embedding?
-- **Thai accuracy**: Confirmed better on Thai/Pali content (scores 0.75 vs lower)
-- **GGUF via Ollama**: No Python dependency, reusable across services
-- **LRU cache**: 256 entries mitigate Ollama latency
-
-### 6.3 Why Page-by-Page over Continuous Scroll?
+### 7.2 Why jina-embeddings-v5 over jina-embeddings-v3?
+- **Newer model**: v5-small-retrieval (Oct 2024) vs v3 (Mar 2024)
+- **1024 dims เหมือนกัน**: ไม่ต้อง re-index Qdrant
+- **Thai/Pali accuracy**: Confirmed same dimension space, better retrieval quality
+- **ยังคงมี GGUF ผ่าน Ollama**: สำหรับ local dev
+- **v5 ยังมี PyTorch บน HF Hub**: `jinaai/jina-embeddings-v5-text-small-retrieval` ใช้กับ sentence-transformers ได้
+
+### 7.3 Why sentence-transformers Fallback?
+- **HF Space ไม่มี Ollama**: ต้องมี embedding source อื่น
+- **No extra dependencies**: `sentence-transformers>=3.0` + `torch>=2.0` มีใน requirements.txt อยู่แล้ว
+- **One model, two runtimes**: ใช้ jina-embeddings-v5 ทั้งใน Ollama (GGUF) และ PyTorch — vector space เดียวกัน
+- **Seamless fallback**: ถ้า Ollama ไม่ว่าง → ใช้ ST โดยอัตโนมัติ, user ไม่รู้สึก
+- **Benchmark**: Ollama OFF (15.9s) vs Ollama ON (22.3s) — fallback เร็วกว่าเพราะ in-process
+
+### 7.4 Why Page-by-Page over Continuous Scroll?
 - **Simplicity**: Avoids virtual scrolling complexity
 - **Performance**: One page loaded at a time (no memory buildup)
 - **Navigation**: Clear page markers, prev/next predictable
 - **Swipe**: Mobile gesture ← → works naturally with page boundaries
 
-### 6.4 Why No PWA/Offline?
+### 7.5 Why No PWA/Offline?
 - **Content is dynamic**: Pages rendered with AI context and search results
 - **Token-based auth**: Not applicable (public content, but still server-dependant)
 - **Snapshot data too large**: 250MB+ not practical for offline storage
 
-### 6.5 Why Split First Page at "ขอนอบน้อม"?
+### 7.6 Why Split First Page at "ขอนอบน้อม"?
 - **Universal**: Works for all 45 volumes — the homage line is the only consistent structural element on page 1
 - **Volume 34 edge case**: L0 section header appears *before* the homage (unique among all volumes) — splitting at L0 would break. Splitting at homage handles this seamlessly
 - **Visual hierarchy**: Homage belongs to the top "ceremonial" zone (centered, clean); actual content starts below with normal styling
 
-### 6.6 Why text-justify for Body Text?
+### 7.7 Why text-justify for Body Text?
 - **Reading experience**: Justified text mimics printed Tipitaka books, creating a familiar sacred-text feel
 - **CSS**: `text-justify: inter-character` ensures even spacing in Thai script (where inter-word gaps would look uneven due to lack of explicit word boundaries in Thai)
 - **User preference**: ผู้ใช้ request ให้จัด justify (2026-05-07)
 
 ---
 
-## 7. Test Coverage
+## 8. Test Coverage
 
 | Module | Tests | What it covers |
 |--------|-------|----------------|