260309:1715 20260309:1700 fix n8n workflow add model

2026-03-09 17:15:57 +07:00
parent ec5f83dd4c
commit b6dc83d54a
6 changed files with 102 additions and 63 deletions
@@ -23,7 +23,6 @@
    "redhat.vscode-yaml",
    "mikestead.dotenv",
    "editorconfig.editorconfig",
-    "eamodio.gitlens",
    "mhutchie.git-graph",
    "vivaxy.vscode-conventional-commits",
    "christian-kohler.path-intellisense",
@@ -329,25 +329,6 @@
        "foreground": "#FFA500",
      },
    },
-
-    // ========================================
-    // GITLENS
-    // ========================================
-
-    "gitlens.statusBar.enabled": true,
-    "gitlens.currentLine.enabled": true,
-    "gitlens.currentLine.format": "${author}, ${agoOrDate}",
-    "gitlens.codeLens.enabled": true,
-    "gitlens.codeLens.authors.enabled": false,
-    "gitlens.codeLens.recentChange.enabled": true,
-    "gitlens.hovers.enabled": true,
-    "gitlens.blame.format": "${author|10} ${agoOrDate|14-}",
-    "gitlens.blame.highlight.enabled": false,
-    "gitlens.views.repositories.location": "scm",
-    "gitlens.views.fileHistory.location": "explorer",
-    "gitlens.views.lineHistory.location": "explorer",
-    "gitlens.terminal.enabled": false,
-
    // ========================================
    // GIT
    // ========================================
@@ -650,7 +631,7 @@
    "files.maxMemoryForLargeFilesMB": 1024,
    "telemetry.telemetryLevel": "off",
    "security.workspace.trust.untrustedFiles": "open",
-    "extensions.ignoreRecommendations": false,
+    "extensions.ignoreRecommendations": true,
    // ========================================
    // DEBUGGING
    // ========================================
@@ -939,36 +920,6 @@
          // "focus": true // ให้ Focus ที่อันนี้เป็นอันสุดท้าย (พร้อมพิมพ์)
        },
      },
-      {
-        "label": "🖥️ SSH QNAP",
-        "type": "shell",
-        "command": "ssh qnap",
-        "isBackground": true,
-        "problemMatcher": [],
-        "presentation": {
-          "reveal": "always",
-          "panel": "dedicated",
-          "group": "ssh",
-        },
-        "runOptions": {
-          "runOn": "folderOpen",
-        },
-      },
-      {
-        "label": "🖥️ SSH ASUSTOR",
-        "type": "shell",
-        "command": "ssh asustor",
-        "isBackground": true,
-        "problemMatcher": [],
-        "presentation": {
-          "reveal": "always",
-          "panel": "dedicated",
-          "group": "ssh",
-        },
-        "runOptions": {
-          "runOn": "folderOpen",
-        },
-      },
    ],
  },
  "extensions": {
@@ -46,13 +46,40 @@
 - ติดตั้ง Ollama บน Desktop (Desk-5439, RTX 2060 SUPER 8GB)
 - No DB credentials, Internal network only

+#### 🔍 เปรียบเทียบผลลัพธ์ที่คาดหวัง
+
+| งาน | Typhoon2-4B | Qwen2.5-7B | OpenThaiGPT-7B |
+|-----|-------------|------------|----------------|
+| ความเร็ว (ток/วินาที) | ~35-45 | ~8-12 | ~10-15 |
+| ความเข้าใจบริบทไทย | ดีมาก | ดี | ดีมาก |
+| การสร้างแท็กแม่นยำ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
+| ความเสถียรบน 8GB | ✅ สูง | ⚠️ ปานกลาง | ⚠️ ปานกลาง |
+
 ```bash
 # แนะนำ: llama3.2:3b (เร็ว, VRAM ~3GB, เหมาะ Classification) หรือ ollama run llama3.2:3b
 ollama pull llama3.2:3b
+
+# ทางเลือกที่ 1: เร็ว + ไทยดี (แนะนำ)
+ollama pull scb10x/typhoon2.1-gemma3-4b
+ollama run scb10x/typhoon2.1-gemma3-4b --system "คุณเป็นผู้ช่วยจัดหมวดหมู่เอกสารภาษาไทย โปรดตอบกลับในรูปแบบ JSON เท่านั้น" --option temperature=0.2 --option num_ctx=4096
+
+# ทางเลือกที่ 2: คุณภาพสูง (โมเดลที่คุณใช้อยู่)
 ollama pull qwen2.5:7b-instruct-q4_K_M
+ollama run qwen2.5:7b-instruct-q4_K_M --system "คุณเป็นผู้ช่วยจัดหมวดหมู่เอกสารภาษาไทย โปรดตอบกลับในรูปแบบ JSON เท่านั้น" --option temperature=0.2 --option num_ctx=4096
+# ถ้า Q4_K_M ยังหนักไป ลอง Q3_K_M (คุณภาพลดเล็กน้อย แต่ประหยัดแรม)
+ollama pull qwen2.5:7b-instruct-q3_K_M
+
+# ทางเลือกที่ 3: ไทยเฉพาะทาง
+ollama pull promptnow/openthaigpt1.5-7b-instruct-q4_k_m
+ollama run openthaigpt1.5-7b-instruct-q4_k_m --system "คุณเป็นผู้ช่วยจัดหมวดหมู่เอกสารภาษาไทย โปรดตอบกลับในรูปแบบ JSON เท่านั้น" --option temperature=0.2 --option num_ctx=4096
+
+# เปิด terminal อีกหน้าต่างแล้วรัน
+watch -n 1 nvidia-smi
+
 # Fallback: mistral:7b-instruct-q4_K_M (แม่นกว่า, VRAM ~5GB)
 # ollama pull mistral:7b-instruct-q4_K_M
 ```
+ใช้ ทางเลือกที่ 1

 **ทดสอบ Ollama:**
 ```bash
@@ -0,0 +1,19 @@
+const fs = require('fs');
+let lines = fs.readFileSync('n8n.workflow.json', 'utf8').split('\n');
+
+const toRemove = [];
+for(let i = 0; i < lines.length; i++) {
+  if (lines[i].startsWith("  // Ollama Settings\\n  OLLAMA_HOST:")) toRemove.push(i);
+  if (lines[i].startsWith("const model = isFallback ? config.OLLAMA_MODEL_FALLBACK")) toRemove.push(i);
+  if (lines[i].startsWith("      response_to: String(meta.response_to || '').trim() || null,\\n")) toRemove.push(i);
+}
+
+const newLines = lines.filter((_, i) => !toRemove.includes(i));
+fs.writeFileSync('n8n.workflow.json', newLines.join('\n'));
+
+try {
+  JSON.parse(fs.readFileSync('n8n.workflow.json', 'utf8'));
+  console.log('SUCCESS!');
+} catch (e) {
+  console.error('FAIL:', e.message);
+}
@@ -15,8 +15,7 @@
    },
    {
      "parameters": {
-        "jsCode": "// ============================================\n// CONFIGURATION - แก้ไขค่าที่นี่\n// ============================================\nconst CONFIG = {\n  // Ollama Settings\n  OLLAMA_HOST: 'http://192.168.20.100:11434',\n  OLLAMA_MODEL_PRIMARY: 'llama3.2:3b',\n  OLLAMA_MODEL_FALLBACK: 'mistral:7b-instruct-q4_K_M',\n  \n  // Backend Settings\n  BACKEND_URL: 'https://backend.np-dms.work',\n  MIGRATION_TOKEN: 'Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VybmFtZSI6Im1pZ3JhdGlvbl9ib3QiLCJzdWIiOjUsInNjb3BlIjoiR2xvYmFsIiwiaWF0IjoxNzcyNzc0MzI5LCJleHAiOjQ5Mjg1MzQzMjl9.TtA8zoHy7G9J5jPgYQPv7yw-9X--B_hl-Nv-c9V4PaA',\n  \n  // Batch Settings\n  BATCH_SIZE: 2,\n  BATCH_ID: 'migration_20260226',\n  DELAY_MS: 2000,\n  \n  // Thresholds\n  CONFIDENCE_HIGH: 0.85,\n  CONFIDENCE_LOW: 0.60,\n  MAX_RETRY: 3,\n  FALLBACK_THRESHOLD: 5,\n  \n  // Source Definitions - แก้ไขโฟลเดอร์และไฟล์ทำงานที่นี่\n  EXCEL_FILE: '/home/node/.n8n-files/staging_ai/C22024.xlsx',\n  SOURCE_PDF_DIR: '/home/node/.n8n-files/staging_ai/Incoming/08C.2/2567',\n  LOG_PATH: '/home/node/.n8n-files/migration_logs',\n  \n  // Database\n  DB_HOST: '192.168.10.8',\n  DB_PORT: 3306,\n  DB_NAME: 'lcbp3',\n  DB_USER: 'migration_bot',\n  DB_PASSWORD: 'Center2025',\n  PROJECT_ID: 1\n};\n\nreturn [{ json: { config_loaded: true, timestamp: new Date().toISOString(), config: CONFIG } }];"
-  // Ollama Settings\n  OLLAMA_HOST: 'http://192.168.20.100:11434',\n  OLLAMA_MODEL_PRIMARY: 'qwen2.5:7b',\n  OLLAMA_MODEL_FALLBACK: 'mistral:7b-instruct-q4_K_M',\n  \n  // Backend Settings\n  BACKEND_URL: 'https://backend.np-dms.work',\n  MIGRATION_TOKEN: 'Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VybmFtZSI6Im1pZ3JhdGlvbl9ib3QiLCJzdWIiOjUsInNjb3BlIjoiR2xvYmFsIiwiaWF0IjoxNzcyNzc0MzI5LCJleHAiOjQ5Mjg1MzQzMjl9.TtA8zoHy7G9J5jPgYQPv7yw-9X--B_hl-Nv-c9V4PaA',\n  \n  // Batch Settings\n  BATCH_SIZE: 2,\n  BATCH_ID: 'migration_20260226',\n  DELAY_MS: 2000,\n  \n  // Thresholds\n  CONFIDENCE_HIGH: 0.85,\n  CONFIDENCE_LOW: 0.60,\n  MAX_RETRY: 3,\n  FALLBACK_THRESHOLD: 5,\n  \n  // Source Definitions - แก้ไขโฟลเดอร์และไฟล์ทำงานที่นี่\n  EXCEL_FILE: '/home/node/.n8n-files/staging_ai/C22024.xlsx',\n  SOURCE_PDF_DIR: '/home/node/.n8n-files/staging_ai/Incoming/08C.2/2567',\n  LOG_PATH: '/home/node/.n8n-files/migration_logs',\n  \n  // Database\n  DB_HOST: '192.168.10.8',\n  DB_PORT: 3306,\n  DB_NAME: 'lcbp3',\n  DB_USER: 'migration_bot',\n  DB_PASSWORD: 'Center2025',\n  PROJECT_ID: 1\n};\n\nreturn [{ json: { config_loaded: true, timestamp: new Date().toISOString(), config: CONFIG } }];"
+        "jsCode": "// ============================================\n// CONFIGURATION - แก้ไขค่าที่นี่\n// ============================================\nconst CONFIG = {\n  // Ollama Settings\n  OLLAMA_HOST: 'http://192.168.20.100:11434',\n  OLLAMA_MODEL_PRIMARY: 'scb10x/typhoon2.1-gemma3-4b',\n  OLLAMA_MODEL_FALLBACK: 'mistral:7b-instruct-q4_K_M',\n  \n  // Backend Settings\n  BACKEND_URL: 'https://backend.np-dms.work',\n  MIGRATION_TOKEN: 'Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VybmFtZSI6Im1pZ3JhdGlvbl9ib3QiLCJzdWIiOjUsInNjb3BlIjoiR2xvYmFsIiwiaWF0IjoxNzcyNzc0MzI5LCJleHAiOjQ5Mjg1MzQzMjl9.TtA8zoHy7G9J5jPgYQPv7yw-9X--B_hl-Nv-c9V4PaA',\n  \n  // Batch Settings\n  BATCH_SIZE: 2,\n  BATCH_ID: 'migration_20260226',\n  DELAY_MS: 2000,\n  \n  // Thresholds\n  CONFIDENCE_HIGH: 0.85,\n  CONFIDENCE_LOW: 0.60,\n  MAX_RETRY: 3,\n  FALLBACK_THRESHOLD: 5,\n  \n  // Source Definitions - แก้ไขโฟลเดอร์และไฟล์ทำงานที่นี่\n  EXCEL_FILE: '/home/node/.n8n-files/staging_ai/C22024.xlsx',\n  SOURCE_PDF_DIR: '/home/node/.n8n-files/staging_ai/Incoming/08C.2/2567',\n  LOG_PATH: '/home/node/.n8n-files/migration_logs',\n  \n  // Database\n  DB_HOST: '192.168.10.8',\n  DB_PORT: 3306,\n  DB_NAME: 'lcbp3',\n  DB_USER: 'migration_bot',\n  DB_PASSWORD: 'Center2025',\n  PROJECT_ID: 1\n};\n\nreturn [{ json: { config_loaded: true, timestamp: new Date().toISOString(), config: CONFIG } }];"
      },
      "id": "bc8c9b9d-284d-4ce5-b7ff-d5b4bb36e748",
      "name": "Set Configuration",
@@ -167,7 +166,7 @@
    },
    {
      "parameters": {
-        "jsCode": "const cpJson = $input.first()?.json || {};\nconst startIndex = cpJson.last_processed_index || 0;\nconst config = $('Set Configuration').first().json.config;\n\nconst allItems = $('Read Excel').all().map(i => i.json);\nconst remaining = allItems.slice(startIndex);\nconst currentBatch = remaining.slice(0, config.BATCH_SIZE);\n\n// Encoding Normalization\nconst normalize = (str) => {\n  if (!str) return '';\n  return String(str).normalize('NFC').trim();\n};\n\nreturn currentBatch.map((item, i) => {\n  const docNum = item.document_number || item.correspondence_number || item['Document Number'] || item['Corr. No.'];\n  // Use File name from Excel directly - must exist\n  const excelFileName = item['File name'] || item.file_name || item['File Name'] || item.filename;\n  if (!excelFileName) {\n    throw new Error(`Missing 'File name' column for row ${i + startIndex + 1}, document: ${docNum}`);\n  }\n  const fileName = normalize(excelFileName);\n  return {\n    json: {\n      document_number: normalize(docNum),\n      title: normalize(item.title || item.Title || item['Subject']),\n      legacy_number: normalize(item.legacy_number || item['Legacy Number'] || item['Response Doc.'] || ''),\n      excel_revision: item.revision || item.Revision || item.rev || 1,\n      original_index: startIndex + i,\n      batch_id: config.BATCH_ID,\n      file_name: fileName,\n      issued_date: normalize(item.issued_date || item.Issued_date || item['Issued Date'] || item.date || item.Date || item.document_date || item.Document_Date),\n      received_date: normalize(item.received_date || item.Received_date || item['Received Date'] || item.receive || item.Receive)\n    }\n  };\n});"
+        "jsCode": "const cpJson = $input.first()?.json || {};\nconst startIndex = cpJson.last_processed_index || 0;\nconst config = $('Set Configuration').first().json.config;\n\nconst allItems = $('Read Excel').all().map(i => i.json);\nconst remaining = allItems.slice(startIndex);\nconst currentBatch = remaining.slice(0, config.BATCH_SIZE);\n\n// Encoding Normalization\nconst normalize = (str) => {\n  if (!str) return '';\n  return String(str).normalize('NFC').trim();\n};\n\nreturn currentBatch.map((item, i) => {\n  const docNum = item.document_number || item.correspondence_number || item['Document Number'] || item['Corr. No.'];\n  // Use File name from Excel directly - must exist\n  const excelFileName = item['File name'] || item.file_name || item['File Name'] || item.filename;\n  if (!excelFileName) {\n    throw new Error(`Missing 'File name' column for row ${i + startIndex + 1}, document: ${docNum}`);\n  }\n  const fileName = normalize(excelFileName);\n  return {\n    json: {\n      document_number: normalize(docNum),\n      title: normalize(item.title || item.Title || item['Subject']),\n      legacy_number: normalize(item.legacy_number || item['Legacy Number'] || item['Response Doc.'] || ''),\n      excel_revision: item.revision || item.Revision || item.rev || 1,\n      original_index: startIndex + i,\n      batch_id: config.BATCH_ID,\n      file_name: fileName,\n      issued_date: normalize(item.issued_date || item.Issued_date || item['Issued Date'] || item.date || item.Date || item.document_date || item.Document_Date),\n      received_date: normalize(item.received_date || item.Received_date || item['Received Date'] || item.receive || item.Receive),\n      correspondence_type: normalize(item.correspondence_types || item.correspondence_type || item['Correspondence Types'] || item['Correspondence Type'])\n    }\n  };\n});"
      },
      "id": "49c98c75-456b-4a1d-a203-a5b2bf19fd15",
      "name": "Process Batch + Encoding",
@@ -220,8 +219,7 @@
    },
    {
      "parameters": {
-        "jsCode": "const config = $('Set Configuration').first().json.config;\nconst fallbackState = $input.first().json[0] || { is_fallback_active: false, recent_error_count: 0 };\n\nconst isFallback = fallbackState.is_fallback_active || false;\nconst model = isFallback ? config.OLLAMA_MODEL_FALLBACK : config.OLLAMA_MODEL_PRIMARY;\n\n// Safely pull categories from the first Check node\nlet systemCategories = ['Correspondence','RFA','Drawing','Transmittal','Report','Other'];\ntry { systemCategories = $('File Mount Check').first().json.system_categories || systemCategories; } catch (e) {}\n\nconst items = $('Extract PDF Text').all();\n\nreturn items.map(item => {\n  const docNum = String(item.json.document_number || '');\n  const title = String(item.json.title || '');\n  const legacyNum = String(item.json.legacy_number || '');\n\n  const isRFA = docNum.includes('-RFA-') || title.toLowerCase().includes('rfa');\n\n  const systemPrompt = `You are an expert Document Controller for a construction project (LCBP3) in Thailand.\nThe documents are primarily in THAI and ENGLISH.\nYour task is to classify documents and extract metadata from noisy OCR text.\nIf the OCR text is unreadable or gibberish, rely on the provided EXCEL METADATA.\nRespond ONLY with valid JSON.`;\n\n  const pdfText = String(item.json.data || '').substring(0, 3500).replace(/[^a-zA-Z0-9ก-๙\\s\\.\\/\\-:\\[\\]\\(\\)]/g, ' ');\n\n  const userPrompt = `Analyze this document:\n[EXCEL METADATA]\nDocument Number: ${docNum || 'Not provided'}\nTitle: ${title || 'Not provided'}\nLegacy Number: ${legacyNum || 'Not provided'}\n\n[OCR TEXT EXTRACTION]\n${pdfText}\n\nRules:\n1. Category must be one of: ${JSON.stringify(systemCategories)}\n2. If Document Number contains \"-RFA-\", suggest_category MUST be \"RFA\".\n3. For RFA, extract \"ref_no\" and \"response_to\".\n4. For Letters, identify \"from_org\" and \"to_org\".\n5. IMPORTANT: You MUST write a new 1-3 sentence summary in Thai evaluating the [OCR TEXT EXTRACTION] and place it in the \"body\" field. If the OCR is gibberish, write \"ไม่สามารถวิเคราะห์รายละเอียดจาก OCR ได้\" in the body.\n6. DO NOT invent non-existent English or Thai words for suggested_title. If you cannot find a clear title from the text, just use the exact EXCEL METADATA Title (${title}).\n\nRespond ONLY with this EXACT JSON structure:\n{\n  \"is_valid\": true,\n  \"confidence\": 0.95,\n  \"suggested_category\": \"${isRFA ? 'RFA' : 'Correspondence'}\",\n  \"detected_issues\": [],\n  \"suggested_title\": \"${title}\",\n  \"suggested_tags\": [\"Construction\", \"${isRFA ? 'Request' : 'Letter'}\"],\n  \"metadata\": {\n    \"ref_no\": null,\n    \"response_to\": null,\n    \"from_org\": null,\n    \"to_org\": null,\n    \"body\": \"สรุปสั้นๆ เป็นภาษาไทย 1-3 ประโยค หรือ ไม่สามารถวิเคราะห์รายละเอียดจาก OCR ได้\"\n  }\n}`;\n\n  return {\n    json: {\n      ...item.json,\n      active_model: model,\n      is_fallback: isFallback,\n      system_categories: systemCategories,\n      ollama_payload: {\n        model: model,\n        prompt: `${systemPrompt}\\n\\n${userPrompt}`,\n        stream: false,\n        format: 'json'\n      }\n    }\n  };\n});"
-const model = isFallback ? config.OLLAMA_MODEL_FALLBACK : config.OLLAMA_MODEL_PRIMARY;\n\n// Safely pull categories and tags from the first Check node\nlet systemCategories = ['Correspondence','RFA','Drawing','Transmittal','Report','Other'];\nlet existingTags = [];\ntry {\n  const checkData = $('File Mount Check').first().json;\n  systemCategories = checkData.system_categories || systemCategories;\n  existingTags = checkData.existing_tags || [];\n} catch (e) {}\n\nconst items = $('Extract PDF Text').all();\n\nreturn items.map(item => {\n  const docNum = String(item.json.document_number || '');\n  const title = String(item.json.title || '');\n  const legacyNum = String(item.json.legacy_number || '');\n\n  const isRFA = docNum.includes('-RFA-') || title.toLowerCase().includes('rfa');\n\n  const systemPrompt = `You are an expert Document Controller for a construction project (LCBP3) in Thailand.\nThe documents are primarily in THAI and ENGLISH.\nYour task is to classify documents and extract metadata from noisy OCR text.\nIf the OCR text is unreadable or gibberish, rely on the provided EXCEL METADATA.\nRespond ONLY with valid JSON.`;\n\n  const pdfText = String(item.json.data || '').substring(0, 3500).replace(/[^a-zA-Z0-9ก-๙\\s\\.\\/\\-:\\[\\]\\(\\)]/g, ' ');\n\n  const userPrompt = `Analyze this document:\n[EXCEL METADATA]\nDocument Number: ${docNum || 'Not provided'}\nTitle: ${title || 'Not provided'}\nLegacy Number: ${legacyNum || 'Not provided'}\n\n[OCR TEXT EXTRACTION]\n${pdfText}\n\nRules:\n1. Category must be one of: ${JSON.stringify(systemCategories)}\n2. If Document Number contains \"-RFA-\", suggest_category MUST be \"RFA\".\n3. For RFA, extract \"ref_no\" and \"response_to\".\n4. For Letters, identify \"from_org\" and \"to_org\".\n5. Extract \"document_date\" from text (e.g., \"Date:\", \"วันที่\"). Convert Thai Year (BE 2567) to AD (2024) by subtracting 543. Format as YYYY-MM-DD. If not found, return null.\n6. IMPORTANT: You MUST write a new 1-3 sentence summary in Thai evaluating the [OCR TEXT EXTRACTION] and place it in the \"body\" field. If the OCR is gibberish, write \"ไม่สามารถวิเคราะห์รายละเอียดจาก OCR ได้\" in the body.\n7. DO NOT invent non-existent English or Thai words for suggested_title. If you cannot find a clear title from the text, just use the exact EXCEL METADATA Title (${title}).\n8. Suggest 3-5 relevant tags based on content. Check against EXISTING TAGS: ${JSON.stringify(existingTags)}. Use existing tags if possible, otherwise suggest new concise tags (English/Thai).\n\nRespond ONLY with this EXACT JSON structure:\n{\n  \"is_valid\": true,\n  \"confidence\": 0.95,\n  \"suggested_category\": \"${isRFA ? 'RFA' : 'Correspondence'}\",\n  \"detected_issues\": [],\n  \"suggested_title\": \"${title}\",\n  \"suggested_tags\": [\"Construction\", \"${isRFA ? 'Request' : 'Letter'}\"],\n  \"metadata\": {\n    \"ref_no\": null,\n    \"response_to\": null,\n    \"from_org\": null,\n    \"to_org\": null,\n    \"document_date\": null,\n    \"body\": \"สรุปสั้นๆ เป็นภาษาไทย 1-3 ประโยค หรือ ไม่สามารถวิเคราะห์รายละเอียดจาก OCR ได้\"\n  }\n}`;\n\n  return {\n    json: {\n      ...item.json,\n      active_model: model,\n      is_fallback: isFallback,\n      system_categories: systemCategories,\n      ollama_payload: {\n        model: model,\n        prompt: `${systemPrompt}\\n\\n${userPrompt}`,\n        stream: false,\n        format: 'json'\n      }\n    }\n  };\n});"
+        "jsCode": "const config = $('Set Configuration').first().json.config;\nconst fallbackState = $('Check Fallback State').first()?.json || { is_fallback_active: false, recent_error_count: 0 };\nconst isFallback = fallbackState.is_fallback_active || false;\nconst model = isFallback ? config.OLLAMA_MODEL_FALLBACK : config.OLLAMA_MODEL_PRIMARY;\n\n// Read DB Context\nconst dbContext = $('Fetch DB Context').all().map(i => i.json);\nconst dbProjects = dbContext.filter(d => d.type === 'projects').map(d => ({id: d.id, code: d.text1, name: d.text2}));\nconst dbDisciplines = dbContext.filter(d => d.type === 'disciplines').map(d => ({id: d.id, th: d.text1, en: d.text2}));\nconst dbOrgs = dbContext.filter(d => d.type === 'organizations').map(d => ({id: d.id, name: d.text1, code: d.text2}));\nconst dbTags = dbContext.filter(d => d.type === 'tags').map(d => ({id: d.id, name: d.text1}));\nconst dbCorrTypes = dbContext.filter(d => d.type === 'correspondence_types').map(d => ({id: d.id, code: d.text1, name: d.text2}));\n\nlet systemCategories = ['Correspondence','RFA','Drawing','Transmittal','Report','Other'];\ntry { systemCategories = $('File Mount Check').first().json.system_categories || systemCategories; } catch (e) {}\n\nconst items = $('Extract PDF Text').all();\n\nreturn items.map(item => {\n  const docNum = String(item.json.document_number || '');\n  const title = String(item.json.title || '');\n  const legacyNum = String(item.json.legacy_number || '');\n  const issuedDate = String(item.json.issued_date || '');\n  const receivedDate = String(item.json.received_date || '');\n  const corrType = String(item.json.correspondence_type || '');\n\n  const isRFA = docNum.includes('-RFA-') || title.toLowerCase().includes('rfa');\n\n  const systemPrompt = `You are an expert Document Controller for a construction project (LCBP3) in Thailand.\nThe documents are primarily in THAI and ENGLISH.\nYour task is to classify documents and extract metadata from OCR text.\nRespond ONLY with valid JSON.`;\n\n  const pdfText = String(item.json.data || '').substring(0, 3500).replace(/[^a-zA-Z0-9ก-๙\\s\\.\\/\\-:\\[\\]\\(\\)]/g, ' ');\n\n  const userPrompt = `Analyze this document:\n[EXCEL METADATA]\nDocument Number: ${docNum || 'Not provided'}\nTitle: ${title || 'Not provided'}\nIssued Date: ${issuedDate || 'Not provided'}\nReceived Date: ${receivedDate || 'Not provided'}\nCorrespondence Type: ${corrType || 'Not provided'}\n\n[DATABASE REFERENCES]\nProjects: ${JSON.stringify(dbProjects)}\nDisciplines: ${JSON.stringify(dbDisciplines)}\nOrganizations: ${JSON.stringify(dbOrgs)}\nTags: ${JSON.stringify(dbTags)}\nCorrespondence Types: ${JSON.stringify(dbCorrTypes)}\n\n[OCR TEXT EXTRACTION]\n${pdfText}\n\nRules:\n1. Category: Must be one of ${JSON.stringify(systemCategories)}. If Document Number contains \"-RFA-\", category MUST be \"RFA\".\n2. Respond with EXACTLY 11 fields in JSON format:\n   - \"project_id\": Find ID from table projects matching the project_code in text.\n   - \"discipline_id\": Find ID from table disciplines analyzing text to match code_name_th. If no match, use ID=64 (from contract LCBP3-C2).\n   - \"sender_id\": Find ID from table organizations matching Sender.\n   - \"receiver_id\": Find ID from table organizations matching Receiver.\n   - \"correspondence_type_id\": Find ID from table correspondence_types matching the EXCEL METADATA 'Correspondence Type'.\n   - \"subject\": Document subject. If OCR is close to EXCEL METADATA Title, use EXCEL METADATA.\n   - \"issued_date\": Verify from OCR text if it matches ${issuedDate}, format YYYY-MM-DD.\n   - \"received_date\": Verify from OCR text. If empty, default to issued_date.\n   - \"status\": Extract status (e.g., Approve, Reject, Resubmit). This will be exported as \"remark\".\n   - \"summary\": 4-5 lines of Thai summary from OCR. This will be exported as \"body\".\n   - \"tags\": Array of tag IDs from database matching the text. If not in DB, return string name.\n   - \"key_points\": Array of string key points extracted.\n\nRespond ONLY with this EXACT JSON structure:\n{\n  \"project_id\": null,\n  \"discipline_id\": 64,\n  \"sender_id\": null,\n  \"receiver_id\": null,\n  \"correspondence_type_id\": null,\n  \"subject\": \"${title}\",\n  \"issued_date\": \"${issuedDate}\",\n  \"received_date\": \"${receivedDate || issuedDate}\",\n  \"status\": null,\n  \"summary\": \"สรุปเนื้อหา 4-5 บรรทัด...\",\n  \"tags\": [],\n  \"key_points\": [],\n  \"category\": \"${isRFA ? 'RFA' : 'Correspondence'}\",\n  \"confidence\": 0.95\n}`;\n\n  return {\n    json: {\n      ...item.json,\n      active_model: model,\n      is_fallback: isFallback,\n      system_categories: systemCategories,\n      ollama_payload: {\n        model: model,\n        prompt: `${systemPrompt}\\n\\n${userPrompt}`,\n        stream: false,\n        format: 'json'\n      }\n    }\n  };\n});"
      },
      "id": "9f82950f-7533-4cbd-8e1e-8e441c1cb2a5",
      "name": "Build AI Prompt",
@@ -229,7 +227,7 @@ const model = isFallback ? config.OLLAMA_MODEL_FALLBACK : config.OLLAMA_MODEL_PR
      "typeVersion": 2,
      "position": [
        6032,
-        3696
+        3896
      ],
      "notes": "สร้าง Prompt โดยใช้ Categories จาก System"
    },
@@ -256,9 +254,7 @@ const model = isFallback ? config.OLLAMA_MODEL_FALLBACK : config.OLLAMA_MODEL_PR
    },
    {
      "parameters": {
-        "jsCode": "const ollamaItems = $input.all();\nconst originalItems = $('Build AI Prompt').all();\nconst results = [];\n\nfor (let i = 0; i < ollamaItems.length; i++) {\n  const ollamaItem = ollamaItems[i];\n  const originalItem = originalItems[i];\n\n  if (!originalItem) continue; // safety check\n\n  // Reconstruct original JSON\n  const baseJson = originalItem.json;\n\n  try {\n    let raw = ollamaItem.json.response || '';\n\n    // Clean markdown and whitespace\n    raw = raw.replace(/\\`\\`\\`json/gi, '').replace(/\\`\\`\\`/g, '').trim();\n    if (!raw) throw new Error('Empty response from AI');\n\n    const result = JSON.parse(raw);\n\n    // Metadata mapping & normalization\n    const meta = result.metadata || {};\n    const metadata = {\n      ref_no: String(meta.ref_no || '').trim() || null,\n      response_to: String(meta.response_to || '').trim() || null,\n      from_org: String(meta.from_org || '').trim() || null,\n      to_org: String(meta.to_org || '').trim() || null,\n      body: String(result.body || meta.body || '').trim() || null\n    };\n\n    // Tag Validation\n    let tags = Array.isArray(result.suggested_tags) ? result.suggested_tags : [];\n    tags = [...new Set(tags.map(t => String(t).trim()).filter(t => t.length > 0))];\n\n    // Enum Validation for Category\n    const systemCategories = baseJson.system_categories || [];\n    let finalCategory = result.suggested_category;\n    if (!systemCategories.includes(finalCategory)) {\n      finalCategory = String(baseJson.document_number || '').includes('-RFA-') ? 'RFA' : 'Correspondence';\n    }\n\n    const d_issued = baseJson.issued_date || null;\n    const d_received = baseJson.received_date || d_issued;\n    results.push({\n      json: {\n        ...baseJson,\n        ai_result: { ...result, suggested_category: finalCategory, suggested_tags: tags, body: result.body || meta.body || null },\n        metadata: metadata,\n        issued_date: d_issued,\n        received_date: d_received,\n        parse_error: null\n      }\n    });\n  } catch (err) {\n    results.push({\n      json: {\n        ...baseJson,\n        ai_result: null,\n        parse_error: err.message,\n        raw_ai_response: ollamaItem.json.response,\n        error_type: 'AI_PARSE_ERROR'\n      }\n    });\n  }\n}\n\nreturn results;"
-      response_to: String(meta.response_to || '').trim() || null,\n      from_org: String(meta.from_org || '').trim() || null,\n      to_org: String(meta.to_org || '').trim() || null,\n      document_date: String(meta.document_date || '').trim() || null,\n      body: String(result.body || meta.body || '').trim() || null\n    };\n\n    // Tag Validation\n    let tags = Array.isArray(result.suggested_tags) ? result.suggested_tags : [];\n    tags = [...new Set(tags.map(t => String(t).trim()).filter(t => t.length > 0))];\n\n    // Enum Validation for Category\n    const systemCategories = baseJson.system_categories || [];\n    let finalCategory = result.suggested_category;\n    if (!systemCategories.includes(finalCategory)) {\n      finalCategory = String(baseJson.document_number || '').includes('-RFA-') ? 'RFA' : 'Correspondence';\n    }\n\n    const d_issued = baseJson.issued_date || null;\n    const d_received = baseJson.received_date || d_issued;\n    results.push({\n      json: {\n        ...baseJson,\n        ai_result: { ...result, suggested_category: finalCategory, suggested_tags: tags, body: result.body || meta.body || null },\n        metadata: metadata,\n        issued_date: d_issued,\n        received_date: d_received,\n        parse_error: null\n      }\n    });\n  } catch (err) {\n    results.push({\n      json: {\n        ...baseJson,\n        ai_result: null,\n        parse_error: err.message,\n        raw_ai_response: ollamaItem.json.response,\n        error_type: 'AI_PARSE_ERROR'\n      }\n    });\n  }\n}\n\nreturn results;"
-      response_to: String(meta.response_to || '').trim() || null,\n      from_org: String(meta.from_org || '').trim() || null,\n      to_org: String(meta.to_org || '').trim() || null,\n      document_date: String(meta.document_date || '').trim() || null,\n      body: String(result.body || meta.body || '').trim() || null\n    };\n\n    // Tag Validation\n    let tags = Array.isArray(result.suggested_tags) ? result.suggested_tags : [];\n    tags = [...new Set(tags.map(t => String(t).trim()).filter(t => t.length > 0))];\n\n    // Enum Validation for Category\n    const systemCategories = baseJson.system_categories || [];\n    let finalCategory = result.suggested_category;\n    if (!systemCategories.includes(finalCategory)) {\n      finalCategory = String(baseJson.document_number || '').includes('-RFA-') ? 'RFA' : 'Correspondence';\n    }\n\n    const d_issued = baseJson.issued_date || metadata.document_date || null;\n    const d_received = baseJson.received_date || d_issued;\n    results.push({\n      json: {\n        ...baseJson,\n        ai_result: { ...result, suggested_category: finalCategory, suggested_tags: tags, body: result.body || meta.body || null },\n        metadata: metadata,\n        issued_date: d_issued,\n        received_date: d_received,\n        parse_error: null\n      }\n    });\n  } catch (err) {\n    results.push({\n      json: {\n        ...baseJson,\n        ai_result: null,\n        parse_error: err.message,\n        raw_ai_response: ollamaItem.json.response,\n        error_type: 'AI_PARSE_ERROR'\n      }\n    });\n  }\n}\n\nreturn results;"
+        "jsCode": "const ollamaItems = $input.all();\nconst originalItems = $('Build AI Prompt').all();\nconst results = [];\n\nfor (let i = 0; i < ollamaItems.length; i++) {\n  const ollamaItem = ollamaItems[i];\n  const originalItem = originalItems[i];\n  if (!originalItem) continue;\n  const baseJson = originalItem.json;\n\n  try {\n    let raw = ollamaItem.json.response || '';\n    raw = raw.replace(/\\x60\\x60\\x60json/gi, '').replace(/\\x60\\x60\\x60/g, '').trim();\n    if (!raw) throw new Error('Empty response from AI');\n\n    const result = JSON.parse(raw);\n\n    // Map AI result to database fields\n    let tags = Array.isArray(result.tags) ? result.tags : [];\n\n    // Enum Validation for Category\n    const systemCategories = baseJson.system_categories || [];\n    let finalCategory = result.category;\n    if (!systemCategories.includes(finalCategory)) {\n      finalCategory = String(baseJson.document_number || '').includes('-RFA-') ? 'RFA' : 'Correspondence';\n    }\n\n    results.push({\n      json: {\n        ...baseJson,\n        ai_result: {\n          suggested_category: finalCategory,\n          confidence: result.confidence || 0.8,\n          project_id: result.project_id,\n          discipline_id: result.discipline_id || 64,\n          sender_id: result.sender_id,\n          receiver_id: result.receiver_id,\n          correspondence_type_id: result.correspondence_type_id || null,\n          subject: result.subject || baseJson.title,\n          issued_date: result.issued_date || baseJson.issued_date,\n          received_date: result.received_date || baseJson.received_date || result.issued_date || baseJson.issued_date,\n          remark: result.status,\n          body: result.summary,\n          tags: tags,\n          key_points: result.key_points || []\n        },\n        parse_error: null\n      }\n    });\n  } catch (err) {\n    results.push({\n      json: {\n        ...baseJson,\n        ai_result: null,\n        parse_error: err.message,\n        raw_ai_response: ollamaItem.json.response,\n        error_type: 'AI_PARSE_ERROR'\n      }\n    });\n  }\n}\nreturn results;"
      },
      "id": "281dc950-a3b6-4412-a0b4-76663b8c37ea",
      "name": "Parse & Validate AI Response",
@@ -325,7 +321,7 @@ const model = isFallback ? config.OLLAMA_MODEL_FALLBACK : config.OLLAMA_MODEL_PR
        },
        "sendBody": true,
        "specifyBody": "json",
-        "jsonBody": "={\n  \"document_number\": \"{{$json.document_number}}\",\n  \"title\": \"{{$json.ai_result.suggested_title || $json.title}}\",\n  \"category\": \"{{$json.ai_result.suggested_category}}\",\n  \"source_file_path\": \"{{$json.file_path}}\",\n  \"ai_confidence\": {{$json.ai_result.confidence}},\n  \"ai_issues\": {{JSON.stringify($json.ai_result.detected_issues || [])}},\n  \"migrated_by\": \"SYSTEM_IMPORT\",\n  \"batch_id\": \"{{$('Set Configuration').first().json.config.BATCH_ID}}\",\n  \"project_id\": {{$('Set Configuration').first().json.config.PROJECT_ID}},\n  \"issued_date\": \"{{$json.issued_date}}\",\n  \"received_date\": \"{{$json.received_date}}\",\n  \"body\": {{JSON.stringify($json.ai_result.body || \"\")}},\n  \"details\": {\n    \"legacy_number\": \"{{$json.legacy_number}}\",\n    \"ref_no\": \"{{$json.metadata.ref_no}}\",\n    \"response_to\": \"{{$json.metadata.response_to}}\",\n    \"from_org\": \"{{$json.metadata.from_org}}\",\n    \"to_org\": \"{{$json.metadata.to_org}}\"\n  }\n}",
+        "jsonBody": "={\n  \"document_number\": \"{{$json.document_number}}\",\n  \"title\": \"{{$json.ai_result.subject}}\",\n  \"category\": \"{{$json.ai_result.suggested_category}}\",\n  \"source_file_path\": \"{{$json.file_path}}\",\n  \"ai_confidence\": {{$json.ai_result.confidence}},\n  \"migrated_by\": \"SYSTEM_IMPORT\",\n  \"batch_id\": \"{{$('Set Configuration').first().json.config.BATCH_ID}}\",\n  \"project_id\": {{$json.ai_result.project_id || $('Set Configuration').first().json.config.PROJECT_ID}},\n  \"discipline_id\": {{$json.ai_result.discipline_id}},\n  \"sender_id\": {{$json.ai_result.sender_id || null}},\n  \"receiver_id\": {{$json.ai_result.receiver_id || null}},\n  \"correspondence_type_id\": {{$json.ai_result.correspondence_type_id || null}},\n  \"issued_date\": \"{{$json.ai_result.issued_date}}\",\n  \"received_date\": \"{{$json.ai_result.received_date}}\",\n  \"body\": {{JSON.stringify($json.ai_result.body || \"\")}},\n  \"details\": {\n    \"legacy_number\": \"{{$json.legacy_number}}\",\n    \"remark\": \"{{$json.ai_result.remark}}\",\n    \"key_points\": {{JSON.stringify($json.ai_result.key_points || [])}},\n    \"tags\": {{JSON.stringify($json.ai_result.tags || [])}}\n  }\n}",
        "options": {
          "timeout": 30000
        }
@@ -605,13 +601,26 @@ const model = isFallback ? config.OLLAMA_MODEL_FALLBACK : config.OLLAMA_MODEL_PR
            {
              "name": "X-Tika-OCRLanguage",
              "value": "tha+eng"
+            },
+            {
+              "name": "X-Tika-PDFOcrStrategy",
+              "value": "ocr_only"
            }
          ]
        },
        "sendBody": true,
        "contentType": "binaryData",
        "inputDataFieldName": "data",
-        "options": {}
+        "options": {},
+        "sendQuery": true,
+        "queryParameters": {
+          "parameters": [
+            {
+              "name": "maxPages",
+              "value": "2"
+            }
+          ]
+        }
      },
      "id": "node-extract-pdf-1",
      "name": "Extract PDF Text",
@@ -622,6 +631,29 @@ const model = isFallback ? config.OLLAMA_MODEL_FALLBACK : config.OLLAMA_MODEL_PR
        3064
      ],
      "onError": "continueErrorOutput"
+    },
+    {
+      "parameters": {
+        "operation": "executeQuery",
+        "query": "SELECT 'projects' as type, id, project_code as text1, project_name as text2 FROM projects\nUNION ALL\nSELECT 'disciplines' as type, id, code_name_th as text1, code_name_en as text2 FROM disciplines\nUNION ALL\nSELECT 'organizations' as type, id, organization_name as text1, organization_code as text2 FROM organizations\nUNION ALL\nSELECT 'tags' as type, id, tag_name as text1, description as text2 FROM tags\nUNION ALL\nSELECT 'correspondence_types' as type, id, type_code as text1, type_name as text2 FROM correspondence_types",
+        "options": {}
+      },
+      "id": "fetch-db-context-node-id",
+      "name": "Fetch DB Context",
+      "type": "n8n-nodes-base.mySql",
+      "typeVersion": 2.4,
+      "position": [
+        6032,
+        3696
+      ],
+      "credentials": {
+        "mySql": {
+          "id": "CHHfbKhMacNo03V4",
+          "name": "MySQL account"
+        }
+      },
+      "alwaysOutputData": true,
+      "notes": "ดึงข้อมูลจาก Database ส่งให้ AI"
    }
  ],
  "pinData": {},
@@ -751,7 +783,7 @@ const model = isFallback ? config.OLLAMA_MODEL_FALLBACK : config.OLLAMA_MODEL_PR
      "main": [
        [
          {
-            "node": "Build AI Prompt",
+            "node": "Fetch DB Context",
            "type": "main",
            "index": 0
          }
@@ -943,6 +975,17 @@ const model = isFallback ? config.OLLAMA_MODEL_FALLBACK : config.OLLAMA_MODEL_PR
          }
        ]
      ]
+    },
+    "Fetch DB Context": {
+      "main": [
+        [
+          {
+            "node": "Build AI Prompt",
+            "type": "main",
+            "index": 0
+          }
+        ]
+      ]
    }
  },
  "active": false,