{ "meta": { "templateCredsSetupCompleted": true }, "name": "Extract structured data from Gmail attachments to Google Sheets using GPT-4o Vision", "nodes": [ { "id": "762adf7a-01c4-44f8-800b-b084812cf0c3", "name": "Workflow Description", "type": "n8n-nodes-base.stickyNote", "position": [ 2384, 1216 ], "parameters": { "width": 380, "height": 776, "content": "## Email Attachment Processor Template\n\nAutomatically extract structured information from emails using AI.\n\n### What this workflow does:\n1. **Monitors Gmail** for emails from specified domains\n2. **Classifies emails** into custom categories\n3. **Processes attachments** (Excel, PPTX, PDF, images) using GPT Vision\n4. **Extracts structured data** based on your schema\n5. **Saves to Google Sheets** with classification\n6. **Labels processed emails** in Gmail\n7. **Sends to custom API** (optional)\n8. **Notifies via Slack** (optional)\n\n### Requirements:\n- Gmail OAuth2 credentials\n- OpenAI API key (for GPT-4 Vision)\n- Google Sheets OAuth2 credentials\n- AWS S3 bucket (for image processing)\n- ConvertAPI account (for PPTX/PDF conversion)\n- Slack OAuth2 (optional)\n\n### Setup:\n1. Configure all credentials\n2. Update Google Sheets document ID in Variables\n3. Update S3 bucket name in Variables\n4. Customize domain filter in 'Domain Filter' node\n5. Customize classification keywords as needed" }, "typeVersion": 1 }, { "id": "1a385d9d-4e9a-4a56-beb3-4d3e7dd9649b", "name": "Email Classification Info", "type": "n8n-nodes-base.stickyNote", "position": [ 3040, 1184 ], "parameters": { "color": 7, "width": 332, "height": 336, "content": "### Email Type Detection\n\n**Customize keywords for your use case:**\n\n**Category A (e.g., Cancellation):**\n- Add your keywords here\n\n**Category B (e.g., Updates):**\n- Add your keywords here\n\n**Category C (e.g., New Items):**\n- Default category\n\nCustomize keywords in the 'Domain Filter' code node." }, "typeVersion": 1 }, { "id": "579e95c7-8c34-4446-a229-061652df9808", "name": "Gmail Labels Info", "type": "n8n-nodes-base.stickyNote", "position": [ 6560, 1264 ], "parameters": { "color": 3, "width": 280, "height": 216, "content": "### Gmail Labels\n\nProcessed emails are labeled:\n- Processed_CategoryA\n- Processed_CategoryB\n- Processed_CategoryC\n\nLabels are created automatically if they don't exist.\n\n**Customize label names in the code nodes.**" }, "typeVersion": 1 }, { "id": "e852a330-e3ae-4b4f-b887-eec29afafa25", "name": "File Processing Info", "type": "n8n-nodes-base.stickyNote", "position": [ 3696, 1152 ], "parameters": { "color": 7, "width": 300, "height": 344, "content": "### File Processing Routes\n\n**Excel files (Category C):**\n\u2192 Direct data extraction\n\n**Excel files (Category A/B):**\n\u2192 GPT Vision analysis\n\n**PPTX/PDF files:**\n\u2192 Convert to images \u2192 GPT Vision\n\n**Image attachments:**\n\u2192 Direct GPT Vision analysis\n\n**Text-only emails:**\n\u2192 GPT text analysis" }, "typeVersion": 1 }, { "id": "eb599c2d-491a-4824-85b9-a503a4b65c47", "name": "ConvertAPI Info", "type": "n8n-nodes-base.stickyNote", "position": [ 4176, 1760 ], "parameters": { "color": 3, "width": 300, "height": 284, "content": "### ConvertAPI Setup\n\nUsed for converting PPTX/PDF to images for GPT Vision analysis.\n\nhttps://www.convertapi.com\n\n*This service is paid but offers free initial conversions.*\n\nReplace the HTTP Header Auth credential with your ConvertAPI secret." }, "typeVersion": 1 }, { "id": "5b69b7ca-b4e1-42d3-ab8b-cfb915f745a1", "name": "Google Sheets Info", "type": "n8n-nodes-base.stickyNote", "position": [ 6128, 1136 ], "parameters": { "color": 3, "width": 300, "height": 368, "content": "### Google Sheets Output\n\nExtracted data is saved with columns:\n- email_type (Category)\n- field_1 (Date)\n- field_2 (Time)\n- field_3 (Name)\n- field_4 (Amount)\n- And more...\n\n**Customize the output schema in the 'Parse All Results' node.**\n\n**Update the Document ID** in Variables or directly in the Google Sheets node." }, "typeVersion": 1 }, { "id": "9a32ec51-62fb-49c8-9941-60bf6a1adc13", "name": "AWS S3 Info", "type": "n8n-nodes-base.stickyNote", "position": [ 4208, 1024 ], "parameters": { "color": 7, "width": 300, "height": 288, "content": "### AWS S3 Setup\n\nImages are temporarily uploaded to S3 for GPT Vision processing.\n\n**Required:**\n1. Create an S3 bucket\n2. Configure AWS credentials in n8n\n3. Update bucket name in Variables\n\nImages are used for GPT Vision API calls and can be cleaned up periodically." }, "typeVersion": 1 }, { "id": "61bd3fd0-9fdd-4cf0-9edf-28721a2358da", "name": "Schedule Trigger", "type": "n8n-nodes-base.scheduleTrigger", "position": [ 2832, 1536 ], "parameters": { "rule": { "interval": [ { "field": "minutes", "minutesInterval": 30 } ] } }, "typeVersion": 1.2 }, { "id": "11ce6b59-1436-4ef5-a0ef-71f9aa98c763", "name": "Gmail - Get Emails", "type": "n8n-nodes-base.gmail", "position": [ 3040, 1536 ], "parameters": { "limit": 1, "filters": { "q": "-label:Processed_CategoryA -label:Processed_CategoryB -label:Processed_CategoryC", "receivedAfter": "={{ $now.minus({ days: 60 }).toISO() }}" }, "operation": "getAll" }, "typeVersion": 2.1 }, { "id": "a6dff01e-96d5-4c57-8def-5bd8c1216c26", "name": "Domain Filter", "type": "n8n-nodes-base.code", "position": [ 3264, 1536 ], "parameters": { "mode": "runOnceForEachItem", "jsCode": "// ============================================================\n// DOMAIN FILTER & EMAIL CLASSIFICATION\n// ============================================================\n// Customize this node for your specific use case\n// ============================================================\n\nconst json = $input.item.json;\nconst subject = json.subject || json.Subject || '';\nconst snippet = json.snippet || '';\nconst fromRaw = json.from || json.From || '';\nconst fromText = typeof fromRaw === 'object' ? (fromRaw.text || JSON.stringify(fromRaw)) : String(fromRaw);\nconst text = (subject + ' ' + snippet + ' ' + fromText).toLowerCase();\n\n// ============================================================\n// STEP 1: Configure your allowed domains\n// ============================================================\n// Add the domains you want to monitor (without @ symbol)\n// Example: ['company.com', 'partner.co.jp', 'supplier.net']\nconst allowedDomains = [\n // Add your domains here\n // 'example.com',\n // 'example.co.jp',\n];\n\n// Check if email is from an allowed domain\nconst fromLower = fromText.toLowerCase();\nconst isFromAllowedDomain = allowedDomains.length === 0 || allowedDomains.some(d => fromLower.includes(d));\n\n// ============================================================\n// STEP 2: Configure classification keywords\n// ============================================================\n\n// Category A keywords (e.g., Cancellations, Deletions)\nconst categoryAKeywords = [\n // Add your Category A keywords here\n // 'cancel', 'cancelled', 'delete', 'remove'\n];\n\n// Category B keywords (e.g., Updates, Changes)\nconst categoryBKeywords = [\n // Add your Category B keywords here\n // 'update', 'change', 'modify', 'reschedule'\n];\n\n// Category C keywords (e.g., New items - this is the default)\nconst categoryCKeywords = [\n // Add your Category C keywords here (optional)\n // 'new', 'offer', 'proposal', 'available'\n];\n\n// ============================================================\n// STEP 3: Configure identifier codes (optional)\n// ============================================================\n// If your emails contain specific codes like [ABC] or \u3010XYZ\u3011\nconst identifierCodes = [\n // Add your identifier codes here (lowercase)\n // 'abc', 'xyz', 'code1'\n];\n\n// Normalize full-width characters to ASCII\nconst normalizeAscii = value => String(value || '').replace(/[\uff21-\uff3a\uff10-\uff19]/g, ch => String.fromCharCode(ch.charCodeAt(0) - 0xFEE0));\nconst textUpper = normalizeAscii(subject + ' ' + snippet + ' ' + fromText).toUpperCase();\n\n// Check for identifier codes in brackets\nconst hasIdentifierCode = identifierCodes.some(c => \n textUpper.includes('\u3010' + c.toUpperCase() + '\u3011') || \n textUpper.includes('[' + c.toUpperCase() + ']')\n);\n\n// ============================================================\n// Classification Logic\n// ============================================================\n\n// Skip if not from allowed domain and no identifier match\nif (!isFromAllowedDomain && !hasIdentifierCode) {\n return { json: { _skip: true } };\n}\n\n// Check if email matches any category keywords\nconst isCategoryA = categoryAKeywords.some(k => text.includes(k.toLowerCase()));\nconst isCategoryB = categoryBKeywords.some(k => text.includes(k.toLowerCase()));\nconst hasCategoryC = categoryCKeywords.length === 0 || categoryCKeywords.some(k => text.includes(k.toLowerCase()));\n\n// If no keywords configured and domain doesn't match, skip\nif (categoryAKeywords.length === 0 && categoryBKeywords.length === 0 && categoryCKeywords.length === 0) {\n // Process all emails from allowed domains when no keywords configured\n if (!isFromAllowedDomain) {\n return { json: { _skip: true } };\n }\n}\n\n// Determine email type/category\nlet emailType = 'CategoryC'; // Default\nif (isCategoryA) emailType = 'CategoryA';\nelse if (isCategoryB) emailType = 'CategoryB';\n\nreturn { json: { ...json, _skip: false, _email_type: emailType } };" }, "typeVersion": 2 }, { "id": "e303d6d7-5c68-4fc6-97d4-4e5e6c171472", "name": "Filter Valid Emails", "type": "n8n-nodes-base.filter", "position": [ 3488, 1536 ], "parameters": { "options": {}, "conditions": { "options": { "version": 1, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "operator": { "type": "boolean", "operation": "notEquals" }, "leftValue": "={{ $json._skip }}", "rightValue": true } ] } }, "typeVersion": 2 }, { "id": "6a426693-2bfc-4d4a-a17a-9f89973f9dc9", "name": "Gmail - Get Attachments", "type": "n8n-nodes-base.gmail", "position": [ 3712, 1536 ], "parameters": { "simple": false, "options": { "downloadAttachments": true, "dataPropertyAttachmentsPrefixName": "attachment_" }, "messageId": "={{ $json.id }}", "operation": "get" }, "typeVersion": 2.1 }, { "id": "7ce1026b-c28f-4e63-95fc-8dcb61814c3f", "name": "Detect File Types", "type": "n8n-nodes-base.code", "position": [ 3920, 1536 ], "parameters": { "mode": "runOnceForEachItem", "jsCode": "// ============================================================\n// FILE TYPE DETECTION\n// ============================================================\n// Identifies attachment types and preserves metadata\n// ============================================================\n\nconst item = $input.item;\nconst binary = item.binary || {};\nconst json = item.json;\n\nconst subject = json.Subject || json.subject || '';\nconst fromData = json.From || json.from || '';\n\nlet emailType = json._email_type || '';\nif (!emailType) {\n try {\n const currentId = json.id;\n const filteredItems = $('Filter Valid Emails').all();\n for (const f of filteredItems) {\n if (f.json.id === currentId && f.json._email_type) {\n emailType = f.json._email_type;\n break;\n }\n }\n } catch(e) {}\n}\nemailType = emailType || 'CategoryC';\n\nlet fileInfo = { excel: null, pptx: null, pdf: null, image: null };\nlet attachments = [];\n\nfor (const key of Object.keys(binary)) {\n const bin = binary[key];\n if (!bin || !bin.fileName) continue;\n const fn = bin.fileName.toLowerCase();\n const mime = (bin.mimeType || '').toLowerCase();\n attachments.push({ filename: bin.fileName, mimeType: bin.mimeType, key: key });\n \n if (fn.endsWith('.xlsx') || fn.endsWith('.xls')) fileInfo.excel = { key, fileName: bin.fileName };\n else if (fn.endsWith('.pptx') || fn.endsWith('.ppt')) fileInfo.pptx = { key, fileName: bin.fileName };\n else if (fn.endsWith('.pdf') || mime.includes('pdf')) fileInfo.pdf = { key, fileName: bin.fileName };\n else if (fn.endsWith('.png') || fn.endsWith('.jpg') || fn.endsWith('.jpeg') || mime.startsWith('image/')) fileInfo.image = { key, fileName: bin.fileName, mimeType: bin.mimeType || 'image/png' };\n}\n\n// Variable format types need GPT Vision even for Excel\nconst isVariableFormat = emailType === 'CategoryA' || emailType === 'CategoryB';\nconst hasExcelDirect = fileInfo.excel !== null && !isVariableFormat;\nconst hasFileConvert = fileInfo.pptx !== null || fileInfo.pdf !== null || (fileInfo.excel !== null && isVariableFormat);\n\nlet convType = null, convKey = null;\nif (fileInfo.pptx) { convType = 'pptx'; convKey = fileInfo.pptx.key; }\nelse if (fileInfo.pdf) { convType = 'pdf'; convKey = fileInfo.pdf.key; }\nelse if (fileInfo.excel && isVariableFormat) { convType = 'xlsx'; convKey = fileInfo.excel.key; }\n\nconst output = {\n ...json,\n id: json.id,\n Subject: subject,\n subject: subject,\n From: fromData,\n from: fromData,\n attachments: attachments,\n has_excel: hasExcelDirect,\n has_pptx_pdf: hasFileConvert,\n has_image: fileInfo.image !== null,\n excel_binary_key: fileInfo.excel ? fileInfo.excel.key : null,\n conversion_type: convType,\n conversion_binary_key: convKey,\n image_binary_key: fileInfo.image ? fileInfo.image.key : null,\n _email_type: emailType\n};\n\nreturn {\n json: output,\n binary: item.binary\n};" }, "typeVersion": 2 }, { "id": "64aef15e-2374-4845-acc6-0dfe45d35c8f", "name": "IF - Has Excel?", "type": "n8n-nodes-base.if", "position": [ 4144, 1536 ], "parameters": { "options": {}, "conditions": { "options": { "version": 1, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "operator": { "type": "boolean", "operation": "equals" }, "leftValue": "={{ $json.has_excel }}", "rightValue": true } ] } }, "typeVersion": 2 }, { "id": "7c133bed-216b-49fe-a6d9-8e7292391f87", "name": "Extract from Excel", "type": "n8n-nodes-base.extractFromFile", "position": [ 4368, 1344 ], "parameters": { "options": {}, "operation": "xlsx", "binaryPropertyName": "={{ $json.excel_binary_key || 'attachment_0' }}" }, "typeVersion": 1 }, { "id": "ae047edd-7921-47cd-84c1-98f9fd5b3ae2", "name": "Parse Excel Data", "type": "n8n-nodes-base.code", "position": [ 5008, 1216 ], "parameters": { "jsCode": "// ============================================================\n// EXCEL DATA EXTRACTION\n// ============================================================\n// Customize this node to parse your specific Excel format\n// ============================================================\n\n// Map codes to full names (customize for your use case)\nconst codeToNameMap = {\n // 'CODE1': 'Full Name 1',\n // 'CODE2': 'Full Name 2',\n};\n\nfunction excelTimeToHHMM(fraction) {\n if (!fraction && fraction !== 0) return '';\n const totalMinutes = Math.round(fraction * 24 * 60);\n const hours = Math.floor(totalMinutes / 60);\n const minutes = totalMinutes % 60;\n return String(hours).padStart(2, '0') + ':' + String(minutes).padStart(2, '0');\n}\n\nlet excelSourceItems = [];\ntry { excelSourceItems = $('IF - Has Excel?').all(); } catch(e) {}\n\nconst allRows = $input.all().map(i => i.json);\nconst results = [];\n\nlet hintYear = new Date().getFullYear();\nlet hintMonth = new Date().getMonth() + 1;\nlet currentName = '';\nlet currentType = 'Regular';\n\nconst dataBySource = {};\n\nfor (const row of allRows) {\n const allText = JSON.stringify(row);\n const firstKey = Object.keys(row)[0] || '';\n const firstVal = row[firstKey] || '';\n \n // Extract year/month from text (customize pattern as needed)\n const ymMatch = allText.match(/(20\\d{2})[-\\/\u5e74]\\s*(\\d{1,2})/);\n if (ymMatch) {\n hintYear = parseInt(ymMatch[1]);\n hintMonth = parseInt(ymMatch[2]);\n }\n \n // Extract source name from brackets (customize pattern as needed)\n const nameMatch = allText.match(/\u3010([A-Z]+)[^\u3011]*\u3011/i);\n if (nameMatch) {\n const code = nameMatch[1].toUpperCase();\n currentName = codeToNameMap[code] || code;\n }\n \n // Check for codes in the text\n for (const code of Object.keys(codeToNameMap)) {\n if (allText.toUpperCase().includes(code)) {\n currentName = codeToNameMap[code] || code;\n break;\n }\n }\n \n // Skip header rows (customize as needed)\n if (allText.includes('Header1') && allText.includes('Header2')) continue;\n\n // ============================================================\n // CUSTOMIZE: Add your Excel parsing logic here\n // ============================================================\n \n // Example: Parse rows with specific structure\n if (row.__EMPTY_1 !== undefined && typeof row.__EMPTY_1 === 'number') {\n const startTime = typeof row.__EMPTY_2 === 'number'\n ? excelTimeToHHMM(row.__EMPTY_2)\n : excelTimeToHHMM(row.__EMPTY_1);\n const endTime = typeof row.__EMPTY_4 === 'number'\n ? excelTimeToHHMM(row.__EMPTY_4)\n : excelTimeToHHMM(row.__EMPTY_2);\n\n if (startTime) {\n const sourceName = currentName || 'Unknown';\n const amount = typeof row.__EMPTY_7 === 'number' ? row.__EMPTY_7 : (row.__EMPTY_5 || 0);\n const dataItem = {\n date: hintYear + '-' + String(hintMonth).padStart(2,'0') + '-' + String(row.__EMPTY_1).padStart(2,'0'),\n day_of_week: firstVal,\n start_time: startTime,\n end_time: endTime,\n amount: amount,\n type: currentType,\n name: '',\n source_name: sourceName\n };\n if (!dataBySource[sourceName]) dataBySource[sourceName] = [];\n dataBySource[sourceName].push(dataItem);\n }\n }\n}\n\n// Build results\nfor (const srcItem of excelSourceItems) {\n const meta = srcItem.json;\n const emailId = meta.id || '';\n const emailType = meta._email_type || 'CategoryC'; \n \n let defaultSource = '';\n const subjectText = (meta.Subject || meta.subject || '');\n const sourceMatch = subjectText.match(/\u3010([A-Za-z]+)\u3011/i);\n if (sourceMatch) defaultSource = codeToNameMap[sourceMatch[1].toUpperCase()] || sourceMatch[1].toUpperCase();\n\n let dataForEmail = dataBySource[defaultSource] || [];\n\n if (!defaultSource && dataForEmail.length == 0) {\n const allSources = Object.keys(dataBySource);\n if (allSources.length === 1) {\n dataForEmail = dataBySource[allSources[0]] || [];\n } else if (allSources.length > 1) {\n dataForEmail = allSources.flatMap(name => dataBySource[name] || []);\n }\n }\n\n results.push({\n json: {\n _meta_subject: meta.Subject || meta.subject,\n _meta_from: meta.from || meta.From,\n _meta_id: emailId,\n email_id: emailId, \n _meta_source_name: defaultSource,\n _email_type: emailType,\n choices: [{ message: { content: JSON.stringify(dataForEmail.length ? dataForEmail : [{notes: '\u26a0\ufe0fNo data found'}]) } }]\n }\n });\n}\n\nreturn results.length ? results : [{ json: { _no_data: true } }];" }, "typeVersion": 2 }, { "id": "edbfd491-dbab-411d-a3d2-c674ee34020d", "name": "IF - Has PPTX/PDF?", "type": "n8n-nodes-base.if", "position": [ 4368, 1536 ], "parameters": { "options": {}, "conditions": { "options": { "version": 1, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "operator": { "type": "boolean", "operation": "equals" }, "leftValue": "={{ $json.has_pptx_pdf }}", "rightValue": true } ] } }, "typeVersion": 2 }, { "id": "49b24d00-f787-4b0d-8162-d41f62f6ca0d", "name": "ConvertAPI to PNG", "type": "n8n-nodes-base.httpRequest", "position": [ 4592, 1488 ], "parameters": { "url": "=https://v2.convertapi.com/convert/{{ $json.conversion_type }}/to/png", "method": "POST", "options": {}, "sendBody": true, "contentType": "multipart-form-data", "authentication": "genericCredentialType", "bodyParameters": { "parameters": [ { "name": "File", "parameterType": "formBinaryData", "inputDataFieldName": "={{ $json.conversion_binary_key }}" } ] }, "genericAuthType": "httpHeaderAuth" }, "typeVersion": 4.2 }, { "id": "bdd00767-8b17-4d7f-bc38-588ba5a923b9", "name": "Prepare for S3 Upload", "type": "n8n-nodes-base.code", "position": [ 4800, 1488 ], "parameters": { "jsCode": "// ============================================================\n// PREPARE IMAGES FOR S3 UPLOAD\n// ============================================================\n\nconst allItems = $input.all();\nconst results = [];\n\nlet pptxPdfItems = [];\ntry {\n const allFileItems = $('Detect File Types').all();\n pptxPdfItems = allFileItems.filter(item => item.json.has_pptx_pdf === true);\n} catch(e) {}\n\nlet filterItems = [];\ntry { filterItems = $('Filter Valid Emails').all(); } catch(e) {}\n\nfor (let idx = 0; idx < allItems.length; idx++) {\n const item = allItems[idx];\n const response = item.json;\n \n let emailId='', subject='', fromData='', attachments=[], emailType='';\n \n if (pptxPdfItems[idx]) {\n const meta = pptxPdfItems[idx].json;\n emailId = meta.id || '';\n subject = meta.Subject || '';\n fromData = meta.From || '';\n attachments = meta.attachments || [];\n emailType = meta._email_type || '';\n }\n \n if (!emailType && emailId) {\n for (const f of filterItems) {\n if (f.json.id === emailId && f.json._email_type) {\n emailType = f.json._email_type;\n break;\n }\n }\n }\n emailType = emailType || 'CategoryC';\n \n if (response.Files && response.Files.length > 0) {\n const maxPages = Math.min(response.Files.length, 15);\n const ts = Date.now();\n \n for (let i = 0; i < maxPages; i++) {\n const fileData = response.Files[i];\n if (fileData && fileData.FileData) {\n const fileName = 'convert-' + emailId + '-page-' + (i+1) + '-' + ts + '.png';\n results.push({\n json: {\n _meta_subject: subject,\n _meta_from: fromData,\n _meta_id: emailId,\n _meta_attachments: attachments,\n _meta_process_type: 'pptx_pdf',\n _email_type: emailType,\n _page_index: i,\n _page_filename: fileName,\n _total_pages: maxPages\n },\n binary: {\n data: {\n data: fileData.FileData,\n mimeType: 'image/png',\n fileName: fileName,\n fileExtension: 'png'\n }\n }\n });\n }\n }\n }\n}\n\nif (results.length === 0) return [{ json: { _skip: true } }];\nreturn results;" }, "typeVersion": 2 }, { "id": "9a844a79-cb50-439f-abcf-ea9ef20d3510", "name": "S3 Upload (PPTX)", "type": "n8n-nodes-base.awsS3", "position": [ 5024, 1488 ], "parameters": { "fileName": "={{ $json._page_filename }}", "operation": "upload", "bucketName": "={{ $vars.S3_BUCKET_NAME || 'your-bucket-name' }}", "additionalFields": {} }, "typeVersion": 2 }, { "id": "22f24843-db51-4bc5-aac7-bf9cf8034222", "name": "Collect S3 URLs", "type": "n8n-nodes-base.code", "position": [ 5248, 1488 ], "parameters": { "jsCode": "// ============================================================\n// COLLECT S3 URLs FOR GPT VISION\n// ============================================================\n\nlet prepItems = [];\ntry { prepItems = $('Prepare for S3 Upload').all(); } catch(e) {}\n\nconst bucket = $vars.S3_BUCKET_NAME || 'your-bucket-name';\nconst region = $vars.AWS_REGION || 'us-east-1';\nconst emailGroups = new Map();\n\nfor (const prep of prepItems) {\n const p = prep.json;\n const emailId = p._meta_id || '';\n const filename = p._page_filename || '';\n if (!emailId || !filename) continue;\n \n const s3Url = 'https://' + bucket + '.s3.' + region + '.amazonaws.com/' + filename;\n \n if (!emailGroups.has(emailId)) {\n emailGroups.set(emailId, {\n subject: p._meta_subject || '',\n from: p._meta_from || '',\n emailId: emailId,\n attachments: p._meta_attachments || [],\n emailType: p._email_type || 'CategoryC',\n sourceName: p._meta_source_name || '',\n urls: []\n });\n }\n emailGroups.get(emailId).urls.push({ url: s3Url, index: p._page_index || 0 });\n}\n\nconst results = [];\nfor (const [emailId, data] of emailGroups) {\n data.urls.sort((a, b) => a.index - b.index);\n results.push({\n json: {\n _meta_subject: data.subject,\n _meta_from: data.from,\n _meta_id: data.emailId,\n _meta_attachments: data.attachments,\n _meta_source_name: data.sourceName,\n _email_type: data.emailType,\n _s3_urls: data.urls.map(u => u.url)\n }\n });\n}\n\nif (results.length === 0) return [{ json: { _skip: true } }];\nreturn results;" }, "typeVersion": 2 }, { "id": "f92643b3-d9a2-4053-b2ca-8f76294e0e67", "name": "Prepare GPT Request (PPTX)", "type": "n8n-nodes-base.code", "position": [ 5472, 1488 ], "parameters": { "mode": "runOnceForEachItem", "jsCode": "// ============================================================\n// PREPARE GPT VISION REQUEST FOR PPTX/PDF\n// ============================================================\n// Customize the system prompt and extraction fields\n// ============================================================\n\n// Map codes to full names (customize for your use case)\nconst codeToNameMap = {\n // 'CODE1': 'Full Name 1',\n // 'CODE2': 'Full Name 2',\n};\n\nconst item = $input.item.json;\nconst subject = item._meta_subject || '';\nconst emailId = item._meta_id || '';\nconst s3Urls = item._s3_urls || [];\nconst emailType = item._email_type || 'CategoryC';\n\n// Extract source name from subject\nconst sourceMatch = subject.match(/\u3010([A-Za-z]+)\u3011/i);\nconst sourceCode = sourceMatch ? sourceMatch[1].toUpperCase() : '';\nconst sourceName = codeToNameMap[sourceCode] || sourceCode;\n\n// Build prompt based on email type\nlet prompt = '';\nif (emailType === 'CategoryA') {\n prompt = 'Subject: ' + subject + '\\n\\nThis is a CANCELLATION notice. Extract cancellation details.';\n} else if (emailType === 'CategoryB') {\n prompt = 'Subject: ' + subject + '\\n\\nThis is an UPDATE notice. Extract the changes.';\n} else {\n prompt = 'Subject: ' + subject + '\\n\\nExtract all relevant information from this image.';\n}\n\nlet userContent = [{ type: 'text', text: prompt }];\nfor (const url of s3Urls) {\n userContent.push({ type: 'image_url', image_url: { url: url, detail: 'high' } });\n}\n\n// ============================================================\n// CUSTOMIZE: Update the system prompt and fields for your use case\n// ============================================================\nconst systemPrompt = `You are a data extraction expert. Extract structured information accurately from images.\n\n\u3010Fields to extract\u3011\n- date (YYYY-MM-DD format)\n- day_of_week\n- start_time (HH:MM format)\n- end_time (HH:MM format)\n- source_code\n- name\n- amount (numbers only)\n- category\n- type\n- notes\n\n\u3010Output\u3011JSON array only, no explanation`;\n\nconst gptBody = {\n model: 'gpt-4o',\n messages: [\n { role: 'system', content: systemPrompt },\n { role: 'user', content: userContent }\n ],\n max_tokens: 4096,\n temperature: 0.1\n};\n\nreturn {\n json: {\n _meta_subject: subject,\n _meta_from: item._meta_from,\n _meta_id: emailId,\n _meta_attachments: item._meta_attachments || [],\n _meta_process_type: 'pptx_pdf',\n _meta_source_name: sourceName,\n _email_type: emailType,\n gpt_request_body: JSON.stringify(gptBody)\n }\n};" }, "typeVersion": 2 }, { "id": "eeb04c4a-6278-4f95-b7a1-270ea8052e46", "name": "GPT Vision Analysis", "type": "n8n-nodes-base.httpRequest", "position": [ 5664, 1264 ], "parameters": { "url": "https://api.openai.com/v1/chat/completions", "method": "POST", "options": { "timeout": 300000 }, "jsonBody": "={{ $json.gpt_request_body }}", "sendBody": true, "specifyBody": "json", "authentication": "predefinedCredentialType", "nodeCredentialType": "openAiApi" }, "typeVersion": 4.2 }, { "id": "59b8220a-6ea1-42d6-88c4-91225668facf", "name": "IF - Has Image?", "type": "n8n-nodes-base.if", "position": [ 4592, 1648 ], "parameters": { "options": {}, "conditions": { "options": { "version": 1, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "operator": { "type": "boolean", "operation": "equals" }, "leftValue": "={{ $json.has_image }}", "rightValue": true } ] } }, "typeVersion": 2 }, { "id": "68b29f63-ef7c-437a-806b-19ae1e9e1be3", "name": "Prepare Image Metadata", "type": "n8n-nodes-base.code", "position": [ 4800, 1616 ], "parameters": { "mode": "runOnceForEachItem", "jsCode": "// ============================================================\n// PREPARE IMAGE METADATA FOR S3 UPLOAD\n// ============================================================\n\nconst item = $input.item;\nconst json = item.json;\nconst subject = json.Subject || '';\nconst emailId = json.id || 'unknown';\n\nlet emailType = json._email_type || '';\nif (!emailType && emailId) {\n try {\n const filteredItems = $('Filter Valid Emails').all();\n for (const f of filteredItems) {\n if (f.json.id === emailId && f.json._email_type) {\n emailType = f.json._email_type;\n break;\n }\n }\n } catch(e) {}\n}\nemailType = emailType || 'CategoryC';\n\nconst timestamp = Date.now();\nconst fileName = 'email-image-' + emailId + '-' + timestamp + '.png';\n\nreturn {\n json: {\n _meta_subject: subject,\n _meta_from: json.From,\n _meta_id: json.id,\n _meta_attachments: json.attachments || [],\n _meta_process_type: 'image',\n _email_type: emailType,\n _image_key: json.image_binary_key || 'attachment_0',\n _s3_filename: fileName\n },\n binary: item.binary\n};" }, "typeVersion": 2 }, { "id": "45f24d85-b9d3-4532-997c-55d6036aaa4a", "name": "S3 Upload (Image)", "type": "n8n-nodes-base.awsS3", "position": [ 5024, 1648 ], "parameters": { "fileName": "={{ $json._s3_filename }}", "operation": "upload", "bucketName": "={{ $vars.S3_BUCKET_NAME || 'your-bucket-name' }}", "additionalFields": {}, "binaryPropertyName": "={{ $json._image_key }}" }, "typeVersion": 2 }, { "id": "e6c8c547-bd98-4e4e-9c7f-8efb515349f1", "name": "Prepare GPT Request (Image)", "type": "n8n-nodes-base.code", "position": [ 5248, 1648 ], "parameters": { "mode": "runOnceForEachItem", "jsCode": "// ============================================================\n// PREPARE GPT VISION REQUEST FOR IMAGE ATTACHMENTS\n// ============================================================\n\nconst codeToNameMap = {\n // 'CODE1': 'Full Name 1',\n};\n\nconst item = $input.item.json;\nlet meta = {};\ntry { meta = $('Prepare Image Metadata').first().json; } catch(e) {}\n\nconst subject = meta._meta_subject || '';\nconst emailId = meta._meta_id || '';\nconst emailType = meta._email_type || 'CategoryC';\n\nconst sourceMatch = subject.match(/\u3010([A-Za-z]+)\u3011/i);\nconst sourceCode = sourceMatch ? sourceMatch[1].toUpperCase() : '';\nconst sourceName = codeToNameMap[sourceCode] || sourceCode;\n\nconst imageUrl = item.Location || '';\nif (!imageUrl) return { json: { _skip: true } };\n\nlet prompt = '';\nif (emailType === 'CategoryA') {\n prompt = 'Subject: ' + subject + '\\n\\nThis is a CANCELLATION notice.';\n} else if (emailType === 'CategoryB') {\n prompt = 'Subject: ' + subject + '\\n\\nThis is an UPDATE notice.';\n} else {\n prompt = 'Subject: ' + subject + '\\n\\nExtract all relevant data from this image.';\n}\n\nconst gptBody = {\n model: 'gpt-4o',\n messages: [\n { role: 'system', content: 'You are a data extraction expert. Extract structured information from images.\\n\\n\u3010Fields\u3011date(YYYY-MM-DD), day_of_week, start_time(HH:MM), end_time(HH:MM), source_code, name, amount(numbers only), category, type, notes\\n\\n\u3010Output\u3011JSON array only' },\n { role: 'user', content: [{ type: 'text', text: prompt }, { type: 'image_url', image_url: { url: imageUrl, detail: 'high' } }] }\n ],\n max_tokens: 4096\n};\n\nreturn {\n json: {\n _meta_subject: subject,\n _meta_from: meta._meta_from || '',\n _meta_id: emailId,\n _meta_attachments: meta._meta_attachments || [],\n _meta_process_type: 'image',\n _meta_source_name: sourceName,\n _email_type: emailType,\n gpt_request_body: JSON.stringify(gptBody)\n }\n};" }, "typeVersion": 2 }, { "id": "16937348-70e9-46da-89eb-9ab047956476", "name": "GPT Image Analysis", "type": "n8n-nodes-base.httpRequest", "position": [ 5472, 1760 ], "parameters": { "url": "https://api.openai.com/v1/chat/completions", "method": "POST", "options": {}, "jsonBody": "={{ $json.gpt_request_body }}", "sendBody": true, "specifyBody": "json", "authentication": "predefinedCredentialType", "nodeCredentialType": "openAiApi" }, "typeVersion": 4.2 }, { "id": "3b289245-1499-4dd3-849f-2b39cf04f65a", "name": "Prepare GPT Request (Text)", "type": "n8n-nodes-base.code", "position": [ 4800, 1840 ], "parameters": { "mode": "runOnceForEachItem", "jsCode": "// ============================================================\n// PREPARE GPT REQUEST FOR TEXT-ONLY EMAILS\n// ============================================================\n\nconst item = $input.item.json;\nconst subject = item.Subject || '';\nconst emailId = item.id || '';\n\nlet emailType = item._email_type || '';\nif (!emailType && emailId) {\n try {\n const filteredItems = $('Filter Valid Emails').all();\n for (const f of filteredItems) {\n if (f.json.id === emailId && f.json._email_type) {\n emailType = f.json._email_type;\n break;\n }\n }\n } catch(e) {}\n}\nemailType = emailType || 'CategoryC';\n\nconst textBody = item.textPlain || item.text || '';\nlet userPrompt = 'Subject: ' + subject + '\\n\\nBody:\\n' + textBody.substring(0, 3000);\nif (emailType === 'CategoryA') userPrompt = '\u3010CANCELLATION\u3011' + userPrompt;\nelse if (emailType === 'CategoryB') userPrompt = '\u3010UPDATE\u3011' + userPrompt;\n\nconst gptBody = {\n model: 'gpt-4o-mini',\n messages: [\n { role: 'system', content: 'You are a data extraction expert. Extract structured information from email text.\\n\\n\u3010Fields\u3011date, day_of_week, start_time, end_time, source_code, name, amount, category, type, notes\\n\\n\u3010Output\u3011JSON array only' },\n { role: 'user', content: userPrompt }\n ],\n max_tokens: 2048\n};\n\nreturn {\n json: {\n _meta_subject: subject,\n _meta_from: item.From,\n _meta_id: emailId,\n _meta_attachments: item.attachments || [],\n _meta_process_type: 'text',\n _email_type: emailType,\n gpt_request_body: JSON.stringify(gptBody)\n }\n};" }, "typeVersion": 2 }, { "id": "27d9b48b-9762-43e5-b624-f810651664e3", "name": "GPT Text Analysis", "type": "n8n-nodes-base.httpRequest", "position": [ 5024, 1840 ], "parameters": { "url": "https://api.openai.com/v1/chat/completions", "method": "POST", "options": {}, "jsonBody": "={{ $json.gpt_request_body }}", "sendBody": true, "specifyBody": "json", "authentication": "predefinedCredentialType", "nodeCredentialType": "openAiApi" }, "typeVersion": 4.2 }, { "id": "f0ac5f4d-bf3b-4a45-ab48-b1cf17f43ce1", "name": "Merge All Paths", "type": "n8n-nodes-base.merge", "position": [ 5904, 1536 ], "parameters": {}, "typeVersion": 3, "alwaysOutputData": true }, { "id": "0a1f3790-23fa-4b48-a1c6-857c88758b86", "name": "Parse All Results", "type": "n8n-nodes-base.code", "position": [ 6128, 1536 ], "parameters": { "jsCode": "// ============================================================\n// PARSE ALL GPT RESPONSES AND FORMAT FOR OUTPUT\n// ============================================================\n// Customize the output schema for your Google Sheets\n// ============================================================\n\n// Map codes to full names (customize for your use case)\nconst codeToNameMap = {\n // 'CODE1': 'Full Name 1',\n // 'CODE2': 'Full Name 2',\n};\n\nconst allItems = $input.all();\nconst results = [];\n\nlet pptxPrepItems = [], imagePrepItems = [], textPrepItems = [];\ntry { pptxPrepItems = $('Prepare GPT Request (PPTX)').all(); } catch(e) {}\ntry { imagePrepItems = $('Prepare GPT Request (Image)').all(); } catch(e) {}\ntry { textPrepItems = $('Prepare GPT Request (Text)').all(); } catch(e) {}\n\nlet pptxPrepIndex = 0, imagePrepIndex = 0, textPrepIndex = 0;\n\nfor (let i = 0; i < allItems.length; i++) {\n const item = allItems[i];\n const data = item.json;\n \n const hasMetadata = !!data._meta_id;\n let subject, from, emailId, attachments, sourceName, emailType;\n \n if (hasMetadata) {\n subject = data._meta_subject || '';\n from = data._meta_from || '';\n emailId = data._meta_id || '';\n attachments = data._meta_attachments || [];\n sourceName = data._meta_source_name || '';\n emailType = data._email_type || 'CategoryC';\n } else {\n const modelName = data.model || '';\n const isTextModel = modelName.includes('gpt-4o-mini');\n let meta = null;\n if (isTextModel && textPrepIndex < textPrepItems.length) {\n meta = textPrepItems[textPrepIndex].json;\n textPrepIndex++;\n } else if (imagePrepIndex < imagePrepItems.length) {\n meta = imagePrepItems[imagePrepIndex].json;\n imagePrepIndex++;\n } else if (pptxPrepIndex < pptxPrepItems.length) {\n meta = pptxPrepItems[pptxPrepIndex].json;\n pptxPrepIndex++;\n }\n\n if (meta) {\n subject = meta._meta_subject || '';\n from = meta._meta_from || '';\n emailId = meta._meta_id || '';\n attachments = meta._meta_attachments || [];\n sourceName = meta._meta_source_name || '';\n emailType = meta._email_type || 'CategoryC';\n } else {\n subject = ''; from = ''; emailId = ''; attachments = []; sourceName = ''; emailType = 'CategoryC';\n }\n }\n \n if (from && typeof from === 'object') {\n from = from.text || from.address || (from.value && from.value[0] ? from.value[0].address : '') || '';\n }\n from = String(from || '');\n \n let content = '';\n if (data.choices && data.choices[0] && data.choices[0].message) {\n content = data.choices[0].message.content || '';\n }\n \n // Check for unreadable content\n const unreadablePatterns = ['sorry', 'cannot read', 'unable to', 'not able to'];\n const isUnreadable = unreadablePatterns.some(p => content.toLowerCase().includes(p));\n \n if (isUnreadable) {\n results.push({\n json: {\n email_type: emailType,\n date: '', day_of_week: '', start_time: '', end_time: '',\n source_name: sourceName, name: '', amount: '0', category: '', type: '',\n status: 'Needs Review', client: '',\n notes: '\u26a0\ufe0fFile unreadable - manual review required',\n created_at: new Date().toISOString(),\n email_subject: subject, sender: from, email_id: emailId,\n attachments: attachments.map(a => a.filename || '').join(', '),\n _unreadable: true\n }\n });\n continue;\n }\n \n // Parse JSON from GPT response\n let items = [];\n try {\n let jsonStr = content;\n const codeMatch = content.match(/```json\\s*([\\s\\S]*?)\\s*```/);\n if (codeMatch) jsonStr = codeMatch[1];\n else {\n const arrMatch = content.match(/\\[[\\s\\S]*\\]/);\n if (arrMatch) jsonStr = arrMatch[0];\n }\n const parsed = JSON.parse(jsonStr);\n items = Array.isArray(parsed) ? parsed : [parsed];\n } catch(e) { \n if (emailId) {\n results.push({\n json: {\n email_type: emailType,\n date: '', day_of_week: '', start_time: '', end_time: '',\n source_name: sourceName, name: '', amount: '0', category: '', type: '',\n status: 'Needs Review', client: '',\n notes: '\u26a0\ufe0fParse failed - manual review required',\n created_at: new Date().toISOString(),\n email_subject: subject, sender: from, email_id: emailId,\n attachments: attachments.map(a => a.filename || '').join(', '),\n _unreadable: true\n }\n });\n }\n continue;\n }\n \n if (items.length === 0) continue;\n \n // ============================================================\n // CUSTOMIZE: Map extracted data to your output schema\n // ============================================================\n for (const s of items) {\n let sourceNameFinal = sourceName || s.source_name || '';\n if (s.source_code) {\n const code = s.source_code.toUpperCase();\n sourceNameFinal = codeToNameMap[code] || sourceNameFinal || code;\n }\n \n // Determine category from time (customize as needed)\n let category = s.category || '';\n if (!category && s.start_time) {\n const h = parseInt(s.start_time.split(':')[0]) || 0;\n if (h >= 5 && h < 10) category = 'Morning';\n else if (h >= 10 && h < 15) category = 'Afternoon';\n else if (h >= 15 && h < 23) category = 'Evening';\n else category = 'Night';\n }\n\n results.push({\n json: {\n email_type: emailType,\n date: s.date || '',\n day_of_week: s.day_of_week || '',\n start_time: s.start_time || '',\n end_time: s.end_time || '',\n source_name: sourceNameFinal,\n name: s.name || '',\n amount: String(s.amount || 0),\n category: category,\n type: s.type || 'Regular',\n status: 'Pending',\n client: '',\n notes: s.notes || '',\n created_at: new Date().toISOString(),\n email_subject: subject,\n sender: from,\n email_id: emailId,\n attachments: attachments.map(a => a.filename || '').join(', ')\n }\n });\n }\n}\n\nif (results.length === 0) return [{ json: { _no_data: true } }];\nreturn results;" }, "typeVersion": 2 }, { "id": "3b91edb4-d360-4441-ac58-bcaf1da85514", "name": "Filter Valid Data", "type": "n8n-nodes-base.filter", "position": [ 6352, 1536 ], "parameters": { "options": {}, "conditions": { "options": { "version": 1, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "operator": { "type": "string", "operation": "notEmpty", "singleValue": true }, "leftValue": "={{ $json[\"email_type\"] }}", "rightValue": true } ] } }, "typeVersion": 2 }, { "id": "07942913-2997-42f5-a93f-b4459ca4c119", "name": "Save to Google Sheets", "type": "n8n-nodes-base.googleSheets", "position": [ 6560, 1536 ], "parameters": { "columns": { "value": {}, "mappingMode": "autoMapInputData" }, "options": {}, "operation": "append", "sheetName": { "__rl": true, "mode": "name", "value": "Sheet1" }, "documentId": { "__rl": true, "mode": "id", "value": "={{ $vars.GOOGLE_SHEET_ID || 'your-google-sheet-id' }}" } }, "typeVersion": 4.4 }, { "id": "93650748-aff3-423d-afa6-48dd53b5230b", "name": "Extract Email IDs", "type": "n8n-nodes-base.code", "position": [ 6784, 1536 ], "parameters": { "jsCode": "// ============================================================\n// EXTRACT UNIQUE EMAIL IDs FOR LABELING\n// ============================================================\n\nconst allItems = $input.all();\nconst emailMap = new Map();\n\nfunction addEmail(emailId, emailType, isUnreadable) {\n if (!emailId) return;\n if (!emailMap.has(emailId)) {\n emailMap.set(emailId, { type: isUnreadable ? 'NeedsReview' : emailType });\n }\n if (isUnreadable) emailMap.get(emailId).type = 'NeedsReview';\n}\n\nfor (const item of allItems) {\n const emailId = item.json.email_id;\n const emailType = item.json.email_type || 'CategoryC';\n const isUnreadable = item.json._unreadable === true;\n addEmail(emailId, emailType, isUnreadable);\n}\n\ntry {\n const filteredItems = $('Filter Valid Emails').all();\n for (const f of filteredItems) {\n addEmail(f.json.id, f.json._email_type || 'CategoryC', false);\n }\n} catch (e) {}\n\nreturn Array.from(emailMap.entries()).map(([id, data]) => ({\n json: { email_id: id, email_type: data.type }\n}));" }, "typeVersion": 2 }, { "id": "613a3c8a-e875-48f5-b75b-1b121b92f173", "name": "Get Gmail Labels", "type": "n8n-nodes-base.httpRequest", "position": [ 6784, 1760 ], "parameters": { "url": "https://gmail.googleapis.com/gmail/v1/users/me/labels", "options": {}, "authentication": "predefinedCredentialType", "nodeCredentialType": "gmailOAuth2" }, "typeVersion": 4.2 }, { "id": "dab2e312-225d-4df4-a45d-4b1ab1a37558", "name": "Check Labels", "type": "n8n-nodes-base.code", "position": [ 7008, 1760 ], "parameters": { "jsCode": "// ============================================================\n// CHECK WHICH LABELS EXIST\n// ============================================================\n// Customize label names for your use case\n// ============================================================\n\nconst response = $input.first().json;\nconst labels = response.labels || [];\n\n// Define your label names here\nconst labelNames = [\n 'Processed_CategoryA',\n 'Processed_CategoryB', \n 'Processed_CategoryC'\n];\n\nconst existingLabels = {};\nconst missingLabels = [];\n\nfor (const name of labelNames) {\n const found = labels.find(l => l.name === name);\n if (found) existingLabels[name] = found.id;\n else missingLabels.push(name);\n}\n\nreturn [{ \n json: { \n existing_labels: existingLabels, \n missing_labels: missingLabels, \n needs_creation: missingLabels.length > 0 \n } \n}];" }, "typeVersion": 2 }, { "id": "f169d45b-e952-4df5-bd93-7e61a4f07980", "name": "IF - Need Labels?", "type": "n8n-nodes-base.if", "position": [ 7232, 1760 ], "parameters": { "options": {}, "conditions": { "options": { "version": 1, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "operator": { "type": "boolean", "operation": "equals" }, "leftValue": "={{ $json.needs_creation }}", "rightValue": true } ] } }, "typeVersion": 2 }, { "id": "137c484a-e4ce-4996-b603-4f2164309dc3", "name": "Prepare Label Creation", "type": "n8n-nodes-base.code", "position": [ 7440, 1664 ], "parameters": { "jsCode": "// Prepare label creation requests\nconst items = $input.all();\nconst results = [];\nfor (const item of items) {\n const data = item.json || {};\n const missingLabels = data.missing_labels || [];\n const existingLabels = data.existing_labels || {};\n if (missingLabels.length === 0) results.push({ json: { label_name: '', existing_labels: existingLabels, _skip_creation: true } });\n else for (const name of missingLabels) results.push({ json: { label_name: name, existing_labels: existingLabels, _skip_creation: false } });\n}\nif (results.length === 0) return [{ json: { label_name: '', existing_labels: {}, _skip_creation: true } }];\nreturn results;" }, "typeVersion": 2 }, { "id": "28717cfd-d0dc-4040-b3fa-dd2982e05819", "name": "Filter Labels to Create", "type": "n8n-nodes-base.filter", "position": [ 7664, 1664 ], "parameters": { "options": {}, "conditions": { "options": { "version": 1, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "operator": { "type": "boolean", "operation": "notEquals" }, "leftValue": "={{ $json._skip_creation }}", "rightValue": true } ] } }, "typeVersion": 2 }, { "id": "5f7f79dc-0cb8-43b8-b938-6539881c02d8", "name": "Create Gmail Label", "type": "n8n-nodes-base.httpRequest", "position": [ 7888, 1664 ], "parameters": { "url": "https://gmail.googleapis.com/gmail/v1/users/me/labels", "method": "POST", "options": {}, "jsonBody": "={\"name\": \"{{ $json.label_name }}\", \"labelListVisibility\": \"labelShow\", \"messageListVisibility\": \"show\"}", "sendBody": true, "specifyBody": "json", "authentication": "predefinedCredentialType", "nodeCredentialType": "gmailOAuth2" }, "typeVersion": 4.2 }, { "id": "13873411-e770-4f62-a957-f0dd3690b26d", "name": "Get New Label IDs", "type": "n8n-nodes-base.code", "position": [ 8112, 1664 ], "parameters": { "jsCode": "// Collect newly created label IDs\nconst allItems = $input.all();\nconst createdLabels = {};\nfor (const item of allItems) {\n const data = item.json;\n if (data.id && data.name) createdLabels[data.name] = data.id;\n}\nlet existingLabels = {};\ntry { const labelCheckData = $('Check Labels').first().json; existingLabels = labelCheckData.existing_labels || {}; } catch(e) {}\nreturn [{ json: { all_labels: { ...existingLabels, ...createdLabels } } }];" }, "typeVersion": 2 }, { "id": "8ca950ac-7f74-4596-adf7-ca74e6b191d3", "name": "Pass Existing Labels", "type": "n8n-nodes-base.code", "position": [ 7440, 1856 ], "parameters": { "jsCode": "// Pass through existing labels when no creation needed\nconst items = $input.all();\nlet existingLabels = {};\nfor (const item of items) {\n const data = item.json || {};\n if (data.existing_labels) existingLabels = { ...existingLabels, ...data.existing_labels };\n}\nreturn [{ json: { all_labels: existingLabels } }];" }, "typeVersion": 2 }, { "id": "bebcee89-dc11-4bab-8a9c-e72744778b07", "name": "Merge Label IDs", "type": "n8n-nodes-base.code", "position": [ 8112, 1856 ], "parameters": { "jsCode": "// Merge all label IDs\nconst items = $input.all();\nlet allLabels = {};\nfor (const item of items) {\n const data = item.json || {};\n const labels = data.all_labels || data.existing_labels || {};\n allLabels = { ...allLabels, ...labels };\n}\nreturn [{ json: { all_labels: allLabels } }];" }, "typeVersion": 2 }, { "id": "ec6d8a00-ade2-4047-84fa-2f448cceb979", "name": "Prepare Label Application", "type": "n8n-nodes-base.code", "position": [ 8320, 1776 ], "parameters": { "jsCode": "// ============================================================\n// PREPARE LABEL APPLICATION FOR EACH EMAIL\n// ============================================================\n// Customize the type-to-label mapping\n// ============================================================\n\nconst inputItems = $input.all();\nconst labelInfo = inputItems[0]?.json?.all_labels\n ? inputItems[0].json\n : (() => {\n try { return $('Merge Label IDs').first().json; } catch (e) { return {}; }\n })();\n\nlet emailItems = [];\ntry { emailItems = $('Extract Email IDs').all(); } catch (e) { emailItems = []; }\nif (emailItems.length === 0) {\n try {\n const filteredItems = $('Filter Valid Emails').all();\n emailItems = filteredItems\n .map(item => ({\n json: {\n email_id: item.json.id || '',\n email_type: item.json._email_type || 'CategoryC'\n }\n }))\n .filter(item => item.json.email_id);\n } catch (e) {\n emailItems = [];\n }\n}\n\nconst allLabels = labelInfo.all_labels || {};\n\n// Map email types to label names (customize these)\nconst typeToLabel = { \n 'CategoryA': 'Processed_CategoryA', \n 'CategoryB': 'Processed_CategoryB', \n 'CategoryC': 'Processed_CategoryC' \n};\n\nconst results = [];\n\nfor (const item of emailItems) {\n const emailType = item.json.email_type || 'CategoryC';\n const labelName = typeToLabel[emailType] || 'Processed_CategoryC';\n const labelId = allLabels[labelName];\n if (labelId) {\n results.push({\n json: {\n email_id: item.json.email_id,\n email_type: emailType,\n label_name: labelName,\n label_id: labelId,\n request_body: JSON.stringify({ addLabelIds: [labelId] })\n }\n });\n }\n}\n\nif (results.length === 0) return [{ json: { _no_emails: true } }];\nreturn results;" }, "typeVersion": 2 }, { "id": "b6a78bf7-4974-47ee-8500-440c30991c39", "name": "Has Emails?", "type": "n8n-nodes-base.filter", "position": [ 8544, 1776 ], "parameters": { "options": {}, "conditions": { "options": { "version": 1, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "operator": { "type": "boolean", "operation": "notEquals" }, "leftValue": "={{ $json._no_emails }}", "rightValue": true } ] } }, "typeVersion": 2 }, { "id": "f1e17c5b-24d1-4168-9b06-f24be083c53f", "name": "Apply Gmail Labels", "type": "n8n-nodes-base.httpRequest", "position": [ 8768, 1776 ], "parameters": { "url": "=https://gmail.googleapis.com/gmail/v1/users/me/messages/{{ $json.email_id }}/modify", "method": "POST", "options": {}, "jsonBody": "={{ $json.request_body }}", "sendBody": true, "specifyBody": "json", "authentication": "predefinedCredentialType", "nodeCredentialType": "gmailOAuth2" }, "typeVersion": 4.2 }, { "id": "config-variables-note", "name": "Configuration Variables", "type": "n8n-nodes-base.stickyNote", "position": [ 2384, 2040 ], "parameters": { "color": 4, "width": 500, "height": 300, "content": "### Required Variables\n\nSet these in n8n Settings > Variables:\n\n| Variable | Description |\n|----------|-------------|\n| `S3_BUCKET_NAME` | Your AWS S3 bucket name |\n| `AWS_REGION` | AWS region (e.g., us-east-1) |\n| `GOOGLE_SHEET_ID` | Google Sheets document ID |\n\n### Required Credentials\n\n- **Gmail OAuth2** - For email access\n- **OpenAI API** - For GPT Vision\n- **AWS S3** - For image storage\n- **Google Sheets OAuth2** - For data output\n- **ConvertAPI HTTP Header Auth** - For file conversion" }, "typeVersion": 1 } ], "connections": { "Has Emails?": { "main": [ [ { "node": "Apply Gmail Labels", "type": "main", "index": 0 } ] ] }, "Check Labels": { "main": [ [ { "node": "IF - Need Labels?", "type": "main", "index": 0 } ] ] }, "Domain Filter": { "main": [ [ { "node": "Filter Valid Emails", "type": "main", "index": 0 } ] ] }, "Collect S3 URLs": { "main": [ [ { "node": "Prepare GPT Request (PPTX)", "type": "main", "index": 0 } ] ] }, "IF - Has Excel?": { "main": [ [ { "node": "Extract from Excel", "type": "main", "index": 0 } ], [ { "node": "IF - Has PPTX/PDF?", "type": "main", "index": 0 } ] ] }, "IF - Has Image?": { "main": [ [ { "node": "Prepare Image Metadata", "type": "main", "index": 0 } ], [ { "node": "Prepare GPT Request (Text)", "type": "main", "index": 0 } ] ] }, "Merge All Paths": { "main": [ [ { "node": "Parse All Results", "type": "main", "index": 0 } ] ] }, "Merge Label IDs": { "main": [ [ { "node": "Prepare Label Application", "type": "main", "index": 0 } ] ] }, "Get Gmail Labels": { "main": [ [ { "node": "Check Labels", "type": "main", "index": 0 } ] ] }, "Parse Excel Data": { "main": [ [ { "node": "Merge All Paths", "type": "main", "index": 0 } ] ] }, "S3 Upload (PPTX)": { "main": [ [ { "node": "Collect S3 URLs", "type": "main", "index": 0 } ] ] }, "Schedule Trigger": { "main": [ [ { "node": "Gmail - Get Emails", "type": "main", "index": 0 } ] ] }, "ConvertAPI to PNG": { "main": [ [ { "node": "Prepare for S3 Upload", "type": "main", "index": 0 } ] ] }, "Detect File Types": { "main": [ [ { "node": "IF - Has Excel?", "type": "main", "index": 0 } ] ] }, "Filter Valid Data": { "main": [ [ { "node": "Save to Google Sheets", "type": "main", "index": 0 }, { "node": "Extract Email IDs", "type": "main", "index": 0 } ] ] }, "GPT Text Analysis": { "main": [ [ { "node": "Merge All Paths", "type": "main", "index": 0 } ] ] }, "Get New Label IDs": { "main": [ [ { "node": "Merge Label IDs", "type": "main", "index": 0 } ] ] }, "IF - Need Labels?": { "main": [ [ { "node": "Prepare Label Creation", "type": "main", "index": 0 } ], [ { "node": "Pass Existing Labels", "type": "main", "index": 0 } ] ] }, "Parse All Results": { "main": [ [ { "node": "Filter Valid Data", "type": "main", "index": 0 } ] ] }, "S3 Upload (Image)": { "main": [ [ { "node": "Prepare GPT Request (Image)", "type": "main", "index": 0 } ] ] }, "Create Gmail Label": { "main": [ [ { "node": "Get New Label IDs", "type": "main", "index": 0 } ] ] }, "Extract from Excel": { "main": [ [ { "node": "Parse Excel Data", "type": "main", "index": 0 } ] ] }, "GPT Image Analysis": { "main": [ [ { "node": "Merge All Paths", "type": "main", "index": 0 } ] ] }, "Gmail - Get Emails": { "main": [ [ { "node": "Domain Filter", "type": "main", "index": 0 } ] ] }, "IF - Has PPTX/PDF?": { "main": [ [ { "node": "ConvertAPI to PNG", "type": "main", "index": 0 } ], [ { "node": "IF - Has Image?", "type": "main", "index": 0 } ] ] }, "Filter Valid Emails": { "main": [ [ { "node": "Gmail - Get Attachments", "type": "main", "index": 0 } ] ] }, "GPT Vision Analysis": { "main": [ [ { "node": "Merge All Paths", "type": "main", "index": 0 } ] ] }, "Pass Existing Labels": { "main": [ [ { "node": "Merge Label IDs", "type": "main", "index": 0 } ] ] }, "Prepare for S3 Upload": { "main": [ [ { "node": "S3 Upload (PPTX)", "type": "main", "index": 0 } ] ] }, "Save to Google Sheets": { "main": [ [ { "node": "Get Gmail Labels", "type": "main", "index": 0 } ] ] }, "Prepare Image Metadata": { "main": [ [ { "node": "S3 Upload (Image)", "type": "main", "index": 0 } ] ] }, "Prepare Label Creation": { "main": [ [ { "node": "Filter Labels to Create", "type": "main", "index": 0 } ] ] }, "Filter Labels to Create": { "main": [ [ { "node": "Create Gmail Label", "type": "main", "index": 0 } ] ] }, "Gmail - Get Attachments": { "main": [ [ { "node": "Detect File Types", "type": "main", "index": 0 } ] ] }, "Prepare Label Application": { "main": [ [ { "node": "Has Emails?", "type": "main", "index": 0 } ] ] }, "Prepare GPT Request (PPTX)": { "main": [ [ { "node": "GPT Vision Analysis", "type": "main", "index": 0 } ] ] }, "Prepare GPT Request (Text)": { "main": [ [ { "node": "GPT Text Analysis", "type": "main", "index": 0 } ] ] }, "Prepare GPT Request (Image)": { "main": [ [ { "node": "GPT Image Analysis", "type": "main", "index": 0 } ] ] } }, "description": "Automatically extract structured information from emails using AI-powered document analysis. This workflow processes emails from specified domains, classifies them by type, and extracts structured data from various attachment formats.\n\n## Who is this for\nOperations teams, coordinators, and business professionals who receive proposals or reports from multiple sources via email and need to consolidate the information into a structured database.\n\n## What this workflow does\n1. **Monitors Gmail** every 30 minutes for emails from specified domains\n2. **Classifies emails** into three categories based on customizable keywords\n3. **Processes attachments** intelligently based on file type and email classification\n4. **Extracts structured data**: dates, times, names, amounts, and other fields\n5. **Saves to Google Sheets** with full metadata and classification\n6. **Labels processed emails** in Gmail for tracking\n\n## Setup requirements\n- Gmail OAuth2 credentials\n- OpenAI API key (GPT-4 Vision)\n- Google Sheets OAuth2 credentials\n- AWS S3 bucket for temporary image storage\n- ConvertAPI account for PPTX/PDF conversion\n\n## How to customize\nEdit the domain list and classification keywords in the code nodes to adapt for your specific use case." }