홈 데코 AI (Google Nano Banana) - Santhej Kallada
고급
이것은Content Creation, Multimodal AI분야의자동화 워크플로우로, 28개의 노드를 포함합니다.주로 If, Set, Code, Merge, Airtable 등의 노드를 사용하며. Google Gemini 기반 AI 이미지 생성 및 편집과 Telegram 봇
사전 요구사항
- •Airtable API Key
- •Telegram Bot Token
- •Google Drive API 인증 정보
- •대상 API의 인증 정보가 필요할 수 있음
사용된 노드 (28)
워크플로우 미리보기
노드 연결 관계를 시각적으로 표시하며, 확대/축소 및 이동을 지원합니다
워크플로우 내보내기
다음 JSON 구성을 복사하여 n8n에 가져오면 이 워크플로우를 사용할 수 있습니다
{
"id": "dOynwI3sAYdHex7x",
"meta": {
"instanceId": "d3c17a28b831505b1128b4be827611328e2ecd2d4b635d3c4ca5c08fd677a016",
"templateCredsSetupCompleted": true
},
"name": "Home Furnishing AI (Google Nano Banana) - Santhej Kallada",
"tags": [
{
"id": "o5lUbXtRS653224b",
"name": "tutorials",
"createdAt": "2025-10-29T07:23:26.446Z",
"updatedAt": "2025-10-29T07:23:26.446Z"
}
],
"nodes": [
{
"id": "47fbda3a-c350-4dc8-8e4d-a02b989b44fc",
"name": "Telegram 트리거",
"type": "n8n-nodes-base.telegramTrigger",
"position": [
-528,
1680
],
"webhookId": "fb264149-5f6f-41ef-9b49-aca9304879cc",
"parameters": {
"updates": [
"message"
],
"additionalFields": {}
},
"typeVersion": 1.2
},
{
"id": "54e2b068-775c-47d4-a1bc-9ddfe17e3d88",
"name": "If1",
"type": "n8n-nodes-base.if",
"position": [
-304,
1680
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "loose"
},
"combinator": "and",
"conditions": [
{
"id": "a43f58c6-dccc-41fd-bbaf-7bb3f299adc7",
"operator": {
"type": "object",
"operation": "exists",
"singleValue": true
},
"leftValue": "={{ $json.message.photo[0] }}",
"rightValue": ""
}
]
},
"looseTypeValidation": true
},
"typeVersion": 2.2
},
{
"id": "093a6d6a-ae91-4906-a7f3-5e6a63b382ee",
"name": "필드 편집1",
"type": "n8n-nodes-base.set",
"position": [
1040,
1760
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "2cb56c0e-aa56-4fe4-830e-8e42f2dee528",
"name": "message",
"type": "string",
"value": "={{ $('Telegram Trigger').item.json.message.text }}"
},
{
"id": "f7063ef9-30fe-4364-9ef2-043f55bcf591",
"name": "id",
"type": "string",
"value": "={{ $('Telegram Trigger').item.json.message.from.id }}"
},
{
"id": "e05afa5d-3824-41a6-8f7b-6cee3b0f5f36",
"name": "base_image_link",
"type": "string",
"value": "={{ $('Search records').item.json.base_image_link }}"
},
{
"id": "95219c2c-700b-41c6-a8d3-b635270a232f",
"name": "base_image64",
"type": "string",
"value": "={{ $json.data }}"
},
{
"id": "107928c3-4056-4e14-b7cd-13d6dccb0dac",
"name": "image_exits",
"type": "boolean",
"value": true
}
]
}
},
"typeVersion": 3.4
},
{
"id": "3a62cd46-9b63-4dac-ab7b-ea562e62a828",
"name": "레코드 검색",
"type": "n8n-nodes-base.airtable",
"position": [
-80,
1696
],
"parameters": {
"base": {
"__rl": true,
"mode": "list",
"value": "apppCImnrYx31QQ4S",
"cachedResultUrl": "https://airtable.com/apppCImnrYx31QQ4S",
"cachedResultName": "Home Furnish AI"
},
"table": {
"__rl": true,
"mode": "list",
"value": "tblfIbgIrLUoUXFyA",
"cachedResultUrl": "https://airtable.com/apppCImnrYx31QQ4S/tblfIbgIrLUoUXFyA",
"cachedResultName": "Table 1"
},
"options": {},
"operation": "search",
"filterByFormula": "={ID} = \"{{ $('Telegram Trigger').item.json.message.from.id }}\""
},
"typeVersion": 2.1,
"alwaysOutputData": true
},
{
"id": "5ef4c48c-1c9e-4272-bb80-22b08720dec1",
"name": "이미지 편집",
"type": "n8n-nodes-base.httpRequest",
"position": [
1264,
1760
],
"parameters": {
"url": "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image-preview:generateContent",
"method": "POST",
"options": {},
"jsonBody": "={\n \"contents\": [{\n \"parts\": [\n {\n \"text\": {{ JSON.stringify($json.message) }}\n },\n {\n \"inline_data\": {\n \"mime_type\": \"image/jpeg\",\n \"data\": \"{{ $('Edit Fields1').item.json.base_image64 }}\"\n }\n }\n ]\n }]\n}",
"sendBody": true,
"jsonHeaders": "{\n \"x-goog-api-key\": \"put your api key here\",\n \"Content-Type\": \"application/json\"\n}",
"sendHeaders": true,
"specifyBody": "json",
"specifyHeaders": "json"
},
"typeVersion": 4.2
},
{
"id": "ea28d393-8e4f-47ce-8ac6-766aca28f795",
"name": "사진 메시지 전송",
"type": "n8n-nodes-base.telegram",
"position": [
1840,
1824
],
"webhookId": "f12c1e45-334d-4629-bb73-f9caae62f4f1",
"parameters": {
"chatId": "={{ $('Telegram Trigger').item.json.message.from.id }}",
"operation": "sendPhoto",
"binaryData": true,
"additionalFields": {}
},
"typeVersion": 1.2
},
{
"id": "b4225b8e-eae3-4ad0-8ffe-bfe5932bf56a",
"name": "레코드 생성 또는 업데이트1",
"type": "n8n-nodes-base.airtable",
"position": [
1888,
1616
],
"parameters": {
"base": {
"__rl": true,
"mode": "list",
"value": "apppCImnrYx31QQ4S",
"cachedResultUrl": "https://airtable.com/apppCImnrYx31QQ4S",
"cachedResultName": "Home Furnish AI"
},
"table": {
"__rl": true,
"mode": "list",
"value": "tblfIbgIrLUoUXFyA",
"cachedResultUrl": "https://airtable.com/apppCImnrYx31QQ4S/tblfIbgIrLUoUXFyA",
"cachedResultName": "Table 1"
},
"columns": {
"value": {
"ID": "={{ $('Telegram Trigger').item.json.message.from.id }}",
"base_image_link": "={{ $json.webContentLink }}"
},
"schema": [
{
"id": "ID",
"type": "number",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "ID",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "base_image_link",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "base_image_link",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "baseImage_64",
"type": "string",
"display": true,
"removed": true,
"readOnly": false,
"required": false,
"displayName": "baseImage_64",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"ID"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "upsert"
},
"typeVersion": 2.1
},
{
"id": "a7ce2cca-93a0-4b0d-8ba3-eb51d24e1362",
"name": "이미지 다운로드1",
"type": "n8n-nodes-base.telegram",
"position": [
-352,
928
],
"webhookId": "fc1c495a-005e-454e-b92b-66cf1f130634",
"parameters": {
"fileId": "={{ $json.message.photo[2].file_id }}",
"resource": "file",
"additionalFields": {}
},
"typeVersion": 1.2
},
{
"id": "fed96b49-66fa-4479-8c66-b89370907341",
"name": "Base64 변환",
"type": "n8n-nodes-base.extractFromFile",
"position": [
-160,
928
],
"parameters": {
"options": {},
"operation": "binaryToPropery"
},
"typeVersion": 1
},
{
"id": "2bcd11a9-94a2-4b78-86a7-3e31529ee4c0",
"name": "파일 업로드",
"type": "n8n-nodes-base.googleDrive",
"position": [
-48,
1264
],
"parameters": {
"name": "={{ $json.result.file_unique_id }}",
"driveId": {
"__rl": true,
"mode": "list",
"value": "My Drive",
"cachedResultUrl": "https://drive.google.com/drive/my-drive",
"cachedResultName": "My Drive"
},
"options": {},
"folderId": {
"__rl": true,
"mode": "list",
"value": "1Wu1bv38J1izuaCRQmXE0RfEwKR3o3Iwi",
"cachedResultUrl": "https://drive.google.com/drive/folders/1Wu1bv38J1izuaCRQmXE0RfEwKR3o3Iwi",
"cachedResultName": "Home Furnishing AI"
}
},
"typeVersion": 3
},
{
"id": "1d00c8bf-191f-4086-a385-1840f56767a2",
"name": "병합",
"type": "n8n-nodes-base.merge",
"position": [
208,
1264
],
"parameters": {},
"typeVersion": 3.2
},
{
"id": "7de5184c-2ab8-40b8-8e5d-af68f42c8f36",
"name": "집계",
"type": "n8n-nodes-base.aggregate",
"position": [
400,
1264
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData"
},
"typeVersion": 1
},
{
"id": "845d6a60-14fb-4c94-9b97-fc44d04a54c2",
"name": "필드 편집",
"type": "n8n-nodes-base.set",
"position": [
624,
1264
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "32b9d6c1-61ed-4f6a-8757-3209ebb7e4d4",
"name": "image_link",
"type": "string",
"value": "={{ $json.data[0].webContentLink }}"
},
{
"id": "55cb05ec-b1b8-4b54-9a08-fb6c545ef32b",
"name": "image_hash",
"type": "string",
"value": "={{ $json.data[1].data }}"
},
{
"id": "d0511aaf-95f2-46fb-afcb-ad0eefa18485",
"name": "id",
"type": "string",
"value": "={{ $('Telegram Trigger').item.json.message.from.id }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "53655955-c77f-4a95-ade0-021895f197ba",
"name": "레코드 생성 또는 업데이트",
"type": "n8n-nodes-base.airtable",
"disabled": true,
"position": [
864,
1264
],
"parameters": {
"base": {
"__rl": true,
"mode": "list",
"value": "apppCImnrYx31QQ4S",
"cachedResultUrl": "https://airtable.com/apppCImnrYx31QQ4S",
"cachedResultName": "Home Furnish AI"
},
"table": {
"__rl": true,
"mode": "list",
"value": "tblfIbgIrLUoUXFyA",
"cachedResultUrl": "https://airtable.com/apppCImnrYx31QQ4S/tblfIbgIrLUoUXFyA",
"cachedResultName": "Table 1"
},
"columns": {
"value": {
"ID": "={{ $json.id }}",
"base_image_link": "={{ $json.image_link }}"
},
"schema": [
{
"id": "id",
"type": "string",
"display": true,
"removed": false,
"readOnly": true,
"required": false,
"displayName": "id",
"defaultMatch": true
},
{
"id": "ID",
"type": "number",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "ID",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "base_image_link",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "base_image_link",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "baseImage_64",
"type": "string",
"display": true,
"removed": true,
"readOnly": false,
"required": false,
"displayName": "baseImage_64",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"ID"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "upsert"
},
"typeVersion": 2.1
},
{
"id": "0b8c5466-58ca-47ea-89cf-5d5ee95e90ef",
"name": "텍스트 메시지 전송1",
"type": "n8n-nodes-base.telegram",
"position": [
1072,
1264
],
"webhookId": "7a160598-996f-4e02-ada6-2149a9a1c12b",
"parameters": {
"text": "Thank you for providing the image. Please let us know the edits you require",
"chatId": "={{ $('Telegram Trigger').item.json.message.from.id }}",
"additionalFields": {
"appendAttribution": false
}
},
"typeVersion": 1.2
},
{
"id": "e06f6e8f-5f00-43ee-bec3-75e4deca92a3",
"name": "파일에서 추출",
"type": "n8n-nodes-base.extractFromFile",
"position": [
832,
1760
],
"parameters": {
"options": {},
"operation": "binaryToPropery"
},
"typeVersion": 1
},
{
"id": "97d63a01-3a54-4e4f-8941-928e98185b3b",
"name": "파일 다운로드",
"type": "n8n-nodes-base.httpRequest",
"position": [
672,
1760
],
"parameters": {
"url": "={{ $('Search records').item.json.base_image_link }}",
"options": {}
},
"typeVersion": 4.2
},
{
"id": "623e5e22-e227-4aa1-b866-ee74301bc407",
"name": "파일 업로드1",
"type": "n8n-nodes-base.googleDrive",
"position": [
1664,
1616
],
"parameters": {
"name": "={{ $('Telegram Trigger').item.json.message.from.id }}_base",
"driveId": {
"__rl": true,
"mode": "list",
"value": "My Drive"
},
"options": {},
"folderId": {
"__rl": true,
"mode": "list",
"value": "1Wu1bv38J1izuaCRQmXE0RfEwKR3o3Iwi",
"cachedResultUrl": "https://drive.google.com/drive/folders/1Wu1bv38J1izuaCRQmXE0RfEwKR3o3Iwi",
"cachedResultName": "Home Furnishing AI"
}
},
"typeVersion": 3
},
{
"id": "5db3e15a-fbbc-487d-93c5-c71e042e8478",
"name": "파일로 변환",
"type": "n8n-nodes-base.code",
"position": [
1424,
1760
],
"parameters": {
"jsCode": "// Enhanced n8n code node for base64 to PNG conversion\nconst inputData = $input.all();\nconst outputData = [];\n\n// Helper function to safely get nested property\nfunction getNestedProperty(obj, path) {\n return path.split('.').reduce((current, key) => {\n return current && current[key] !== undefined ? current[key] : null;\n }, obj);\n}\n\n// Helper function to validate base64\nfunction isValidBase64(str) {\n if (!str || typeof str !== 'string') return false;\n try {\n // Basic base64 pattern check\n const base64Pattern = /^[A-Za-z0-9+/]*={0,2}$/;\n return base64Pattern.test(str) && str.length % 4 === 0;\n } catch (err) {\n return false;\n }\n}\n\n// Helper function to detect image format from base64\nfunction detectImageFormat(base64String) {\n try {\n const header = base64String.substring(0, 20);\n const decoded = Buffer.from(header, 'base64');\n \n // PNG signature: 89 50 4E 47\n if (decoded[0] === 0x89 && decoded[1] === 0x50 && decoded[2] === 0x4E && decoded[3] === 0x47) {\n return 'png';\n }\n // JPEG signature: FF D8 FF\n if (decoded[0] === 0xFF && decoded[1] === 0xD8 && decoded[2] === 0xFF) {\n return 'jpeg';\n }\n // GIF signature: 47 49 46\n if (decoded[0] === 0x47 && decoded[1] === 0x49 && decoded[2] === 0x46) {\n return 'gif';\n }\n // WebP signature: starts with \"RIFF\" and contains \"WEBP\"\n const str = decoded.toString('ascii', 0, 12);\n if (str.startsWith('RIFF') && str.includes('WEBP')) {\n return 'webp';\n }\n return 'unknown';\n } catch (error) {\n return 'unknown';\n }\n}\n\nfor (const [index, item] of inputData.entries()) {\n try {\n let base64String = null;\n let sourcePath = null;\n \n // Define possible paths where base64 data might be located\n const possiblePaths = [\n 'candidates.0.content.parts.1.inlineData.data',\n 'candidates.0.content.parts.0.inlineData.data',\n // Add other possible variations\n 'candidates.0.content.parts.1.inline_data.data',\n 'candidates.0.content.parts.0.inline_data.data',\n // Check if there are multiple parts\n 'candidates.0.content.parts.2.inlineData.data',\n 'candidates.0.content.parts.3.inlineData.data',\n // Fallback to common base64 field names\n 'base64',\n 'data',\n 'image',\n 'content'\n ];\n \n // Try to find base64 data in any of the possible paths\n for (const path of possiblePaths) {\n const value = getNestedProperty(item.json, path);\n if (value && typeof value === 'string' && value.length > 100) { // Minimum length check for image data\n // Handle data URL format if present\n let cleanValue = value;\n if (value.includes('data:image/') && value.includes('base64,')) {\n cleanValue = value.split('base64,')[1];\n }\n \n // Validate if it's proper base64\n if (isValidBase64(cleanValue)) {\n base64String = cleanValue;\n sourcePath = path;\n break;\n }\n }\n }\n \n // Also check if there are multiple parts and scan through them\n if (!base64String && item.json.candidates && item.json.candidates[0] && item.json.candidates[0].content && item.json.candidates[0].content.parts) {\n const parts = item.json.candidates[0].content.parts;\n for (let i = 0; i < parts.length; i++) {\n if (parts[i] && parts[i].inlineData && parts[i].inlineData.data) {\n const value = parts[i].inlineData.data;\n if (isValidBase64(value)) {\n base64String = value;\n sourcePath = `candidates.0.content.parts.${i}.inlineData.data`;\n break;\n }\n }\n // Also check for inline_data (underscore variant)\n if (parts[i] && parts[i].inline_data && parts[i].inline_data.data) {\n const value = parts[i].inline_data.data;\n if (isValidBase64(value)) {\n base64String = value;\n sourcePath = `candidates.0.content.parts.${i}.inline_data.data`;\n break;\n }\n }\n }\n }\n \n if (!base64String) {\n throw new Error('No valid base64 image data found in any of the expected paths');\n }\n \n // Convert to buffer\n const imageBuffer = Buffer.from(base64String, 'base64');\n \n // Detect original format\n const originalFormat = detectImageFormat(base64String);\n \n // Generate filename\n const timestamp = new Date().getTime();\n const filename = `converted_image_${timestamp}_${index}.png`;\n \n // Create output item\n const outputItem = {\n json: {\n success: true,\n filename: filename,\n mimeType: 'image/png',\n fileSize: imageBuffer.length,\n originalFormat: originalFormat,\n sourcePath: sourcePath,\n convertedAt: new Date().toISOString(),\n itemIndex: index\n },\n binary: {\n data: {\n data: base64String, // Use the clean base64 string\n mimeType: 'image/png',\n fileName: filename,\n fileExtension: 'png'\n }\n }\n };\n \n outputData.push(outputItem);\n \n } catch (error) {\n // Create error output with debugging information\n const errorItem = {\n json: {\n success: false,\n error: error.message,\n itemIndex: index,\n availablePaths: Object.keys(item.json).length > 0 ? \n JSON.stringify(item.json, null, 2).substring(0, 500) + '...' : 'No data',\n timestamp: new Date().toISOString()\n }\n };\n \n outputData.push(errorItem);\n }\n}\n\nreturn outputData;"
},
"typeVersion": 2
},
{
"id": "e6d8ca2d-e16e-48e0-9521-0318f4756c96",
"name": "If",
"type": "n8n-nodes-base.if",
"position": [
144,
1808
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "bc63b8aa-dd00-4672-920d-d08033c701ee",
"operator": {
"type": "number",
"operation": "exists",
"singleValue": true
},
"leftValue": "={{ $json.ID }}",
"rightValue": ""
}
]
},
"looseTypeValidation": true
},
"typeVersion": 2.2
},
{
"id": "3480ecd9-ed69-4d51-a749-e447bb97a0c6",
"name": "텍스트 메시지 전송",
"type": "n8n-nodes-base.telegram",
"position": [
288,
1664
],
"webhookId": "3f4a95c3-1460-4b5a-a85b-c6033977a1c2",
"parameters": {
"text": "Please wait 30 Seconds while we work our magic.",
"chatId": "={{ $json.ID }}",
"additionalFields": {
"appendAttribution": false
}
},
"typeVersion": 1.2
},
{
"id": "9c5ca8a8-ca2f-4c64-8091-e1616c598713",
"name": "텍스트 메시지 전송2",
"type": "n8n-nodes-base.telegram",
"position": [
288,
1952
],
"webhookId": "843019ac-000f-4de3-95ef-c476bb29f46c",
"parameters": {
"text": "Please upload a image to to edit.",
"chatId": "={{ $('Telegram Trigger').item.json.message.from.id }}",
"additionalFields": {
"appendAttribution": false
}
},
"typeVersion": 1.2
},
{
"id": "5d13dd2c-44fa-4f0b-aa3e-f806f12336f5",
"name": "스티커 노트",
"type": "n8n-nodes-base.stickyNote",
"position": [
-688,
288
],
"parameters": {
"width": 992,
"height": 464,
"content": "## Google Nano Banana Image Generator: Overview\n### How it works\nThis workflow is used to create AI images through Telegram messages. When a user sends any text or command in Telegram, that message is collected by n8n using the Telegram trigger. Then the text is cleaned and used as a prompt for Nano Banana, which is a small AI image model. The workflow sends this prompt to Nano Banana by using HTTP request node. The model then returns one generated image file. This image is again sent to the same user in Telegram chat automatically.\n\nThe whole process is done inside n8n without any other tools. You can also add a small OpenAI node in between if you want to make better image descriptions. The workflow is built in a simple way so anyone can test, learn, and extend it easily.\n\n### Setup steps\n1. Create your Telegram Bot by using BotFather and copy its token.\n2. Add your Telegram credentials in n8n.\n3. Copy your Nano Banana API key and paste it in HTTP Request node.\n4. (Optional) Add OpenAI credentials for refining prompt.\n5. Turn the workflow on and test by sending a message from your Telegram app."
},
"typeVersion": 1
},
{
"id": "969c8e2e-8175-47aa-9873-c2a36863f0e2",
"name": "스티커 노트1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-704,
1536
],
"parameters": {
"color": 6,
"width": 576,
"height": 384,
"content": "## Telegram Input\nThis part collects user messages or images from Telegram. The trigger starts the workflow each time user sends something. IF node checks message type and sends it to the correct branch for next action.\n"
},
"typeVersion": 1
},
{
"id": "eedd4464-fb3b-494f-9d5a-b905b0fd7282",
"name": "스티커 노트2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-688,
816
],
"parameters": {
"color": 6,
"width": 992,
"height": 272,
"content": "## Image Download and Preparation\nHere the workflow downloads the image that came from Telegram and changes it into base64 string. This format is easy to send further to Drive or to any API that needs encoded file.\n"
},
"typeVersion": 1
},
{
"id": "4e7bc232-9a87-49de-b65f-ea06d5d0ce6d",
"name": "스티커 노트3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-96,
1136
],
"parameters": {
"color": 6,
"width": 1344,
"height": 320,
"content": "## Upload and Record Update\nThis top flow uploads the file to Google Drive and then updates the connected record. After that, a text message is sent in Telegram to confirm that the upload and update is completed properly.\n"
},
"typeVersion": 1
},
{
"id": "c3588192-7477-44d5-bc05-d5a1a6d0a630",
"name": "스티커 노트4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-96,
1504
],
"parameters": {
"color": 6,
"width": 672,
"height": 608,
"content": "## Conditional Message Handling\nThis part is used for checking if the incoming message matches any saved record or keyword. Depending on result, Telegram sends one of the two reply messages to guide the user further.\n"
},
"typeVersion": 1
},
{
"id": "02ec6043-51a2-46d8-889f-0c3ec3e2fac2",
"name": "스티커 노트5",
"type": "n8n-nodes-base.stickyNote",
"position": [
640,
1520
],
"parameters": {
"color": 6,
"width": 1472,
"height": 480,
"content": "## Image Editing and Return Flow\nThis section manages full image process. It downloads the earlier stored image, extracts the content, and edits it using image API. The final image file is made and uploaded again, then sent back to Telegram chat as photo message.\n"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {
"Telegram Trigger": [
{
"json": {
"message": {
"chat": {
"id": 6397287561,
"type": "private",
"last_name": "K",
"first_name": "S"
},
"date": 1757313325,
"from": {
"id": 6397287561,
"is_bot": false,
"last_name": "K",
"first_name": "S",
"language_code": "en"
},
"photo": [
{
"width": 90,
"height": 90,
"file_id": "AgACAgUAAxkBAAOwaL55LZCAqkqmrjN0scLJuSFYZY0AAsnDMRtJZfBVFUPlS6nhAAFYAQADAgADcwADNgQ",
"file_size": 1322,
"file_unique_id": "AQADycMxG0ll8FV4"
},
{
"width": 320,
"height": 320,
"file_id": "AgACAgUAAxkBAAOwaL55LZCAqkqmrjN0scLJuSFYZY0AAsnDMRtJZfBVFUPlS6nhAAFYAQADAgADbQADNgQ",
"file_size": 15801,
"file_unique_id": "AQADycMxG0ll8FVy"
},
{
"width": 775,
"height": 775,
"file_id": "AgACAgUAAxkBAAOwaL55LZCAqkqmrjN0scLJuSFYZY0AAsnDMRtJZfBVFUPlS6nhAAFYAQADAgADeAADNgQ",
"file_size": 39961,
"file_unique_id": "AQADycMxG0ll8FV9"
}
],
"message_id": 176
},
"update_id": 388279181
}
}
]
},
"settings": {
"executionOrder": "v1"
},
"versionId": "273d8db8-3328-4877-a4b9-8061e360fe36",
"connections": {
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},
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},
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},
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[
{
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}
]
]
},
"5db3e15a-fbbc-487d-93c5-c71e042e8478": {
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"a7ce2cca-93a0-4b0d-8ba3-eb51d24e1362": {
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"type": "main",
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},
"47fbda3a-c350-4dc8-8e4d-a02b989b44fc": {
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[
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},
"e06f6e8f-5f00-43ee-bec3-75e4deca92a3": {
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[
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},
"fed96b49-66fa-4479-8c66-b89370907341": {
"main": [
[
{
"node": "1d00c8bf-191f-4086-a385-1840f56767a2",
"type": "main",
"index": 1
}
]
]
},
"3480ecd9-ed69-4d51-a749-e447bb97a0c6": {
"main": [
[
{
"node": "97d63a01-3a54-4e4f-8941-928e98185b3b",
"type": "main",
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}
]
]
},
"53655955-c77f-4a95-ade0-021895f197ba": {
"main": [
[
{
"node": "0b8c5466-58ca-47ea-89cf-5d5ee95e90ef",
"type": "main",
"index": 0
}
]
]
}
}
}자주 묻는 질문
이 워크플로우를 어떻게 사용하나요?
위의 JSON 구성 코드를 복사하여 n8n 인스턴스에서 새 워크플로우를 생성하고 "JSON에서 가져오기"를 선택한 후, 구성을 붙여넣고 필요에 따라 인증 설정을 수정하세요.
이 워크플로우는 어떤 시나리오에 적합한가요?
고급 - 콘텐츠 제작, 멀티모달 AI
유료인가요?
이 워크플로우는 완전히 무료이며 직접 가져와 사용할 수 있습니다. 다만, 워크플로우에서 사용하는 타사 서비스(예: OpenAI API)는 사용자 직접 비용을 지불해야 할 수 있습니다.
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