GPT-4o 기반 레스토랑 주문 챗봇 (POS 통합)
고급
이것은Sales, Support, AI분야의자동화 워크플로우로, 19개의 노드를 포함합니다.주로 If, Code, GoogleSheets, SplitInBatches, Agent 등의 노드를 사용하며인공지능 기술을 결합하여 스마트 자동화를 구현합니다. GPT-4o 기반 레스토랑 주문 챗봇으로, POS 시스템 통합을 지원합니다.
사전 요구사항
- •Google Sheets API 인증 정보
- •OpenAI API Key
사용된 노드 (19)
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
{
"meta": {
"instanceId": "d73282515b90623d4a8783919a2d772c706425d649e1512792f37ac51e07e4a8"
},
"nodes": [
{
"id": "62b3c7cb-1993-44f1-8b86-38a34ca1d029",
"name": "정보 추출기",
"type": "@n8n/n8n-nodes-langchain.informationExtractor",
"position": [
-200,
500
],
"parameters": {
"text": "={{ $json.query }}",
"options": {},
"schemaType": "fromJson",
"jsonSchemaExample": "{\n \"name\": \"Information Extractor\",\n \"type\": \"n8n-nodes-base.informationExtractor\",\n \"parameters\": {\n \"extract\": [\n {\n \"name\": \"items\",\n \"pattern\": \"(latte|coffee|tea|cappuccino)\"\n },\n {\n \"name\": \"quantity\",\n \"pattern\": \"\\\\d+\"\n },\n {\n \"name\": \"table\",\n \"pattern\": \"table number (\\\\d+)\"\n }\n ]\n }\n}\n"
},
"typeVersion": 1
},
{
"id": "75883f27-af58-4791-9e1a-a70b83e1cead",
"name": "OpenAI 채팅 모델1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-180,
740
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "OizdHUANhz9NIHyd",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "aeefdd4b-bf7d-4824-97d8-0afc356fb7d6",
"name": "조건문",
"type": "n8n-nodes-base.if",
"position": [
120,
540
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "8a5dda0c-a567-4305-83a3-68d6fb573dd3",
"operator": {
"type": "array",
"operation": "empty",
"singleValue": true
},
"leftValue": "={{ $json.output.parameters.extract }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "9e3f8a1b-ccd8-4f4d-91cb-b99cc46f412f",
"name": "Google 스프레드시트",
"type": "n8n-nodes-base.googleSheets",
"position": [
840,
420
],
"parameters": {
"columns": {
"value": {
"Item": "={{ $json.item }}",
"Quantity": "={{ $json.quantity }}",
"Table No": "={{ $json.table }}",
"Timestamp": "={{ $now }}"
},
"schema": [
{
"id": "Timestamp",
"type": "string",
"display": true,
"required": false,
"displayName": "Timestamp",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Table No",
"type": "string",
"display": true,
"required": false,
"displayName": "Table No",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Item",
"type": "string",
"display": true,
"required": false,
"displayName": "Item",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Quantity",
"type": "string",
"display": true,
"required": false,
"displayName": "Quantity",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/16fXaxEcfnq_-oif9tp94-3uTeHSFWoSnuBPNTljuW-k/edit#gid=0",
"cachedResultName": "Order log"
},
"documentId": {
"__rl": true,
"mode": "url",
"value": "https://docs.google.com/spreadsheets/d/16fXaxEcfnq_-oif9tp94-3uTeHSFWoSnuBPNTljuW-k/edit?usp=sharing"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "0RSJGMBcFzxY9GkS",
"name": "Google Sheets account"
}
},
"typeVersion": 4.5
},
{
"id": "4cc1818f-1585-42e1-a111-7b55557aebcb",
"name": "코드",
"type": "n8n-nodes-base.code",
"position": [
380,
560
],
"parameters": {
"language": "python",
"pythonCode": "# Input from n8n\ninput_data = items\n\n# Get the extracted list\nextract_data = input_data[0].get('json', {}).get('output', {}).get('parameters', {}).get('extract', [])\n\n# Prepare variables\norder_items = []\ntable_number = None\n\n# Separate entries by type\nitems_list = []\nquantities = []\n\n# Parse all entries\nfor entry in extract_data:\n if entry['name'] == 'table number':\n table_number = entry['pattern']\n elif entry['name'] == 'item':\n items_list.append(entry['pattern'])\n elif entry['name'] == 'quantity':\n quantities.append(int(entry['pattern']))\n\n# Pair items and quantities\nfor i in range(len(items_list)):\n item_data = {\n 'item': items_list[i],\n 'quantity': quantities[i] if i < len(quantities) else None,\n 'table': table_number\n }\n order_items.append(item_data)\n\n# Set final output\noutput = [{'json': item} for item in order_items]\n\nreturn output"
},
"typeVersion": 2
},
{
"id": "a92d2745-148b-4e2a-b8f7-82d3993ff34f",
"name": "항목 반복",
"type": "n8n-nodes-base.splitInBatches",
"position": [
620,
500
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "aea89e6c-37a9-4859-adc8-b7e449701503",
"name": "대체 노드",
"type": "n8n-nodes-base.noOp",
"position": [
800,
660
],
"parameters": {},
"typeVersion": 1
},
{
"id": "b31dba52-b27e-4267-be32-a7730b4d08a8",
"name": "아무 작업 없음",
"type": "n8n-nodes-base.noOp",
"position": [
440,
400
],
"parameters": {},
"typeVersion": 1
},
{
"id": "d7f9a381-6bc2-44d0-81ac-6e0fbe77d70a",
"name": "스티커 노트",
"type": "n8n-nodes-base.stickyNote",
"position": [
-260,
220
],
"parameters": {
"color": 3,
"width": 340,
"height": 680,
"content": "## JSON PARSER\n\n1.converts the textual data final order like\nitem name \nquantity \nand table name in a json.\n\n2.if the data doesn't include the above it returns null."
},
"typeVersion": 1
},
{
"id": "acc7a528-f767-4576-b08d-6fc386f57648",
"name": "스티커 노트1",
"type": "n8n-nodes-base.stickyNote",
"position": [
100,
220
],
"parameters": {
"color": 2,
"width": 460,
"height": 680,
"content": "## Refine/Split the jsons into multiple items\n\nIf the data from previous item is not null the custom code block splits the data into multiple json items in a list."
},
"typeVersion": 1
},
{
"id": "857a3102-f5e1-4db5-afb4-154544414701",
"name": "스티커 노트2",
"type": "n8n-nodes-base.stickyNote",
"position": [
580,
220
],
"parameters": {
"color": 4,
"width": 440,
"height": 680,
"content": "## Send each item as a record in Google sheet\n\n\n**Each item is looped over and produce a batch of 1 item and appended as row in sheet with timestamp.\n"
},
"typeVersion": 1
},
{
"id": "a1ff2b0f-0b48-4ea2-8121-4e2d72197ef7",
"name": "레스토랑 채팅 워크플로에서 트리거됨",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
-440,
500
],
"parameters": {
"inputSource": "passthrough"
},
"typeVersion": 1.1
},
{
"id": "8689b773-a1c4-4de4-a66e-fab8c9eb6244",
"name": "채팅 메시지 수신 시",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-140,
-280
],
"webhookId": "d931c4a7-02f5-4359-918f-7ad3fae7b144",
"parameters": {
"public": true,
"options": {}
},
"typeVersion": 1.1
},
{
"id": "de310ce2-3868-4a0f-aa9b-38253e75dbda",
"name": "AI 에이전트",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
100,
-260
],
"parameters": {
"options": {
"systemMessage": "\n\nYou are a polite and efficient restaurant assistant.\n\nYour job is to take customer orders, verify the order details, correct any mistakes, and confirm the order.\n\nFollow these steps:\n\nGreeting and Asking for the Order\n\nIf the customer greets you (e.g., \"Hello\", \"Hi\", \"Good evening\"), respond with:\n\n\"Hello! How can I assist you today? What would you like to order?\"\n\nOrder Parsing and Understanding\n\nAccept orders in flexible formats, such as:\n\n\"1 latte, 2 coffee, table number 5\"\n\n\"latte 2, pepsi 1, table 3\"\n\n\"1 cappucino\"\n\n\"1 tea table no 4\"\n\nYour goal is to extract the following:\n\nItem names (e.g., latte, coffee, chocolate, tea, pepsi)\n\nQuantities (must be numeric)\n\nTable number (must be numeric)\n\nVerify and Handle Missing or Incorrect Information\n\nFor each item in the order:\n\nIf the item name is missing, respond:\n\"Sorry, the item name is missing. What would you like to order?\"\n\nIf the quantity is missing, respond:\n\"How many [item] would you like?\"\n\nIf the table number is missing, respond:\n\"Could you please provide a table number?\"\n\nIf there are spelling mistakes in the item name, suggest corrections. Example:\n\"Did you mean chocolate instead of chocolat? Please confirm.\"\n\nUse fuzzy matching to detect common variations and typos.\n\nFinal Confirmation\n\nOnce all necessary details are collected, present an order summary like this:\n\nHere’s your order summary:\n\n1 latte\n\n2 coffee\n\nTable number: 5\nShall I confirm this order?\n\nOn Confirmation: Use the Tool\n\nWhen the user confirms, use the tool ConfirmOrder to send the final confirmation message as plain text in this format:\n\nThank you for confirming! Your order will be prepared shortly. Enjoy your time with us!\n\nOrder details are following:\nitem quantity\nlatte 1\ncoffee 2\n\nAdded to table number 5\n\nEnsure numeric values (quantities and table numbers) are correctly extracted, even if they appear at the start or end. Always confirm with the user if there is any uncertainty.\n\n\n\n\n\n\n\n\n"
}
},
"typeVersion": 1.9
},
{
"id": "9dda45ee-0a92-448c-8a7e-8daa99282cda",
"name": "OpenAI 채팅 모델",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-20,
20
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {
"responseFormat": "text"
}
},
"credentials": {
"openAiApi": {
"id": "OizdHUANhz9NIHyd",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "0c0189d5-8fb4-4679-b2e2-221a3e2a4c88",
"name": "n8n 워크플로 도구 호출",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"position": [
360,
20
],
"parameters": {
"workflowId": {
"__rl": true,
"mode": "list",
"value": "wgaJ0eJQtYA8oKSC",
"cachedResultName": "Restaurant POS workflow"
},
"description": "This tool sends the text output generated by the AI Agent node to another n8n workflow for additional handling or automation.",
"workflowInputs": {
"value": {},
"schema": [],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
}
},
"notesInFlow": false,
"typeVersion": 2.2
},
{
"id": "9292db7f-6ffc-486e-b31a-bcbd6ef7ab98",
"name": "최근 5개 대화 메모리",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
140,
40
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "2782d5b6-d33b-4c89-ac79-90bf380f0828",
"name": "스티커 노트3",
"type": "n8n-nodes-base.stickyNote",
"position": [
60,
-380
],
"parameters": {
"width": 340,
"height": 300,
"content": "## Restaurant Order Chat bot\n** It chats with the user and refines the order for the pos system in another workflow."
},
"typeVersion": 1
},
{
"id": "7c298718-e9e3-40d3-a612-94c578bd3100",
"name": "스티커 노트4",
"type": "n8n-nodes-base.stickyNote",
"position": [
500,
-20
],
"parameters": {
"color": 5,
"content": "## Call the subworkflow\nit passes the data to the subworkflow for further process\n"
},
"typeVersion": 1
}
],
"pinData": {},
"connections": {
"aeefdd4b-bf7d-4824-97d8-0afc356fb7d6": {
"main": [
[
{
"node": "b31dba52-b27e-4267-be32-a7730b4d08a8",
"type": "main",
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}
],
[
{
"node": "4cc1818f-1585-42e1-a111-7b55557aebcb",
"type": "main",
"index": 0
}
]
]
},
"4cc1818f-1585-42e1-a111-7b55557aebcb": {
"main": [
[
{
"node": "a92d2745-148b-4e2a-b8f7-82d3993ff34f",
"type": "main",
"index": 0
}
]
]
},
"de310ce2-3868-4a0f-aa9b-38253e75dbda": {
"main": [
[]
]
},
"aea89e6c-37a9-4859-adc8-b7e449701503": {
"main": [
[
{
"node": "a92d2745-148b-4e2a-b8f7-82d3993ff34f",
"type": "main",
"index": 0
}
]
]
},
"a92d2745-148b-4e2a-b8f7-82d3993ff34f": {
"main": [
[
{
"node": "9e3f8a1b-ccd8-4f4d-91cb-b99cc46f412f",
"type": "main",
"index": 0
}
],
[
{
"node": "aea89e6c-37a9-4859-adc8-b7e449701503",
"type": "main",
"index": 0
}
]
]
},
"9dda45ee-0a92-448c-8a7e-8daa99282cda": {
"ai_languageModel": [
[
{
"node": "de310ce2-3868-4a0f-aa9b-38253e75dbda",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"75883f27-af58-4791-9e1a-a70b83e1cead": {
"ai_languageModel": [
[
{
"node": "62b3c7cb-1993-44f1-8b86-38a34ca1d029",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"62b3c7cb-1993-44f1-8b86-38a34ca1d029": {
"main": [
[
{
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"type": "main",
"index": 0
}
]
]
},
"0c0189d5-8fb4-4679-b2e2-221a3e2a4c88": {
"ai_tool": [
[
{
"node": "de310ce2-3868-4a0f-aa9b-38253e75dbda",
"type": "ai_tool",
"index": 0
}
]
]
},
"8689b773-a1c4-4de4-a66e-fab8c9eb6244": {
"main": [
[
{
"node": "de310ce2-3868-4a0f-aa9b-38253e75dbda",
"type": "main",
"index": 0
}
]
]
},
"9292db7f-6ffc-486e-b31a-bcbd6ef7ab98": {
"ai_memory": [
[
{
"node": "de310ce2-3868-4a0f-aa9b-38253e75dbda",
"type": "ai_memory",
"index": 0
}
]
]
},
"a1ff2b0f-0b48-4ea2-8121-4e2d72197ef7": {
"main": [
[
{
"node": "62b3c7cb-1993-44f1-8b86-38a34ca1d029",
"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 - 영업, 지원, 인공지능
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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