AI 어시스턴트와 Airtable 대화 및 데이터 분석
고급
이것은Engineering, AI분야의자동화 워크플로우로, 41개의 노드를 포함합니다.주로 If, Set, Merge, Switch, Airtable 등의 노드를 사용하며인공지능 기술을 결합하여 스마트 자동화를 구현합니다. AI스마트어시스턴트与Airtable对话及데이터분석
사전 요구사항
- •Airtable API Key
- •대상 API의 인증 정보가 필요할 수 있음
- •OpenAI API Key
사용된 노드 (41)
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
{
"nodes": [
{
"id": "799d2e0c-29b9-494c-b11a-d79c7ed4a06d",
"name": "OpenAI 채팅 모델",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
920,
480
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "zJhr5piyEwVnWtaI",
"name": "OpenAi club"
}
},
"typeVersion": 1
},
{
"id": "6254ef4e-9699-404e-96a4-569326cce48d",
"name": "AI 에이전트",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1160,
200
],
"parameters": {
"text": "={{ $('When chat message received').item.json.chatInput }}",
"agent": "openAiFunctionsAgent",
"options": {
"maxIterations": 10,
"systemMessage": "You are Airtable assistant. \nYou need to process user's requests and run relevant tools for that. \n\nPlan and execute in right order runs of tools to get data for user's request.\n\nFeel free to ask questions before do actions - especially if you noticed some inconcistency in user requests that might be error/misspelling. \n\nIMPORTANT Always check right table and base ids before doing queries.\n\nIMPORTANT Use Code function to do aggregation functions that requires math like - count, sum, average and etc. Aggegation function could be recognized by words like \"how many\",\"count\",\"what number\" and etc.\nUse Code function to generate graph and images.\n\nIMPORTANT If search with filter failed - try to fetch records without filter\n\nIMPORTANT Ask yourself before answering - am I did everything is possible? Is the answer is right? Is the answer related to user request?\n\nIMPORTANT Always return in response name of Base and Table where records from. "
},
"promptType": "define"
},
"typeVersion": 1.6
},
{
"id": "227a5427-c270-47dc-bc08-4bb321314926",
"name": "메모",
"type": "n8n-nodes-base.stickyNote",
"position": [
1740,
620
],
"parameters": {
"height": 80,
"content": "### Replace Mapbox public key - <your_public_key> in code"
},
"typeVersion": 1
},
{
"id": "667751f4-9815-45b7-8dd2-9a0821a7a5a7",
"name": "메모1",
"type": "n8n-nodes-base.stickyNote",
"position": [
840,
640
],
"parameters": {
"height": 80,
"content": "### Replace OpenAI connection"
},
"typeVersion": 1
},
{
"id": "a9cdec25-4167-44a9-9d3c-fb04aac7bb32",
"name": "윈도우 버퍼 메모리",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
1080,
480
],
"parameters": {
"sessionKey": "={{ $('When chat message received').item.json.sessionId }}",
"sessionIdType": "customKey"
},
"typeVersion": 1.3
},
{
"id": "dfab4eb2-ba30-4756-8a52-5d73de9fba53",
"name": "채팅 메시지 수신 시",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
940,
200
],
"webhookId": "abf9ab75-eaca-4b91-b3ba-c0f83d3daba4",
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "259e3d13-ca92-4756-af69-34065dbe08f3",
"name": "워크플로우 실행 트리거",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
760,
1340
],
"parameters": {},
"typeVersion": 1
},
{
"id": "5b80c2c8-7649-40f2-b9be-d090d8bd5ae9",
"name": "Response",
"type": "n8n-nodes-base.set",
"position": [
2740,
1360
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "cfdbe2f5-921e-496d-87bd-9c57fdc22a7a",
"name": "response",
"type": "object",
"value": "={{$json}}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "761f5593-f85c-44cd-abbd-aeac78bc31f8",
"name": "스위치",
"type": "n8n-nodes-base.switch",
"position": [
980,
1320
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "get_bases",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $('Execute Workflow Trigger').item.json.command }}",
"rightValue": "get_bases"
}
]
},
"renameOutput": true
},
{
"outputKey": "get_base_tables_schema",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "26a3ffe8-c8a6-4564-8d18-5494a8059372",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $('Execute Workflow Trigger').item.json.command }}",
"rightValue": "get_base_tables_schema"
}
]
},
"renameOutput": true
},
{
"outputKey": "search",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "0f51cc26-2e42-42e1-a5c2-cb1d2e384962",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $('Execute Workflow Trigger').item.json.command }}",
"rightValue": "search"
}
]
},
"renameOutput": true
},
{
"outputKey": "code",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "51031140-5ceb-48aa-9f33-d314131a9653",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $('Execute Workflow Trigger').item.json.command }}",
"rightValue": "code"
}
]
},
"renameOutput": true
}
]
},
"options": {}
},
"typeVersion": 3.2
},
{
"id": "d6252c5b-a820-4ded-b59b-ab2fb2e277c3",
"name": "집계",
"type": "n8n-nodes-base.aggregate",
"position": [
1780,
980
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData"
},
"typeVersion": 1
},
{
"id": "1442ca2e-1793-4029-b398-61d6e6f1c346",
"name": "집계1",
"type": "n8n-nodes-base.aggregate",
"position": [
1780,
1140
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData"
},
"typeVersion": 1
},
{
"id": "a81b4dcc-c999-43be-a0ea-e37f3c7c9f9d",
"name": "병합",
"type": "n8n-nodes-base.merge",
"position": [
1960,
1360
],
"parameters": {},
"typeVersion": 3
},
{
"id": "8029213c-fd8a-4673-a2a0-11b90fd23971",
"name": "집계2",
"type": "n8n-nodes-base.aggregate",
"position": [
2260,
1360
],
"parameters": {
"options": {
"mergeLists": true
},
"fieldsToAggregate": {
"fieldToAggregate": [
{
"fieldToAggregate": "records"
}
]
}
},
"typeVersion": 1
},
{
"id": "f5f99038-9d19-49ed-9f50-3cd0270bf9ce",
"name": "If1",
"type": "n8n-nodes-base.if",
"position": [
2120,
1720
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "fcb24127-53f9-4498-b0fd-463bd4966ac9",
"operator": {
"type": "string",
"operation": "notExists",
"singleValue": true
},
"leftValue": "={{ $json.data[0].attachments[0].file_id }}",
"rightValue": ""
},
{
"id": "016ecba7-f6af-4881-a7d6-780dcb43223c",
"operator": {
"type": "string",
"operation": "notExists",
"singleValue": true
},
"leftValue": "={{ $json.data[0].content.find(x=>x.type==\"image_file\").image_file.file_id }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "abc7ddae-9ca9-4cf6-89a4-a63da8c1e036",
"name": "Response1",
"type": "n8n-nodes-base.set",
"position": [
2760,
1720
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "cfdbe2f5-921e-496d-87bd-9c57fdc22a7a",
"name": "response",
"type": "string",
"value": "={{ $json.data.url.replace('org/','org/dl/') }}"
}
]
},
"includeOtherFields": true
},
"typeVersion": 3.4
},
{
"id": "6f40d50f-70e8-4b64-aa42-ae9262fb8381",
"name": "메모4",
"type": "n8n-nodes-base.stickyNote",
"position": [
2080,
1520
],
"parameters": {
"width": 160,
"height": 80,
"content": "### Replace Airtable connection"
},
"typeVersion": 1
},
{
"id": "de99a161-5ab3-4b54-bdf7-340d74aa5a93",
"name": "메모5",
"type": "n8n-nodes-base.stickyNote",
"position": [
1740,
1600
],
"parameters": {
"width": 160,
"height": 80,
"content": "### Replace OpenAI connection"
},
"typeVersion": 1
},
{
"id": "c1e030fd-4449-43ca-a4e7-a863f9487614",
"name": "메모6",
"type": "n8n-nodes-base.stickyNote",
"position": [
1540,
860
],
"parameters": {
"width": 160,
"height": 80,
"content": "### Replace Airtable connection"
},
"typeVersion": 1
},
{
"id": "4375d3a4-0b3b-4de6-9db7-42af4148af2b",
"name": "메모3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1360,
1900
],
"parameters": {
"width": 1180,
"height": 80,
"content": "### Replace OpenAI connection"
},
"typeVersion": 1
},
{
"id": "138f813c-d0b0-4a2b-8833-69f1decc9253",
"name": "메모7",
"type": "n8n-nodes-base.stickyNote",
"position": [
700,
0
],
"parameters": {
"color": 6,
"width": 1320,
"height": 780,
"content": "### Workflow 1"
},
"typeVersion": 1
},
{
"id": "ca87c7b7-ab34-4ff9-8d74-cef90e6f1e5e",
"name": "메모8",
"type": "n8n-nodes-base.stickyNote",
"position": [
700,
840
],
"parameters": {
"color": 6,
"width": 2240,
"height": 1180,
"content": "### Workflow 2"
},
"typeVersion": 1
},
{
"id": "a5cdf41a-f2ca-4203-94ce-45795395ea92",
"name": "메모9",
"type": "n8n-nodes-base.stickyNote",
"position": [
300,
680
],
"parameters": {
"color": 7,
"width": 330.5152611046425,
"height": 239.5888196628349,
"content": "### ... or watch set up video [20 min]\n[](https://youtu.be/SotqsAZEhdc)\n"
},
"typeVersion": 1
},
{
"id": "697889c4-15e7-4099-89b8-f4e2e3a3abac",
"name": "메모10",
"type": "n8n-nodes-base.stickyNote",
"position": [
0,
0
],
"parameters": {
"color": 7,
"width": 636,
"height": 657,
"content": "\n## AI Agent to chat with Airtable and analyze data\n**Made by [Mark Shcherbakov](https://www.linkedin.com/in/marklowcoding/) from community [5minAI](https://www.skool.com/5minai)**\n\nEngaging with data stored in Airtable often requires manual navigation and time-consuming searches. This workflow allows users to interact conversationally with their datasets, retrieving essential information quickly while minimizing the need for complex queries.\n\nThis workflow enables an AI agent to facilitate chat interactions over Airtable data. The agent can:\n- Retrieve order records, product details, and other relevant data.\n- Execute mathematical functions to analyze data such as calculating averages and totals.\n- Optionally generate maps for geographic data visualization.\n\n1. **Dynamic Data Retrieval**: The agent uses user prompts to dynamically query the dataset.\n2. **Memory Management**: It retains context during conversations, allowing users to engage in a more natural dialogue.\n3. **Search and Filter Capabilities**: Users can perform tailored searches with specific parameters or filters to refine their results."
},
"typeVersion": 1
},
{
"id": "a9f7c4fd-c07a-4c7c-875d-74b27e3f1fbf",
"name": "메모11",
"type": "n8n-nodes-base.stickyNote",
"position": [
0,
680
],
"parameters": {
"color": 7,
"width": 280,
"height": 346,
"content": "### Set up steps\n\n1. **Separate workflows**:\n\t- Create additional workflow and move there Workflow 2.\n\n2. **Replace credentials**:\n\t- Replace connections and credentials in all nodes.\n\n3. **Start chat**:\n\t- Ask questions and don't forget to mention required base name."
},
"typeVersion": 1
},
{
"id": "0c86638f-7220-415d-a920-13761da925a6",
"name": "Search records",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"position": [
1500,
480
],
"parameters": {
"name": "search",
"fields": {
"values": [
{
"name": "command",
"stringValue": "search"
}
]
},
"schemaType": "manual",
"workflowId": {
"__rl": true,
"mode": "list",
"value": "zVd0G4m33K6KrBvV",
"cachedResultName": "Airtable Agent Tools"
},
"description": "Search records in specific base and table.\n\n- Use Filter (optional) rules for filtering - describe what logic you want to see in filter including field names. \nIMPORTANT - specify all related fields with types for Filter query with right names based on schema. Tool doesn't know schema and type of fields.\n\n- Use Limit (optional) to get more/less records - default = All records. IMPORTANT use default value only when user ask to get all records for analysis.\n\n- Always try to limit list of fields based on user request or in case of number of fields > 30. IMPORTANT Use fields names only.\n \n- Sort by one/multiple fields if needed - order in array is order of level for sorting.\n\nInput example:\nbase_id - appHwXgLVrBujox4J\ntable_id - tblrGzFneREP5Dktl\nlimit - 100\nsort (optional) - [{\"field\":\"Name\",\"direction\":\"asc\"}]\nfilter_desc (optional) - field Name (string) should be equal/contains Mark\nfields (optional) - [\"Name\",\"Email\"]\n\nOutput example:\nRecord 1 - value 1, value 2",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"base_id\": {\n \"type\": \"string\",\n \"description\": \"ID of the base to search in\"\n },\n \"table_id\": {\n \"type\": \"string\",\n \"description\": \"ID of the table to search in\"\n },\n \"limit\": {\n \"type\": \"number\",\n \"description\": \"Number of records to retrieve (default is all records)\"\n },\n \"filter_desc\": {\n \"type\": \"string\",\n \"description\": \"Text description of the filter logic\"\n },\n \"sort\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"field\": { \"type\": \"string\" },\n \"direction\": { \"type\": \"string\", \"enum\": [\"asc\", \"desc\"] }\n },\n \"required\": [\"field\", \"direction\"]\n },\n \"description\": \"Array of sorting rules for the query\"\n },\n \"fields\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" },\n \"description\": \"List of fields to retrieve\"\n }\n },\n \"required\": [\"base_id\", \"table_id\"]\n}",
"specifyInputSchema": true
},
"typeVersion": 1.2
},
{
"id": "7ba1d6ac-f1a2-4b8d-a9a5-ce92eaa4e7fa",
"name": "Process data with code",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"position": [
1640,
480
],
"parameters": {
"name": "code",
"fields": {
"values": [
{
"name": "command",
"stringValue": "code"
}
]
},
"schemaType": "manual",
"workflowId": {
"__rl": true,
"mode": "list",
"value": "zVd0G4m33K6KrBvV",
"cachedResultName": "Airtable Agent Tools"
},
"description": "Process data with code. Use for math functions and image (graphs) generation. \nIMPORTANT Provide raw data only, don't preprocess or use math functions by yourself\n\nInput example:\nrequest - Count average\ndata - 1,2,3\n\nOutput example:\nAverage is 2\nImage file",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"request\": {\n \"type\": \"string\",\n \"description\": \"Description of the operation to perform.\"\n },\n \"data\": {\n \"type\": \"string\",\n \"description\": \"Stringified data - JSON, strings, arrays and etc.\"\n }\n },\n \"required\": [\"request\", \"data\"]\n}",
"specifyInputSchema": true
},
"typeVersion": 1.2
},
{
"id": "3754175c-6f74-4750-b2e7-00e2bd3caf6d",
"name": "Create map image",
"type": "@n8n/n8n-nodes-langchain.toolCode",
"position": [
1800,
480
],
"parameters": {
"name": "create_map",
"jsCode": "// Example: convert the incoming query to uppercase and return it\n\nreturn `https://api.mapbox.com/styles/v1/mapbox/streets-v12/static/${query.markers}/-96.9749,41.8219,3.31,0/800x500?before_layer=admin-0-boundary&access_token=<your_public_key>`;",
"schemaType": "manual",
"description": "Create link with image for map graph.\nUse addresses' longitude and latitude to create input data.\n\nInput Example:\npin-s+555555(-74.006,40.7128),pin-s+555555(-118.2437,34.0522)\n\nOutput Example:\nImage link.",
"inputSchema": "{\n\"type\": \"object\",\n\"properties\": {\n\t\"markers\": {\n\t\t\"type\": \"string\",\n\t\t\"description\": \"List of markers with longitude and latitude data separated by comma. Keep the same color 555555|Example: pin-s+555555(-74.006,40.7128),pin-s+555555(-118.2437,34.0522)\"\n\t\t}\n\t}\n}",
"specifyInputSchema": true
},
"typeVersion": 1.1
},
{
"id": "135078ea-6a3f-4aee-9f60-c6d5832e446e",
"name": "Get list of bases",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"position": [
1220,
480
],
"parameters": {
"name": "get_bases",
"fields": {
"values": [
{
"name": "command",
"stringValue": "get_bases"
}
]
},
"workflowId": {
"__rl": true,
"mode": "list",
"value": "zVd0G4m33K6KrBvV",
"cachedResultName": "Airtable Agent Tools"
},
"description": "Fetches the list of available bases.\n\nOutput:\n- List of bases with their IDs and names."
},
"typeVersion": 1.2
},
{
"id": "cd4781d0-f873-4aea-951c-6809358c1db6",
"name": "Get base schema",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"position": [
1360,
480
],
"parameters": {
"name": "get_base_tables_schema",
"fields": {
"values": [
{
"name": "command",
"stringValue": "get_base_tables_schema"
}
]
},
"schemaType": "manual",
"workflowId": {
"__rl": true,
"mode": "list",
"value": "zVd0G4m33K6KrBvV",
"cachedResultName": "Airtable Agent Tools"
},
"description": "Fetches the schema of tables in a specific base by id.\n\nInput:\nbase_id: appHwXgLVrBujox4J\n\nOutput:\ntable 1: field 1 - type string, fields 2 - type number",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"base_id\": {\n \"type\": \"string\",\n \"description\": \"ID of the base to retrieve the schema for. Format - appHwXgLVrBujox4J\"\n }\n },\n \"required\": [\"base_id\"]\n}",
"specifyInputSchema": true
},
"typeVersion": 1.2
},
{
"id": "45c8b2eb-f43a-48b1-a270-9caeda9da0b0",
"name": "Get Bases",
"type": "n8n-nodes-base.airtable",
"position": [
1580,
980
],
"parameters": {
"options": {},
"resource": "base"
},
"credentials": {
"airtableTokenApi": {
"id": "xZwG0YpqsxpWrzVM",
"name": "Mark Airtable account"
}
},
"typeVersion": 2.1
},
{
"id": "bb8036bc-1c23-461b-bd03-2461e31c6cb6",
"name": "Get Base/Tables schema",
"type": "n8n-nodes-base.airtable",
"position": [
1580,
1140
],
"parameters": {
"base": {
"__rl": true,
"mode": "id",
"value": "={{ $('Execute Workflow Trigger').item.json.query.base_id }}"
},
"resource": "base",
"operation": "getSchema"
},
"credentials": {
"airtableTokenApi": {
"id": "xZwG0YpqsxpWrzVM",
"name": "Mark Airtable account"
}
},
"typeVersion": 2.1
},
{
"id": "dab309d9-3629-44ba-9f0a-ede55f96488f",
"name": "If filter description exists",
"type": "n8n-nodes-base.if",
"position": [
1340,
1360
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "fcb24127-53f9-4498-b0fd-463bd4966ac9",
"operator": {
"type": "string",
"operation": "notExists",
"singleValue": true
},
"leftValue": "={{ $('Execute Workflow Trigger').item.json.query.filter_desc }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "4cc416aa-50bd-4b60-ae51-887c4ee97c88",
"name": "Airtable - Search records",
"type": "n8n-nodes-base.httpRequest",
"onError": "continueErrorOutput",
"position": [
2100,
1360
],
"parameters": {
"url": "=https://api.airtable.com/v0/{{ $('Execute Workflow Trigger').item.json.query.base_id }}/{{ $('Execute Workflow Trigger').item.json.query.table_id }}/listRecords",
"method": "POST",
"options": {
"pagination": {
"pagination": {
"parameters": {
"parameters": [
{
"name": "offset",
"type": "body",
"value": "={{ $response.body.offset}}"
}
]
},
"completeExpression": "={{ $response.body.offset==undefined}}",
"paginationCompleteWhen": "other"
}
}
},
"jsonBody": "={{ \n Object.fromEntries(\n Object.entries({\n sort: $('Execute Workflow Trigger').item.json.query.sort,\n limit: $('Execute Workflow Trigger').item.json.query.limit,\nfields: $('Execute Workflow Trigger').item.json.query.fields,\nfilterByFormula: $('Merge').item.json.choices == undefined ? undefined : JSON.parse($json.choices[0].message.content).filter\n }).filter(([key, value]) => value !== undefined)\n )\n}}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "airtableTokenApi"
},
"credentials": {
"httpQueryAuth": {
"id": "1DXeuNaLSixqGPaU",
"name": "Query Auth account Youtube"
},
"airtableTokenApi": {
"id": "xZwG0YpqsxpWrzVM",
"name": "Mark Airtable account"
}
},
"typeVersion": 4.2
},
{
"id": "9dc71d31-8499-4b69-b87c-898217447d50",
"name": "OpenAI - Generate search filter",
"type": "n8n-nodes-base.httpRequest",
"position": [
1760,
1420
],
"parameters": {
"url": "=https://api.openai.com/v1/chat/completions",
"method": "POST",
"options": {},
"jsonBody": "={\n \"model\": \"gpt-4o-mini\",\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": {{ JSON.stringify($('Set schema and prompt').item.json.prompt) }}\n },\n {\n \"role\": \"user\",\n \"content\": \"{{ $('Execute Workflow Trigger').item.json.query.filter_desc }}\"\n }],\n \"response_format\":{ \"type\": \"json_schema\", \"json_schema\": {{ $('Set schema and prompt').item.json.schema }}\n\n }\n }",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"id": "zJhr5piyEwVnWtaI",
"name": "OpenAi club"
}
},
"typeVersion": 4.2
},
{
"id": "16e4ea97-ea73-45a0-aa88-0f9a2969a6a3",
"name": "설정 schema and prompt",
"type": "n8n-nodes-base.set",
"position": [
1560,
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "dc09a5b4-ff6a-4cee-b87e-35de7336ac05",
"name": "prompt",
"type": "string",
"value": "=Analyse user request for Airtable filtration. User filter rules to build right formula. Think smart about filter (e.g. instead of search where Name equal to value - search where name contains lowercase value)\nIMPORTANT Check examples and best practices before building formula. \n\nIMPORTANT best practices:\n\nSEARCH(LOWER('example'), LOWER({Field})) ensures both the search term and field are compared in lowercase for consistent case-insensitive matching\n\nIMPORTANT Examples:\n\n- AND(SEARCH('urgent', {Notes}), {Priority} > 3) fetch records where “Notes” contain “urgent” and “Priority” is greater than 3\n- AND({Status} = 'Pending', IS_BEFORE({Due Date}, TODAY())) fetch records where “Status” is “Pending” and “Due Date” is before today\n- OR(SEARCH('error', {Logs}), SEARCH('warning', {Logs})) fetch records where “Logs” contain “error” or “warning”\n- AND(LEN({Description}) > 10, {Price} > 50) fetch records where “Description” is longer than 10 characters and “Price” is greater than 50\n- RECORD_ID() = 'rec12345' fetch a specific record by its ID\n- SEARCH('rec67890', ARRAYJOIN({Linked Records}, ',')) fetch records linked to a specific record ID rec67890\n- AND(SEARCH('rec12345', ARRAYJOIN({Linked Records}, ',')), {Status} = 'Active') fetch records where “Linked Records” contain rec12345 and “Status” is “Active”\n\nFormula rules:\nOperators - =,!=,>,<,>=,<= \n- AND(condition1, condition2, ...) logical AND\n- OR(condition1, condition2, ...) logical OR\n- NOT(condition) logical NOT\n- SEARCH('substring', {Field}) finds position of substring, case-insensitive\n- FIND('substring', {Field}) finds position of substring, case-sensitive\n- IS_BEFORE({Date}, 'YYYY-MM-DD') checks if date is before\n- IS_AFTER({Date}, 'YYYY-MM-DD') checks if date is after\n- IS_SAME({Date1}, {Date2}, 'unit') checks if dates are the same by unit\n- RECORD_ID() = 'recXXXXXX' filters by record ID\n- {Field} = '' field is blank\n- {Field} != '' field is not blank\n- ARRAYJOIN({Linked Field}, ',') joins linked records into a string\n- LOWER({Field}) converts to lowercase for case-insensitive comparison\n- UPPER({Field}) converts to uppercase for case-insensitive comparison\n- VALUE({Text}) converts text to number for numeric comparisons\n- LEN({Field}) gets text length\n- ROUND(number, precision) rounds number\n- TODAY() current date\n- NOW() current timestamp\n- IF(condition, true_value, false_value) conditional logic\n- DATETIME_FORMAT({Date}, 'format') formats date as text\n- DATETIME_DIFF(date1, date2, 'unit') difference between dates\n- DATEADD({Date}, number, 'unit') adds time to date\n- LEFT({Text}, number) extracts leftmost characters\n- RIGHT({Text}, number) extracts rightmost characters\n- AND({Field1} = 'Value1', {Field2} > 50) multiple conditions\n- SEARCH('Value', {Field}) substring match\n- ROUND({Field1} / {Field2}, 2) numeric calculation\n- AND(IS_BEFORE({Date}, TODAY()), {Status} = 'Active') filter by date and status\n- ISERROR(expression) checks if an expression has an error\n- ABS(number) absolute value\n- MIN(value1, value2) minimum value\n- MAX(value1, value2) maximum value\n\n"
},
{
"id": "4e0f9af6-517f-42af-9ced-df0e8a7118b0",
"name": "schema",
"type": "string",
"value": "={\n \"name\": \"filter\",\n \"schema\": {\n \"type\": \"object\",\n \"properties\": {\n \"filter\": {\n \"type\": \"string\"\n }\n },\n \"required\": [\n \"filter\"\n ],\n \"additionalProperties\": false\n },\n \"strict\": true\n}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "6e670074-8508-4282-9c40-600cc445b10f",
"name": "Upload file to get link",
"type": "n8n-nodes-base.httpRequest",
"onError": "continueRegularOutput",
"position": [
2580,
1720
],
"parameters": {
"url": "=https://tmpfiles.org/api/v1/upload",
"method": "POST",
"options": {},
"sendBody": true,
"contentType": "multipart-form-data",
"bodyParameters": {
"parameters": [
{
"name": "file",
"parameterType": "formBinaryData",
"inputDataFieldName": "data"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "b7569d19-3a10-41e5-932b-4be04260a58e",
"name": "OpenAI - Download File",
"type": "n8n-nodes-base.httpRequest",
"position": [
2360,
1720
],
"parameters": {
"url": "=https://api.openai.com/v1/files/{{ $json.data[0].attachments[0]?.file_id ?? $json.data[0].content.find(x=>x.type==\"image_file\")?.image_file.file_id }}/content",
"options": {},
"sendHeaders": true,
"authentication": "predefinedCredentialType",
"headerParameters": {
"parameters": [
{
"name": "OpenAI-Beta",
"value": "assistants=v2"
}
]
},
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"id": "vBLHyjEnMK9EaWwQ",
"name": "Mark OpenAi "
}
},
"typeVersion": 4.2
},
{
"id": "bf378b21-07fb-4f9e-bfc5-9623ebcb8236",
"name": "OpenAI - Get messages",
"type": "n8n-nodes-base.httpRequest",
"position": [
1960,
1720
],
"parameters": {
"url": "=https://api.openai.com/v1/threads/{{ $('OpenAI - Create thread').item.json.id }}/messages",
"options": {},
"sendHeaders": true,
"authentication": "predefinedCredentialType",
"headerParameters": {
"parameters": [
{
"name": "OpenAI-Beta",
"value": "assistants=v2"
}
]
},
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"id": "zJhr5piyEwVnWtaI",
"name": "OpenAi club"
}
},
"typeVersion": 4.2
},
{
"id": "9874eec1-61e2-45fe-8c57-556957a15473",
"name": "OpenAI - Run assistant",
"type": "n8n-nodes-base.httpRequest",
"position": [
1760,
1720
],
"parameters": {
"url": "=https://api.openai.com/v1/threads/{{ $('OpenAI - Create thread').item.json.id }}/runs",
"method": "POST",
"options": {},
"sendBody": true,
"sendHeaders": true,
"authentication": "predefinedCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "assistant_id",
"value": "asst_PGUuvzEGJWOE8p8vwV56INLO"
},
{
"name": "stream",
"value": "={{true}}"
},
{
"name": "tool_choice",
"value": "={{ {\"type\": \"code_interpreter\"} }}"
},
{
"name": "tools",
"value": "={{ [{\"type\": \"code_interpreter\"}] }}"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "OpenAI-Beta",
"value": "assistants=v2"
}
]
},
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"id": "fLfRtaXbR0EVD0pl",
"name": "OpenAi account"
}
},
"typeVersion": 4.2
},
{
"id": "e5339ad2-36c7-40c5-846b-2bd242f41ea5",
"name": "OpenAI - Send message",
"type": "n8n-nodes-base.httpRequest",
"position": [
1560,
1720
],
"parameters": {
"url": "=https://api.openai.com/v1/threads/{{ $('OpenAI - Create thread').item.json.id }}/messages ",
"method": "POST",
"options": {},
"sendBody": true,
"sendHeaders": true,
"authentication": "predefinedCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "role",
"value": "user"
},
{
"name": "content",
"value": "=Request:\n{{ $('Execute Workflow Trigger').item.json.query.request }}\n\nData:\n{{ $('Execute Workflow Trigger').item.json.query.data }}"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "OpenAI-Beta",
"value": "assistants=v2"
}
]
},
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"id": "fLfRtaXbR0EVD0pl",
"name": "OpenAi account"
}
},
"typeVersion": 4.2
},
{
"id": "5b822c15-af63-43f6-ac30-61a34dcd91ee",
"name": "OpenAI - Create thread",
"type": "n8n-nodes-base.httpRequest",
"position": [
1360,
1720
],
"parameters": {
"url": "https://api.openai.com/v1/threads",
"method": "POST",
"options": {},
"sendHeaders": true,
"authentication": "predefinedCredentialType",
"headerParameters": {
"parameters": [
{
"name": "OpenAI-Beta",
"value": "assistants=v2"
}
]
},
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"id": "vBLHyjEnMK9EaWwQ",
"name": "Mark OpenAi "
}
},
"typeVersion": 4.2
}
],
"pinData": {},
"connections": {
"f5f99038-9d19-49ed-9f50-3cd0270bf9ce": {
"main": [
[
{
"node": "5b80c2c8-7649-40f2-b9be-d090d8bd5ae9",
"type": "main",
"index": 0
}
],
[
{
"node": "b7569d19-3a10-41e5-932b-4be04260a58e",
"type": "main",
"index": 0
}
]
]
},
"Merge": {
"main": [
[
{
"node": "4cc416aa-50bd-4b60-ae51-887c4ee97c88",
"type": "main",
"index": 0
}
]
]
},
"Switch": {
"main": [
[
{
"node": "45c8b2eb-f43a-48b1-a270-9caeda9da0b0",
"type": "main",
"index": 0
}
],
[
{
"node": "bb8036bc-1c23-461b-bd03-2461e31c6cb6",
"type": "main",
"index": 0
}
],
[
{
"node": "dab309d9-3629-44ba-9f0a-ede55f96488f",
"type": "main",
"index": 0
}
],
[
{
"node": "5b822c15-af63-43f6-ac30-61a34dcd91ee",
"type": "main",
"index": 0
}
]
]
},
"Aggregate": {
"main": [
[
{
"node": "5b80c2c8-7649-40f2-b9be-d090d8bd5ae9",
"type": "main",
"index": 0
}
]
]
},
"45c8b2eb-f43a-48b1-a270-9caeda9da0b0": {
"main": [
[
{
"node": "Aggregate",
"type": "main",
"index": 0
}
]
]
},
"Aggregate1": {
"main": [
[
{
"node": "5b80c2c8-7649-40f2-b9be-d090d8bd5ae9",
"type": "main",
"index": 0
}
]
]
},
"Aggregate2": {
"main": [
[
{
"node": "5b80c2c8-7649-40f2-b9be-d090d8bd5ae9",
"type": "main",
"index": 0
}
]
]
},
"0c86638f-7220-415d-a920-13761da925a6": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"cd4781d0-f873-4aea-951c-6809358c1db6": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"3754175c-6f74-4750-b2e7-00e2bd3caf6d": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"135078ea-6a3f-4aee-9f60-c6d5832e446e": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Window Buffer Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"bf378b21-07fb-4f9e-bfc5-9623ebcb8236": {
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[
{
"node": "f5f99038-9d19-49ed-9f50-3cd0270bf9ce",
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}
]
]
},
"e5339ad2-36c7-40c5-846b-2bd242f41ea5": {
"main": [
[
{
"node": "9874eec1-61e2-45fe-8c57-556957a15473",
"type": "main",
"index": 0
}
]
]
},
"Set schema and prompt": {
"main": [
[
{
"node": "9dc71d31-8499-4b69-b87c-898217447d50",
"type": "main",
"index": 0
}
]
]
},
"bb8036bc-1c23-461b-bd03-2461e31c6cb6": {
"main": [
[
{
"node": "Aggregate1",
"type": "main",
"index": 0
}
]
]
},
"5b822c15-af63-43f6-ac30-61a34dcd91ee": {
"main": [
[
{
"node": "e5339ad2-36c7-40c5-846b-2bd242f41ea5",
"type": "main",
"index": 0
}
]
]
},
"b7569d19-3a10-41e5-932b-4be04260a58e": {
"main": [
[
{
"node": "6e670074-8508-4282-9c40-600cc445b10f",
"type": "main",
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}
]
]
},
"9874eec1-61e2-45fe-8c57-556957a15473": {
"main": [
[
{
"node": "bf378b21-07fb-4f9e-bfc5-9623ebcb8236",
"type": "main",
"index": 0
}
]
]
},
"7ba1d6ac-f1a2-4b8d-a9a5-ce92eaa4e7fa": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"6e670074-8508-4282-9c40-600cc445b10f": {
"main": [
[
{
"node": "abc7ddae-9ca9-4cf6-89a4-a63da8c1e036",
"type": "main",
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}
]
]
},
"Execute Workflow Trigger": {
"main": [
[
{
"node": "Switch",
"type": "main",
"index": 0
}
]
]
},
"4cc416aa-50bd-4b60-ae51-887c4ee97c88": {
"main": [
[
{
"node": "Aggregate2",
"type": "main",
"index": 0
}
],
[
{
"node": "5b80c2c8-7649-40f2-b9be-d090d8bd5ae9",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"dab309d9-3629-44ba-9f0a-ede55f96488f": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 0
}
],
[
{
"node": "Set schema and prompt",
"type": "main",
"index": 0
}
]
]
},
"9dc71d31-8499-4b69-b87c-898217447d50": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 1
}
]
]
}
}
}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 - 엔지니어링, 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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저자
Mark Shcherbakov
@lowcodingdevI am a business analyst with a development background, dedicated to helping small businesses and entrepreneurs leverage cloud services for increased efficiency. My expertise lies in automating manual workflows, integrating data from multiple cloud service providers, creating insightful dashboards, and building custom CRM systems.
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