브랜드 보이콧성 검사 - AI 연구소 데모 프로젝트
고급
이것은Market Research, AI Summarization분야의자동화 워크플로우로, 48개의 노드를 포함합니다.주로 If, Set, Limit, Perplexity, HttpRequest 등의 노드를 사용하며. 跨AI검색工具의品牌可见性및情感분석 (OpenAI、Perplexity、ChatGPT)
사전 요구사항
- •대상 API의 인증 정보가 필요할 수 있음
- •Google Sheets API 인증 정보
- •OpenAI API Key
사용된 노드 (48)
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": "eoiCUdr68Q41iEua",
"meta": {
"instanceId": "88b34e051213082619adc89dcb3c4c6a3611f57a17080c0af86efd3b8840b94f",
"templateCredsSetupCompleted": true
},
"name": "LLMO Brand Visibility Check - AI Lab (28.08) Demo Project",
"tags": [],
"nodes": [
{
"id": "6023f97b-3528-4390-8e58-1c506dedc75d",
"name": "수동 트리거",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-2240,
256
],
"parameters": {},
"typeVersion": 1
},
{
"id": "fc60def4-55f5-4838-bae8-ef18ab63730e",
"name": "응답 감성 분석1",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-528,
-704
],
"parameters": {
"text": "=You task is to analyse the sentiment of a text message. Your input is the result of an Perplexity AI API Call in the JSON format. \nTake this message and evaluate its content: \"{{ $json.Message }} \"\n\nYour output is a JSON with three classifications:\n1. the Basic Polarity (KEY), with values from Positive, Neutral to Negative \n2. Emotion Category (Key) with values from Joy, Sadness,Anger,Fear, Disgust, Surprise.\n3. Third, Brand Hierachy (key), you evaluate the hierachy of brands mentioned in the LLM Response.\n\nExample Output for Brand Hierarchy: Nike>Adidas>Puma",
"options": {},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 2.2
},
{
"id": "0baea2a7-b19a-4aa7-b121-f1455215102b",
"name": "Sheet/Excel 업데이트",
"type": "n8n-nodes-base.googleSheets",
"position": [
224,
-704
],
"parameters": {
"columns": {
"value": {
"Response": "={{ $json.Message }}"
},
"schema": [
{
"id": "Prompt",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Prompt",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Brand Mentioning",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Brand Mentioning",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Response",
"type": "string",
"display": true,
"required": false,
"displayName": "Response",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Emotion Category",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Emotion Category",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Basic Polarity",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Basic Polarity",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Quelle1",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Quelle1",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Quelle2",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Quelle2",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Quelle3",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Quelle3",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Quelle4",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Quelle4",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Quelle5",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Quelle5",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Message",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Message",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Tool",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Tool",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Anfrage",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Anfrage",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "autoMapInputData",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": 568802405,
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1ObN4sF1fBBXsrg6aBayZdPXETRFNlAtp5H92mEiH33o/edit#gid=568802405",
"cachedResultName": "Output"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1ObN4sF1fBBXsrg6aBayZdPXETRFNlAtp5H92mEiH33o",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1ObN4sF1fBBXsrg6aBayZdPXETRFNlAtp5H92mEiH33o/edit?usp=drivesdk",
"cachedResultName": "AI Lab Prompts"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "qdhEdg8zimJRSIxl",
"name": "Google Sheets account (aoetesting gmail)"
}
},
"typeVersion": 4.6
},
{
"id": "0dccf99d-c7ca-4a54-9f81-094b0cf61b15",
"name": "스티커 노트4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1152,
-800
],
"parameters": {
"width": 464,
"height": 416,
"content": "## LLMO GEO Visibility Research"
},
"typeVersion": 1
},
{
"id": "033a6e5a-4614-41b2-9055-91690ca20975",
"name": "스티커 노트5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-656,
-800
],
"parameters": {
"width": 528,
"height": 416,
"content": "## Sentiment analysis und brand evaluation"
},
"typeVersion": 1
},
{
"id": "0bf0f1c1-7faf-48a0-972a-7d38b1fa5c07",
"name": "스티커 노트6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-80,
-800
],
"parameters": {
"width": 496,
"height": 416,
"content": "## Reporting"
},
"typeVersion": 1
},
{
"id": "7529683e-cc21-4351-b603-96c1ab5196a0",
"name": "스티커 노트7",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1472,
-800
],
"parameters": {
"width": 288,
"height": 416,
"content": "## Data source"
},
"typeVersion": 1
},
{
"id": "67a8ef42-42da-4729-8818-6782625e46b2",
"name": "스티커 노트8",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1936,
-864
],
"parameters": {
"color": 7,
"width": 2672,
"height": 656,
"content": "## Simplified Flow"
},
"typeVersion": 1
},
{
"id": "0ce10377-ac99-4f8e-97de-407716b12053",
"name": "LLM 프롬프트",
"type": "n8n-nodes-base.set",
"position": [
-1376,
-704
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "306f8ce3-e140-4d8b-a8b4-a57c5c131066",
"name": "Prompt",
"type": "string",
"value": "Are Asics running shoes any good"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "d76ce3fd-91cd-4c1c-9a4d-579b42b08123",
"name": "채팅 모델",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-528,
-512
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini",
"cachedResultName": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "M0gBX6dGinkN0Qym",
"name": "OpenAi account (n8n project)"
}
},
"typeVersion": 1.2
},
{
"id": "13244b6e-40b4-494c-b5b2-2c6693e3807f",
"name": "출력 파서",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
-384,
-512
],
"parameters": {
"jsonSchemaExample": "{\n \n \"Basic Polarity\": \"Negative\",\n \"Emotion Category\": \"Anger\",\n \"Brand Hierachy\": \"Nike>Adidas>Puma\"\n}"
},
"typeVersion": 1.3
},
{
"id": "f4455754-0af0-4ede-a0ab-eb6a77d1b6d5",
"name": "시트에 행 추가",
"type": "n8n-nodes-base.googleSheets",
"position": [
928,
304
],
"parameters": {
"columns": {
"value": {},
"schema": [
{
"id": "Prompt",
"type": "string",
"display": true,
"required": false,
"displayName": "Prompt",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "LLM",
"type": "string",
"display": true,
"required": false,
"displayName": "LLM",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Response",
"type": "string",
"display": true,
"required": false,
"displayName": "Response",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Brand mentioned",
"type": "string",
"display": true,
"required": false,
"displayName": "Brand mentioned",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Brand Hierarchy",
"type": "string",
"display": true,
"required": false,
"displayName": "Brand Hierarchy",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Basic Polarity",
"type": "string",
"display": true,
"required": false,
"displayName": "Basic Polarity",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Emotion Category",
"type": "string",
"display": true,
"required": false,
"displayName": "Emotion Category",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Source 1",
"type": "string",
"display": true,
"required": false,
"displayName": "Source 1",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Source 2",
"type": "string",
"display": true,
"required": false,
"displayName": "Source 2",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Source 3",
"type": "string",
"display": true,
"required": false,
"displayName": "Source 3",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "autoMapInputData",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": 1051572958,
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1ObN4sF1fBBXsrg6aBayZdPXETRFNlAtp5H92mEiH33o/edit#gid=1051572958",
"cachedResultName": "Output many models"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1ObN4sF1fBBXsrg6aBayZdPXETRFNlAtp5H92mEiH33o",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1ObN4sF1fBBXsrg6aBayZdPXETRFNlAtp5H92mEiH33o/edit?usp=drivesdk",
"cachedResultName": "AI Lab Prompts"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "qdhEdg8zimJRSIxl",
"name": "Google Sheets account (aoetesting gmail)"
}
},
"typeVersion": 4.7
},
{
"id": "fb6acc43-81a2-4b6f-85d9-74715960213d",
"name": "OpenAI 채팅 모델1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
336,
528
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "M0gBX6dGinkN0Qym",
"name": "OpenAi account (n8n project)"
}
},
"typeVersion": 1.2
},
{
"id": "f6d1180b-8dbc-4ae2-ad7d-02e3a17dbb7f",
"name": "응답 감성 분석3",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
320,
320
],
"parameters": {
"text": "=Take this message and evaluate its content: \"{{ $json.Response }}\"\n",
"options": {
"systemMessage": "You task is to analyse the sentiment of a text message. Your input is the result of an Perplexity AI API Call in JSON format. \n\n--\n\n\nYour output is a JSON with three classifications:\n1. the Basic Polarity (KEY), with values from Positive, Neutral to Negative \n2. Emotion Category (Key) with values from Joy, Sadness,Anger,Fear, Disgust, Surprise.\n3. Third, Brand Hierachy (key), you evaluate the hierachy of brands mentioned in the LLM Response.\n\nExample Output for Brand Hierarchy: Nike>Adidas>Puma"
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 2.2
},
{
"id": "c8786ef3-5147-4f02-9d71-bbee87d3a19d",
"name": "구조화 출력 파서3",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
480,
528
],
"parameters": {
"jsonSchemaExample": "{\n \"Basic Polarity\": \"Negative\",\n \"Emotion Category\": \"Anger\",\n \"Brand Hierachy\": \"Nike>Adidas>Puma\"\n}"
},
"typeVersion": 1.3
},
{
"id": "e3398784-fe71-4d86-bfcd-96ec8ae6e816",
"name": "스티커 노트10",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2416,
-208
],
"parameters": {
"color": 7,
"width": 4000,
"height": 1360,
"content": "## multi-model prompting"
},
"typeVersion": 1
},
{
"id": "89625abb-4027-42ea-83d0-b180b42bcd24",
"name": "스티커 노트11",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1168,
-48
],
"parameters": {
"color": 2,
"width": 1008,
"height": 80,
"content": "## LLMO GEO Brand Visibility Research"
},
"typeVersion": 1
},
{
"id": "dfdf11bb-f6f3-4af9-93dd-8ccb540ea09b",
"name": "스티커 노트12",
"type": "n8n-nodes-base.stickyNote",
"position": [
-112,
48
],
"parameters": {
"width": 704,
"height": 640,
"content": "## Sentiment Analysis and Brandevaluation"
},
"typeVersion": 1
},
{
"id": "8125153b-44e3-4caf-b48e-966abc5f88a3",
"name": "스티커 노트13",
"type": "n8n-nodes-base.stickyNote",
"position": [
-3360,
-1280
],
"parameters": {
"color": 4,
"width": 896,
"height": 384,
"content": "# Use Case Explanation\n\nUsers are continuously more and more using AI tools like ChatGPT, Perplexity etc to find what they need. Therefore, it's more and more important for brands and organization to be visible when users ask relevant questions. \n\nThe first step to optimize for visibility in these AI tools is to know where your brand stand. \n\nThis workflow helps in automating the analysis of the current visibility in tools like:\n- native open AI knowledge\n- Perplexity\n- chatGPT\n\nIt can be extended for more tools. See this workflow as a kickstart. There's much more you can do. The benefit of using a workflow for these analysis is that you can add your specific evaluations and your specific reasonings, even such as potential optimizations to increase visibility.\n\nInterested in professional AI automation - feel free to [check our services](https://www.aoe.com/de/services/automation-ai/n8n)"
},
"typeVersion": 1
},
{
"id": "46cf70fd-a8f5-4310-b226-8f0bbca0519a",
"name": "OpenAI 채팅 모델 (GPT 5)",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-704,
256
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-5",
"cachedResultName": "gpt-5"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "M0gBX6dGinkN0Qym",
"name": "OpenAi account (n8n project)"
}
},
"typeVersion": 1.2
},
{
"id": "7260d41f-2349-45ab-bcec-6c8d73fab4e5",
"name": "OpenAI 요청",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
-704,
112
],
"parameters": {
"text": "={{ $json.Prompt }}",
"batching": {},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "f83b4579-7eee-437c-bdc7-34273e19925b",
"name": "OpenAI",
"type": "n8n-nodes-base.set",
"position": [
-352,
96
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "51253e72-9fc7-45de-bdfb-52087d1e6fc2",
"name": "Response",
"type": "string",
"value": "={{ $json.text }}"
},
{
"id": "1f6d60ae-2599-4371-baf9-d833bf03ad98",
"name": "LLM",
"type": "string",
"value": "OpenAI"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "81b6c166-e656-45e1-a630-d899fe850841",
"name": "스티커 노트14",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1168,
48
],
"parameters": {
"width": 1008,
"height": 288,
"content": "## OpenAI (API / LLM Knowledge)"
},
"typeVersion": 1
},
{
"id": "a5778474-4314-4347-abd3-f13e095d4b39",
"name": "스티커 노트15",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1968,
-48
],
"parameters": {
"width": 752,
"height": 624,
"content": "## Input\n(Replace to your need)"
},
"typeVersion": 1
},
{
"id": "7f4cb9f8-941f-42bd-aba5-833b15dee6d8",
"name": "스티커 노트16",
"type": "n8n-nodes-base.stickyNote",
"position": [
-112,
-48
],
"parameters": {
"color": 2,
"width": 704,
"height": 80,
"content": "## Result Evaluation"
},
"typeVersion": 1
},
{
"id": "8f53dcf4-7957-478a-9f67-bf8b8b701775",
"name": "스티커 노트17",
"type": "n8n-nodes-base.stickyNote",
"position": [
624,
-48
],
"parameters": {
"color": 2,
"width": 576,
"height": 80,
"content": "## Store Result"
},
"typeVersion": 1
},
{
"id": "b997e680-41f9-4163-b6e3-47bb734b06b8",
"name": "스티커 노트18",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1168,
384
],
"parameters": {
"width": 1008,
"height": 256,
"content": "## Perplexity"
},
"typeVersion": 1
},
{
"id": "44c0457c-ce38-4285-be10-13be5af2046e",
"name": "스티커 노트19",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1168,
672
],
"parameters": {
"width": 1008,
"height": 272,
"content": "## ChatGPT\n\n"
},
"typeVersion": 1
},
{
"id": "1fc655af-c428-4bad-8df3-7dbb193d8005",
"name": "APIfy 호출 ChatGPT 스크레이퍼",
"type": "n8n-nodes-base.httpRequest",
"onError": "continueRegularOutput",
"position": [
-864,
736
],
"parameters": {
"url": "https://api.apify.com/v2/acts/automation_nerd~chatgpt-prompt-actor/run-sync-get-dataset-items",
"method": "POST",
"options": {},
"jsonBody": "={\n \"prompts\": [{{ JSON.stringify($json[\"Prompt\"]) }}],\n \"proxyCountry\": \"DE\"\n}",
"sendBody": true,
"specifyBody": "json",
"authentication": "genericCredentialType",
"genericAuthType": "httpQueryAuth"
},
"credentials": {
"httpQueryAuth": {
"id": "0jsRZDiuGwwQPHPB",
"name": "APIFy Token (Test account)"
}
},
"retryOnFail": false,
"typeVersion": 4.2,
"alwaysOutputData": false
},
{
"id": "0c0d89a3-8c5a-4d18-82af-2c0cf3129e85",
"name": "스티커 노트20",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1168,
992
],
"parameters": {
"color": 3,
"width": 464,
"height": 256,
"content": "Use this node with care - only for testing and to your own risk. \nIt's using an APIfy actor that tries to prompt ChatGPT through the web interface.\n\nOpen AI might restrict access and you might violate usage conditions. \nSo use at your own risk and check the APIfy documentations for more details on how to use this.\n"
},
"typeVersion": 1
},
{
"id": "a66095ef-09fd-4d7e-a33c-ea7ac731fc4a",
"name": "최종 매핑",
"type": "n8n-nodes-base.set",
"position": [
16,
-704
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "81d3aa0e-4db1-4a08-9d17-a2e88708a262",
"name": "source #1",
"type": "string",
"value": "={{ $('Map LLM Output').item.json.Citation1 }}"
},
{
"id": "ec22de77-1bb3-4acd-889a-23866068014f",
"name": "source #2",
"type": "string",
"value": "={{ $('Map LLM Output').item.json.Citation2 }}"
},
{
"id": "d8ada9a6-8be5-4042-928b-f88364fe6c20",
"name": "source #3",
"type": "string",
"value": "={{ $('Map LLM Output').item.json.Citation3 }}"
},
{
"id": "30a63ecd-ab3d-4de5-9a9e-0010f40beb4d",
"name": "source #4",
"type": "string",
"value": "={{ $('Map LLM Output').item.json.Citation4 }}"
},
{
"id": "e87fcbbd-a77d-408b-a27d-ecec890d0cc1",
"name": "source #5",
"type": "string",
"value": "={{ $('Map LLM Output').item.json.Citation5 }}"
},
{
"id": "0972cb5b-a561-44a3-8872-477a54c4d64e",
"name": "Message",
"type": "string",
"value": "={{ $('Map LLM Output').item.json.Message }}"
},
{
"id": "ac0895c7-df51-46a4-b6bd-6a736ecd4eea",
"name": "Emotion Category",
"type": "string",
"value": "={{ $json.output['Emotion Category'] }}"
},
{
"id": "dd9c8884-b58d-4d01-b5c1-a1d1b9b38ac4",
"name": "Basic Polarity",
"type": "string",
"value": "={{ $json.output['Basic Polarity'] }}"
},
{
"id": "e47cbb34-9bfe-4a5f-8ade-80754a9e9d1a",
"name": "Brand Hierachy",
"type": "string",
"value": "={{ $json.output[\"Brand Hierachy\"] }}"
},
{
"id": "11e7f8c8-e965-4cbb-8d48-ec59b95eb7b6",
"name": "Prompt",
"type": "string",
"value": "={{ $('LLM-Prompts').item.json.Prompt }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "ec91e5db-13ab-4c68-91d3-c15fbd391c57",
"name": "Perplexity 요청",
"type": "n8n-nodes-base.perplexity",
"position": [
-656,
464
],
"parameters": {
"model": "sonar",
"options": {},
"messages": {
"message": [
{
"content": "={{ $json.Prompt }}"
}
]
},
"requestOptions": {}
},
"credentials": {
"perplexityApi": {
"id": "hGRSmzGiDNMOmljL",
"name": "LLM-SEO PoC Key"
}
},
"typeVersion": 1
},
{
"id": "8912b8d9-266c-4b9f-9e4e-d12dd5c7a97d",
"name": "Perplexity 요청1",
"type": "n8n-nodes-base.perplexity",
"position": [
-1072,
-704
],
"parameters": {
"model": "sonar",
"options": {},
"messages": {
"message": [
{
"content": "={{ $json.Prompt }}"
}
]
},
"requestOptions": {}
},
"credentials": {
"perplexityApi": {
"id": "hGRSmzGiDNMOmljL",
"name": "LLM-SEO PoC Key"
}
},
"typeVersion": 1
},
{
"id": "410190c4-513c-4524-b8ca-1383bd00f8fe",
"name": "LLM 출력 매핑",
"type": "n8n-nodes-base.set",
"position": [
-864,
-704
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "81d3aa0e-4db1-4a08-9d17-a2e88708a262",
"name": "Citation1",
"type": "string",
"value": "={{ $json.citations[0] }}"
},
{
"id": "ec22de77-1bb3-4acd-889a-23866068014f",
"name": "Citation2",
"type": "string",
"value": "={{ $json.citations[1] }}"
},
{
"id": "d8ada9a6-8be5-4042-928b-f88364fe6c20",
"name": "Citation3",
"type": "string",
"value": "={{ $json.citations[2] }}"
},
{
"id": "30a63ecd-ab3d-4de5-9a9e-0010f40beb4d",
"name": "Citation4",
"type": "string",
"value": "={{ $json.citations[3] }}"
},
{
"id": "e87fcbbd-a77d-408b-a27d-ecec890d0cc1",
"name": "Citation5",
"type": "string",
"value": "={{ $json.citations[4] }}"
},
{
"id": "0972cb5b-a561-44a3-8872-477a54c4d64e",
"name": "Message",
"type": "string",
"value": "={{ $json.choices[0].message.content }}"
},
{
"id": "d06c8225-5bfe-41a8-8d56-06201c5de6c7",
"name": "Prompt",
"type": "string",
"value": "={{ $('LLM-Prompts').item.json.Prompt }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "b1b67d26-323f-43d6-af99-6a751e7dc75e",
"name": "스티커 노트21",
"type": "n8n-nodes-base.stickyNote",
"position": [
-3328,
-848
],
"parameters": {
"color": 4,
"width": 896,
"height": 384,
"content": "# Using the simple Perplexity Flow\n\nThe simple flow calls the Perplexity API with the hardcoded Prompt in the first node. \n\nTo use it, just connect your openAI credentials and create a Google Sheet in your Google account with the proper fields to collect the result. \n\nThis simple flow acts as a demo. Use it to extend with your logic.\n"
},
"typeVersion": 1
},
{
"id": "a3fac816-4abc-4869-9ab1-19ffa4f5a53a",
"name": "스티커 노트22",
"type": "n8n-nodes-base.stickyNote",
"position": [
-3360,
-144
],
"parameters": {
"color": 4,
"width": 896,
"height": 480,
"content": "# Using the multi model flow\n\nThis flow uses a Google Sheet to get the input prompts and then executes that prompts towards three different AI tools:\n\n1) just the openAI API to check the basic knowledge.\n2) Perplexity \n3) Uses APIfy to call a chatGPT scraping actor (use at own risk)\n\n--\n## To use the flow:\n\n- Connect your google sheet and prepare two sheets: \n - one with the input prompts (containing just one Column \"Prompt\")\n - and one for the output w\n\n\n- Create apify credential: \"Generic Credential Type\" > \"Query Auth\". Use Name \"token\" and paste the \nt te Column \"Prompt\tLLM\tResponse\tBrand mentioned\tBrand Hierarchy\tBasic Polarity\tEmotion Category\tSource 1\tSource 2\tSource 3\tSource4\""
},
"typeVersion": 1
},
{
"id": "84a55a32-071a-43c6-a8c8-6e50dc31c705",
"name": "프롬프트 읽기1",
"type": "n8n-nodes-base.googleSheets",
"position": [
-1936,
240
],
"parameters": {
"range": "A1:A100",
"options": {},
"sheetId": "1ObN4sF1fBBXsrg6aBayZdPXETRFNlAtp5H92mEiH33o"
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "qdhEdg8zimJRSIxl",
"name": "Google Sheets account (aoetesting gmail)"
}
},
"typeVersion": 2
},
{
"id": "d22d72c3-9a88-4fa9-8e13-4f6c84100ae6",
"name": "프롬프트 순환 처리",
"type": "n8n-nodes-base.splitInBatches",
"position": [
-1456,
240
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "67f32fd1-5f4a-40d9-825c-a90157dae006",
"name": "순환 전 입력",
"type": "n8n-nodes-base.noOp",
"position": [
-1616,
240
],
"parameters": {},
"typeVersion": 1
},
{
"id": "f92edafd-a458-42f5-acfc-c2a0e9eae6af",
"name": "수동 입력",
"type": "n8n-nodes-base.set",
"position": [
-1904,
448
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "05e2a95b-2e64-43c1-873d-744a9fd4b656",
"name": "Prompt",
"type": "string",
"value": "Was sind die besten Laufschuhe?"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "53519447-0bb2-4975-8c91-2b3b729f837c",
"name": "테스트 제한",
"type": "n8n-nodes-base.limit",
"position": [
-1792,
240
],
"parameters": {
"maxItems": 2
},
"typeVersion": 1
},
{
"id": "438305e4-b1e0-4ce9-b54e-86c93ea850b7",
"name": "Perplexity 매퍼",
"type": "n8n-nodes-base.set",
"position": [
-336,
480
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "44594b4e-0665-4a34-b7af-9eb8993dca3e",
"name": "Response",
"type": "string",
"value": "={{ $json.choices[0].message.content }}"
},
{
"id": "1add090f-9bc2-44ba-b996-7946ffb0fc17",
"name": "LLM",
"type": "string",
"value": "Perplexity"
},
{
"id": "bb33a029-c428-490d-a51b-0e11556a04bf",
"name": "Source1",
"type": "string",
"value": "={{ $json.citations[0]}}"
},
{
"id": "71b8cc6a-f6a7-484e-ab76-8b20a15217d6",
"name": "Source2",
"type": "string",
"value": "={{ $json.citations[1]}}"
},
{
"id": "512b2a94-91c7-4046-be6f-ffee545a8502",
"name": "Source3",
"type": "string",
"value": "={{ $json.citations[3]}}"
},
{
"id": "13e874d5-bbb1-4624-809b-2631f2e915ad",
"name": "Source4",
"type": "string",
"value": "={{ $json.citations[3]}}"
},
{
"id": "85f0a9a3-22ac-482d-9ba6-31cf18a33af9",
"name": "Source5",
"type": "string",
"value": "={{ $json.citations[4]}}"
},
{
"id": "0d7bfe8c-a9de-442d-a186-1d10bdf31985",
"name": "Source6",
"type": "string",
"value": "={{ $json.citations[5]}}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "4086a4bc-527c-41ad-8f46-7e9ab78f30f5",
"name": "ChatGPT 매퍼",
"type": "n8n-nodes-base.set",
"position": [
-320,
688
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "44594b4e-0665-4a34-b7af-9eb8993dca3e",
"name": "Response",
"type": "string",
"value": "={{ $json.response }}"
},
{
"id": "1add090f-9bc2-44ba-b996-7946ffb0fc17",
"name": "LLM",
"type": "string",
"value": "ChatGPT"
},
{
"id": "bb33a029-c428-490d-a51b-0e11556a04bf",
"name": "Source1",
"type": "string",
"value": "={{ $json.citations[0].url}}"
},
{
"id": "71b8cc6a-f6a7-484e-ab76-8b20a15217d6",
"name": "Source2",
"type": "string",
"value": "={{ $json.citations[1].url}}"
},
{
"id": "512b2a94-91c7-4046-be6f-ffee545a8502",
"name": "Source3",
"type": "string",
"value": "={{ $json.citations[3].url}}"
},
{
"id": "13e874d5-bbb1-4624-809b-2631f2e915ad",
"name": "Source4",
"type": "string",
"value": "={{ $json.citations[3].url}}"
},
{
"id": "85f0a9a3-22ac-482d-9ba6-31cf18a33af9",
"name": "Source5",
"type": "string",
"value": "={{ $json.citations[4].url}}"
},
{
"id": "0d7bfe8c-a9de-442d-a186-1d10bdf31985",
"name": "Source6",
"type": "string",
"value": "={{ $json.citations[5].url}}"
},
{
"id": "998de421-8d87-4f4b-a605-e7cc54dc7872",
"name": "NewsListing1",
"type": "string",
"value": "={{ $json.newsListing[0].url}}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "cbe3ef1b-49c9-438f-bcdb-96476e293cd9",
"name": "시트 열 준비",
"type": "n8n-nodes-base.set",
"position": [
704,
304
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "08e610b4-563f-4f6d-b79e-cfc40b0d2f81",
"name": "Prompt",
"type": "string",
"value": "={{ $('loop-input').item.json.Prompt }}"
},
{
"id": "d34deaf5-2918-4afa-911c-d0922dcd7925",
"name": "Response",
"type": "string",
"value": "={{ $('normalized-tool-response').item.json.Response }}"
},
{
"id": "9f4ccf36-cfe0-4920-8f72-5c683a345806",
"name": "Brand mentioned",
"type": "string",
"value": "={{ $('normalized-tool-response').item.json.Response.toLowerCase().includes(\"asics\") }}"
},
{
"id": "b8b67f47-747b-41f7-9e8c-e31ac5f8047f",
"name": "Brand Hierarchy",
"type": "string",
"value": "={{ $json.output['Brand Hierachy'] || \"\" }}"
},
{
"id": "f954ed8a-d432-4a30-bbc5-2d864901b356",
"name": "Basic Polarity",
"type": "string",
"value": "={{ $json.output['Basic Polarity'] || \"\" }}"
},
{
"id": "817ce1aa-f913-4933-9646-340ff66c784b",
"name": "Emotion Category",
"type": "string",
"value": "={{ $json.output['Emotion Category'] || \"\"}}"
},
{
"id": "25feac61-7a0d-469b-ba5f-061e75a66c99",
"name": "Source 1",
"type": "string",
"value": "={{ $('normalized-tool-response').item.json.Source1?$('normalized-tool-response').item.json.Source1:\"\" }}"
},
{
"id": "9d7b5abd-64ac-4032-a765-d8d995a898e4",
"name": "Source 2",
"type": "string",
"value": "={{ $('normalized-tool-response').item.json.Source2?$('normalized-tool-response').item.json.Source2:\"\" }}"
},
{
"id": "d1bd2690-777b-419d-9b8c-e3d746525fe0",
"name": "Source 3",
"type": "string",
"value": "={{ $('normalized-tool-response').item.json.Source3?$('normalized-tool-response').item.json.Source3:\"\" }}"
},
{
"id": "7b53cb12-1e02-4ce3-ad0a-6c4f930eeb6c",
"name": "Source4",
"type": "string",
"value": "={{ $('normalized-tool-response').item.json.Source4?$('normalized-tool-response').item.json.Source4:\"\" }}"
},
{
"id": "e3bf318d-5986-451c-9a5c-e4d46055951b",
"name": "LLM",
"type": "string",
"value": "={{ $('normalized-tool-response').item.json.LLM }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "ad845eea-f2ae-48a2-ae5f-ddf050ee648a",
"name": "정규화 도구 응답",
"type": "n8n-nodes-base.noOp",
"position": [
32,
336
],
"parameters": {},
"typeVersion": 1
},
{
"id": "24cd700e-5ff1-4a5a-9564-0857046347d5",
"name": "순환 입력",
"type": "n8n-nodes-base.noOp",
"position": [
-1296,
320
],
"parameters": {},
"typeVersion": 1
},
{
"id": "67ab9566-baa4-4cc6-ba9f-598919abf806",
"name": "성공 시",
"type": "n8n-nodes-base.if",
"position": [
-656,
736
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "221e7600-61b9-4f53-a904-a04bacc391f9",
"operator": {
"type": "string",
"operation": "notEmpty",
"singleValue": true
},
"leftValue": "={{ $json.prompt }}",
"rightValue": 0
}
]
}
},
"typeVersion": 2.2
},
{
"id": "f5329382-468c-4a50-ba99-c941693fcf54",
"name": "순환 종료",
"type": "n8n-nodes-base.noOp",
"position": [
1136,
704
],
"parameters": {},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "f43370b9-890c-4081-8787-39ac1033a57c",
"connections": {
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"main": [
[
{
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"type": "main",
"index": 0
}
]
]
},
"f5329382-468c-4a50-ba99-c941693fcf54": {
"main": [
[
{
"node": "d22d72c3-9a88-4fa9-8e13-4f6c84100ae6",
"type": "main",
"index": 0
}
]
]
},
"d76ce3fd-91cd-4c1c-9a4d-579b42b08123": {
"ai_languageModel": [
[
{
"node": "fc60def4-55f5-4838-bae8-ef18ab63730e",
"type": "ai_languageModel",
"index": 0
}
]
]
},
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[
{
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"type": "main",
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},
{
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"type": "main",
"index": 0
},
{
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"type": "main",
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}
]
]
},
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{
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}
]
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},
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},
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"type": "ai_outputParser",
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}
]
]
},
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}
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}
]
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},
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}
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},
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}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 - 시장 조사, AI 요약
유료인가요?
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AOE Agent Lab
@aoepeopleWe are AOE’s AI & Automation Team – engineers, architects, and AI specialists. We build production-ready, agent-based automation using n8n, LLMs, vector stores, and secure toolchains. Our focus: ideation, evaluation-driven development, and scalable AI architecture. All workflows are modular, reusable, and built for real-world application – by practitioners
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