Markensichtbarkeitsprüfung - AI-Labor-Demonstrationsprojekt
Experte
Dies ist ein Market Research, AI Summarization-Bereich Automatisierungsworkflow mit 48 Nodes. Hauptsächlich werden If, Set, Limit, Perplexity, HttpRequest und andere Nodes verwendet. Markenbekanntheit und Sentimentanalyse über KI-Suchwerkzeuge (OpenAI, Perplexity, ChatGPT)
Voraussetzungen
- •Möglicherweise sind Ziel-API-Anmeldedaten erforderlich
- •Google Sheets API-Anmeldedaten
- •OpenAI API Key
Verwendete Nodes (48)
Kategorie
Workflow-Vorschau
Visualisierung der Node-Verbindungen, mit Zoom und Pan
Workflow exportieren
Kopieren Sie die folgende JSON-Konfiguration und importieren Sie sie in n8n
{
"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": "Manueller Trigger",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-2240,
256
],
"parameters": {},
"typeVersion": 1
},
{
"id": "fc60def4-55f5-4838-bae8-ef18ab63730e",
"name": "Antwort-Stimmungsanalyse1",
"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 aktualisieren",
"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": "Haftnotiz4",
"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": "Haftnotiz5",
"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": "Haftnotiz6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-80,
-800
],
"parameters": {
"width": 496,
"height": 416,
"content": "## Reporting"
},
"typeVersion": 1
},
{
"id": "7529683e-cc21-4351-b603-96c1ab5196a0",
"name": "Haftnotiz7",
"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": "Haftnotiz8",
"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-Prompts",
"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": "Chat-Modell",
"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": "Ausgabe-Parser",
"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": "Zeile in Sheet anhängen",
"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 Chat-Modell1",
"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": "Antwort-Stimmungsanalyse3",
"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": "Strukturierter Ausgabe-Parser3",
"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": "Haftnotiz10",
"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": "Haftnotiz11",
"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": "Haftnotiz12",
"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": "Haftnotiz13",
"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 Chat-Modell (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 Anfrage",
"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": "={{ $json.text }}"
},
{
"id": "1f6d60ae-2599-4371-baf9-d833bf03ad98",
"name": "LLM",
"type": "string",
"value": "OpenAI"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "81b6c166-e656-45e1-a630-d899fe850841",
"name": "Haftnotiz14",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1168,
48
],
"parameters": {
"width": 1008,
"height": 288,
"content": "## OpenAI (API / LLM Knowledge)"
},
"typeVersion": 1
},
{
"id": "a5778474-4314-4347-abd3-f13e095d4b39",
"name": "Haftnotiz15",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1968,
-48
],
"parameters": {
"width": 752,
"height": 624,
"content": "## Input\n(Replace to your need)"
},
"typeVersion": 1
},
{
"id": "7f4cb9f8-941f-42bd-aba5-833b15dee6d8",
"name": "Haftnotiz16",
"type": "n8n-nodes-base.stickyNote",
"position": [
-112,
-48
],
"parameters": {
"color": 2,
"width": 704,
"height": 80,
"content": "## Result Evaluation"
},
"typeVersion": 1
},
{
"id": "8f53dcf4-7957-478a-9f67-bf8b8b701775",
"name": "Haftnotiz17",
"type": "n8n-nodes-base.stickyNote",
"position": [
624,
-48
],
"parameters": {
"color": 2,
"width": 576,
"height": 80,
"content": "## Store Result"
},
"typeVersion": 1
},
{
"id": "b997e680-41f9-4163-b6e3-47bb734b06b8",
"name": "Haftnotiz18",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1168,
384
],
"parameters": {
"width": 1008,
"height": 256,
"content": "## Perplexity"
},
"typeVersion": 1
},
{
"id": "44c0457c-ce38-4285-be10-13be5af2046e",
"name": "Haftnotiz19",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1168,
672
],
"parameters": {
"width": 1008,
"height": 272,
"content": "## ChatGPT\n\n"
},
"typeVersion": 1
},
{
"id": "1fc655af-c428-4bad-8df3-7dbb193d8005",
"name": "APIfy Aufruf ChatGPT Scraper",
"type": "n8n-nodes-base.httpRequest",
"onError": "continueRegularOutput",
"position": [
-864,
736
],
"parameters": {
"url": "https://api.apify.com/v2/acts/automation_nerd~chatgpt-prompt-actor/run-sync-get-dataset-items",
"method": "POST",
"options": {},
"jsonBody": "={\n \"prompts\": [{{ JSON.stringify($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": "Haftnotiz20",
"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": "Finales Mapping",
"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 Anfrage",
"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 Anfrage1",
"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-Ausgabe zuordnen",
"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": "Haftnotiz21",
"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": "Haftnotiz22",
"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": "Prompts lesen1",
"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": "Schleife über Prompts",
"type": "n8n-nodes-base.splitInBatches",
"position": [
-1456,
240
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "67f32fd1-5f4a-40d9-825c-a90157dae006",
"name": "Vor-Schleifen-Eingabe",
"type": "n8n-nodes-base.noOp",
"position": [
-1616,
240
],
"parameters": {},
"typeVersion": 1
},
{
"id": "f92edafd-a458-42f5-acfc-c2a0e9eae6af",
"name": "Manuelle Eingabe",
"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": "Limit für Tests",
"type": "n8n-nodes-base.limit",
"position": [
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240
],
"parameters": {
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},
"typeVersion": 1
},
{
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"name": "Perplexity Mapper",
"type": "n8n-nodes-base.set",
"position": [
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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]}}"
},
{
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"name": "Source4",
"type": "string",
"value": "={{ $json.citations[3]}}"
},
{
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"name": "Source5",
"type": "string",
"value": "={{ $json.citations[4]}}"
},
{
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"name": "Source6",
"type": "string",
"value": "={{ $json.citations[5]}}"
}
]
}
},
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},
{
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"name": "ChatGPT Mapper",
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"position": [
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688
],
"parameters": {
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"assignments": {
"assignments": [
{
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"name": "Response",
"type": "string",
"value": "={{ $json.response }}"
},
{
"id": "1add090f-9bc2-44ba-b996-7946ffb0fc17",
"name": "LLM",
"type": "string",
"value": "ChatGPT"
},
{
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"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}}"
},
{
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"name": "Source6",
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"value": "={{ $json.citations[5].url}}"
},
{
"id": "998de421-8d87-4f4b-a605-e7cc54dc7872",
"name": "NewsListing1",
"type": "string",
"value": "={{ $json.newsListing[0].url}}"
}
]
}
},
"typeVersion": 3.4
},
{
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"type": "n8n-nodes-base.set",
"position": [
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304
],
"parameters": {
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{
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"name": "Prompt",
"type": "string",
"value": "={{ $('loop-input').item.json.Prompt }}"
},
{
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"name": "Response",
"type": "string",
"value": "={{ $('normalized-tool-response').item.json.Response }}"
},
{
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"name": "Brand mentioned",
"type": "string",
"value": "={{ $('normalized-tool-response').item.json.Response.toLowerCase().includes(\"asics\") }}"
},
{
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"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'] || \"\" }}"
},
{
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"name": "Emotion Category",
"type": "string",
"value": "={{ $json.output['Emotion Category'] || \"\"}}"
},
{
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"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:\"\" }}"
},
{
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"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 }}"
}
]
}
},
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},
{
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"name": "Normalisierte Tool-Antwort",
"type": "n8n-nodes-base.noOp",
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336
],
"parameters": {},
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},
{
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"name": "Schleifen-Eingabe",
"type": "n8n-nodes-base.noOp",
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],
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},
{
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"name": "Wenn erfolgreich",
"type": "n8n-nodes-base.if",
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736
],
"parameters": {
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"conditions": {
"options": {
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"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
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"operator": {
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"operation": "notEmpty",
"singleValue": true
},
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"rightValue": 0
}
]
}
},
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},
{
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],
"active": false,
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]
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},
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}Häufig gestellte Fragen
Wie verwende ich diesen Workflow?
Kopieren Sie den obigen JSON-Code, erstellen Sie einen neuen Workflow in Ihrer n8n-Instanz und wählen Sie "Aus JSON importieren". Fügen Sie die Konfiguration ein und passen Sie die Anmeldedaten nach Bedarf an.
Für welche Szenarien ist dieser Workflow geeignet?
Experte - Marktforschung, KI-Zusammenfassung
Ist es kostenpflichtig?
Dieser Workflow ist völlig kostenlos. Beachten Sie jedoch, dass Drittanbieterdienste (wie OpenAI API), die im Workflow verwendet werden, möglicherweise kostenpflichtig sind.
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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
Externe Links
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