Workflow n8n
Automatisation Google Sheets avec n8n : analyse des LLM en temps réel
Ce workflow n8n a pour objectif de tester plusieurs modèles de LLM (Language Learning Models) en utilisant LM Studio et d'analyser les résultats en temps réel. Il est particulièrement utile pour les équipes de développement et de recherche qui souhaitent évaluer l'efficacité de différents modèles linguistiques. En intégrant des outils comme Google Sheets, ce workflow permet de stocker et d'analyser les résultats de manière structurée. Le déclencheur principal est un message de chat reçu, qui initie le processus d'analyse.
- Étape 1 : Lorsque le message est reçu, le workflow commence par capturer l'heure de début.
- Étape 2 : Il récupère les modèles disponibles via une requête HTTP, puis prépare les données pour l'analyse.
- Étape 3 : Les modèles sont exécutés avec des entrées dynamiques, et les résultats sont analysés à l'aide d'un code JavaScript.
- Étape 4 : Les résultats finaux sont ensuite sauvegardés dans Google Sheets pour une consultation ultérieure. Ce processus permet d'éliminer les pertes de temps liées à l'analyse manuelle et d'assurer une évaluation précise des performances des modèles. En utilisant ce workflow, les utilisateurs bénéficient d'une automatisation efficace qui améliore la productivité et la précision des analyses.
Tags clés :automatisationGoogle SheetsLLMn8nanalyse
Catégorie: Webhook · Tags: automatisation, Google Sheets, LLM, n8n, analyse❤ 0
Workflow n8n Google Sheets, LLM, analyse : vue d'ensemble
Schéma des nœuds et connexions de ce workflow n8n, généré à partir du JSON n8n.
Workflow n8n Google Sheets, LLM, analyse : détail des nœuds
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S'inscrire gratuitementBesoin d'aide ?{
"id": "WulUYgcXvako9hBy",
"meta": {
"instanceId": "d6b86682c7e02b79169c1a61ad0484dcda5bc8b0ea70f1a95dac239c2abfd057",
"templateCredsSetupCompleted": true
},
"name": "Testing Mulitple Local LLM with LM Studio",
"tags": [
{
"id": "RkTiZTdbLvr6uzSg",
"name": "Training",
"createdAt": "2024-06-18T16:09:35.806Z",
"updatedAt": "2024-06-18T16:09:35.806Z"
},
{
"id": "W3xdiSeIujD7XgBA",
"name": "Template",
"createdAt": "2024-06-18T22:15:34.874Z",
"updatedAt": "2024-06-18T22:15:34.874Z"
}
],
"nodes": [
{
"id": "08c457ef-5c1f-46d8-a53e-f492b11c83f9",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
1600,
420
],
"parameters": {
"color": 6,
"width": 478.38709677419376,
"height": 347.82258064516134,
"content": "## 🧠Text Analysis\n### Readability Score Ranges:\nWhen testing model responses, readability scores can range across different levels. Here’s a breakdown:\n\n- **90–100**: Very easy to read (5th grade or below)\n- **80–89**: Easy to read (6th grade)\n- **70–79**: Fairly easy to read (7th grade)\n- **60–69**: Standard (8th to 9th grade)\n- **50–59**: Fairly difficult (10th to 12th grade)\n- **30–49**: Difficult (College)\n- **0–29**: Very difficult (College graduate)\n- **Below 0**: Extremely difficult (Post-graduate level)\n"
},
"typeVersion": 1
},
{
"id": "7801734c-5eb9-4abd-b234-e406462931f7",
"name": "Get Models",
"type": "n8n-nodes-base.httpRequest",
"onError": "continueErrorOutput",
"position": [
20,
180
],
"parameters": {
"url": "http://192.168.1.179:1234/v1/models",
"options": {
"timeout": 10000,
"allowUnauthorizedCerts": false
}
},
"typeVersion": 4.2
},
{
"id": "5ee93d9a-ad2e-4ea9-838e-2c12a168eae6",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-140,
-100
],
"parameters": {
"width": 377.6129032258063,
"height": 264.22580645161304,
"content": "## ⚙️ 2. Update Local IP\nUpdate the **'Base URL'** `http://192.168.1.1:1234/v1/models` in the workflow to match the IP of your LM Studio server. (Running LM Server)[https://lmstudio.ai/docs/basics/server]\n\nThis node will query the LM Studio server to retrieve a list of all loaded model IDs at the time of the query. If you change or add models to LM Studio, you’ll need to rerun this node to get an updated list of active LLMs.\n"
},
"typeVersion": 1
},
{
"id": "f2b6a6ed-0ef1-4f2c-8350-9abd59d08e61",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-300,
180
],
"webhookId": "39c3c6d5-ea06-4faa-b0e3-4e77a05b0297",
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "dbaf0ad1-9027-4317-a996-33a3fcc9e258",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-740,
200
],
"parameters": {
"width": 378.75806451612857,
"height": 216.12903225806457,
"content": "## 🛠️1. Setup - LM Studio\nFirst, download and install [LM Studio](https://lmstudio.ai/). Identify which LLM models you want to use for testing.\n\nNext, the selected models are loaded into the server capabilities to prepare them for testing. For a detailed guide on how to set up multiple models, refer to the [LM Studio Basics](https://lmstudio.ai/docs/basics) documentation.\n"
},
"typeVersion": 1
},
{
"id": "36770fd1-7863-4c42-a68d-8d240ae3683b",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
360,
400
],
"parameters": {
"width": 570.0000000000002,
"height": 326.0645161290325,
"content": "## 3. 💡Update the LM Settings\n\nFrom here, you can modify the following\n parameters to fine-tune model behavior:\n\n- **Temperature**: Controls randomness. Higher values (e.g., 1.0) produce more diverse results, while lower values (e.g., 0.2) make responses more focused and deterministic.\n- **Top P**: Adjusts nucleus sampling, where the model considers only a subset of probable tokens. A lower value (e.g., 0.5) narrows the response range.\n- **Presence Penalty**: Penalizes new tokens based on their presence in the input, encouraging the model to generate more varied responses.\n"
},
"typeVersion": 1
},
{
"id": "6b36f094-a3bf-4ff7-9385-4f7a2c80d54f",
"name": "Get timeDifference",
"type": "n8n-nodes-base.dateTime",
"position": [
1600,
160
],
"parameters": {
"endDate": "={{ $json.endDateTime }}",
"options": {},
"operation": "getTimeBetweenDates",
"startDate": "={{ $('Capture Start Time').item.json.startDateTime }}"
},
"typeVersion": 2
},
{
"id": "a0b8f29d-2f2f-4fcf-a54a-dff071e321e5",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
1900,
-260
],
"parameters": {
"width": 304.3225806451618,
"height": 599.7580645161281,
"content": "## 📊4. Create Google Sheet (Optional)\n1. First, create a Google Sheet with the following headers:\n - Prompt\n - Time Sent\n - Time Received\n - Total Time Spent\n - Model\n - Response\n - Readability Score\n - Average Word Length\n - Word Count\n - Sentence Count\n - Average Sentence Length\n2. After creating the sheet, update the corresponding Google Sheets node in the workflow to map the data fields correctly.\n"
},
"typeVersion": 1
},
{
"id": "d376a5fb-4e07-42a3-aa0c-8ccc1b9feeb7",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-760,
-200
],
"parameters": {
"color": 5,
"width": 359.2903225806448,
"height": 316.9032258064518,
"content": "## 🏗️Setup Steps\n1. **Download and Install LM Studio**: Ensure LM Studio is correctly installed on your machine.\n2. **Update the Base URL**: Replace the base URL with the IP address of your LLM instance. Ensure the connection is established.\n3. **Configure LLM Settings**: Verify that your LLM models are properly set up and configured in LM Studio.\n4. **Create a Google Sheet**: Set up a Google Sheet with the necessary headers (Prompt, Time Sent, Time Received, etc.) to track your testing results.\n"
},
"typeVersion": 1
},
{
"id": "b21cad30-573e-4adf-a1d0-f34cf9628819",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
560,
-160
],
"parameters": {
"width": 615.8064516129025,
"height": 272.241935483871,
"content": "## 📖Prompting Multiple LLMs\n\nWhen testing for specific outcomes (such as conciseness or readability), you can add a **System Prompt** in the LLM Chain to guide the models' responses.\n\n**System Prompt Suggestion**:\n- Focus on ensuring that responses are concise, clear, and easily understandable by a 5th-grade reading level. \n- This prompt will help you compare models based on how well they meet readability standards and stay on point.\n \nAdjust the prompt to fit your desired testing criteria.\n"
},
"typeVersion": 1
},
{
"id": "dd5f7e7b-bc69-4b67-90e6-2077b6b93148",
"name": "Run Model with Dunamic Inputs",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1020,
400
],
"parameters": {
"model": "={{ $node['Extract Model IDsto Run Separately'].json.id }}",
"options": {
"topP": 1,
"baseURL": "http://192.168.1.179:1234/v1",
"timeout": 250000,
"temperature": 1,
"presencePenalty": 0
}
},
"credentials": {
"openAiApi": {
"id": "LBE5CXY4yeWrZCsy",
"name": "OpenAi account"
}
},
"typeVersion": 1
},
{
"id": "a0ee6c9a-cf76-4633-9c43-a7dc10a1f73e",
"name": "Analyze LLM Response Metrics",
"type": "n8n-nodes-base.code",
"position": [
2000,
160
],
"parameters": {
"jsCode": "// Get the input data from n8n\nconst inputData = items.map(item => item.json);\n\n// Function to count words in a string\nfunction countWords(text) {\n return text.trim().split(/\\s+/).length;\n}\n\n// Function to count sentences in a string\nfunction countSentences(text) {\n const sentences = text.match(/[^.!?]+[.!?]+/g) || [];\n return sentences.length;\n}\n\n// Function to calculate average sentence length\nfunction averageSentenceLength(text) {\n const sentences = text.match(/[^.!?]+[.!?]+/g) || [];\n const sentenceLengths = sentences.map(sentence => sentence.trim().split(/\\s+/).length);\n const totalWords = sentenceLengths.reduce((acc, val) => acc + val, 0);\n return sentenceLengths.length ? (totalWords / sentenceLengths.length) : 0;\n}\n\n// Function to calculate average word length\nfunction averageWordLength(text) {\n const words = text.trim().split(/\\s+/);\n const totalCharacters = words.reduce((acc, word) => acc + word.length, 0);\n return words.length ? (totalCharacters / words.length) : 0;\n}\n\n// Function to calculate Flesch-Kincaid Readability Score\nfunction fleschKincaidReadability(text) {\n // Split text into sentences (approximate)\n const sentences = text.match(/[^.!?]+[.!?]*[\\n]*/g) || [];\n // Split text into words\n const words = text.trim().split(/\\s+/);\n // Estimate syllable count by matching vowel groups\n const syllableCount = (text.toLowerCase().match(/[aeiouy]{1,2}/g) || []).length;\n\n const sentenceCount = sentences.length;\n const wordCount = words.length;\n\n // Avoid division by zero\n if (wordCount === 0 || sentenceCount === 0) return 0;\n\n const averageWordsPerSentence = wordCount / sentenceCount;\n const averageSyllablesPerWord = syllableCount / wordCount;\n\n // Flesch-Kincaid formula\n return 206.835 - (1.015 * averageWordsPerSentence) - (84.6 * averageSyllablesPerWord);\n}\n\n\n// Prepare the result array for n8n output\nconst resultArray = [];\n\n// Loop through the input data and analyze each LLM response\ninputData.forEach(item => {\n const llmResponse = item.llm_response;\n\n // Perform the analyses\n const wordCount = countWords(llmResponse);\n const sentenceCount = countSentences(llmResponse);\n const avgSentenceLength = averageSentenceLength(llmResponse);\n const readabilityScore = fleschKincaidReadability(llmResponse);\n const avgWordLength = averageWordLength(llmResponse);\n\n // Structure the output to include original input and new calculated values\n resultArray.push({\n json: {\n llm_response: item.llm_response,\n prompt: item.prompt,\n model: item.model,\n start_time: item.start_time,\n end_time: item.end_time,\n time_diff: item.time_diff,\n word_count: wordCount,\n sentence_count: sentenceCount,\n average_sent_length: avgSentenceLength,\n readability_score: readabilityScore,\n average_word_length: avgWordLength\n }\n });\n});\n\n// Return the result array to n8n\nreturn resultArray;\n"
},
"typeVersion": 2
},
{
"id": "adef5d92-cb7e-417e-acbb-1a5d6c26426a",
"name": "Save Results to Google Sheets",
"type": "n8n-nodes-base.googleSheets",
"position": [
2180,
160
],
"parameters": {
"columns": {
"value": {
"Model": "={{ $('Extract Model IDsto Run Separately').item.json.id }}",
"Prompt": "={{ $json.prompt }}",
"Response ": "={{ $('LLM Response Analysis').item.json.text }}",
"TIme Sent": "={{ $json.start_time }}",
"Word_count": "={{ $json.word_count }}",
"Sentence_count": "={{ $json.sentence_count }}",
"Time Recieved ": "={{ $json.end_time }}",
"Total TIme spent ": "={{ $json.time_diff }}",
"readability_score": "={{ $json.readability_score }}",
"Average_sent_length": "={{ $json.average_sent_length }}",
"average_word_length": "={{ $json.average_word_length }}"
},
"schema": [
{
"id": "Prompt",
"type": "string",
"display": true,
"required": false,
"displayName": "Prompt",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "TIme Sent",
"type": "string",
"display": true,
"required": false,
"displayName": "TIme Sent",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Time Recieved ",
"type": "string",
"display": true,
"required": false,
"displayName": "Time Recieved ",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Total TIme spent ",
"type": "string",
"display": true,
"required": false,
"displayName": "Total TIme spent ",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Model",
"type": "string",
"display": true,
"required": false,
"displayName": "Model",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Response ",
"type": "string",
"display": true,
"required": false,
"displayName": "Response ",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "readability_score",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "readability_score",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "average_word_length",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "average_word_length",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Word_count",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Word_count",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Sentence_count",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Sentence_count",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Average_sent_length",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Average_sent_length",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": []
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1GdoTjKOrhWOqSZb-AoLNlXgRGBdXKSbXpy-EsZaPGvg/edit#gid=0",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1GdoTjKOrhWOqSZb-AoLNlXgRGBdXKSbXpy-EsZaPGvg",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1GdoTjKOrhWOqSZb-AoLNlXgRGBdXKSbXpy-EsZaPGvg/edit?usp=drivesdk",
"cachedResultName": "Teacking LLM Success"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "DMnEU30APvssJZwc",
"name": "Google Sheets account"
}
},
"typeVersion": 4.5
},
{
"id": "2e147670-67af-4dde-8ba8-90b685238599",
"name": "Capture End Time",
"type": "n8n-nodes-base.dateTime",
"position": [
1380,
160
],
"parameters": {
"options": {},
"outputFieldName": "endDateTime"
},
"typeVersion": 2
},
{
"id": "5a8d3334-b7f8-4f14-8026-055db795bb1f",
"name": "Capture Start Time",
"type": "n8n-nodes-base.dateTime",
"position": [
520,
160
],
"parameters": {
"options": {},
"outputFieldName": "startDateTime"
},
"typeVersion": 2
},
{
"id": "c42d1748-a10d-4792-8526-5ea1c542eeec",
"name": "Prepare Data for Analysis",
"type": "n8n-nodes-base.set",
"position": [
1800,
160
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "920ffdcc-2ae1-4ccb-bc54-049d9d84bd42",
"name": "llm_response",
"type": "string",
"value": "={{ $('LLM Response Analysis').item.json.text }}"
},
{
"id": "c3e70e1b-055c-4a91-aeb0-3d00d41af86d",
"name": "prompt",
"type": "string",
"value": "={{ $('When chat message received').item.json.chatInput }}"
},
{
"id": "cfa45a85-7e60-4a09-b1ed-f9ad51161254",
"name": "model",
"type": "string",
"value": "={{ $('Extract Model IDsto Run Separately').item.json.id }}"
},
{
"id": "a49758c8-4828-41d9-b1d8-4e64dc06920b",
"name": "start_time",
"type": "string",
"value": "={{ $('Capture Start Time').item.json.startDateTime }}"
},
{
"id": "6206be8f-f088-4c4d-8a84-96295937afe2",
"name": "end_time",
"type": "string",
"value": "={{ $('Capture End Time').item.json.endDateTime }}"
},
{
"id": "421b52f9-6184-4bfa-b36a-571e1ea40ce4",
"name": "time_diff",
"type": "string",
"value": "={{ $json.timeDifference.days }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "04679ba8-f13c-4453-99ac-970095bffc20",
"name": "Extract Model IDsto Run Separately",
"type": "n8n-nodes-base.splitOut",
"position": [
300,
160
],
"parameters": {
"options": {},
"fieldToSplitOut": "data"
},
"typeVersion": 1
},
{
"id": "97cdd050-5538-47e1-a67a-ea6e90e89b19",
"name": "Sticky Note7",
"type": "n8n-nodes-base.stickyNote",
"position": [
2240,
-160
],
"parameters": {
"width": 330.4677419354838,
"height": 182.9032258064516,
"content": "### Optional\nYou can just delete the google sheet node, and review the results by hand. \n\nUtilizing the google sheet, allows you to provide mulitple prompts and review the analysis against all of those runs."
},
"typeVersion": 1
},
{
"id": "5a1558ec-54e8-4860-b3db-edcb47c52413",
"name": "Add System Prompt",
"type": "n8n-nodes-base.set",
"position": [
740,
160
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "fd48436f-8242-4c01-a7c3-246d28a8639f",
"name": "system_prompt",
"type": "string",
"value": "Ensure that messages are concise and to the point readable by a 5th grader."
}
]
},
"includeOtherFields": true
},
"typeVersion": 3.4
},
{
"id": "74df223b-17ab-4189-a171-78224522e1c7",
"name": "LLM Response Analysis",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
1000,
160
],
"parameters": {
"text": "={{ $('When chat message received').item.json.chatInput }}",
"messages": {
"messageValues": [
{
"message": "={{ $json.system_prompt }}"
}
]
},
"promptType": "define"
},
"typeVersion": 1.4
},
{
"id": "65d8b0d3-7285-4c64-8ca5-4346e68ec075",
"name": "Sticky Note8",
"type": "n8n-nodes-base.stickyNote",
"position": [
380,
780
],
"parameters": {
"color": 3,
"width": 570.0000000000002,
"height": 182.91935483870984,
"content": "## 🚀Pro Tip \n\nIf you are getting strange results, ensure that you are Deleting the previous chat (next to the Chat Button) to ensure you aren't bleeding responses into the next chat. "
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"timezone": "America/Denver",
"callerPolicy": "workflowsFromSameOwner",
"executionOrder": "v1",
"saveManualExecutions": true
},
"versionId": "a80bee71-8e21-40ff-8803-42d38f316bfb",
"connections": {
"Get Models": {
"main": [
[
{
"node": "Extract Model IDsto Run Separately",
"type": "main",
"index": 0
}
]
]
},
"Capture End Time": {
"main": [
[
{
"node": "Get timeDifference",
"type": "main",
"index": 0
}
]
]
},
"Add System Prompt": {
"main": [
[
{
"node": "LLM Response Analysis",
"type": "main",
"index": 0
}
]
]
},
"Capture Start Time": {
"main": [
[
{
"node": "Add System Prompt",
"type": "main",
"index": 0
}
]
]
},
"Get timeDifference": {
"main": [
[
{
"node": "Prepare Data for Analysis",
"type": "main",
"index": 0
}
]
]
},
"LLM Response Analysis": {
"main": [
[
{
"node": "Capture End Time",
"type": "main",
"index": 0
}
]
]
},
"Prepare Data for Analysis": {
"main": [
[
{
"node": "Analyze LLM Response Metrics",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Get Models",
"type": "main",
"index": 0
}
]
]
},
"Analyze LLM Response Metrics": {
"main": [
[
{
"node": "Save Results to Google Sheets",
"type": "main",
"index": 0
}
]
]
},
"Run Model with Dunamic Inputs": {
"ai_languageModel": [
[
{
"node": "LLM Response Analysis",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Extract Model IDsto Run Separately": {
"main": [
[
{
"node": "Capture Start Time",
"type": "main",
"index": 0
}
]
]
}
}
}Workflow n8n Google Sheets, LLM, analyse : pour qui est ce workflow ?
Ce workflow s'adresse aux équipes de développement et de recherche, ainsi qu'aux entreprises qui travaillent avec des modèles de langage. Il est idéal pour les organisations de taille moyenne à grande qui cherchent à automatiser l'analyse des performances de leurs modèles linguistiques. Un niveau technique intermédiaire est recommandé pour une personnalisation optimale.
Workflow n8n Google Sheets, LLM, analyse : problème résolu
Ce workflow résout le problème de l'analyse manuelle des performances des modèles de langage, qui peut être chronophage et sujet à des erreurs. En automatisant ce processus, les utilisateurs peuvent rapidement obtenir des résultats précis et fiables, réduisant ainsi le risque d'erreurs humaines. Cela permet également de libérer du temps pour se concentrer sur des tâches à plus forte valeur ajoutée, tout en garantissant une évaluation rigoureuse des modèles.
Workflow n8n Google Sheets, LLM, analyse : étapes du workflow
Étape 1 : Lorsque le message de chat est reçu, l'heure de début est capturée.
- Étape 1 : Une requête HTTP est effectuée pour récupérer les modèles disponibles.
- Étape 2 : Les données sont préparées pour l'analyse.
- Étape 3 : Les modèles sont exécutés avec des entrées dynamiques.
- Étape 4 : Les résultats sont analysés à l'aide d'un code JavaScript.
- Étape 5 : L'heure de fin est capturée pour calculer le temps écoulé.
- Étape 6 : Les résultats finaux sont sauvegardés dans Google Sheets.
Workflow n8n Google Sheets, LLM, analyse : guide de personnalisation
Pour personnaliser ce workflow, vous pouvez modifier l'URL de la requête HTTP pour récupérer des modèles spécifiques. Il est également possible d'ajuster les paramètres des noeuds Sticky Note pour personnaliser les messages affichés. Pour intégrer d'autres outils, vous pouvez ajouter des noeuds supplémentaires ou modifier les connexions existantes. Assurez-vous de sécuriser le flux en configurant correctement les autorisations d'accès à Google Sheets et en surveillant les performances du workflow via les logs n8n.