Mon workflow 3
Ceci est unHR, AI Summarization, Multimodal AIworkflow d'automatisation du domainecontenant 23 nœuds.Utilise principalement des nœuds comme Set, Code, Merge, Airtable, FormTrigger. Tri et notation automatisés des CV, en utilisant AI, Gmail, GoogleDrive et Airtable
- •Clé API Airtable
- •Informations d'identification Google Drive API
- •Compte Google et informations d'identification Gmail API
- •Informations d'identification Google Sheets API
Nœuds utilisés (23)
{
"id": "7S4ihndpWguEUgPR",
"meta": {
"instanceId": "b2b5a36da7eac7de99012b5a90e67cd124f5c20d9168d5fb4eef7aa2b75f2f80",
"templateCredsSetupCompleted": true
},
"name": "My workflow 3",
"tags": [],
"nodes": [
{
"id": "14df0331-5d44-471e-a60b-9931f108764c",
"name": "Gmail Trigger",
"type": "n8n-nodes-base.gmailTrigger",
"position": [
-128,
64
],
"parameters": {
"simple": false,
"filters": {
"q": "Senior Software Engineer"
},
"options": {
"downloadAttachments": true,
"dataPropertyAttachmentsPrefixName": "CV"
},
"pollTimes": {
"item": [
{
"mode": "everyMinute"
}
]
}
},
"credentials": {
"gmailOAuth2": {
"id": "8jLBWmrnkH59W1tP",
"name": "Gmail account"
}
},
"typeVersion": 1.3
},
{
"id": "6c3f54bf-26d8-4863-b91c-d6760b54bfc4",
"name": "Téléverser un fichier",
"type": "n8n-nodes-base.googleDrive",
"position": [
144,
-48
],
"parameters": {
"name": "={{ $json.from.value[0].name }}",
"driveId": {
"__rl": true,
"mode": "list",
"value": "My Drive"
},
"options": {},
"folderId": {
"__rl": true,
"mode": "list",
"value": "13yu3QH6GO5Kx0HbEkwXPiceBH1yDVzTO",
"cachedResultUrl": "https://drive.google.com/drive/folders/13yu3QH6GO5Kx0HbEkwXPiceBH1yDVzTO",
"cachedResultName": "Software Engineer Resume"
},
"inputDataFieldName": "CV0"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "WV2QCnuShiBUUxQX",
"name": "Google Drive account"
}
},
"typeVersion": 3
},
{
"id": "8b8fb671-bd8d-42cc-8a21-1a518eb8c42b",
"name": "Télécharger un fichier",
"type": "n8n-nodes-base.googleDrive",
"position": [
368,
-48
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.id }}"
},
"options": {},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "WV2QCnuShiBUUxQX",
"name": "Google Drive account"
}
},
"typeVersion": 3
},
{
"id": "436d2b81-56a7-4cca-a4f3-73fa174ef3d5",
"name": "Extraire du fichier",
"type": "n8n-nodes-base.extractFromFile",
"position": [
592,
-48
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "659a2bfe-607c-46f4-a8c0-748f900dac7d",
"name": "Extracteur d'informations",
"type": "@n8n/n8n-nodes-langchain.informationExtractor",
"position": [
976,
-224
],
"parameters": {
"text": "={{ $json.text }}",
"options": {},
"schemaType": "manual",
"inputSchema": "={\n\t\"type\": \"object\",\n\t\"properties\": {\n \t\"candidate_name\": {\n\t\t\"type\": \"string\"\n\t},\n \"email_address\": {\n\t\t\"type\": \"string\",\n\t\t\"format\": \"email\"\n },\n \"contact_number\": {\n \"type\": \"string\",\n \"pattern\": \"^(\\\\+\\\\d{1,3}[- ]?)?\\\\d{10}$\"\n }\n }\n}\n"
},
"typeVersion": 1.2
},
{
"id": "88b31b2a-4e61-485f-a472-d689b198ac9e",
"name": "Modèle de chat OpenRouter",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
"position": [
976,
-32
],
"parameters": {
"model": "openai/gpt-oss-20b:free",
"options": {}
},
"credentials": {
"openRouterApi": {
"id": "ONkqc0B0l2xlY8Mu",
"name": "OpenRouter account"
}
},
"typeVersion": 1
},
{
"id": "6476ff9c-5460-48d2-9dee-b7109692c87c",
"name": "Agent IA",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
960,
112
],
"parameters": {
"text": "=CV:\n{{ $json.text }}",
"options": {
"systemMessage": "=YOU ARE THE WORLD'S MOST ACCURATE AND EFFICIENT CV SUMMARIZER, KNOWN FOR PRODUCING CONCISE AND INFORMATIVE SUMMARIES THAT CAPTURE ALL ESSENTIAL DETAILS.\nYOUR TASK IS TO SUMMARIZE A PROVIDED CV INTO THREE CLEAR SECTIONS: EDUCATIONAL QUALIFICATIONS, JOBN HISTORY, AND SKILL SET. IN ADDITION, YOU MUST EVALUATE THE CANDIDATE'S SUITABILITY FOR A SPECIFIED JOB ROLE AND ASSIGN A SCORE FROM 1 TO 10 BASED ON HOW WELL THEIR QUALIFICATIONS MATCH THE ROLE.\n\nINSTRUCTIONS\n1. EXTRACT AND SUMMARIZE INFORMATION FROM THE CV:\nEDUCATIONAL QUALIFICATIONS: INCLUDE DEGREE NAMES, INSTITUTIONS, AND GRADUATION YEARS.\nJOB HISTORY: LIS JOB TITLES, COMPANIES, AND EMPLOYMENT DATES, WITH A BRIEF OVERVIEW OF KEY RESPONSIBILITIES OR ACHIEVEMENTS.\nSKILL SET: COMPLETE RELEVANT TECHNICAL, SOFT, AND INDUSTRY-SPECIFIC SKILLS.\n\n2. EVALUATE THE CANDIDATE BASED ON THE PROVIDED JOB POST:\nANALYZE RELEVANCE: Compare the candidate's education, work experience, and skill set with the provided job post.\nASSIGN A SCORE (1-10):\n1-3: Weak match (lacks key qualifications or experience).\n4-6: Moderate match (some relevant qalifications but gaps exists).\n7-8: Strong match (meets most job criteria with relevant experience).\n9-10: Excellent match (perfect fit exceeding expectations).\n\nPROVIDE A BRIEF JUSTIFICATION for the assigned score, highlighting key strengths or missing qualifications.\n\n3. OUTPUT FORMAT:\nEducational Qualifications\n\n[Degree], [Institution], [Year]\nJob History\n\n[Job Title], [Company], [Dates]: [Key responsibilities or Achievements]\nSkill Ste\n\n[Skill 1], [Skill 2], [Skill 3], [Skill 4], etc.\nCandidates Evaluation\n\nScore: [1-10]\nJustification: [Brief explanation of why the candidate received this score]\nWHAT TO DO\nDO NOT INCLUDE PERSONAL INFORMATION such as contact details or addresses.\nDO NOT OMIT RELEVANT EDUCATION, JOB, OR SKILL INFORMATION.\nDO NOT ADD YOUR OWN INTERPRETATION OR ASSUMPTIONS ABOUT THE CV CONTENT.\nDO NOT USE INFORMAL LANGUAGE OR EXCESSIVE DETAIL.\nEXAMPLE OUTPUT:\nEducational Qualifications\n\nBachelor of Science in Computer Science, University of Karachi, 2020.\nJob History\n\nSoftware Engineer, Techcorp, 2021-2025: Developed Scalable web applications and optimized database performance.\nSkill Set\n\nPython, Javascript, React, ReactNative, n8n, Zapier, AI, LLM, Team Leadership, Agile Development.\nCandidate Evaluation\n\nScore: 8/10\nJustification: The candidate has a relevant degree, strong technical skills, and 12 years of industry experience. However, lacks experience with cloud technologies mentioned in the job description.\n\nJob Post:\nWe’re seeking a talented and driven Full-Stack Developer with solid experience in Next.js, SAAS Development, Supabase etc. to join our growing team. In this role, you will be instrumental in building and maintaining scalable, high-performance web applications and backend systems.\n\nKey Responsibilities:\n•\tDevelop and scale web applications using Next.js.\n•\tBuild backend infrastructure using Supabase (database, authentication, storage, etc.).\n•\tCollaborate with cross-functional teams in a SaaS product environment.\n•\tIntegrate AI tools and workflows to enhance development efficiency and innovation.\n•\tWrite optimized, maintainable SQL queries and design robust data structures.\n•\tAnalyze and work with existing codebases to extend features or resolve issues.\n•\tEnsure system performance, stability, and security through best practices.\n\nIdeal Candidate should have:\n•\t3+ years of professional development experience.\n•\tA Bachelors in Computer Science, Engineering, Information Technology, or a relevant Field.\n•\tStrong proficiency in Next.js and Supabase.\n•\tDemonstrated experience in SaaS application development.\n•\tAbility to read and work with existing codebases.\n•\tGood understanding of authentication, authorization, and middleware.\n•\tProficiency in SQL, database schema design, and performance tuning.\n•\tActively incorporates AI tools (like Copilot, ChatGPT, etc.) into development processes.\n•\tAbility to work independently and collaboratively in a fast-paced environment.\n\nWhat we Offer:\n•\tCompetitive compensation\n•\tOpportunity to work on innovative, AI-powered tools and services\n•\tCollaborative, fast-paced, and growth-focused environment\nInterested candidates can share the Resume to baluntechsol@gmail.com with the Position mentioned in the Subject line.\n\nIf you are interested, please feel free to DM me or email your Resume to baluntechsol@gmail.com with the Position mentioned in the Subject line."
},
"promptType": "define"
},
"typeVersion": 2.2
},
{
"id": "930d6fcb-bcb5-4179-b8d2-00037be73b1a",
"name": "Modèle de chat OpenRouter1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
"position": [
960,
352
],
"parameters": {
"model": "openai/gpt-oss-20b:free",
"options": {}
},
"credentials": {
"openRouterApi": {
"id": "ONkqc0B0l2xlY8Mu",
"name": "OpenRouter account"
}
},
"typeVersion": 1
},
{
"id": "ff7b1234-947b-45d8-9693-c2d9a3c82fa6",
"name": "Modifier les champs",
"type": "n8n-nodes-base.set",
"position": [
1344,
128
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "c186b601-19ce-4a98-8097-6f9e1d0f1a9e",
"name": "output",
"type": "string",
"value": "={{ $json.output }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "f0274105-2a9e-490f-af57-73efb0c7d366",
"name": "Code",
"type": "n8n-nodes-base.code",
"position": [
1568,
128
],
"parameters": {
"jsCode": "// Read raw text from previous node\nconst data = items[0].json;\nconst rawText = String(\n data.output ||\n data.outputText ||\n data.Output ||\n data.summary ||\n data.result ||\n \"\"\n);\n\nif (!rawText) {\n return [{\n json: {\n error: \"No input text found in previous node (tried output / outputText / Output / summary / result).\"\n }\n }];\n}\n\n// Helper: extract section\nfunction extractSection(text, sectionName) {\n if (!text) return \"\";\n const nameEsc = sectionName.replace(/[.*+?^${}()|[\\]\\\\]/g, \"\\\\$&\");\n\n // 1) Bold markdown header: **Section Name**\n let regex = new RegExp(`\\\\*\\\\*\\\\s*${nameEsc}\\\\s*\\\\*\\\\*[\\\\r\\\\n]+([\\\\s\\\\S]*?)(?=\\\\n\\\\*\\\\*|\\\\n---|$)`, \"i\");\n let m = text.match(regex);\n if (m) return m[1].trim();\n\n // 2) Plain header line\n regex = new RegExp(`^\\\\s*${nameEsc}\\\\s*$[\\\\r\\\\n]+([\\\\s\\\\S]*?)(?=^\\\\s*\\\\*\\\\*|\\\\n---|$)`, \"im\");\n m = text.match(regex);\n if (m) return m[1].trim();\n\n // 3) Fallback: find the name anywhere\n regex = new RegExp(nameEsc, \"i\");\n m = text.match(regex);\n if (m) {\n const start = m.index + m[0].length;\n const rest = text.slice(start);\n const nextBoundary = rest.search(/\\n\\*\\*|\\n---/i);\n const end = nextBoundary !== -1 ? start + nextBoundary : text.length;\n return text.slice(start, end).trim();\n }\n\n return \"\";\n}\n\n// Extract score + justification\nfunction extractScoreAndJustification(block) {\n if (!block) return [\"\", \"\"];\n const sanitized = block.replace(/\\*/g, \"\").trim();\n\n let score = \"\";\n let justification = \"\";\n\n const scoreMatch = sanitized.match(/Score\\s*[:\\-–—]?\\s*([0-9]{1,2}(?:\\/10)?|N\\/A|NA|n\\/a)/i);\n if (scoreMatch) {\n score = scoreMatch[1].trim();\n if (/^[0-9]{1,2}$/.test(score)) {\n const n = parseInt(score, 10);\n if (n >= 0 && n <= 10) score = `${n}/10`;\n }\n }\n\n const justMatch = sanitized.match(/Justification\\s*[:\\-–—]?\\s*([\\s\\S]*)/i);\n if (justMatch) {\n justification = justMatch[1].trim();\n }\n\n if (!score && sanitized) score = \"N/A\";\n return [score, justification];\n}\n\n// Extract sections\nconst educationalQualification = extractSection(rawText, \"Educational Qualifications\");\nconst jobHistory = extractSection(rawText, \"Job History\");\nconst skillSet = extractSection(rawText, \"Skill Set\");\nconst candidateEvaluation = extractSection(rawText, \"Candidate Evaluation\");\n\n// Get score + justification\nconst [score, justification] = extractScoreAndJustification(candidateEvaluation);\n\nreturn [{\n json: {\n educationalQualification: educationalQualification || \"\",\n jobHistory: jobHistory || \"\",\n skillSet: skillSet || \"\",\n score: score || \"\",\n justification: justification || \"\"\n }\n}];\n"
},
"typeVersion": 2
},
{
"id": "49e498a3-c87b-4d79-9a59-6947324dcb9a",
"name": "Fusionner",
"type": "n8n-nodes-base.merge",
"position": [
1808,
-48
],
"parameters": {
"mode": "combine",
"options": {},
"combineBy": "combineAll"
},
"typeVersion": 3.2
},
{
"id": "607b4f93-7b37-4293-8844-fd17ded34785",
"name": "Ajouter une ligne dans la feuille",
"type": "n8n-nodes-base.googleSheets",
"position": [
2064,
-208
],
"parameters": {
"columns": {
"value": {
"score": "={{ $json.score }}",
"skill set": "={{ $json.skillSet }}",
"Job History": "={{ $json.jobHistory }}",
"Justification": "={{ $json.justification }}",
"email_address": "={{ $json.output.email_address }}",
"candidate_name": "={{ $json.output.candidate_name }}",
"contact_number": "={{ $json.output.contact_number }}",
"Educational Qualifications": "={{ $json.educationalQualification }}"
},
"schema": [
{
"id": "candidate_name",
"type": "string",
"display": true,
"required": false,
"displayName": "candidate_name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "email_address",
"type": "string",
"display": true,
"required": false,
"displayName": "email_address",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "contact_number",
"type": "string",
"display": true,
"required": false,
"displayName": "contact_number",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Educational Qualifications",
"type": "string",
"display": true,
"required": false,
"displayName": "Educational Qualifications",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Job History",
"type": "string",
"display": true,
"required": false,
"displayName": "Job History",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "skill set",
"type": "string",
"display": true,
"required": false,
"displayName": "skill set",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "score",
"type": "string",
"display": true,
"required": false,
"displayName": "score",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Justification",
"type": "string",
"display": true,
"required": false,
"displayName": "Justification",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/12pqhk8m-j2V44jKaNZwG7jKFPUpm4yCe17mHjbr6qUQ/edit#gid=0",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "12pqhk8m-j2V44jKaNZwG7jKFPUpm4yCe17mHjbr6qUQ",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/12pqhk8m-j2V44jKaNZwG7jKFPUpm4yCe17mHjbr6qUQ/edit?usp=drivesdk",
"cachedResultName": "HR_Automation_Workflow"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "ObgvVgjWJYaH5iLJ",
"name": "Google Sheets account"
}
},
"typeVersion": 4.7
},
{
"id": "e9ad17fd-e688-4c34-80ce-a79dc572b794",
"name": "Créer un enregistrement",
"type": "n8n-nodes-base.airtable",
"position": [
2064,
64
],
"parameters": {
"base": {
"__rl": true,
"mode": "list",
"value": "appAN9KciZeolO2PN",
"cachedResultUrl": "https://airtable.com/appAN9KciZeolO2PN",
"cachedResultName": "Senior_Software_Engineer_Resume"
},
"table": {
"__rl": true,
"mode": "list",
"value": "tblgro31x2ktE3aEc",
"cachedResultUrl": "https://airtable.com/appAN9KciZeolO2PN/tblgro31x2ktE3aEc",
"cachedResultName": "Table 1"
},
"columns": {
"value": {
"Score": "={{ $json.score }}",
"Skill set": "={{ $json.skillSet }}",
"Job History": "={{ $json.jobHistory }}",
"Justification": "={{ $json.justification }}",
"email_address": "={{ $json.output.email_address }}",
"candidate_name": "={{ $json.output.candidate_name }}",
"contact_number": "={{ $json.output.contact_number }}",
"Educational Qualifications": "={{ $json.educationalQualification }}"
},
"schema": [
{
"id": "id",
"type": "string",
"display": true,
"removed": false,
"readOnly": true,
"required": false,
"displayName": "id",
"defaultMatch": true
},
{
"id": "candidate_name",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "candidate_name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "email_address",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "email_address",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "contact_number",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "contact_number",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Educational Qualifications",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Educational Qualifications",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Job History",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Job History",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Skill set",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Skill set",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Score",
"type": "number",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Score",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Justification",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Justification",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"id"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "create"
},
"credentials": {
"airtableTokenApi": {
"id": "jgRMszk4kSwSaU3V",
"name": "Airtable Personal Access Token account"
}
},
"typeVersion": 2.1
},
{
"id": "04901267-ea47-4280-9e19-3e88c9fc7993",
"name": "À la soumission du formulaire",
"type": "n8n-nodes-base.formTrigger",
"position": [
-128,
-144
],
"webhookId": "12378e65-adc8-4ca3-9ef6-95cd5d2a412b",
"parameters": {
"options": {},
"formTitle": "Senior Software Engineer"
},
"typeVersion": 2.3
},
{
"id": "34eb4149-0501-4d6d-8dc6-f19a59385d58",
"name": "Note adhésive",
"type": "n8n-nodes-base.stickyNote",
"position": [
-192,
-416
],
"parameters": {
"color": 4,
"height": 912,
"content": "GMAIL TRIGGER:\nListen from emails or forms submissions matching the CV's received for specific job position and fetch attachments."
},
"typeVersion": 1
},
{
"id": "22690685-9e05-4c1b-a798-bd2646e5214d",
"name": "Note adhésive1",
"type": "n8n-nodes-base.stickyNote",
"position": [
64,
-416
],
"parameters": {
"height": 912,
"content": "UPLOAD THE FILE:\nIncoming attachment (CV) is uploaded to the configured Google Drive folder and named from the sender."
},
"typeVersion": 1
},
{
"id": "73d68e7c-705c-49e0-a2af-8bea13a69091",
"name": "Note adhésive2",
"type": "n8n-nodes-base.stickyNote",
"position": [
320,
-416
],
"parameters": {
"color": 3,
"width": 192,
"height": 912,
"content": "DOWNLOAD THE ATTACHMENT (CV):\nThe stored file is downloaded by ID so it can be read."
},
"typeVersion": 1
},
{
"id": "55a5c9bd-9602-4875-aae5-d4497e06b067",
"name": "Note adhésive3",
"type": "n8n-nodes-base.stickyNote",
"position": [
528,
-416
],
"parameters": {
"color": 7,
"height": 912,
"content": "EXTRACT FROM FILE:\nExtract from File converts the CV (PDF) into plain text."
},
"typeVersion": 1
},
{
"id": "05bd2380-4208-4f0e-95be-9f0c0c542721",
"name": "Note adhésive4",
"type": "n8n-nodes-base.stickyNote",
"position": [
864,
-416
],
"parameters": {
"color": 5,
"width": 384,
"height": 912,
"content": "Two parallel AI paths:\n\nQuick structured extraction: Information Extractor uses a small schema (name, email, phone) + LM helper to pull contact fields.\n\nFull CV analysis: AI Agent runs a large system prompt to summarize Education, Job History, Skills and to assign a suitability score (1–10)."
},
"typeVersion": 1
},
{
"id": "fdce16c8-1138-4bb6-9925-efe4357a9f80",
"name": "Note adhésive5",
"type": "n8n-nodes-base.stickyNote",
"position": [
1264,
-416
],
"parameters": {
"color": 2,
"height": 912,
"content": "Normalize agent output: Edit Fields maps the agent response into output."
},
"typeVersion": 1
},
{
"id": "9dd5f08c-6528-4650-b411-5e645413ce6e",
"name": "Note adhésive6",
"type": "n8n-nodes-base.stickyNote",
"position": [
1520,
-416
],
"parameters": {
"width": 208,
"height": 912,
"content": "Parse & clean: \nCode runs JS to extract the three summary sections plus score and justification from the agent text (regex-based)."
},
"typeVersion": 1
},
{
"id": "90db96b2-a8b0-44a4-8f75-05552c5c1ee1",
"name": "Note adhésive7",
"type": "n8n-nodes-base.stickyNote",
"position": [
1744,
-416
],
"parameters": {
"color": 4,
"width": 208,
"height": 912,
"content": "Merge datasets: \nMerge combines the schema extraction (contact info) with the AI-parsed summary/score."
},
"typeVersion": 1
},
{
"id": "d699e192-b79d-4283-b531-59ff75313ffc",
"name": "Note adhésive8",
"type": "n8n-nodes-base.stickyNote",
"position": [
1968,
-416
],
"parameters": {
"color": 6,
"width": 272,
"height": 912,
"content": "Store results: \nFinal record is appended to Google Sheets and inserted into Airtable for tracking, reporting, or downstream workflows."
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "6658e34c-6f2c-418e-84fd-271309c8fcbb",
"connections": {
"f0274105-2a9e-490f-af57-73efb0c7d366": {
"main": [
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"type": "main",
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}
]
]
},
"49e498a3-c87b-4d79-9a59-6947324dcb9a": {
"main": [
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"type": "main",
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},
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"type": "main",
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"6476ff9c-5460-48d2-9dee-b7109692c87c": {
"main": [
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"type": "main",
"index": 0
}
]
]
},
"ff7b1234-947b-45d8-9693-c2d9a3c82fa6": {
"main": [
[
{
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"type": "main",
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}
]
]
},
"6c3f54bf-26d8-4863-b91c-d6760b54bfc4": {
"main": [
[
{
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"type": "main",
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]
]
},
"8b8fb671-bd8d-42cc-8a21-1a518eb8c42b": {
"main": [
[
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"type": "main",
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}
]
]
},
"14df0331-5d44-471e-a60b-9931f108764c": {
"main": [
[
{
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"type": "main",
"index": 0
}
]
]
},
"436d2b81-56a7-4cca-a4f3-73fa174ef3d5": {
"main": [
[
{
"node": "659a2bfe-607c-46f4-a8c0-748f900dac7d",
"type": "main",
"index": 0
},
{
"node": "6476ff9c-5460-48d2-9dee-b7109692c87c",
"type": "main",
"index": 0
}
]
]
},
"04901267-ea47-4280-9e19-3e88c9fc7993": {
"main": [
[
{
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"type": "main",
"index": 0
}
]
]
},
"659a2bfe-607c-46f4-a8c0-748f900dac7d": {
"main": [
[
{
"node": "49e498a3-c87b-4d79-9a59-6947324dcb9a",
"type": "main",
"index": 0
}
]
]
},
"88b31b2a-4e61-485f-a472-d689b198ac9e": {
"ai_languageModel": [
[
{
"node": "659a2bfe-607c-46f4-a8c0-748f900dac7d",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"930d6fcb-bcb5-4179-b8d2-00037be73b1a": {
"ai_languageModel": [
[
{
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"type": "ai_languageModel",
"index": 0
}
]
]
}
}
}Comment utiliser ce workflow ?
Copiez le code de configuration JSON ci-dessus, créez un nouveau workflow dans votre instance n8n et sélectionnez "Importer depuis le JSON", collez la configuration et modifiez les paramètres d'authentification selon vos besoins.
Dans quelles scénarios ce workflow est-il adapté ?
Avancé - Ressources Humaines, Résumé IA, IA Multimodale
Est-ce payant ?
Ce workflow est entièrement gratuit et peut être utilisé directement. Veuillez noter que les services tiers utilisés dans le workflow (comme l'API OpenAI) peuvent nécessiter un paiement de votre part.
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usamaahmed
@usamaahmedPartager ce workflow