Workflow n8n
Automatisation Telegram avec n8n : génération de CV en HTML
Ce workflow n8n a pour objectif de faciliter la génération de CV au format HTML à partir de données collectées via Telegram. Dans un contexte où la présentation de l'information est cruciale, ce processus permet aux utilisateurs de créer des CV attrayants et bien structurés en automatisant la conversion des informations personnelles, de l'historique professionnel, de l'éducation, des projets et des expériences de bénévolat. Le workflow commence par un déclencheur Telegram qui capte les messages entrants. Ensuite, il utilise le modèle de chat OpenAI pour traiter les données et les convertir en HTML. Les différentes sections du CV sont traitées par des nœuds de conversion spécifiques, tels que 'Convert education to HTML' et 'Convert employment history to HTML', qui transforment les données brutes en format HTML. Les données sont ensuite fusionnées et structurées à l'aide de nœuds de fusion pour garantir une présentation cohérente. Enfin, le CV est généré sous forme de document PDF, prêt à être partagé. Cette automatisation n8n permet non seulement de gagner du temps, mais aussi d'assurer une qualité constante dans la création de CV, réduisant ainsi le risque d'erreurs humaines et augmentant l'efficacité des processus de candidature.
Tags clés :automatisationTelegramn8ngénération de CVHTML
Catégorie: Telegram trigger · Tags: automatisation, Telegram, n8n, génération de CV, HTML❤ 0
Workflow n8n Telegram, génération de CV, HTML : vue d'ensemble
Schéma des nœuds et connexions de ce workflow n8n, généré à partir du JSON n8n.
Workflow n8n Telegram, génération de CV, HTML : détail des nœuds
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"nodes": [
{
"id": "79849bb5-00a4-42e6-92c4-b06c7a20eb3e",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1580,
340
],
"parameters": {
"model": "gpt-4-turbo-preview",
"options": {
"temperature": 0,
"responseFormat": "json_object"
}
},
"credentials": {
"openAiApi": {
"id": "jazew1WAaSRrjcHp",
"name": "OpenAI (workfloows@gmail.com)"
}
},
"typeVersion": 1
},
{
"id": "85df0106-1f78-4412-8751-b84d417c8bf9",
"name": "Convert education to HTML",
"type": "n8n-nodes-base.code",
"position": [
2420,
180
],
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "function convertToHTML(list) {\n let html = '';\n\n list.forEach((education, index) => {\n if (index > 0) {\n html += '<br /><br />'; // Add a new line if it's not the first item\n }\n html += `<b>Institution:</b> ${education.institution}<br />\n<b>Start year:</b> ${education.start_year}<br />\n<b>Degree:</b> ${education.degree}`;\n });\n\n return html;\n}\n\n// Assuming payload is already defined\nconst payload = $input.item.json.education;\n\nconst htmlOutput = convertToHTML(payload);\nreturn {\n htmlOutput\n};"
},
"typeVersion": 2
},
{
"id": "da4fc45d-712f-4171-b72a-66b74b4d8e05",
"name": "Auto-fixing Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserAutofixing",
"position": [
1820,
340
],
"parameters": {},
"typeVersion": 1
},
{
"id": "225a7513-6fd4-4672-9b40-b10b00f121a7",
"name": "OpenAI Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1740,
520
],
"parameters": {
"options": {
"temperature": 0
}
},
"credentials": {
"openAiApi": {
"id": "jazew1WAaSRrjcHp",
"name": "OpenAI (workfloows@gmail.com)"
}
},
"typeVersion": 1
},
{
"id": "0606c99d-a080-4277-b071-1bc0c93bb2e3",
"name": "Structured Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
1960,
520
],
"parameters": {
"jsonSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"personal_info\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": { \"type\": \"string\" },\n \"address\": { \"type\": \"string\" },\n \"email\": { \"type\": \"string\", \"format\": \"email\" },\n \"github\": { \"type\": \"string\"},\n \"linkedin\": { \"type\": \"string\" }\n }\n },\n \"employment_history\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"position\": { \"type\": \"string\" },\n \"company\": { \"type\": \"string\" },\n \"duration\": { \"type\": \"string\" },\n \"responsibilities\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" }\n }\n }\n }\n },\n \"education\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"institution\": { \"type\": \"string\" },\n \"start_year\": { \"type\": \"integer\" },\n \"degree\": { \"type\": \"string\" }\n }\n }\n },\n \"projects\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": { \"type\": \"string\" },\n \"year\": { \"type\": \"integer\" },\n \"description\": { \"type\": \"string\" },\n \"technologies\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" }\n }\n }\n }\n },\n \"volunteering\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"activity\": { \"type\": \"string\" },\n \"location\": { \"type\": \"string\" },\n \"date\": { \"type\": \"string\" },\n \"description\": { \"type\": \"string\" }\n }\n }\n },\n \"programming_languages\": {\n \"type\": \"object\",\n \"properties\": {\n \"languages\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" }\n },\n \"tools\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" }\n },\n \"methodologies\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" }\n }\n }\n },\n \"foreign_languages\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"language\": { \"type\": \"string\" },\n \"level\": { \"type\": \"string\" }\n }\n }\n }\n }\n}\n"
},
"typeVersion": 1
},
{
"id": "027975cd-768a-4048-858d-9060f48ab622",
"name": "Convert employment history to HTML",
"type": "n8n-nodes-base.code",
"position": [
2420,
-20
],
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "function convertToHTML(list) {\n let html = '';\n\n list.forEach((item, index) => {\n if (index > 0) {\n html += '<br />'; // Add a new line if it's not the first item\n }\n html += `<b>Position:</b> ${item.position}\n<b>Company:</b> ${item.company}\n<br />\n<b>Duration:</b> ${item.duration}\n<br />\n<b>Responsibilities:</b>\n`;\n\n item.responsibilities.forEach((responsibility, i) => {\n html += `- ${responsibility}`;\n if (i < item.responsibilities.length - 1 || index < list.length - 1) {\n html += '<br />'; // Add new line if it's not the last responsibility in the last item\n }\n });\n });\n\n return html;\n}\n\n// Assuming payload is already defined\nconst payload = $input.item.json.employment_history;\n\nconst htmlOutput = convertToHTML(payload);\nreturn {\n htmlOutput\n};"
},
"typeVersion": 2
},
{
"id": "823a241d-1c68-40a9-8f2c-f1bdfaab7603",
"name": "Convert projects to HTML",
"type": "n8n-nodes-base.code",
"position": [
2420,
380
],
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "function convertToHTML(list) {\n let html = '';\n\n list.forEach((project, index) => {\n if (index > 0) {\n html += '<br />'; // Add a new line if it's not the first project\n }\n html += `<b>Name:</b> ${project.name}<br />\n<b>Year:</b> ${project.year}<br />\n<b>Description:</b> ${project.description}<br /><br />\n<b>Technologies:</b>\n<br />`;\n\n project.technologies.forEach((technology, i) => {\n html += `- ${technology}`;\n if (i < project.technologies.length - 1 || index < list.length - 1) {\n html += '<br />'; // Add new line if it's not the last technology in the last project\n }\n });\n });\n\n return html;\n}\n\n// Assuming payload is already defined\nconst payload = $input.item.json.projects;\n\nconst htmlOutput = convertToHTML(payload);\nreturn {\n htmlOutput\n};\n"
},
"typeVersion": 2
},
{
"id": "a12eb0e1-1cb9-4b83-a1ec-42dd8214f6bc",
"name": "Convert volunteering to HTML",
"type": "n8n-nodes-base.code",
"position": [
2420,
580
],
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "function convertToHTML(list) {\n let html = '';\n\n list.forEach((event, index) => {\n if (index > 0) {\n html += '<br />'; // Add a new line if it's not the first volunteering event\n }\n html += `<b>Activity:</b> ${event.activity}<br />\n<b>Location:</b> ${event.location}<br />\n<b>Date:</b> ${event.date}<br />\n<b>Description:</b> ${event.description}<br />`;\n });\n\n return html;\n}\n\n// Assuming payload is already defined\nconst payload = $input.item.json.volunteering;\n\nconst htmlOutput = convertToHTML(payload);\nreturn {\n htmlOutput\n};\n"
},
"typeVersion": 2
},
{
"id": "70b67b80-d22d-4eea-8c97-3d2cb2b9bbfc",
"name": "Telegram trigger",
"type": "n8n-nodes-base.telegramTrigger",
"position": [
360,
340
],
"webhookId": "d6829a55-a01b-44ac-bad3-2349324c8515",
"parameters": {
"updates": [
"message"
],
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"id": "lStLV4zzcrQO9eAM",
"name": "Telegram (Resume Extractor)"
}
},
"typeVersion": 1.1
},
{
"id": "21bead1d-0665-44d5-b623-b0403c9abd6c",
"name": "Auth",
"type": "n8n-nodes-base.if",
"position": [
600,
340
],
"parameters": {
"options": {},
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "7ca4b4c3-e23b-4896-a823-efc85c419467",
"operator": {
"type": "number",
"operation": "equals"
},
"leftValue": "={{ $json.message.chat.id }}",
"rightValue": 0
}
]
}
},
"typeVersion": 2
},
{
"id": "de76d6ec-3b0e-44e0-943d-55547aac2e46",
"name": "No operation (unauthorized)",
"type": "n8n-nodes-base.noOp",
"position": [
860,
520
],
"parameters": {},
"typeVersion": 1
},
{
"id": "439f5e2c-be7d-486b-a1f1-13b09f77c2c8",
"name": "Check if start message",
"type": "n8n-nodes-base.if",
"position": [
860,
220
],
"parameters": {
"options": {},
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "1031f14f-9793-488d-bb6b-a021f943a399",
"operator": {
"type": "string",
"operation": "notEquals"
},
"leftValue": "={{ $json.message.text }}",
"rightValue": "/start"
}
]
}
},
"typeVersion": 2
},
{
"id": "af5f5622-c338-40c0-af72-90e124ed7ce1",
"name": "No operation (start message)",
"type": "n8n-nodes-base.noOp",
"position": [
1120,
360
],
"parameters": {},
"typeVersion": 1
},
{
"id": "2efae11a-376b-44aa-ab91-9b3dea82ede0",
"name": "Get file",
"type": "n8n-nodes-base.telegram",
"position": [
1120,
120
],
"parameters": {
"fileId": "={{ $json.message.document.file_id }}",
"resource": "file"
},
"credentials": {
"telegramApi": {
"id": "lStLV4zzcrQO9eAM",
"name": "Telegram (Resume Extractor)"
}
},
"typeVersion": 1.1
},
{
"id": "88fd1002-ad2c-445f-92d4-11b571db3788",
"name": "Extract text from PDF",
"type": "n8n-nodes-base.extractFromFile",
"position": [
1380,
120
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "9dfc204b-c567-418a-93a3-9b72cf534a8c",
"name": "Set parsed fileds",
"type": "n8n-nodes-base.set",
"position": [
2040,
120
],
"parameters": {
"options": {}
},
"typeVersion": 3.2
},
{
"id": "314c771a-5ff2-484f-823b-0eab88f43ea3",
"name": "Personal info",
"type": "n8n-nodes-base.set",
"position": [
2420,
-380
],
"parameters": {
"fields": {
"values": [
{
"name": "personal_info",
"stringValue": "=<b><u>Personal info</u></b>\n<br /><br />\n<b>Name:</b> {{ $json.personal_info.name }}\n<br />\n<b>Address:</b> {{ $json.personal_info.address }}\n<br />\n<b>Email:</b> {{ $json.personal_info.email }}\n<br />\n<b>GitHub:</b> {{ $json.personal_info.github }}\n<br />"
}
]
},
"include": "none",
"options": {}
},
"typeVersion": 3.2
},
{
"id": "be6b32e8-6000-4235-a723-0e22828ead45",
"name": "Technologies",
"type": "n8n-nodes-base.set",
"position": [
2420,
-200
],
"parameters": {
"fields": {
"values": [
{
"name": "technologies",
"stringValue": "=<b><u>Technologies</u></b>\n<br /><br />\n<b>Programming languages:</b> {{ $json.programming_languages.languages.join(', ') }}\n<br />\n<b>Tools:</b> {{ $json.programming_languages.tools.join(', ') }}\n<br />\n<b>Methodologies:</b> {{ $json.programming_languages.methodologies.join(', ') }}\n<br />"
}
]
},
"include": "none",
"options": {}
},
"typeVersion": 3.2
},
{
"id": "ab726d61-84b8-4af7-a195-33e1add89153",
"name": "Employment history",
"type": "n8n-nodes-base.set",
"position": [
2640,
-20
],
"parameters": {
"fields": {
"values": [
{
"name": "employment_history",
"stringValue": "=<b><u>Employment history</u></b>\n<br /><br />\n{{ $json[\"htmlOutput\"] }}"
}
]
},
"include": "none",
"options": {}
},
"typeVersion": 3.2
},
{
"id": "692f9555-6102-4d3c-b0a1-868e27e3c343",
"name": "Education",
"type": "n8n-nodes-base.set",
"position": [
2640,
180
],
"parameters": {
"fields": {
"values": [
{
"name": "education",
"stringValue": "=<b><u>Education</u></b>\n<br /><br />\n{{ $json[\"htmlOutput\"] }}"
}
]
},
"include": "none",
"options": {}
},
"typeVersion": 3.2
},
{
"id": "258728f2-1f03-4786-8197-feb9f1bc4dfe",
"name": "Projects",
"type": "n8n-nodes-base.set",
"position": [
2640,
380
],
"parameters": {
"fields": {
"values": [
{
"name": "projects",
"stringValue": "=<b><u>Projects</u></b>\n<br /><br />\n{{ $json[\"htmlOutput\"] }}"
}
]
},
"include": "none",
"options": {}
},
"typeVersion": 3.2
},
{
"id": "3c819ce4-235a-4b12-a396-d33dca9f80da",
"name": "Volunteering",
"type": "n8n-nodes-base.set",
"position": [
2640,
580
],
"parameters": {
"fields": {
"values": [
{
"name": "volunteering",
"stringValue": "=<b><u>Volunteering</u></b>\n<br /><br />\n{{ $json[\"htmlOutput\"] }}"
}
]
},
"include": "none",
"options": {}
},
"typeVersion": 3.2
},
{
"id": "41bd7506-7330-4c25-8b43-aa3c836736fc",
"name": "Merge education and employment history",
"type": "n8n-nodes-base.merge",
"position": [
2880,
100
],
"parameters": {
"mode": "combine",
"options": {},
"combinationMode": "multiplex"
},
"typeVersion": 2.1
},
{
"id": "d788da36-360b-4009-82ad-2f206fad8e53",
"name": "Merge projects and volunteering",
"type": "n8n-nodes-base.merge",
"position": [
2880,
500
],
"parameters": {
"mode": "combine",
"options": {},
"combinationMode": "multiplex"
},
"typeVersion": 2.1
},
{
"id": "57c20e19-3d84-41c0-a415-1d55cb031da1",
"name": "Merge personal info and technologies",
"type": "n8n-nodes-base.merge",
"position": [
3140,
-160
],
"parameters": {
"mode": "combine",
"options": {},
"combinationMode": "multiplex"
},
"typeVersion": 2.1
},
{
"id": "f12be010-8375-4ff7-ba8e-9c2c870f648b",
"name": "Merge all",
"type": "n8n-nodes-base.merge",
"position": [
3400,
200
],
"parameters": {
"mode": "combine",
"options": {},
"combinationMode": "multiplex"
},
"typeVersion": 2.1
},
{
"id": "d6428167-2c75-42a5-a905-7590ff1d6a25",
"name": "Set final data",
"type": "n8n-nodes-base.set",
"position": [
3620,
200
],
"parameters": {
"fields": {
"values": [
{
"name": "output",
"stringValue": "={{ $json.personal_info }}\n<br /><br />\n{{ $json.employment_history }}\n<br /><br />\n{{ $json.education }}\n<br /><br />\n{{ $json.projects }}\n<br /><br />\n{{ $json.volunteering }}\n<br /><br />\n{{ $json.technologies }}"
}
]
},
"include": "none",
"options": {}
},
"typeVersion": 3.2
},
{
"id": "9ea13c62-2e09-4b37-b889-66edaef1fcf1",
"name": "Convert raw to base64",
"type": "n8n-nodes-base.code",
"position": [
3840,
200
],
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "const encoded = Buffer.from($json.output).toString('base64');\n\nreturn { encoded };"
},
"typeVersion": 2
},
{
"id": "c4474fa1-b1b5-432f-b30e-100201c9ec7c",
"name": "Convert to HTML",
"type": "n8n-nodes-base.convertToFile",
"position": [
4060,
200
],
"parameters": {
"options": {
"fileName": "index.html",
"mimeType": "text/html"
},
"operation": "toBinary",
"sourceProperty": "encoded"
},
"typeVersion": 1.1
},
{
"id": "3c4d2010-1bdc-4f01-bb1a-bd0128017787",
"name": "Generate plain PDF doc",
"type": "n8n-nodes-base.httpRequest",
"position": [
4340,
200
],
"parameters": {
"url": "http://gotenberg:3000/forms/chromium/convert/html",
"method": "POST",
"options": {
"response": {
"response": {
"responseFormat": "file"
}
}
},
"sendBody": true,
"contentType": "multipart-form-data",
"bodyParameters": {
"parameters": [
{
"name": "files",
"parameterType": "formBinaryData",
"inputDataFieldName": "data"
}
]
}
},
"typeVersion": 4.1
},
{
"id": "2b3cd55f-21a3-4c14-905f-82b158aa3fd0",
"name": "Send PDF to the user",
"type": "n8n-nodes-base.telegram",
"position": [
4640,
200
],
"parameters": {
"chatId": "={{ $('Telegram trigger').item.json[\"message\"][\"chat\"][\"id\"] }}",
"operation": "sendDocument",
"binaryData": true,
"additionalFields": {
"fileName": "={{ $('Set parsed fileds').item.json[\"personal_info\"][\"name\"].toLowerCase().replace(' ', '-') }}.pdf"
}
},
"credentials": {
"telegramApi": {
"id": "lStLV4zzcrQO9eAM",
"name": "Telegram (Resume Extractor)"
}
},
"typeVersion": 1.1
},
{
"id": "54fe1d2d-eb9d-4fe1-883f-1826e27ac873",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
540,
180
],
"parameters": {
"width": 226.21234567901217,
"height": 312.917333333334,
"content": "### Add chat ID\nRemember to set your actual ID to trigger automation from Telegram."
},
"typeVersion": 1
},
{
"id": "b193a904-260b-4d45-8a66-e3cb46fc7ce4",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
800,
83.43940740740783
],
"parameters": {
"width": 229.64938271604922,
"height": 293.54824691358016,
"content": "### Ignore start message\nWorkflow ignores initial`/start` message sent to the bot."
},
"typeVersion": 1
},
{
"id": "d5c95d8f-b699-4a8e-9460-a4f5856b5e6f",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1066,
-20
],
"parameters": {
"width": 211.00246913580224,
"height": 302.41975308642,
"content": "### Download resume file\nBased on file ID, node performs downloading of the file uploaded by user."
},
"typeVersion": 1
},
{
"id": "2de0751d-8e11-457e-8c38-a6dcca59190c",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
1320,
-20
],
"parameters": {
"width": 217.87654320987633,
"height": 302.41975308642,
"content": "### Extract text from PDF\nNode extracts readable text form PDF."
},
"typeVersion": 1
},
{
"id": "4b9ccab8-ff6c-408f-93fe-f148034860a0",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
1580,
-20
],
"parameters": {
"width": 410.9479506172837,
"height": 302.41975308642,
"content": "### Parse resume data\nCreate structured data from text extracted from resume. Chain uses OpenAI `gpt-4-turbo-preview` model and JSON response mode. **Adjust JSON schema in output parser to your needs.**"
},
"typeVersion": 1
},
{
"id": "bfb1d382-90fa-4bff-8c38-04e53bcf5f58",
"name": "Parse resume data",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
1660,
120
],
"parameters": {
"prompt": "={{ $json.text }}",
"messages": {
"messageValues": [
{
"message": "Your task is to extract all necessary data such as first name, last name, experience, known technologies etc. from the provided resume text and return in well-unified JSON format. Do not make things up."
}
]
}
},
"typeVersion": 1.3
},
{
"id": "7e8eb10a-f21c-4a9c-90b1-b71537b78356",
"name": "Merge other data",
"type": "n8n-nodes-base.merge",
"position": [
3140,
340
],
"parameters": {
"mode": "combine",
"options": {},
"combinationMode": "multiplex"
},
"typeVersion": 2.1
},
{
"id": "7c4398de-7b4d-4095-b38f-eaf099d2991b",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
2340,
-491.4074074074074
],
"parameters": {
"width": 1196.8442469135782,
"height": 1260.345679012346,
"content": "### Format HTML\nFormat HTML for each resume section (employment history, projects etc.)."
},
"typeVersion": 1
},
{
"id": "9de2f504-6ff0-4b00-8e0d-436c789b4e23",
"name": "Sticky Note7",
"type": "n8n-nodes-base.stickyNote",
"position": [
3580,
40
],
"parameters": {
"width": 638.6516543209876,
"height": 322.5837037037037,
"content": "### Create HTML file\nFrom formatted output create `index.html` file in order to run PDF conversion."
},
"typeVersion": 1
},
{
"id": "11abdff5-377e-490d-9136-15c24ff6a05e",
"name": "Sticky Note8",
"type": "n8n-nodes-base.stickyNote",
"position": [
4260,
39.83604938271645
],
"parameters": {
"color": 3,
"width": 262.0096790123454,
"height": 322.5837037037035,
"content": "### Convert file to PDF\nForm `index.html` create PDF using [Gotenberg](https://gotenberg.dev/). If you're not familiar with this software, feel free to check out [my tutorial on YouTube](https://youtu.be/bo15xdjXf1Y?si=hFZMTfjzfSOLOLPK)."
},
"typeVersion": 1
},
{
"id": "73fb81d0-5218-4311-aaec-7fa259d8cbd3",
"name": "Sticky Note9",
"type": "n8n-nodes-base.stickyNote",
"position": [
4560,
40
],
"parameters": {
"width": 262.0096790123454,
"height": 322.5837037037035,
"content": "### Send PDF file to user\nDeliver converted PDF to Telegram user (based on chat ID)."
},
"typeVersion": 1
},
{
"id": "bb5fa375-4cc9-4559-a014-7b618d6c5f32",
"name": "Sticky Note10",
"type": "n8n-nodes-base.stickyNote",
"position": [
-280,
128
],
"parameters": {
"width": 432.69769500990674,
"height": 364.2150828344463,
"content": "## ⚠️ Note\n\nThis is *resume extractor* workflow that I had a pleasure to present during [n8n community hangout](https://youtu.be/eZacuxrhCuo?si=KkJQrgQuvLxj-6FM&t=1701\n) on March 7, 2024.\n\n1. Remember to add your credentials and configure nodes.\n2. This node requires installed [Gotenberg](https://gotenberg.dev/) for PDF generation. If you're not familiar with this software, feel free to check out [my tutorial on YouTube](https://youtu.be/bo15xdjXf1Y?si=hFZMTfjzfSOLOLPK). If you don't want to self-host Gotenberg, you use other PDF generation provider (PDFMonkey, ApiTemplate or similar).\n3. If you like this workflow, please subscribe to [my YouTube channel](https://www.youtube.com/@workfloows) and/or [my newsletter](https://workfloows.com/).\n\n**Thank you for your support!**"
},
"typeVersion": 1
}
],
"connections": {
"Auth": {
"main": [
[
{
"node": "Check if start message",
"type": "main",
"index": 0
}
],
[
{
"node": "No operation (unauthorized)",
"type": "main",
"index": 0
}
]
]
},
"Get file": {
"main": [
[
{
"node": "Extract text from PDF",
"type": "main",
"index": 0
}
]
]
},
"Projects": {
"main": [
[
{
"node": "Merge projects and volunteering",
"type": "main",
"index": 0
}
]
]
},
"Education": {
"main": [
[
{
"node": "Merge education and employment history",
"type": "main",
"index": 1
}
]
]
},
"Merge all": {
"main": [
[
{
"node": "Set final data",
"type": "main",
"index": 0
}
]
]
},
"Technologies": {
"main": [
[
{
"node": "Merge personal info and technologies",
"type": "main",
"index": 1
}
]
]
},
"Volunteering": {
"main": [
[
{
"node": "Merge projects and volunteering",
"type": "main",
"index": 1
}
]
]
},
"Personal info": {
"main": [
[
{
"node": "Merge personal info and technologies",
"type": "main",
"index": 0
}
]
]
},
"Set final data": {
"main": [
[
{
"node": "Convert raw to base64",
"type": "main",
"index": 0
}
]
]
},
"Convert to HTML": {
"main": [
[
{
"node": "Generate plain PDF doc",
"type": "main",
"index": 0
}
]
]
},
"Merge other data": {
"main": [
[
{
"node": "Merge all",
"type": "main",
"index": 1
}
]
]
},
"Telegram trigger": {
"main": [
[
{
"node": "Auth",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Parse resume data",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Parse resume data": {
"main": [
[
{
"node": "Set parsed fileds",
"type": "main",
"index": 0
}
]
]
},
"Set parsed fileds": {
"main": [
[
{
"node": "Convert employment history to HTML",
"type": "main",
"index": 0
},
{
"node": "Convert education to HTML",
"type": "main",
"index": 0
},
{
"node": "Convert projects to HTML",
"type": "main",
"index": 0
},
{
"node": "Personal info",
"type": "main",
"index": 0
},
{
"node": "Convert volunteering to HTML",
"type": "main",
"index": 0
},
{
"node": "Technologies",
"type": "main",
"index": 0
}
]
]
},
"Employment history": {
"main": [
[
{
"node": "Merge education and employment history",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model1": {
"ai_languageModel": [
[
{
"node": "Auto-fixing Output Parser",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Convert raw to base64": {
"main": [
[
{
"node": "Convert to HTML",
"type": "main",
"index": 0
}
]
]
},
"Extract text from PDF": {
"main": [
[
{
"node": "Parse resume data",
"type": "main",
"index": 0
}
]
]
},
"Check if start message": {
"main": [
[
{
"node": "Get file",
"type": "main",
"index": 0
}
],
[
{
"node": "No operation (start message)",
"type": "main",
"index": 0
}
]
]
},
"Generate plain PDF doc": {
"main": [
[
{
"node": "Send PDF to the user",
"type": "main",
"index": 0
}
]
]
},
"Convert projects to HTML": {
"main": [
[
{
"node": "Projects",
"type": "main",
"index": 0
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "Auto-fixing Output Parser",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Auto-fixing Output Parser": {
"ai_outputParser": [
[
{
"node": "Parse resume data",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Convert education to HTML": {
"main": [
[
{
"node": "Education",
"type": "main",
"index": 0
}
]
]
},
"Convert volunteering to HTML": {
"main": [
[
{
"node": "Volunteering",
"type": "main",
"index": 0
}
]
]
},
"Merge projects and volunteering": {
"main": [
[
{
"node": "Merge other data",
"type": "main",
"index": 1
}
]
]
},
"Convert employment history to HTML": {
"main": [
[
{
"node": "Employment history",
"type": "main",
"index": 0
}
]
]
},
"Merge personal info and technologies": {
"main": [
[
{
"node": "Merge all",
"type": "main",
"index": 0
}
]
]
},
"Merge education and employment history": {
"main": [
[
{
"node": "Merge other data",
"type": "main",
"index": 0
}
]
]
}
}
}Workflow n8n Telegram, génération de CV, HTML : pour qui est ce workflow ?
Ce workflow s'adresse aux professionnels des ressources humaines, aux recruteurs et aux candidats à la recherche d'une solution efficace pour créer des CV. Il est idéal pour les entreprises de taille petite à moyenne qui souhaitent automatiser leur processus de candidature tout en ayant une maîtrise technique de base.
Workflow n8n Telegram, génération de CV, HTML : problème résolu
Ce workflow résout le problème de la création manuelle de CV, qui peut être chronophage et sujet à des erreurs. En automatisant la collecte et la conversion des données, il permet aux utilisateurs de générer rapidement des CV professionnels et bien formatés. Cela réduit également le stress lié à la préparation des candidatures et améliore la réactivité des candidats sur le marché du travail.
Workflow n8n Telegram, génération de CV, HTML : étapes du workflow
Étape 1 : Le workflow est déclenché par un message reçu sur Telegram.
- Étape 1 : Les données sont récupérées et traitées par le modèle OpenAI pour extraire les informations pertinentes.
- Étape 2 : Les sections du CV, telles que l'éducation et l'historique professionnel, sont converties en HTML à l'aide de nœuds dédiés.
- Étape 3 : Les données sont fusionnées pour créer une structure cohérente.
- Étape 4 : Le CV final est généré en PDF, prêt à être envoyé ou partagé.
Workflow n8n Telegram, génération de CV, HTML : guide de personnalisation
Pour personnaliser ce workflow, vous pouvez modifier les paramètres du nœud 'Telegram trigger' pour adapter les types de messages que vous souhaitez traiter. Vous pouvez également ajuster les modèles de conversion dans les nœuds 'Convert education to HTML' et 'Convert employment history to HTML' pour répondre à vos besoins spécifiques. Pensez à sécuriser le flux en ajoutant des vérifications d'autorisation dans le nœud 'Auth'. Enfin, vous pouvez intégrer d'autres outils ou services en ajoutant des nœuds supplémentaires pour enrichir les fonctionnalités de votre workflow.