Workflow n8n
Automatisation Telegram avec n8n : conversion de CV en HTML
Ce workflow n8n a pour objectif de faciliter la conversion de CV en différents formats, notamment HTML et PDF, en utilisant des données extraites via Telegram. Idéal pour les recruteurs ou les professionnels des ressources humaines, ce processus permet d'automatiser la gestion des candidatures en rendant les informations facilement accessibles et présentables. Le workflow commence par un déclencheur Telegram qui capte les messages entrants. Ensuite, il utilise plusieurs modèles OpenAI pour traiter et structurer les données du CV. Les étapes incluent la conversion des sections du CV, telles que l'éducation, l'historique professionnel, les projets et le bénévolat, en HTML. Chaque section est traitée par des noeuds dédiés, comme 'Convert education to HTML' et 'Convert employment history to HTML', garantissant une présentation cohérente. Les données sont ensuite fusionnées et converties en un document final, prêt à être envoyé ou stocké. Grâce à cette automatisation n8n, les utilisateurs gagnent un temps précieux, réduisent les erreurs manuelles et améliorent l'efficacité de leur processus de recrutement.
Tags clés :automatisationTelegramn8nCVOpenAI
Catégorie: Telegram · Tags: automatisation, Telegram, n8n, CV, OpenAI❤ 0
Workflow n8n Telegram, CV, OpenAI : vue d'ensemble
Schéma des nœuds et connexions de ce workflow n8n, généré à partir du JSON n8n.
Workflow n8n Telegram, CV, OpenAI : 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, CV, OpenAI : pour qui est ce workflow ?
Ce workflow s'adresse principalement aux recruteurs, aux responsables des ressources humaines et aux professionnels du recrutement qui cherchent à automatiser la gestion des candidatures. Il convient également aux entreprises de taille petite à moyenne qui souhaitent optimiser leur processus de sélection. Une connaissance de base des outils numériques est recommandée.
Workflow n8n Telegram, CV, OpenAI : problème résolu
Ce workflow résout le problème de la gestion manuelle des CV, souvent chronophage et sujette à des erreurs. En automatisant la conversion des données de CV en formats HTML et PDF, il permet aux utilisateurs de gagner du temps et d'améliorer la précision des informations présentées. Les recruteurs peuvent ainsi se concentrer sur des tâches à plus forte valeur ajoutée, comme l'évaluation des candidats.
Workflow n8n Telegram, CV, OpenAI : étapes du workflow
Étape 1 : Le workflow est déclenché par un message sur Telegram.
- Étape 1 : Les données du CV sont extraites à l'aide du noeud 'Get file'.
- Étape 2 : Les sections du CV, telles que l'éducation et l'historique professionnel, sont traitées par des noeuds spécifiques pour les convertir en HTML.
- Étape 3 : Les données sont fusionnées à l'aide de plusieurs noeuds 'Merge' pour créer un document cohérent.
- Étape 4 : Le document final est converti en HTML et PDF, prêt à être utilisé.
Workflow n8n Telegram, CV, OpenAI : guide de personnalisation
Pour personnaliser ce workflow, vous pouvez modifier les paramètres du déclencheur Telegram pour spécifier le type de messages à traiter. Les noeuds de conversion, comme 'Convert education to HTML', peuvent être ajustés pour s'adapter à votre format de CV spécifique. Vous pouvez également intégrer d'autres services n8n pour enrichir le processus, comme l'envoi automatique des documents par email. Assurez-vous de sécuriser les données en configurant correctement les autorisations et en surveillant les flux pour détecter toute anomalie.