This workflow follows the Googlegemini → HTTP Request recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"name": "My workflow",
"nodes": [
{
"parameters": {
"httpMethod": "POST",
"path": "generate-interview-question",
"responseMode": "responseNode",
"options": {}
},
"type": "n8n-nodes-base.webhook",
"typeVersion": 2.1,
"position": [
-3904,
688
],
"id": "e9402224-d7d4-4036-a8c9-301359e95527",
"name": "Webhook"
},
{
"parameters": {
"operation": "pdf",
"options": {}
},
"type": "n8n-nodes-base.extractFromFile",
"typeVersion": 1,
"position": [
-2032,
64
],
"id": "ce363184-2903-4a4c-ba5e-ddb1a1a69eb3",
"name": "Extract from File"
},
{
"parameters": {
"modelId": {
"__rl": true,
"value": "gpt-4o-mini",
"mode": "list",
"cachedResultName": "GPT-4O-MINI"
},
"messages": {
"values": [
{
"content": "Generate 10 Interview question with their answer depending on user resume data provided by user. Make sure response will be in question and answer field.\n\nMake sure the key for the array of interview questions and answer object is stricly \"interview_questions\" and nothing else.\n\nFor example:\n[\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": {\n \"interview_questions\": [\n {\n \"question\": \"\",\n \"answer\": \"\"\n }\n ]\n }\n }\n }\n]\n\nImportant: Do not wrap the above example in any additional or originally returned structure. It's just an example of how the key should be \"interview_questions\" only and not to be confused for nesting it inside the orignal response",
"role": "system"
},
{
"content": "={{ $json.cleanedContent }}"
}
]
},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [
-864,
64
],
"id": "c7478588-0d6a-4d21-8cd3-af65a10ca222",
"name": "Message a model",
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"options": {}
},
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.4,
"position": [
-528,
64
],
"id": "88a66353-b18b-464e-83f1-666edd8a9384",
"name": "Respond to Webhook"
},
{
"parameters": {
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 2
},
"conditions": [
{
"id": "3ec71f64-6e62-421b-8448-2470a549072c",
"leftValue": "={{ $json.body.resumeUrl }}",
"rightValue": "",
"operator": {
"type": "string",
"operation": "exists",
"singleValue": true
}
}
],
"combinator": "and"
},
"options": {}
},
"type": "n8n-nodes-base.if",
"typeVersion": 2.2,
"position": [
-3696,
688
],
"id": "2120e1c2-016f-4e18-9cf2-8cf1f084f0f9",
"name": "If"
},
{
"parameters": {
"modelId": {
"__rl": true,
"value": "gpt-4o-mini",
"mode": "list",
"cachedResultName": "GPT-4O-MINI"
},
"messages": {
"values": [
{
"content": "Depends on User Job Title and Job Description, generate 10 interview questions. Give me question and answer inside questions object list with field question and answer.\n\nMake sure the key for the array of interview questions and answer object is stricly \"questions\" and nothing else.\n\nFor example:\n[\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": {\n \"questions\": [\n {\n \"question\": \"\",\n \"answer\": \"\"\n }\n ]\n }\n }\n }\n]\n\nImportant: Do not wrap the above example in any additional or originally returned structure. It's just an example of how the key should be \"questions\" only and not to be confused for nesting it inside the orignal response",
"role": "system"
},
{
"content": "={{ $json.cleanedContent }}"
}
]
},
"jsonOutput": true,
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [
-2592,
1152
],
"id": "0fb67400-84b4-499d-acef-dd052420b9bf",
"name": "Message a model1",
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"options": {}
},
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.4,
"position": [
-2192,
1152
],
"id": "62ae6b30-ff1c-47b2-b4cc-e307bd5942c4",
"name": "Respond to Webhook1"
},
{
"parameters": {
"modelId": {
"__rl": true,
"value": "models/gemini-1.5-flash",
"mode": "list",
"cachedResultName": "models/gemini-1.5-flash"
},
"messages": {
"values": [
{
"content": "=Extract the following fields from {{ $json.text }}:\n\n- jobTitle: The primary or most recent job title.\n- yoe: Total years of professional experience (YOE).\n- keywords: List of up to 5 relevant skills or technologies found in the resume.\n\nReturn your answer strictly in the following JSON format only with no other fields:\n{\n \"jobTitle\": \"...\",\n \"yoe\": ...,\n \"keywords\": [ ... ]\n}"
}
]
},
"jsonOutput": true,
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.googleGemini",
"typeVersion": 1,
"position": [
-1808,
64
],
"id": "eb110a54-4498-4a18-b350-185398566183",
"name": "Message a model2",
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"zone": {
"__rl": true,
"mode": "list",
"value": "web_unlocker1"
},
"country": {
"__rl": true,
"mode": "list",
"value": "us"
},
"url": "=https://www.linkedin.com/jobs/search/?keywords={{ encodeURIComponent($json.jobTitle) }}",
"requestOptions": {}
},
"type": "@brightdata/n8n-nodes-brightdata.brightData",
"typeVersion": 1,
"position": [
-1248,
64
],
"id": "f0476be5-a3c3-48b2-ab20-607390b88ffc",
"name": "Access and extract data from a specific URL",
"credentials": {
"brightdataApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"instructions": "// Make sure not to do any changement to code. Use this exact one\nconst items = $input.all();\nconst text = items[0].json.content.parts[0].text;\nconst parsed = JSON.parse(text);\n\n// Always convert to array format, then slice\nconst dataArray = Array.isArray(parsed) ? parsed : [parsed];\nconst numberOfResults = 1;\n\nreturn dataArray.slice(0, numberOfResults).map(item => ({ json: item }));",
"codeGeneratedForPrompt": "// Make sure not to do any changement to code. Use this exact one\nconst items = $input.all();\nconst text = items[0].json.content.parts[0].text;\nconst parsed = JSON.parse(text);\n\n// Always convert to array format, then slice\nconst dataArray = Array.isArray(parsed) ? parsed : [parsed];\nconst numberOfResults = 1;\n\nreturn dataArray.slice(0, numberOfResults).map(item => ({ json: item }));",
"jsCode": "const items = $input.all();\nconst text = items[0].json.content.parts[0].text;\nconst parsed = JSON.parse(text);\n\n// Always convert to array format, then slice\nconst dataArray = Array.isArray(parsed) ? parsed : [parsed];\nconst numberOfResults = 1;\n\nreturn dataArray.slice(0, numberOfResults).map((item) => ({ json: item }));\n"
},
"type": "n8n-nodes-base.aiTransform",
"typeVersion": 1,
"position": [
-1472,
64
],
"id": "a793f70f-de32-4aae-b190-abb27c4010c4",
"name": "AI Transform"
},
{
"parameters": {
"jsCode": "const outputItems = [];\n\nfor (const item of $input.all()) {\n let htmlContent;\n \n if (typeof item.json === 'string') {\n htmlContent = item.json;\n } else if (Array.isArray(item.json)) {\n htmlContent = item.json[0]; \n } else if (item.json && typeof item.json === 'object') {\n htmlContent = item.json.html || item.json.body || item.json.content;\n }\n \n if (htmlContent && typeof htmlContent === 'string') {\n const originalSize = htmlContent.length;\n \n htmlContent = htmlContent\n .replace(/<script\\b[^<]*(?:(?!<\\/script>)<[^<]*)*<\\/script>/gi, '')\n .replace(/<style\\b[^<]*(?:(?!<\\/style>)<[^<]*)*<\\/style>/gi, '')\n .replace(/<nav\\b[^<]*(?:(?!<\\/nav>)<[^<]*)*<\\/nav>/gi, '')\n .replace(/<header\\b[^<]*(?:(?!<\\/header>)<[^<]*)*<\\/header>/gi, '')\n .replace(/<footer\\b[^<]*(?:(?!<\\/footer>)<[^<]*)*<\\/footer>/gi, '')\n .replace(/<!--[\\s\\S]*?-->/g, '') \n .replace(/<meta\\b[^>]*>/gi, '') \n .replace(/<link\\b[^>]*>/gi, ''); \n \n \n const cleanText = htmlContent\n .replace(/<[^>]+>/g, ' ')\n .replace(/\\s+/g, ' ') \n .replace(/\\n+/g, ' ') \n .trim();\n \n\n outputItems.push({\n json: {\n cleanedContent: cleanText,\n originalSize: originalSize,\n optimizedSize: cleanText.length,\n compressionRatio: Math.round((1 - cleanText.length / originalSize) * 100) + '%'\n }\n });\n }\n}\n\nreturn outputItems;"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
-1040,
64
],
"id": "bab32a1d-21e8-4e4c-82b5-64faf6f111c0",
"name": "Code"
},
{
"parameters": {
"zone": {
"__rl": true,
"mode": "list",
"value": "web_unlocker1"
},
"country": {
"__rl": true,
"mode": "list",
"value": "us"
},
"url": "=https://www.linkedin.com/jobs/search/?keywords={{ encodeURIComponent($json.body.jobTitle) }}",
"requestOptions": {}
},
"type": "@brightdata/n8n-nodes-brightdata.brightData",
"typeVersion": 1,
"position": [
-3040,
1152
],
"id": "4224f86e-6331-4016-abed-d2728a7bc2cd",
"name": "Access and extract data from a specific URL2",
"credentials": {
"brightdataApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"jsCode": "const outputItems = [];\n\nfor (const item of $input.all()) {\n let htmlContent;\n \n if (typeof item.json === 'string') {\n htmlContent = item.json;\n } else if (Array.isArray(item.json)) {\n htmlContent = item.json[0]; \n } else if (item.json && typeof item.json === 'object') {\n htmlContent = item.json.html || item.json.body || item.json.content;\n }\n \n if (htmlContent && typeof htmlContent === 'string') {\n const originalSize = htmlContent.length;\n \n htmlContent = htmlContent\n .replace(/<script\\b[^<]*(?:(?!<\\/script>)<[^<]*)*<\\/script>/gi, '')\n .replace(/<style\\b[^<]*(?:(?!<\\/style>)<[^<]*)*<\\/style>/gi, '')\n .replace(/<nav\\b[^<]*(?:(?!<\\/nav>)<[^<]*)*<\\/nav>/gi, '')\n .replace(/<header\\b[^<]*(?:(?!<\\/header>)<[^<]*)*<\\/header>/gi, '')\n .replace(/<footer\\b[^<]*(?:(?!<\\/footer>)<[^<]*)*<\\/footer>/gi, '')\n .replace(/<!--[\\s\\S]*?-->/g, '') \n .replace(/<meta\\b[^>]*>/gi, '') \n .replace(/<link\\b[^>]*>/gi, ''); \n \n \n const cleanText = htmlContent\n .replace(/<[^>]+>/g, ' ')\n .replace(/\\s+/g, ' ') \n .replace(/\\n+/g, ' ') \n .trim();\n \n\n outputItems.push({\n json: {\n cleanedContent: cleanText,\n originalSize: originalSize,\n optimizedSize: cleanText.length,\n compressionRatio: Math.round((1 - cleanText.length / originalSize) * 100) + '%'\n }\n });\n }\n}\n\nreturn outputItems;"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
-2832,
1152
],
"id": "35cc00f1-79fe-43f0-8b88-3e46c8c01501",
"name": "Code1"
},
{
"parameters": {
"url": "={{ $json.body.resumeUrl }}",
"options": {}
},
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
-3056,
416
],
"id": "c6265599-9683-4c24-8de8-74b1da697e13",
"name": "HTTP Request"
},
{
"parameters": {},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-2432,
-448
],
"id": "c3781c3c-38db-4430-8c5c-68a6fcb157db",
"name": "Sticky Note1"
}
],
"connections": {
"Webhook": {
"main": [
[
{
"node": "If",
"type": "main",
"index": 0
}
]
]
},
"Extract from File": {
"main": [
[
{
"node": "Message a model2",
"type": "main",
"index": 0
}
]
]
},
"Message a model": {
"main": [
[
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
}
]
]
},
"If": {
"main": [
[
{
"node": "HTTP Request",
"type": "main",
"index": 0
}
],
[
{
"node": "Access and extract data from a specific URL2",
"type": "main",
"index": 0
}
]
]
},
"Message a model1": {
"main": [
[
{
"node": "Respond to Webhook1",
"type": "main",
"index": 0
}
]
]
},
"Message a model2": {
"main": [
[
{
"node": "AI Transform",
"type": "main",
"index": 0
}
]
]
},
"Access and extract data from a specific URL": {
"main": [
[
{
"node": "Code",
"type": "main",
"index": 0
}
]
]
},
"AI Transform": {
"main": [
[
{
"node": "Access and extract data from a specific URL",
"type": "main",
"index": 0
}
]
]
},
"Code": {
"main": [
[
{
"node": "Message a model",
"type": "main",
"index": 0
}
]
]
},
"Access and extract data from a specific URL2": {
"main": [
[
{
"node": "Code1",
"type": "main",
"index": 0
}
]
]
},
"Code1": {
"main": [
[
{
"node": "Message a model1",
"type": "main",
"index": 0
}
]
]
},
"HTTP Request": {
"main": [
[
{
"node": "Extract from File",
"type": "main",
"index": 0
}
]
]
}
},
"active": true,
"settings": {
"executionOrder": "v1"
},
"versionId": "b4e5a630-cd3c-4bcd-b583-dd9a316dfe77",
"meta": {
"templateCredsSetupCompleted": true
},
"id": "PEm0WHbHvUzUW0NQ",
"tags": []
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
brightdataApigooglePalmApiopenAiApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
Interviewgeneration. Uses openAi, googleGemini, @brightdata/n8n-nodes-brightdata, aiTransform. Webhook trigger; 15 nodes.
Source: https://github.com/Arjunhg/dev/blob/752e0d4656bea2b1f1c75b26530452e32fc866c2/n8n/InterviewGeneration.json — original creator credit. Request a take-down →
Related workflows
Workflows that share integrations, category, or trigger type with this one. All free to copy and import.
How it works Runs on schedule (Monday-Friday at 9 AM) to automate lead generation Searches for companies on Google Maps by location and category Extracts owner information from company websites and im
HTTP Request, Anthropic, Google Gemini +3
AI Institutional Stock Valuation Engine with Risk Scoring & Scenario Targets
Google Sheets, XML, HTTP Request +3
Overview This is a production-grade, fully automated stock analysis system built entirely in n8n. It combines institutional-level financial analysis, dual AI model consensus, and a self-improving back
Google Sheets, XML, HTTP Request +3
This powerful n8n automation workflow is designed to execute advanced B2B lead enrichment and hyper-personalization for cold email outreach. By orchestrating a complex chain of data scraping, AI analy
OpenAI, HTTP Request, Airtable
This workflow bridges the gap between raw product data and revenue sales tools. It automates the entire Product Qualified Lead (PQL) lifecycle—from real-time intent routing to churn prevention—reducin
HTTP Request, Anthropic, OpenAI