Oneclick AI Squad This workflow corresponds to n8n.io template #5474 — we link there as the canonical source.
This workflow follows the Chainllm → Chat Trigger 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 →
{
"id": "FLn2skSh92HNO2SS",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Airline Web Check-in Scraper with AI & Vector DB Storage using n8n",
"tags": [],
"nodes": [
{
"id": "ee7a49bf-a2dc-4d12-aef0-9add291a398c",
"name": "Loop Over Items",
"type": "n8n-nodes-base.splitInBatches",
"position": [
-220,
175
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "049cfbd5-bbc7-483c-964e-a32cdab1e6b8",
"name": "Fetch Airline URLs\t",
"type": "n8n-nodes-base.googleSheets",
"position": [
-440,
175
],
"parameters": {
"options": {},
"sheetName": {
"__rl": true,
"mode": "list",
"value": 2125635496,
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1ws8YonQyc32SveWQdfihYOW_OzOS-2REIrwSYS37oQ8/edit#gid=2125635496",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1ws8YonQyc32SveWQdfihYOW_OzOS-2REIrwSYS37oQ8",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1ws8YonQyc32SveWQdfihYOW_OzOS-2REIrwSYS37oQ8/edit?usp=drivesdk",
"cachedResultName": "airline_faq_urls"
},
"authentication": "serviceAccount"
},
"credentials": {
"googleApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.5
},
{
"id": "7e2ca713-229f-490c-bd2e-481cf8f18184",
"name": "Chat Trigger - Start\t",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-660,
175
],
"parameters": {
"public": true,
"options": {}
},
"typeVersion": 1.1
},
{
"id": "c11c66ea-3e36-4c12-a263-109d03d8be1a",
"name": "Scrape Airline Webpage\t",
"type": "n8n-nodes-base.httpRequest",
"position": [
0,
0
],
"parameters": {
"url": "=https://r.jina.ai/{{ $json['WEB CHECK IN URL'] }}",
"method": "POST",
"options": {},
"jsonHeaders": "{\n \"Cookie\": \"cookie-keyname1=cookie-value1; cookie-keyname2=cookie-value2; cookie-keyname3=cookie-value3\"\n}\n",
"sendHeaders": true,
"authentication": "genericCredentialType",
"specifyHeaders": "json",
"genericAuthType": "httpHeaderAuth"
},
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "27072e20-58dc-49e2-ae6b-1053750607f9",
"name": "Extract Info with LLM\t",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
220,
0
],
"parameters": {
"text": "={{ $json.data }}",
"messages": {
"messageValues": [
{
"message": "=You are an intelligent parser trained to extract structured data from messy airline webpages.\n\nYour task is to extract and return well-structured airline check-in and policy details from raw text. Always return the result as a clean, valid JSON object using the exact schema described below.\n\n---\n\nExtraction Guidelines:\n\n* Ensure consistent JSON structure for every airline.\n* If a key has no value or content, **remove it** (do not return null, empty arrays, or empty objects).\n* Include any other useful data under `\"additional_info\"` if it doesn\u2019t fit existing keys.\n* Always extract **direct URLs** wherever available.\n* Your output should be compact, valid, and readable JSON.\n\n---\n\nJSON Structure Format:\n\n1. Web Check-in Details\n\n* \"web\\_checkin\\_available\": true/false\n* \"checkin\\_url\": \"<URL>\"\n* \"checkin\\_methods\": \\[\"Online\", \"Mobile App\", \"Kiosk\", \"Airport Counter\"]\n* \"checkin\\_start\": \"<Start Time>\"\n* \"checkin\\_deadline\": \"<Deadline Time>\"\n* \"boarding\\_pass\\_options\":\n\n * \"mobile\\_boarding\\_pass\\_available\": true/false\n * \"printed\\_boarding\\_pass\\_required\": true/false\n * \"additional\\_checkin\\_info\": \"<Extra instructions>\"\n\n2. Customer Support\n\n* \"customer\\_support\":\n\n * \"phone\": \"<Phone Number>\"\n * \"email\": \"<Email>\"\n * \"support\\_url\": \"<Support URL>\"\n * \"chat\\_url\": \"<Chat URL>\"\n * \"operating\\_hours\": \"<Hours>\"\n * \"additional\\_help\\_channels\": \\[\"WhatsApp\", \"Twitter Support\", \"Chatbot\"]\n\n3. Baggage Allowance\n\n* \"baggage\\_allowance\":\n\n * \"hand\\_baggage\":\n\n * \"weight\\_limit\": \"<Weight Limit>\"\n * \"size\\_limit\": \"<Size Limit>\"\n * \"additional\\_items\\_allowed\": \\[\"Handbag\", \"Laptop\", \"Baby Items\", \"Medical Equipment\"]\n * \"special\\_conditions\": \"<Any special baggage conditions>\"\n * \"checked\\_baggage\":\n\n * \"general\\_rules\": \"<Baggage Rules>\"\n * \"class\\_specific\\_limits\": \"<Limits for different travel classes>\"\n * \"baggage\\_calculator\\_url\": \"<URL>\"\n * \"oversized\\_special\\_baggage\": \"\\<Details on sports/music equipment>\"\n\n4. Refund & Cancellation Policy\n\n* \"refund\\_policy\":\n\n * \"conditions\": \"<Refund conditions>\"\n * \"processing\\_time\": \"<Processing Time>\"\n * \"refund\\_policy\\_url\": \"<URL>\"\n* \"cancellation\\_policy\":\n\n * \"conditions\": \"<Cancellation conditions>\"\n * \"fees\\_or\\_penalties\": \"<Cancellation Fees>\"\n * \"cancellation\\_policy\\_url\": \"<URL>\"\n\n5. Airport & Travel Guidelines\n\n* \"airport\\_travel\\_guidelines\":\n\n * \"security\\_immigration\\_rules\": \"\\<Security & Immigration Rules>\"\n * \"airport\\_checkin\\_requirements\": \"<Check-in document requirements>\"\n * \"special\\_services\": \\[\"Priority Boarding\", \"Lounge Access\", \"Wheelchair Assistance\"]\n\n6. Frequently Asked Questions (FAQ)\n\n* \"faq\":\n\n * \"faq\\_url\": \"<FAQ Page URL>\"\n * \"questions\\_answers\": \\[\n {\"question\": \"<Q1>\", \"answer\": \"<A1>\"},\n {\"question\": \"<Q2>\", \"answer\": \"<A2>\"}\n ]\n\n7. Additional Details\n\n* \"additional\\_info\": \"<Any extra info from the page not captured above>\"\n\n---\n\nOutput Rules Summary:\n\n* Always return valid JSON.\n* Do **not** include empty fields or structures (null, {}, or \\[]).\n* Place unrelated or extra info under \"additional\\_info\".\n\nUse this structure for every page, even if some values are missing. Just remove the missing fields completely in the output.\n\n\n"
},
{
"type": "AIMessagePromptTemplate",
"message": "=You are an intelligent parser trained to extract structured data from messy airline webpages.\n\nYour task is to extract and return well-structured airline check-in and policy details from raw text. Always return the result as a clean, valid JSON object using the exact schema described below.\n\n---\n\nExtraction Guidelines:\n\n* Ensure consistent JSON structure for every airline.\n* If a key has no value or content, **remove it** (do not return null, empty arrays, or empty objects).\n* Include any other useful data under `\"additional_info\"` if it doesn\u2019t fit existing keys.\n* Always extract **direct URLs** wherever available.\n* Your output should be compact, valid, and readable JSON.\n\n---\n\nJSON Structure Format:\n\n1. Web Check-in Details\n\n* \"web\\_checkin\\_available\": true/false\n* \"checkin\\_url\": \"<URL>\"\n* \"checkin\\_methods\": \\[\"Online\", \"Mobile App\", \"Kiosk\", \"Airport Counter\"]\n* \"checkin\\_start\": \"<Start Time>\"\n* \"checkin\\_deadline\": \"<Deadline Time>\"\n* \"boarding\\_pass\\_options\":\n\n * \"mobile\\_boarding\\_pass\\_available\": true/false\n * \"printed\\_boarding\\_pass\\_required\": true/false\n * \"additional\\_checkin\\_info\": \"<Extra instructions>\"\n\n2. Customer Support\n\n* \"customer\\_support\":\n\n * \"phone\": \"<Phone Number>\"\n * \"email\": \"<Email>\"\n * \"support\\_url\": \"<Support URL>\"\n * \"chat\\_url\": \"<Chat URL>\"\n * \"operating\\_hours\": \"<Hours>\"\n * \"additional\\_help\\_channels\": \\[\"WhatsApp\", \"Twitter Support\", \"Chatbot\"]\n\n3. Baggage Allowance\n\n* \"baggage\\_allowance\":\n\n * \"hand\\_baggage\":\n\n * \"weight\\_limit\": \"<Weight Limit>\"\n * \"size\\_limit\": \"<Size Limit>\"\n * \"additional\\_items\\_allowed\": \\[\"Handbag\", \"Laptop\", \"Baby Items\", \"Medical Equipment\"]\n * \"special\\_conditions\": \"<Any special baggage conditions>\"\n * \"checked\\_baggage\":\n\n * \"general\\_rules\": \"<Baggage Rules>\"\n * \"class\\_specific\\_limits\": \"<Limits for different travel classes>\"\n * \"baggage\\_calculator\\_url\": \"<URL>\"\n * \"oversized\\_special\\_baggage\": \"\\<Details on sports/music equipment>\"\n\n4. Refund & Cancellation Policy\n\n* \"refund\\_policy\":\n\n * \"conditions\": \"<Refund conditions>\"\n * \"processing\\_time\": \"<Processing Time>\"\n * \"refund\\_policy\\_url\": \"<URL>\"\n* \"cancellation\\_policy\":\n\n * \"conditions\": \"<Cancellation conditions>\"\n * \"fees\\_or\\_penalties\": \"<Cancellation Fees>\"\n * \"cancellation\\_policy\\_url\": \"<URL>\"\n\n5. Airport & Travel Guidelines\n\n* \"airport\\_travel\\_guidelines\":\n\n * \"security\\_immigration\\_rules\": \"\\<Security & Immigration Rules>\"\n * \"airport\\_checkin\\_requirements\": \"<Check-in document requirements>\"\n * \"special\\_services\": \\[\"Priority Boarding\", \"Lounge Access\", \"Wheelchair Assistance\"]\n\n6. Frequently Asked Questions (FAQ)\n\n* \"faq\":\n\n * \"faq\\_url\": \"<FAQ Page URL>\"\n * \"questions\\_answers\": \\[\n {\"question\": \"<Q1>\", \"answer\": \"<A1>\"},\n {\"question\": \"<Q2>\", \"answer\": \"<A2>\"}\n ]\n\n7. Additional Details\n\n* \"additional\\_info\": \"<Any extra info from the page not captured above>\"\n\n---\n\nOutput Rules Summary:\n\n* Always return valid JSON.\n* Do **not** include empty fields or structures (null, {}, or \\[]).\n* Place unrelated or extra info under \"additional\\_info\".\n\nUse this structure for every page, even if some values are missing. Just remove the missing fields completely in the output.\n\n\n"
}
]
},
"promptType": "define"
},
"typeVersion": 1.5
},
{
"id": "ba090b45-e6e8-434a-9577-51d281dd4a5b",
"name": "Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOllama",
"position": [
308,
220
],
"parameters": {
"options": {}
},
"credentials": {
"ollamaApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "d557adab-856e-460e-aa81-f929a66ca465",
"name": "Wait for Response",
"type": "n8n-nodes-base.wait",
"position": [
580,
0
],
"parameters": {},
"typeVersion": 1.1
},
{
"id": "279b24fc-e1f3-4a1c-9c70-0177b13f32d8",
"name": "Store Extracted Info\t",
"type": "n8n-nodes-base.googleSheets",
"position": [
816,
0
],
"parameters": {
"columns": {
"value": {
"row_number": "={{ $('Loop Over Items').item.json.row_number }}",
"web check in details": "={{ $json.text.removeTags().replace(/^```json|```$/g, '').trim() }}"
},
"schema": [
{
"id": "Airline",
"type": "string",
"display": true,
"required": false,
"displayName": "Airline",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "WEB CHECK IN URL",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "WEB CHECK IN URL",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "web check in details",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "web check in details",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "output",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "output",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "row_number",
"type": "string",
"display": true,
"removed": false,
"readOnly": true,
"required": false,
"displayName": "row_number",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"row_number"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "update",
"sheetName": {
"__rl": true,
"mode": "list",
"value": 2125635496,
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1ws8YonQyc32SveWQdfihYOW_OzOS-2REIrwSYS37oQ8/edit#gid=2125635496",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1ws8YonQyc32SveWQdfihYOW_OzOS-2REIrwSYS37oQ8",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1ws8YonQyc32SveWQdfihYOW_OzOS-2REIrwSYS37oQ8/edit?usp=drivesdk",
"cachedResultName": "airline_faq_urls"
},
"authentication": "serviceAccount"
},
"credentials": {
"googleApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.5
},
{
"id": "866e9eca-68ad-419e-acf0-c28141bf7727",
"name": "Generate Embeddings\t",
"type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
"position": [
1036,
220
],
"parameters": {},
"credentials": {
"ollamaApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "56553cae-a61f-4b64-8709-06dbab314bce",
"name": "Prepare Text for Vector DB\t",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
1156,
222.5
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "82da65d6-9ecd-451a-b2f8-466795cd07a0",
"name": "Split Long Text\t",
"type": "@n8n/n8n-nodes-langchain.textSplitterTokenSplitter",
"position": [
1244,
420
],
"parameters": {
"chunkSize": 10000
},
"typeVersion": 1
},
{
"id": "a670a7c1-af95-452d-92e5-a82d5be2d0a5",
"name": "Save to Vector DB\t",
"type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
"position": [
1052,
0
],
"parameters": {
"mode": "insert",
"options": {
"collection": {
"values": {
"useCollection": true
}
}
}
},
"credentials": {
"postgres": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "7c4941f0-4dff-49d0-ac9b-901a23987686",
"name": "Wait Before Next Batch\t",
"type": "n8n-nodes-base.wait",
"position": [
1532,
175
],
"parameters": {
"amount": 15
},
"typeVersion": 1.1
},
{
"id": "0fef87ce-bfc3-4edd-aff8-8e10a0e7489a",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-740,
-860
],
"parameters": {
"width": 660,
"height": 860,
"content": "\n\n### \ud83d\udcdd Web Check-in Details Extractor (LLM Prompt Guide)\n\n#### \u2705 What is this?\n\nThis is a powerful AI prompt used inside the **\"Basic LLM Chain\"** node. It tells the AI how to **extract structured airline web check-in data** (like check-in time, baggage policy, cancellation rules) from messy airline webpages.\n\n#### \ud83c\udfaf Why is it used?\n\nAirline websites often present data in unstructured formats. This LLM-based step:\n\n* Cleans the content scraped from airline URLs.\n* Extracts important travel-related info in a consistent JSON format.\n* Helps automate the enrichment of airline data stored in your Google Sheets and Vector DB.\n\n#### \ud83d\udee0\ufe0f How to use it?\n\n1. **Input**: This node receives raw webpage content from the airline\u2019s \"Web Check-in URL\".\n2. **Prompt**: It applies a fixed set of rules (in natural language) to guide the AI to convert the unstructured data into clean JSON.\n3. **Output**: The AI returns a **structured JSON** object with fields like:\n\n * `checkin_url`\n * `baggage_allowance`\n * `refund_policy`\n * `faq`\n * `additional_info`\n4. The next nodes save this output to:\n\n * Google Sheet (for visibility)\n * PGVector (for semantic search)\n\n\ud83d\udca1 **Pro Tip:** This works best when the HTML content is readable and includes useful labels like \u201cCheck-in\u201d, \u201cCancellation\u201d, \u201cSupport\u201d, etc.\n\n\n"
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "b785e5c9-19bf-42c0-8c99-1659b1c2509b",
"connections": {
"Chat Model": {
"ai_languageModel": [
[
{
"node": "Extract Info with LLM\t",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "Scrape Airline Webpage\t",
"type": "main",
"index": 0
}
]
]
},
"Split Long Text\t": {
"ai_textSplitter": [
[
{
"node": "Prepare Text for Vector DB\t",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Wait for Response": {
"main": [
[
{
"node": "Store Extracted Info\t",
"type": "main",
"index": 0
}
]
]
},
"Save to Vector DB\t": {
"main": [
[
{
"node": "Wait Before Next Batch\t",
"type": "main",
"index": 0
}
]
]
},
"Fetch Airline URLs\t": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Generate Embeddings\t": {
"ai_embedding": [
[
{
"node": "Save to Vector DB\t",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Chat Trigger - Start\t": {
"main": [
[
{
"node": "Fetch Airline URLs\t",
"type": "main",
"index": 0
}
]
]
},
"Store Extracted Info\t": {
"main": [
[
{
"node": "Save to Vector DB\t",
"type": "main",
"index": 0
}
]
]
},
"Extract Info with LLM\t": {
"main": [
[
{
"node": "Wait for Response",
"type": "main",
"index": 0
}
]
]
},
"Scrape Airline Webpage\t": {
"main": [
[
{
"node": "Extract Info with LLM\t",
"type": "main",
"index": 0
}
]
]
},
"Wait Before Next Batch\t": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Prepare Text for Vector DB\t": {
"ai_document": [
[
{
"node": "Save to Vector DB\t",
"type": "ai_document",
"index": 0
}
]
]
}
}
}
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.
googleApihttpHeaderAuthollamaApipostgres
Pro
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About this workflow
This workflow retrieves airline web check-in URLs from Google Sheets, scrapes their content, employs an LLM to generate structured JSON data, refreshes the sheet, creates embeddings, and saves them in a Postgres vector DB for future semantic searches or question-answering.…
Source: https://n8n.io/workflows/5474/ — original creator credit. Request a take-down →
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