내용 빈칸에서 PDF 문서를 통해 연구 질문과 AI 키워드 힌트 생성
중급
이것은Document Extraction, AI RAG분야의자동화 워크플로우로, 15개의 노드를 포함합니다.주로 Code, Form, FormTrigger, HttpRequest, ExtractFromFile 등의 노드를 사용하며. 사용InfraNodus 내용 공백 분석으로 PDF에서 연구 질문 생성
사전 요구사항
- •대상 API의 인증 정보가 필요할 수 있음
워크플로우 미리보기
노드 연결 관계를 시각적으로 표시하며, 확대/축소 및 이동을 지원합니다
워크플로우 내보내기
다음 JSON 구성을 복사하여 n8n에 가져오면 이 워크플로우를 사용할 수 있습니다
{
"id": "oOxhkss1gOyLvJyf",
"meta": {
"instanceId": "2a26454b0172ffcb8d70ba77c235b1209f92cd71bf06c79ba609c7173b416d68",
"templateCredsSetupCompleted": true
},
"name": "Generate Research Questions and AI Prompts from PDF Documents based on Content Gaps",
"tags": [
{
"id": "66wgFoDi9Xjl74M3",
"name": "Support",
"createdAt": "2025-05-21T17:06:32.355Z",
"updatedAt": "2025-05-21T17:06:32.355Z"
},
{
"id": "kRM0hQV2zw7VxrON",
"name": "Research",
"createdAt": "2025-05-21T19:44:19.136Z",
"updatedAt": "2025-05-21T19:44:19.136Z"
},
{
"id": "sJk9cUvmMU8FkJXv",
"name": "AI",
"createdAt": "2025-05-20T13:16:15.636Z",
"updatedAt": "2025-05-20T13:16:15.636Z"
}
],
"nodes": [
{
"id": "a2339bb9-abb9-41bf-8064-8c3af94df039",
"name": "Convert File to PDF",
"type": "n8n-nodes-base.httpRequest",
"disabled": true,
"position": [
1880,
180
],
"parameters": {
"url": "https://v2.convertapi.com/convert/pdf/to/txt",
"method": "POST",
"options": {
"response": {
"response": {
"responseFormat": "text"
}
}
},
"sendBody": true,
"contentType": "multipart-form-data",
"sendHeaders": true,
"authentication": "genericCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "file",
"parameterType": "formBinaryData",
"inputDataFieldName": "data"
}
]
},
"genericAuthType": "httpBearerAuth",
"headerParameters": {
"parameters": [
{
"name": "Accept",
"value": "application/octet-stream"
}
]
}
},
"credentials": {
"httpBearerAuth": {
"id": "9fXf9Np7XsCWgxhg",
"name": "Perplexity"
}
},
"notesInFlow": true,
"typeVersion": 4.2
},
{
"id": "989a0d6c-12a1-45d4-8b6b-206855177df7",
"name": "메모5",
"type": "n8n-nodes-base.stickyNote",
"position": [
1840,
-400
],
"parameters": {
"color": 2,
"width": 360,
"height": 820,
"content": "## Optional: Better PDF Conversion\n\n### Standard Map PDF to Text node will split your PDF files into very short chunks, which deteriorates retrieval. \n\nUse can use [ConvertAPI](https://convertapi.com?ref=4l54n) which is a high-quality convertor that will respect the layout of the original document and not cut the paragraphs into short chunks. \n\nHere is an HTTP node that makes a request to their API to convert the PDF into text. If you have a ConvertAPI account, you can replace the \"Extract Text from PDF\" node in Step 3 with this node. \n\nNote that you will need to map the text output from this node correctly in the Step 4 after.\n"
},
"typeVersion": 1
},
{
"id": "4d698efb-6b02-4e95-9a6f-dca8bc1fbea7",
"name": "On form submission",
"type": "n8n-nodes-base.formTrigger",
"position": [
-380,
-60
],
"webhookId": "f35f0686-fbcb-466c-a59e-48ee9d360024",
"parameters": {
"options": {
"appendAttribution": false
},
"formTitle": "Find Content Gaps in Your PDF Files",
"formFields": {
"values": [
{
"fieldType": "file",
"fieldLabel": "Add Your Files",
"acceptFileTypes": ".pdf"
}
]
},
"formDescription": "Upload the files you'd like to analyze and we will extract content gaps and interesting questions based on them."
},
"typeVersion": 2.2
},
{
"id": "5798ca71-eb05-4097-ace1-9457be450e21",
"name": "Convert binary files to PDF",
"type": "n8n-nodes-base.code",
"position": [
-60,
-60
],
"parameters": {
"jsCode": "let results = [];\n\nfor (let item of items) {\n if (item.binary) {\n // If there's binary data in the item, process each binary file\n for (let key in item.binary) {\n // Use the key as the file name\n let binaryKey = key.replace(/\\s/g, '_'); // Replace spaces with underscores for the key\n results.push({\n json: {\n fileName: binaryKey\n },\n binary: {\n [binaryKey]: item.binary[key] // Use the modified key for the binary data\n }\n });\n }\n }\n}\n\nreturn results;\n"
},
"typeVersion": 2
},
{
"id": "6770b616-db38-48a8-8063-f3f5639d0946",
"name": "Extract text from PDF files",
"type": "n8n-nodes-base.extractFromFile",
"position": [
280,
-60
],
"parameters": {
"options": {},
"operation": "pdf",
"binaryPropertyName": "={{ $json.fileName }}"
},
"typeVersion": 1
},
{
"id": "2e47ecb8-cb9e-434a-ae9e-aae2ddb5fb54",
"name": "Prepare for InfraNodus",
"type": "n8n-nodes-base.code",
"position": [
580,
-60
],
"parameters": {
"jsCode": "\nlet plainText = '' // we send plain text from all the PDFs to InfraNodus for analysis\n\nconst randomNum = Math.floor(Math.random() * 3); // replace this with a 0 if you'd like to address the biggest gap in the knowledge graph\n\nfor (let item of items) {\n plainText += item.json.text + '\\n\\n' \n}\n\n\nreturn {text: plainText, randomNum};"
},
"typeVersion": 2
},
{
"id": "422faf1e-1545-4e5e-98aa-75c51a06c863",
"name": "Display on the Form to the User",
"type": "n8n-nodes-base.form",
"position": [
1380,
-60
],
"webhookId": "091aab99-a4cf-40ec-b3bb-655d8e0b9a5c",
"parameters": {
"operation": "completion",
"respondWith": "showText",
"responseText": "=<br>\n<h3>{{ $json.aiAdvice[0].text }}</h3>\n<br>\n"
},
"typeVersion": 1
},
{
"id": "820a3e64-1108-4c41-88e2-90d98fbd548f",
"name": "메모",
"type": "n8n-nodes-base.stickyNote",
"position": [
-420,
-400
],
"parameters": {
"height": 520,
"content": "## Step 1: User uploads the PDF files for analysis\n\n### You can expose this endpoint and make it publicly available via a URL to your organization."
},
"typeVersion": 1
},
{
"id": "8dc2769a-b797-42c4-b531-82ce7f866dac",
"name": "메모1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-120,
-400
],
"parameters": {
"width": 280,
"height": 520,
"content": "## Step 2: Convert uploaded binaries into PDF files\n\n### We need to convert the binaries uploaded to the PDF files so we can extract text from them."
},
"typeVersion": 1
},
{
"id": "8f8cf47a-726c-4db8-9362-f68c94e75254",
"name": "메모2",
"type": "n8n-nodes-base.stickyNote",
"position": [
220,
-400
],
"parameters": {
"width": 220,
"height": 520,
"content": "## Step 3: Extract plain text from PDF files\n\n### For better quality text extraction, you can use the optional [ConvertAPI](https://convertapi.com?ref=4l54n) node to the right, which respects the files' original formatting."
},
"typeVersion": 1
},
{
"id": "f8da708a-ab86-40d7-bef0-dea600e5a032",
"name": "메모3",
"type": "n8n-nodes-base.stickyNote",
"position": [
520,
-400
],
"parameters": {
"width": 220,
"height": 520,
"content": "## Step 4: Combine extracted text into a text string\n\n### Prepare data for InfraNodus: combine all the extracted text into a text string and also tell InfraNodus the gap depth it should use when generating advice"
},
"typeVersion": 1
},
{
"id": "d63bb46c-5d0a-4fb9-8f9c-06a3aba63959",
"name": "메모4",
"type": "n8n-nodes-base.stickyNote",
"position": [
820,
-400
],
"parameters": {
"width": 380,
"height": 820,
"content": "## Step 5: Use InfraNodus GraphRAG to build a knowledge graph, find the gap, and generate a research question based on it.\n\n### [InfraNodus](https://infranodus.com) builds a knowledge graph from all the texts, identifies the topical clusters that are least connected, and generates a research question that has a potential to bridge them in a new way.\n\n🚨 PROVIDE YOUR INFRANODUS API KEY HERE"
},
"typeVersion": 1
},
{
"id": "0eeaef66-b9f5-4589-b663-9a992913fe1e",
"name": "메모6",
"type": "n8n-nodes-base.stickyNote",
"position": [
1280,
-400
],
"parameters": {
"width": 380,
"height": 820,
"content": "## Step 6: Show question / prompt to the user\n\n### Optionally, you can feed the response to your other n8n workflow or expose it via a webhook and show it in your own app using an iframe."
},
"typeVersion": 1
},
{
"id": "b82fefa5-5882-4ecf-8b68-f884b42411c9",
"name": "메모7",
"type": "n8n-nodes-base.stickyNote",
"position": [
-420,
180
],
"parameters": {
"color": 5,
"width": 1160,
"height": 1000,
"content": "# How does InfraNodus GraphRAG generate research questions?\n\n## [InfraNodus](https://infranodus.com) GraphRAG helps avoid generic responses and LLM bias through analyzing your text's structure. Here's how it works:\n\n### 1. It represents your text as a network of concepts and relations building a knowledge graph.\n\n### 2. It then identifies the clusters of cocnepts that are furthest apart from each other — they appear in the same context (your texts) but are not well connected.\n\n### 3. InfraNodus will then use the AI to generate a question / prompt that bridges this gap — touching upon relevant topics but connecting them in a new way.\n\n"
},
"typeVersion": 1
},
{
"id": "da7cf09a-d3f3-41d8-9ae4-4b4b1bcfc80f",
"name": "InfraNodus GraphRAG Question Generator",
"type": "n8n-nodes-base.httpRequest",
"position": [
960,
0
],
"parameters": {
"url": "=https://infranodus.com/api/v1/graphAndAdvice?doNotSave=true&optimize=develop&includeGraph=false&includeGraphSummary=true&gapDepth={{ $json.randomNum }}",
"method": "POST",
"options": {},
"sendBody": true,
"authentication": "genericCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "aiTopics",
"value": "true"
},
{
"name": "requestMode",
"value": "question"
},
{
"name": "text",
"value": "={{ $json.text }}"
}
]
},
"genericAuthType": "httpBearerAuth"
},
"credentials": {
"httpBearerAuth": {
"id": "FPDx6PA5CtzGEIQc",
"name": "InfraNodus DeeMeeTree API Key"
}
},
"typeVersion": 4.2
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "abd96e27-8999-4490-9c4c-8eda846dfc3b",
"connections": {
"4d698efb-6b02-4e95-9a6f-dca8bc1fbea7": {
"main": [
[
{
"node": "5798ca71-eb05-4097-ace1-9457be450e21",
"type": "main",
"index": 0
}
]
]
},
"2e47ecb8-cb9e-434a-ae9e-aae2ddb5fb54": {
"main": [
[
{
"node": "da7cf09a-d3f3-41d8-9ae4-4b4b1bcfc80f",
"type": "main",
"index": 0
}
]
]
},
"5798ca71-eb05-4097-ace1-9457be450e21": {
"main": [
[
{
"node": "6770b616-db38-48a8-8063-f3f5639d0946",
"type": "main",
"index": 0
}
]
]
},
"6770b616-db38-48a8-8063-f3f5639d0946": {
"main": [
[
{
"node": "2e47ecb8-cb9e-434a-ae9e-aae2ddb5fb54",
"type": "main",
"index": 0
}
]
]
},
"da7cf09a-d3f3-41d8-9ae4-4b4b1bcfc80f": {
"main": [
[
{
"node": "422faf1e-1545-4e5e-98aa-75c51a06c863",
"type": "main",
"index": 0
}
]
]
}
}
}자주 묻는 질문
이 워크플로우를 어떻게 사용하나요?
위의 JSON 구성 코드를 복사하여 n8n 인스턴스에서 새 워크플로우를 생성하고 "JSON에서 가져오기"를 선택한 후, 구성을 붙여넣고 필요에 따라 인증 설정을 수정하세요.
이 워크플로우는 어떤 시나리오에 적합한가요?
중급 - 문서 추출, AI RAG
유료인가요?
이 워크플로우는 완전히 무료이며 직접 가져와 사용할 수 있습니다. 다만, 워크플로우에서 사용하는 타사 서비스(예: OpenAI API)는 사용자 직접 비용을 지불해야 할 수 있습니다.
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노드 유형6
저자
InfraNodus
@infranodusI'm Dmitry, the founder of InfraNodus — an AI text network analysis tool. I'm passionate about networks and data visualization and its ability to reveal what everyone else is missing and to highlight different perspectives. I'm sharing the n8n templates that make use of this unique capability of InfraNodus for multiple scenarios.
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