InfraNodus This workflow corresponds to n8n.io template #4755 — we link there as the canonical source.
This workflow follows the Agent → 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": "EQVZKxcCDzTNXLRp",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Chat with PDF / MD / Text Files using GraphRAG (no vector store needed)",
"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": "011910a9-c2a0-49eb-b1eb-8043e0c2accc",
"name": "Search Google Drive",
"type": "n8n-nodes-base.googleDrive",
"position": [
-220,
-120
],
"parameters": {
"filter": {
"folderId": {
"__rl": true,
"mode": "list",
"value": "13tqp0SaI_v4jG1CFLAZo96isx-UBno4v",
"cachedResultUrl": "https://drive.google.com/drive/folders/13tqp0SaI_v4jG1CFLAZo96isx-UBno4v",
"cachedResultName": "GraphRAG"
}
},
"options": {},
"resource": "fileFolder",
"returnAll": true,
"queryString": "*"
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 3
},
{
"id": "22872ac5-14e4-4ea7-b792-30e19a02cb88",
"name": "Loop Over Items",
"type": "n8n-nodes-base.splitInBatches",
"position": [
260,
-120
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "6456b7ac-f936-425a-a0ac-62fe118e985a",
"name": "Retrieve File",
"type": "n8n-nodes-base.googleDrive",
"position": [
500,
-80
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.id }}"
},
"options": {},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 3
},
{
"id": "8c1f2da9-fff4-457d-bc2e-7c84de3500b3",
"name": "Switch",
"type": "n8n-nodes-base.switch",
"position": [
740,
-20
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "pdf",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "7b4e792b-ab6d-4b9b-88a1-d8e51bea6853",
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{$binary[\"data\"].mimeType}}",
"rightValue": "application/pdf"
}
]
},
"renameOutput": true
},
{
"outputKey": "text",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "09b7d7c5-5353-4719-b4e2-072e4da39948",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{$binary[\"data\"].mimeType}}",
"rightValue": "text/plain"
}
]
},
"renameOutput": true
},
{
"outputKey": "md",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "cb2e6726-97d8-4541-8383-deafff9b18e6",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{$binary[\"data\"].mimeType}}",
"rightValue": "text/markdown"
}
]
},
"renameOutput": true
},
{
"outputKey": "json",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "d2763a45-a592-47c8-868f-59dfcd17a71c",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{$binary[\"data\"].mimeType}}",
"rightValue": "application/json"
}
]
},
"renameOutput": true
},
{
"outputKey": "docs",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "0d9b561f-89c5-479e-a4e2-1f5f05fa8417",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{$binary[\"data\"].mimeType}}",
"rightValue": ""
}
]
},
"renameOutput": true
},
{
"outputKey": "csv",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "6b19deed-5d00-4796-bb9d-b4d87564a751",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{$binary[\"data\"].mimeType}}",
"rightValue": "csv"
}
]
},
"renameOutput": true
}
]
},
"options": {}
},
"typeVersion": 3.2
},
{
"id": "fe133ad7-bd7a-43e2-91a7-4b3dd2652490",
"name": "Extract from PDF",
"type": "n8n-nodes-base.extractFromFile",
"position": [
1020,
-240
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "49f89141-a8ea-46c6-888d-cf9dafcc7c2a",
"name": "Extract from Text File",
"type": "n8n-nodes-base.extractFromFile",
"position": [
1020,
-60
],
"parameters": {
"options": {},
"operation": "text"
},
"typeVersion": 1
},
{
"id": "a47d8f07-917e-44ed-a69e-5d6085981d36",
"name": "Extract from Markdown",
"type": "n8n-nodes-base.extractFromFile",
"position": [
1020,
140
],
"parameters": {
"options": {},
"operation": "text"
},
"typeVersion": 1
},
{
"id": "a2298650-5408-45f8-accf-292ac669002a",
"name": "InfraNodus Save to Graph",
"type": "n8n-nodes-base.httpRequest",
"position": [
1480,
200
],
"parameters": {
"url": "https://infranodus.com/api/v1/graphAndStatements?doNotSave=false&includeGraph=false&includeGraphSummary=true&includeGraph=false",
"method": "POST",
"options": {},
"sendBody": true,
"authentication": "genericCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "name",
"value": "graphrag_from_google_drive"
},
{
"name": "text",
"value": "={{ $json.data }}"
},
{
"name": "=categories",
"value": "=[filename: {{ $('Switch').item.json.name }}]"
},
{
"name": "contextSettings",
"value": "={{{ \"squareBracketsProcessing\":\"IGNORE_BRACKETS\"} }}"
}
]
},
"genericAuthType": "httpBearerAuth"
},
"credentials": {
"httpBearerAuth": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "bb06d60a-01c7-4a1e-bc7a-901e1f7f175d",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-560,
-480
],
"parameters": {
"color": 4,
"width": 2200,
"height": 980,
"content": "# Step 1: Upload Your PDF / MD / Text Files to InfraNodus GraphRAG\n\n## Copy your PDF files to a Google drive and then upload them to your InfraNodus knowledge graph\n\n### InfraNodus visualizes your text as a knowledge graph, showing the main topics and ideas inside. It also provides API access to the knowledge graph, so you can use it as a knowledge base instead of the complex vector store setup\n\n1. You need an [InfraNodus](https://infranodus.com) account to use this workflow. Get the API key at [https://infranodus.com/api-access](https://infranodus.com/api-access)\n2. Also, specify the **name of the graph** you want to save the files to in the InfraNodus HTTP node\n3. When the workflow finishes running, you can get a visualization of your knowledge base at [https://infranodus.com/your_user_name/your_graph_name/edit](https://infranodus.com/your_user_name/your_graph_name/edit)\n\n\n"
},
"typeVersion": 1
},
{
"id": "38707a4d-b397-43e7-b55c-da18a00bb480",
"name": "Map PDF to Text",
"type": "n8n-nodes-base.set",
"position": [
1320,
-100
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "fd160009-0b92-46fc-9e34-a1283b810e91",
"name": "data",
"type": "string",
"value": "={{ $json.text }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "49426e56-7d6c-4dc0-a279-348dea59cca2",
"name": "Convert File to PDF",
"type": "n8n-nodes-base.httpRequest",
"disabled": true,
"position": [
1720,
200
],
"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": {
"name": "<your credential>"
}
},
"notesInFlow": true,
"typeVersion": 4.2
},
{
"id": "5d21b250-7b0f-4925-a858-5aa6badf5700",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
1680,
-340
],
"parameters": {
"color": 2,
"width": 360,
"height": 840,
"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 \"Map PDF to Text\" node in step 4 with this node. \n"
},
"typeVersion": 1
},
{
"id": "ceda9ea4-963b-4ffb-a741-6e7eb3fc7411",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"disabled": true,
"position": [
-520,
840
],
"parameters": {
"public": true,
"options": {
"title": "Talk to PDFs"
},
"initialMessages": "=Ask any question about the PDFs or write `/question` to generate an interesting question to discuss."
},
"typeVersion": 1.1
},
{
"id": "d78cf21e-753b-44d3-a2b4-493f85aa5129",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-240,
840
],
"parameters": {
"options": {
"systemMessage": "You provide information about the topic of user's interest. Always use the knowledge base attached to get the final response. "
}
},
"typeVersion": 1.9
},
{
"id": "02497c8b-89d6-4440-bc4d-d3906b9c4872",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-420,
1100
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "6d77fb59-fb03-4d22-ac40-4cf9bde25909",
"name": "Simple Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
-260,
1100
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "978c8309-380d-4fc8-b238-e0d0148b77b9",
"name": "Knowledge Base GraphRAG",
"type": "n8n-nodes-base.httpRequestTool",
"position": [
100,
1060
],
"parameters": {
"url": "https://infranodus.com/api/v1/graphAndAdvice?doNotSave=true&addStats=true&optimize=develop&includeStatements=true&includeGraphSummary=true&includeGraph=false",
"method": "POST",
"options": {},
"sendBody": true,
"authentication": "genericCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "name",
"value": "graphrag_from_google_drive"
},
{
"name": "requestMode",
"value": "response"
},
{
"name": "prompt",
"value": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('parameters2_Value', `User query to send to the expert`, 'string') }}"
},
{
"name": "aiTopics",
"value": "true"
}
]
},
"genericAuthType": "httpBearerAuth",
"toolDescription": "You contain knowledge on the topic of user's interst"
},
"credentials": {
"httpBearerAuth": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "b005f83d-3d4a-42c8-b70a-63e267747fcd",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-560,
540
],
"parameters": {
"width": 2200,
"height": 700,
"content": "# Step 2: Chat with your PDF / MD / Text Files using [InfraNodus](https://infranodus.com) Graph RAG Knowledge Base\n\n## InfraNodus is used instead of the vector store. It combines traditional RAG and GraphRAG, providing a better topical overview of your knowledge base and having a better understanding of relations between entities in your documents.\n\n1. Deactivate the trigger node in Step 1 and activate the trigger Chat node here\n2. Provide your API key at [https://infranodus.com/api-access](https://infranodus.com/api-access) to the HTTP agent tool\n3. Specify the same **name of the graph** as in the Step 1, so you query the graph where saved the files. \n4. If you connect several knowledge bases, make sure to describe them well, so the agent knows what to query. Get the description from the InfraNodus Graph > Project Notes > RAG enhancement"
},
"typeVersion": 1
},
{
"id": "6a6ad150-670d-45d5-a69e-0bafaf8f2ac4",
"name": "Click \u2018Test workflow\u2019 to ingest the documents",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-500,
-120
],
"parameters": {},
"typeVersion": 1
},
{
"id": "0a148a3f-e898-4f83-9ebb-82da7f3f4633",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-540,
100
],
"parameters": {
"width": 680,
"height": 380,
"content": "## [InfraNodus](https://infranodus.com) Knowledege Graph Example\n\n"
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "90ea1bad-6d21-4f20-81b4-0ba48cc7e7fa",
"connections": {
"Switch": {
"main": [
[
{
"node": "Extract from PDF",
"type": "main",
"index": 0
}
],
[
{
"node": "Extract from Text File",
"type": "main",
"index": 0
}
],
[
{
"node": "Extract from Markdown",
"type": "main",
"index": 0
}
],
[],
[]
]
},
"Retrieve File": {
"main": [
[
{
"node": "Switch",
"type": "main",
"index": 0
}
]
]
},
"Simple Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "Retrieve File",
"type": "main",
"index": 0
}
]
]
},
"Map PDF to Text": {
"main": [
[
{
"node": "InfraNodus Save to Graph",
"type": "main",
"index": 0
}
]
]
},
"Extract from PDF": {
"main": [
[
{
"node": "Map PDF to Text",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Search Google Drive": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Extract from Markdown": {
"main": [
[
{
"node": "InfraNodus Save to Graph",
"type": "main",
"index": 0
}
]
]
},
"Extract from Text File": {
"main": [
[
{
"node": "InfraNodus Save to Graph",
"type": "main",
"index": 0
}
]
]
},
"Knowledge Base GraphRAG": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"InfraNodus Save to Graph": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Click \u2018Test workflow\u2019 to ingest the documents": {
"main": [
[
{
"node": "Search Google Drive",
"type": "main",
"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.
googleDriveOAuth2ApihttpBearerAuthopenAiApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
This templates helps you ingest your PDF / text / MD documents into a knowledge graph use the graph as the knowledge base for your AI chatbots (and other workflows) visualize the main topics and gaps in your documents (good for observability and research)
Source: https://n8n.io/workflows/4755/ — original creator credit. Request a take-down →
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