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Chat with PDF / MD / text files using GraphRAG (no vector store needed)

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Set up a chat with your documents without the complex vector store setup. This templates helps you ingest your PDF / text / MD documents into a knowledge graph use the graph as the knowledge base f...

Best for

  • Internal Wiki automation workflows
  • AI RAG automation workflows
  • advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.googledrive, n8n-nodes-base.splitinbatches, n8n-nodes-base.switch, n8n-nodes-base.extractfromfile, n8n-nodes-base.httprequest, n8n-nodes-base.stickynote, n8n-nodes-base.set, @n8n/n8n-nodes-langchain.chattrigger

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by InfraNodus.

Original n8n.io source

1.1 Workflow description

Title
Chat with PDF / MD / text files using GraphRAG (no vector store needed)
Workflow name
Chat with PDF / MD / text files using GraphRAG (no vector store needed)

Set up a chat with your documents without the complex vector store setup.

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)

The knowledge base is provided using the InfraNodus GraphRAG with the knowledge graphs offering high-quality responses without the need to set up complex RAG vector store workflows.

The advantages of using GraphRAG instead of the standard vector stores for knowledge are:

  • Easy and quick to set up and update — no complex data import workflows needed
  • A knowledge graph offers a holistic and interactive view of your knowledge base (accessible via our API or a web interface — also shareable)
  • Better retrieval of relations between the document chunks = higher quality responses

How it works

This template uses the InfraNodus knowledge graph as a knowledge base for your n8n AI agent node.

The knowledge graph contains the documents you can upload using this template from your Google Drive.

When the user asks a question via the chat interface, the agent forwards this question to the InfraNodus knowledge graph, retrieves a response, a summary, and a list of matching statements (based advanced Graph RAG), then delivers the final response back the user.

Here's a description step by step:

Step 1: Upload your documents

  • Put the PDF / text / MD files you want to chat with into a folder on your Google drive
  • Authorize access to that folder using the Google drive node in the template.
  • Add the InfraNodus API key to the InfraNodus Save to Graph HTTP node
  • Optional: change the name of the graph you want to save the data to in the InfraNodus HTTP node (in the name field of the HTTP post request).
  • Run the workflow to ingest all the files and save them into the graph
  • Optional: check the link provided in the Step 1 workflow description to see the visualization of your knowledge base. It will look something like that:

Note: you can replace the PDF to Text convertor node with a better quality PDF convertor from ConvertAPI which respects the original file layout and doesn't split text into small chunks

Step 2: Chat with your documents

  • Deactive the trigger in the Step 1
  • Activate the chat trigger in the Step 2
  • Add your InfraNodus API credentials to Knowledge Base GraphRAG InfraNodus node
  • Optional: change the graph name in the Knowledge Base node to match the name you provided in the step 1 above
  • Run the chat and ask the question
  • Watch the magic

How to use

You need an InfraNodus GraphRAG API account and key to use this workflow.

Requirements

  • An InfraNodus account and API key
  • An OpenAI (or any other LLM) API key
  • A Google Drive OAuth access (follow the n8n instructions)
  • Optional: ConvertAPI API key for better quality PDF conversion

Customizing this workflow

You can customize this workflow by adding several experts to your AI agent.

Check out the complete guide at https://support.noduslabs.com/hc/en-us/articles/20174217658396-Using-InfraNodus-Knowledge-Graphs-as-Experts-for-AI-Chatbot-Agents-in-n8n

Also check out the video tutorial with a demo:

For support and feedback, please, contact us at https://support.noduslabs.com

To learn more about InfraNodus: https://infranodus.com

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Search Google Drive

Type / Role
n8n-nodes-base.googleDrive - googleDrive
Config choices
Version 3

Block 2 - Loop Over Items

Type / Role
n8n-nodes-base.splitInBatches - splitInBatches
Config choices
Version 3

Block 3 - Retrieve File

Type / Role
n8n-nodes-base.googleDrive - googleDrive
Config choices
Version 3

Block 4 - Switch

Type / Role
n8n-nodes-base.switch - switch
Config choices
Version 3.2

Block 5 - Extract from PDF

Type / Role
n8n-nodes-base.extractFromFile - extractFromFile
Config choices
Version 1

Block 6 - Extract from Text File

Type / Role
n8n-nodes-base.extractFromFile - extractFromFile
Config choices
Version 1

Block 7 - Extract from Markdown

Type / Role
n8n-nodes-base.extractFromFile - extractFromFile
Config choices
Version 1

Block 8 - InfraNodus Save to Graph

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.2

Block 9 - Sticky Note4

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 10 - Map PDF to Text

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 11 - Convert File to PDF

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.2

Block 12 - Sticky Note5

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 13 - When chat message received

Type / Role
@n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices
Version 1.1

Block 14 - AI Agent

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 1.9

Block 15 - OpenAI Chat Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

Block 16 - Simple Memory

Type / Role
@n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices
Version 1.3

Block 17 - Knowledge Base GraphRAG

Type / Role
n8n-nodes-base.httpRequestTool - httpRequestTool
Config choices
Version 4.2

Block 18 - Sticky Note

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 19 - Click ‘Test workflow’ to ingest the documents

Type / Role
n8n-nodes-base.manualTrigger - manualTrigger
Config choices
Version 1

Block 20 - Sticky Note1

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

3. Summary Table

Workflow Chat with PDF / MD / text files using GraphRAG (no vector store needed)
Complexity advanced
Nodes 20
Categories Internal Wiki, AI RAG
Author InfraNodus
Published 07 Jun 2025

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/4755/4755.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does Chat with PDF / MD / text files using GraphRAG (no vector store needed) do?

Set up a chat with your documents without the complex vector store setup. This templates helps you ingest your PDF / text / MD documents into a knowledge graph use the graph as the knowledge base f...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Internal Wiki, AI RAG use case.