Block 1 - Search Google Drive
- Type / Role
- n8n-nodes-base.googleDrive - googleDrive
- Config choices
- Version 3
This workflow is provided as-is. Please review and test before using in production.
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...
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
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by InfraNodus.
Original n8n.io sourceThis templates helps you
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:
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
name field of the HTTP post request).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
name in the Knowledge Base node to match the name you provided in the step 1 aboveYou need an InfraNodus GraphRAG API account and key to use 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
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.
| 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 |
Use the JSON export at /data/workflows/4755/4755.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
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.
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...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
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.