Build an AI chatbot with InfraNodus knowledge graph for enhanced responses

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Important notice

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

1. Workflow Overview

Build an embeddable AI chatbot with an access to a knowledge base This is an example of a simp...

Best for

Support Chatbot automation workflows
AI RAG automation workflows
intermediate n8n builders looking for reusable templates

Tools used

n8n-nodes-base.httprequest, n8n-nodes-base.formtrigger, n8n-nodes-base.form, n8n-nodes-base.stickynote

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: Build an AI chatbot with InfraNodus knowledge graph for enhanced responses
Workflow name: Build an AI chatbot with InfraNodus knowledge graph for enhanced responses

Build an embeddable AI chatbot with an access to a knowledge base

This is an example of a simple AI chatbot that has access to external knowledge to augment its responses.

The knowledge can be added manually or imported from multiple sources (text and PDF files, websites, CSVs, Google search results, AI generated, YouTube search results, RSS feeds, etc) using InfraNodus.

• no OpenAI account needed • no vector store needed • easy data import: PDF, text, CSV, Google / YouTube results, RSS feeds, websites, or AI-generated

How it works

First, you add your data into your InfraNodus graph — this will be your knowledge base.
You can import this data from multiple sources or add it manually.
You will have a visual interface available that will show the main concepts and topics in your knowledge base, so you can have an overview of its structure and know how to improve it, if necessary.
Your data is represented as a knowledge graph which contains information about relations and topical clusters in your data, making the LLM responses much more precise.

How to use

Copy the template
Add your InfraNodus API key to the HTTP AI response node
Create a new graph in InfraNodus with your data (or import from an external source)
Add the name of this graph into the name field of the AI response HTTP node.
That's it! You can query it using the embeddable web form available via a URL

Requirements

You only need an InfraNodus account to set this workflow up.

Free 14-day trials are available.

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 - AI Response

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

Block 2 - On form submission

Type / Role: n8n-nodes-base.formTrigger - formTrigger
Config choices: Version 2.2

Block 3 - Form

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

Block 4 - Sticky Note

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

Block 5 - Sticky Note1

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

3. Summary Table

Workflow	Build an AI chatbot with InfraNodus knowledge graph for enhanced responses
Complexity	intermediate
Nodes	5
Categories	Support Chatbot, AI RAG
Author	InfraNodus
Published	01 Aug 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/6818/6818.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

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

Download workflow JSON Original n8n.io source Support Chatbot workflows AI RAG workflows

Frequently asked questions

What does Build an AI chatbot with InfraNodus knowledge graph for enhanced responses do?

Build an embeddable AI chatbot with an access to a knowledge base This is an example of a simp...

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 Support Chatbot, AI RAG use case.

InfraNodus

19 workflows

Nodes

n8n-nodes-base.httprequest n8n-nodes-base.formtrigger n8n-nodes-base.form n8n-nodes-base.stickynote

Complexity

intermediate

Published 01 Aug 2025

Likes 0

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Install path: /data/workflows/6818/6818.json

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Build an AI chatbot with InfraNodus knowledge graph for enhanced responses

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Build an embeddable AI chatbot with an access to a knowledge base

How it works

How to use

Requirements

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - AI Response

Block 2 - On form submission

Block 3 - Form

Block 4 - Sticky Note

Block 5 - Sticky Note1

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions