Enhance AI chatbot responses with InfraNodus knowledge graph reasoning

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2/3/2026

Augment AI chatbot prompts with a knowledge graph reasoning ontology and improve the quality of responses with Graph RAG.

In this workflow, we augment the original prompt using the InfraNodus GraphRAG system that will extract a reasoning ontology from a graph that you create (or that you can copy from our repository of public graphs).

This additional reasoning logic will improve the user's prompt and make it more descriptive and closely related to the logic you want to use.

As the next step, you can send it back to the same graph to generate a high-quality response using Graph RAG or to another graph (or AI model) to apply one type of knowledge in a completely different field.

InfraNodus Graph

How it works

Receives a request from a user (via n8n or a publicly available URL chat bot, you can also connect it to Telegram
Sends the request to the knowledge graph in your InfraNodus account that contains a reasoning ontology represented as a knowledge graph. Reformulates the original prompt to include the reasoning logic provided.
Sends the request to the knowledge graph in your InfraNodus account (same as the previous one or a new one for cross-disciplinary research) to retrieve a high-quality response using GraphRAG

Special sauce: InfraNodus will build a graph from your augmented prompt, then overlap it on the knowledge graph you want to inquire, traverse this graph based on the overlapped parts and extended relations, then retrieve the necessary part of the graph and include it in the context to improve the quality of your response. This helps InfraNodus grasp the relations and nuances that are not usually available through standard RAG.

How to use

• Just get an InfraNodus API key and add it into your Prompt Augmentation and Knowledge Base InfraNodus HTTP nodes for authentication

• Then provide the name of the graphs you want to be using for prompt augmentation and retrieval. Note, these can be two different graphs if you want to apply a reasoning logic from one domain in another (e.g. machine learning in biology or philosophy in electrical engineering).

Support

If you wan to create your own reasoning ontology graphs, please, refer to this article on generating your own knowledge graph ontologies.

You may also be interested to watch this video that explains the logic of this approach in detail:

Help article on the same topic: Using knowledge graphs as reasoning experts.

Enhance AI Chatbot Responses with Infranodus Knowledge Graph Reasoning

This n8n workflow demonstrates a foundational setup for integrating AI chatbot interactions with external knowledge sources, specifically hinting at a future integration with Infranodus for knowledge graph reasoning. While the current workflow is a basic framework, it lays the groundwork for more sophisticated AI-driven applications.

What it does

This workflow currently provides a starting point for building AI chatbot applications within n8n. It includes:

Listens for Chat Messages: The workflow is triggered whenever a chat message is received, acting as the input for an AI chatbot.
Placeholder for External API Request: It includes an HTTP Request node, which is typically used to interact with external APIs. In the context of the workflow's name, this node is intended to be configured to query a knowledge graph like Infranodus or another AI service.
Documentation Note: A sticky note is included to provide context or instructions within the workflow itself.

Prerequisites/Requirements

n8n Instance: A running n8n instance to import and execute the workflow.
Chat Platform Integration: A chat platform configured with n8n (e.g., Slack, Discord, Telegram) to send and receive messages via the Chat Trigger node.
API Endpoint (for HTTP Request node): An API endpoint (e.g., Infranodus API, OpenAI API, custom knowledge base API) that the HTTP Request node will interact with. This needs to be configured based on your specific use case.

Setup/Usage

Import the Workflow: Download the JSON provided and import it into your n8n instance.
Configure Chat Trigger:
- Open the "When chat message received" node.
- Select or create a credential for your desired chat platform (e.g., Slack, Telegram, Discord).
- Ensure the trigger is active to listen for incoming messages.
Configure HTTP Request:
- Open the "HTTP Request" node.
- Configure the URL, Method, Headers, and Body to interact with your chosen API (e.g., Infranodus, OpenAI). This is where you would send the incoming chat message to an external service for processing or knowledge retrieval.
- Add any necessary credentials for the external API.
Activate the Workflow: Once configured, activate the workflow to start processing chat messages.

This workflow serves as a robust foundation. You would typically expand it by adding nodes after the "HTTP Request" to process the API response, perform further AI reasoning, and then send a response back to the chat platform using a "Chat Send" node (not included in this basic template).

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