Who is this workflow for? This n8n workflow enables seamless interaction with your self-hosted Large Language Models (LLMs) through an intuitive chat interface. By integrating with Ollama, a robust tool for managing local LLMs, you can send prompts and receive AI-generated responses directly within n8n, ensuring efficient and private AI communications..

What does this workflow do?

  • Receive Chat Message: The workflow is triggered when a chat message is received through the user interface. This captures the user’s input efficiently.
  • Chat LLM Chain: The captured input is sent to the Ollama server, which processes the prompt using the selected LLM and generates a response.
  • Deliver Response: The AI-generated response from Ollama is sent back to the chat interface, completing the interaction cycle.

🤖 Why Use This Automation Workflow?

  • Data Privacy: Maintain complete control over your data by keeping interactions local.
  • Cost Efficiency: Eliminate recurring cloud API expenses by utilizing your own hardware.
  • Flexibility: Experiment with various LLMs in a controlled environment tailored to your needs.

👨‍💻 Who is This Workflow For?

This workflow is ideal for developers, data scientists, and businesses looking to implement secure, cost-effective AI solutions. It caters to those who require private interactions with AI models and prefer managing their computational resources independently.

🎯 Use Cases

  1. Private AI Interactions
  • Suitable for environments where data sensitivity and confidentiality are paramount.
  1. Cost-Effective LLM Deployment
  • Reduces long-term costs by leveraging existing hardware instead of relying on external cloud services.
  1. Prototyping and Development
  • Facilitates the creation and testing of AI-powered applications without external dependencies.

TL;DR

This n8n workflow provides a streamlined approach to interact with local Large Language Models using Ollama. It ensures data privacy, reduces costs, and offers a flexible environment for developing and experimenting with AI applications. By integrating these tools, users can effectively manage and utilize their own LLMs within a user-friendly chat interface.

Setup Steps

  • Install and Run Ollama: Ensure that Ollama is installed and actively running on your machine before initiating the workflow.
  • Configure Ollama Address: If your Ollama server uses a non-default address, update the workflow settings accordingly to establish a successful connection.

Integrations

  • Webhook: Facilitates the reception of chat messages.
  • ClickUp: (Specify the role if necessary)
  • Respond to Webhook: Sends the AI-generated response back to the chat interface.

Conclusion

Leverage this n8n workflow to harness the power of local LLMs with Ollama, achieving secure, cost-effective, and flexible AI interactions tailored to your specific requirements.

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