Chat with local LLMs using n8n and Ollama

• Developers and Data Scientists: Those who want to integrate local LLMs into their applications or workflows.
• AI Enthusiasts: Individuals interested in experimenting with AI and natural language processing using self-hosted solutions.
• Business Analysts: Professionals looking to automate chat responses or data collection through intelligent chat interfaces.
• Educators and Students: Users who want to create interactive learning tools using conversational AI.

This workflow addresses the challenge of interacting with local Large Language Models (LLMs) in a seamless manner. Users can send messages and receive responses from their self-hosted AI models without needing extensive programming skills. It simplifies the process of integrating AI chat capabilities into various applications.

1. Message Reception: The workflow begins when a chat message is received through the When chat message received node.
2. Processing the Input: The message is then sent to the Chat LLM Chain, which processes the input and prepares it for the LLM.
3. Generating Response: The Ollama Chat Model node interacts with the local Ollama server, sending the processed input and receiving an AI-generated response.
4. Delivering the Response: Finally, the response from the LLM is delivered back to the chat interface, completing the interaction.