Ollama Chatbot: Seamless AI Conversations
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Automate and enhance your chat interactions with this n8n workflow. It leverages Ollama's powerful language models to process and respond to chat messages intelligently, delivering structured JSON output for seamless integration.
About This Workflow
This n8n workflow, powered by LangChain and Ollama, transforms your chat messaging into an intelligent AI-driven experience. It kicks off when a chat message is received, then utilizes a robust LLM chain to process the user's input through a selected Ollama model (like Llama 3.2). The workflow is meticulously designed to return a structured JSON object containing both the original prompt and the AI's response, ensuring predictable and usable data. This makes it ideal for building custom AI assistants, automating customer support, or creating interactive chat features within your existing systems. The onError setting ensures that even if an error occurs during LLM processing, the workflow can continue gracefully.
Key Features
- Real-time Chat Trigger: Automatically initiates the workflow upon receiving a new chat message.
- Powerful LLM Integration: Leverages LangChain and Ollama for advanced language processing capabilities.
- Customizable Prompting: Easily define specific instructions and question formats for your LLM.
- Structured JSON Output: Returns a clean, predictable JSON object with prompt and response fields.
- Error Handling: Includes a dedicated path for managing and responding to processing errors.
How To Use
- Set Up Ollama Credentials: In n8n, configure your Ollama API credentials to connect to your Ollama instance.
- Configure Chat Trigger: Set up the
When chat message receivednode to listen for incoming chat messages on your desired endpoint. - Define LLM Chain Prompt: In the
Basic LLM Chainnode, customize thetextparameter to define how the LLM should process the input. Use{{ $json.chatInput }}to pass the user's message. - Select Ollama Model: In the
Ollama Modelnode, choose your preferred Ollama model (e.g.,llama3.2:latest). - Map Output: Configure the
Structured Responsenode to format the final output as needed, referencing data from previous nodes. - Implement Error Handling: Adjust the
Error Responsenode to define how your workflow should react to processing failures.
Apps Used
Workflow JSON
{
"id": "7d5c39e0-004d-46d9-b5fe-98ce861e4cf1",
"name": "Ollama Chatbot: Seamless AI Conversations",
"nodes": 14,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 7d5c39e0-004d...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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