Ollama Chat Integration with LangChain
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Process chat messages using Ollama LLM via LangChain. The 'When chat message received' node triggers, the 'Basic LLM Chain' node processes the prompt with an Ollama model, and the 'Structured Response' node formats the output.
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About This Workflow
Overview
This n8n workflow demonstrates a powerful integration of Ollama, a popular open-source LLM inference server, with n8n's LangChain nodes. It's designed to handle incoming chat messages, process them through a specified Ollama model (like Llama 3.2), and return a structured response. This workflow is ideal for building AI-powered chatbots or automated response systems directly within n8n.
Key Features
- Integrates with Ollama for local LLM deployment.
- Leverages LangChain nodes for streamlined LLM interaction.
- Processes chat messages received via a trigger.
- Formats LLM output into a structured JSON object.
- Includes error handling for robust operation.
How To Use
- Ensure you have n8n installed and configured.
- Set up Ollama on your server and download the desired model (e.g.,
llama3.2:latest). - Configure your Ollama API credentials within n8n.
- Connect the 'When chat message received' node to your chat platform or webhook.
- Customize the prompt within the 'Basic LLM Chain' node to define the LLM's task and desired output format.
- Select your Ollama model in the 'Ollama Model' node.
- The 'JSON to Object' and 'Structured Response' nodes format the LLM's output for clarity.
- Connect the error output of the 'Basic LLM Chain' to the 'Error Response' node for fallback messaging.
Apps Used
Workflow JSON
{
"id": "ef5c9f30-b05d-4e10-a497-c3b9cec98860",
"name": "Ollama Chat Integration with LangChain",
"nodes": 0,
"category": "OpenAI and LLMs",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: ef5c9f30-b05d...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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