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🗨️ Ollama Chat

This workflow integrates Ollama's Llama 3.2 large language model to achieve intelligent chat message processing and structured responses. After analyzing the user's natural language input, the model returns clear Q&A in JSON format, enhancing interaction efficiency. The workflow supports error handling to ensure system stability and is suitable for scenarios such as intelligent customer service, online Q&A assistants, and internal knowledge base queries, helping enterprises achieve automated and intelligent customer service.

Workflow Diagram
🗨️ Ollama Chat Workflow diagram

Workflow Name

🗨️ Ollama Chat

Key Features and Highlights

This workflow integrates the Llama 3.2 large language model provided by Ollama to enable intelligent processing and structured responses for chat messages. Its standout feature is the ability to trigger in real-time upon user chat input, analyze the input via the language model, and return clear, easy-to-parse Q&A content in a standardized JSON format. It also supports error handling to ensure response stability and coherence.

Core Problems Addressed

It solves the automation challenge of intelligent Q&A based on chat messages, particularly in scenarios requiring the conversion of natural language input into structured data output. This enhances interaction efficiency and automation levels. Additionally, the error response node ensures system robustness by preventing non-responsiveness or crashes caused by model errors.

Application Scenarios

  • Intelligent customer service chatbots
  • Online Q&A assistants
  • Internal knowledge base query interfaces
  • Any automation scenario requiring conversion of user natural language input into structured responses

Main Workflow Steps

  1. Trigger Reception: Listens for and triggers the workflow via the “On Chat Message Received” node.
  2. Language Model Processing: Passes chat content into the “Basic LLM Chain” processing chain and calls Ollama’s Llama 3.2 model for intelligent analysis.
  3. Data Structure Conversion: Uses the “JSON to Object” node to convert the model’s textual output into a structured JSON object.
  4. Response Formatting: Generates the final reply text to the user according to a preset format through the “Structured Response” node.
  5. Error Handling: When exceptions occur in the processing chain, the “Error Response” node provides a default error message to maintain user experience.

Involved Systems or Services

  • Ollama: Provides the Llama 3.2 large language model for natural language understanding and generation.
  • n8n Platform: Facilitates workflow automation, triggering, data transformation, and response control.

Target Users and Value

This workflow is suitable for developers, enterprise automation engineers, and product managers, especially those who need to quickly build intelligent chat-based Q&A systems. It helps users simplify the language model integration process, rapidly achieve intelligent interaction and structured data output, and enhance automation and intelligence in customer service, internal queries, and other business processes.