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Self-coded LLM Chain Node

This workflow utilizes a custom-written chain of large language model nodes, combined with OpenAI's GPT-4o-mini, to achieve flexible natural language processing and question-answering capabilities. Users can customize inputs and outputs, call external knowledge bases such as Wikipedia, and support complex multi-step reasoning and knowledge retrieval. It is suitable for scenarios such as intelligent Q&A, enterprise knowledge base retrieval, and research and development assistance, enhancing the depth and accuracy of automated processing while lowering the barriers to AI integration.

Workflow Diagram
Self-coded LLM Chain Node Workflow diagram

Workflow Name

Self-coded LLM Chain Node

Key Features and Highlights

This workflow integrates a custom-coded chain of large language model (LLM) nodes combined with the OpenAI GPT-4o-mini model, enabling flexible natural language processing and question-answering capabilities. A standout feature is the seamless invocation of language model chains and tool nodes (such as a Wikipedia query tool) through custom code nodes, supporting complex multi-step AI reasoning and knowledge retrieval.

Core Problem Addressed

It solves the challenge within the n8n automation platform where complex natural language tasks cannot directly and flexibly call chained language models and external knowledge bases. By using self-coded nodes, users can freely define input, output, and invocation logic, efficiently integrating large language models with knowledge query tools to enhance the depth and accuracy of intelligent Q&A and automated processing.

Application Scenarios

  • Building intelligent question-answering systems
  • Automated retrieval from enterprise internal knowledge bases
  • Research assistance for rapid access to authoritative information (e.g., Wikipedia data)
  • Automation of complex text generation and processing
  • AI-driven customer support and content creation assistance

Main Workflow Steps

  1. Trigger the workflow manually via a manual trigger node.
  2. Set the initial input question (e.g., “Tell me a joke” or “Einstein’s birth year?”).
  3. The custom LLM chain node receives the input, constructs a prompt template, and calls the OpenAI GPT-4o-mini model to generate a response.
  4. The self-coded tool node invokes the Wikipedia query tool to supplement knowledge retrieval for the input question.
  5. The AI Agent node synthesizes outputs from the language model and external tool results to complete intelligent reasoning and provide an answer.
  6. Return the final response.

Systems or Services Involved

  • OpenAI GPT-4o-mini language model (accessed via OpenAI API)
  • Wikipedia query tool (custom node based on Langchain community tools)
  • Core n8n automation platform nodes (Manual Trigger, Set, Custom Code nodes, etc.)

Target Users and Value

  • Automation developers and tech enthusiasts seeking customizable AI workflow solutions
  • Enterprise knowledge managers needing to build intelligent Q&A and knowledge retrieval systems
  • Content creators and customer support teams leveraging AI for generation and response assistance
  • Researchers and data analysts aiming for rapid multi-source information access and integration
    This workflow significantly lowers the barrier to AI integration, enabling efficient and flexible intelligent automation applications.