LangChain Custom Code Node for LLM Interactions
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Demonstrates creating custom LangChain tools and LLM chains using n8n's Code node. This workflow allows for advanced, programmable interactions with Large Language Models.
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About This Workflow
Overview
This n8n workflow showcases the power of LangChain integration within n8n, specifically focusing on how to leverage the Code node to create custom LLM (Large Language Model) chains and tools. Instead of relying solely on pre-built nodes, this workflow allows users to define complex LLM logic programmatically. The Code node acts as a bridge, enabling you to write JavaScript code that utilizes LangChain's extensive capabilities, such as defining custom tools like the Wikipedia search or constructing LLM chains with specific prompts and language models. This approach offers unparalleled flexibility for advanced AI automation scenarios.
Problem Solved: This workflow addresses the limitation of pre-defined nodes by enabling users to build highly customized LLM interactions. It allows for the creation of bespoke AI agents and chains that can perform specific tasks not covered by standard integrations, providing greater control and extensibility in AI-driven automation.
Key Features
- Utilize n8n's
Codenode to define custom LangChain tools (e.g., Wikipedia search). - Programmatically construct and execute LLM chains using LangChain's JavaScript API.
- Integrate custom-coded LLM logic directly into n8n workflows.
- Connect custom tools and chains with n8n's
OpenAIandChat OpenAInodes.
How To Use
- Configure OpenAI Credentials: Ensure your OpenAI API credentials are set up in n8n.
- Set Input Prompt: Use the
Setnodes to define the input query for your LLM chain or agent. - Define Custom Tool (Optional): The
Custom - Wikipedianode demonstrates how to create a custom tool using theCodenode. This example returns aWikipediaQueryRuninstance. - Define Custom LLM Chain: The
Custom - LLM Chain Nodeuses theCodenode to create a simple chain using aPromptTemplateand an AI language model. It takes the input from theSetnode. - Create an Agent: The
Agentnode orchestrates the interaction between the LLM, tools, and the input. - Execute Workflow: Trigger the workflow manually to see the LLM process the input and generate an output based on your custom logic and available tools.
Apps Used
Workflow JSON
{
"id": "0e28c197-9bb7-406d-a19e-651e8e48fc5a",
"name": "LangChain Custom Code Node for LLM Interactions",
"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: 0e28c197-9bb7...
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