{
  "name": "Chat with local LLMs using n8n and Ollama",
  "nodes": [
    {
      "id": "475385fa-28f3-45c4-bd1a-10dde79f74f2",
      "name": "When chat message received",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        700,
        460
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "61133dc6-dcd9-44ff-85f2-5d8cc2ce813e",
      "name": "Ollama Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOllama",
      "position": [
        900,
        680
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1
    },
    {
      "id": "3e89571f-7c87-44c6-8cfd-4903d5e1cdc5",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        160,
        80
      ],
      "parameters": {
        "width": 485,
        "height": 473,
        "content": "## Chat with local LLMs using n8n and Ollama\nThis n8n workflow allows you to seamlessly interact with your self-hosted Large Language Models (LLMs) through a user-friendly chat interface. By connecting to Ollama, a powerful tool for managing local LLMs, you can send prompts and receive AI-generated responses directly within n8n.\n\n### How it works\n1. When chat message received: Captures the user's input from the chat interface.\n2. Chat LLM Chain: Sends the input to the Ollama server and receives the AI-generated response.\n3. Delivers the LLM's response back to the chat interface.\n\n### Set up steps\n* Make sure Ollama is installed and running on your machine before executing this workflow.\n* Edit the Ollama address if different from the default.\n"
      },
      "typeVersion": 1
    },
    {
      "id": "9345cadf-a72e-4d3d-b9f0-d670744065fe",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1040,
        660
      ],
      "parameters": {
        "color": 6,
        "width": 368,
        "height": 258,
        "content": "## Ollama setup\n* Connect to your local Ollama, usually on http://localhost:11434\n* If running in Docker, make sure that the n8n container has access to the host's network in order to connect to Ollama. You can do this by passing `--net=host` option when starting the n8n Docker container"
      },
      "typeVersion": 1
    },
    {
      "id": "eeffdd4e-6795-4ebc-84f7-87b5ac4167d9",
      "name": "Chat LLM Chain",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        920,
        460
      ],
      "parameters": {},
      "typeVersion": 1.4
    },
    {
      "id": "dec6d5ac-8886-529a-ba00-542f4e87a6d8",
      "name": "Publication · Overview and setup",
      "type": "n8n-nodes-base.stickyNote",
      "typeVersion": 1,
      "position": [
        -800,
        0
      ],
      "parameters": {
        "content": "# Chat with local LLMs using n8n and Ollama\n\n## Purpose and structure\nThis folder contains the **Chat with local LLMs using n8n and Ollama** workflow. Its executable nodes use `chainLlm`, `chatTrigger`, `lmChatOllama`. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.\n\n## Setup\nImport inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.\n\n## Entry points\nWhen chat message received\n\n## Verification\nSanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.\n\n## Attribution\nhttps://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Stickynote/1379_Stickynote_Automation_Triggered.json\nMIT; preserve source notices.",
        "width": 700,
        "height": 800,
        "color": 1
      }
    }
  ],
  "active": false,
  "settings": {
    "executionOrder": "v1"
  },
  "connections": {
    "Ollama Chat Model": {
      "ai_languageModel": [
        [
          {
            "node": "Chat LLM Chain",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "When chat message received": {
      "main": [
        [
          {
            "node": "Chat LLM Chain",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}