n8n-workflows/workflows/Chat with local LLMs using n8n and Ollama.json
2025-05-14 11:58:29 +03:00

116 lines
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JSON

{
"id": "af8RV5b2TWB2LclA",
"meta": {
"instanceId": "95f2ab28b3dabb8da5d47aa5145b95fe3845f47b20d6343dd5256b6a28ba8fab",
"templateCredsSetupCompleted": true
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"name": "Chat with local LLMs using n8n and Ollama",
"tags": [],
"nodes": [
{
"id": "475385fa-28f3-45c4-bd1a-10dde79f74f2",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
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"webhookId": "ebdeba3f-6b4f-49f3-ba0a-8253dd226161",
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{
"id": "61133dc6-dcd9-44ff-85f2-5d8cc2ce813e",
"name": "Ollama Chat Model",
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"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"
},
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"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
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{
"id": "eeffdd4e-6795-4ebc-84f7-87b5ac4167d9",
"name": "Chat LLM Chain",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
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"connections": {
"Ollama Chat Model": {
"ai_languageModel": [
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{
"node": "Chat LLM Chain",
"type": "ai_languageModel",
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"When chat message received": {
"main": [
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{
"node": "Chat LLM Chain",
"type": "main",
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}