Aitor | 1Node This workflow corresponds to n8n.io template #3848 — we link there as the canonical source.
This workflow follows the Agent → Chat Trigger recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "2Eba0OHGtOmoTWOU",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "RAG AI Agent with Milvus and Cohere",
"tags": [
{
"id": "yj7cF3GCsZiargFT",
"name": "rag",
"createdAt": "2025-05-03T17:14:30.099Z",
"updatedAt": "2025-05-03T17:14:30.099Z"
}
],
"nodes": [
{
"id": "361065cc-edbf-47da-8da7-c59b564db6f3",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
0,
320
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "a01b9512-ced1-4e28-a2aa-88077ab79d9a",
"name": "Embeddings Cohere",
"type": "@n8n/n8n-nodes-langchain.embeddingsCohere",
"position": [
-140,
320
],
"parameters": {
"modelName": "embed-multilingual-v3.0"
},
"credentials": {
"cohereApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "1da6ea4b-de88-44d3-a215-78c55b5592a2",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-800,
520
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "23004477-3f6d-4909-a626-0eba0557a5bd",
"name": "Watch New Files",
"type": "n8n-nodes-base.googleDriveTrigger",
"position": [
-800,
100
],
"parameters": {
"event": "fileCreated",
"options": {},
"pollTimes": {
"item": [
{
"mode": "everyMinute"
}
]
},
"triggerOn": "specificFolder",
"folderToWatch": {
"__rl": true,
"mode": "list",
"value": "15gjDQZiHZuBeVscnK8Ic_kIWt3mOaVfs",
"cachedResultUrl": "https://drive.google.com/drive/folders/15gjDQZiHZuBeVscnK8Ic_kIWt3mOaVfs",
"cachedResultName": "RAG template"
}
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "001fbdbe-dfcb-4552-bf09-de416b253389",
"name": "Download New",
"type": "n8n-nodes-base.googleDrive",
"position": [
-580,
100
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.id }}"
},
"options": {},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 3
},
{
"id": "c1116cba-beb9-4d28-843d-c5c21c0643de",
"name": "Insert into Milvus",
"type": "@n8n/n8n-nodes-langchain.vectorStoreMilvus",
"position": [
-124,
100
],
"parameters": {
"mode": "insert",
"options": {
"clearCollection": false
},
"milvusCollection": {
"__rl": true,
"mode": "list",
"value": "collectionName",
"cachedResultName": "collectionName"
}
},
"credentials": {
"milvusApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "2dbc7139-46f6-41d8-8c13-9fafad5aec55",
"name": "RAG Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-540,
520
],
"parameters": {
"options": {}
},
"typeVersion": 1.8
},
{
"id": "a103506e-9019-41f2-9b0d-9b831434c9e9",
"name": "Retrieve from Milvus",
"type": "@n8n/n8n-nodes-langchain.vectorStoreMilvus",
"position": [
-340,
740
],
"parameters": {
"mode": "retrieve-as-tool",
"topK": 10,
"toolName": "vector_store",
"toolDescription": "You are an AI agent that responds based on information received from a vector database.",
"milvusCollection": {
"__rl": true,
"mode": "list",
"value": "collectionName",
"cachedResultName": "collectionName"
}
},
"credentials": {
"milvusApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "74ccdff1-b976-4e1c-a2c4-237ffff19e34",
"name": "OpenAI 4o",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-580,
740
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o",
"cachedResultName": "gpt-4o"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "36e35eaf-f723-4eeb-9658-143d5bc390a0",
"name": "Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
-460,
740
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "ec7b6b92-065c-455c-a3f0-17586d9e48d7",
"name": "Cohere embeddings",
"type": "@n8n/n8n-nodes-langchain.embeddingsCohere",
"position": [
-220,
900
],
"parameters": {
"modelName": "embed-multilingual-v3.0"
},
"credentials": {
"cohereApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "3c3a8900-0b98-4479-8602-16b21e011ba1",
"name": "Set Chunks",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
80,
480
],
"parameters": {
"options": {},
"chunkSize": 700,
"chunkOverlap": 60
},
"typeVersion": 1
},
{
"id": "3a43bf1a-7e22-4b5e-bbb1-6bb2c1798c07",
"name": "Extract from File",
"type": "n8n-nodes-base.extractFromFile",
"position": [
-360,
100
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "e0c9d4d7-5e3e-4e47-bb1f-dbdca360b20a",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1440,
120
],
"parameters": {
"color": 2,
"width": 540,
"height": 600,
"content": "## Why Milvus\nBased on comparisons and user feedback, **Milvus is often considered a more performant and scalable vector database solution compared to Supabase**, particularly for demanding use cases involving large datasets, high-volume vector search operations, and multilingual support.\n\n\n### Requirements\n- Create an account on [Zilliz](https://zilliz.com/) to generate the Milvus cluster. \n- There is no need to create docker containers or your own instance, Zilliz provides the cloud infraestructure to build it easily\n- Get your credentials ready from Drive, Milvus (Zilliz), and [Cohere](https://cohere.com)\n\n### Usage\nEvery time a new pdf is added into the Drive folder, it will be inserted into the Milvus Vector Store, allowing for the interaction with the RAG agent in seconds.\n\n## Calculate your company's RAG costs\n\nWant to run Milvus on your own server on n8n? Zilliz provides a great [cost calculator](https://zilliz.com/rag-cost-calculator/)\n\n### Get in touch with us\nWant to implement a RAG AI agent for your company? [Shoot us a message](https://1node.ai)\n"
},
"typeVersion": 1
}
],
"active": true,
"settings": {
"executionOrder": "v1"
},
"versionId": "8b5fc2b8-50f7-425c-8fc8-94ba4f76ecf3",
"connections": {
"Memory": {
"ai_memory": [
[
{
"node": "RAG Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"OpenAI 4o": {
"ai_languageModel": [
[
{
"node": "RAG Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Set Chunks": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Download New": {
"main": [
[
{
"node": "Extract from File",
"type": "main",
"index": 0
}
]
]
},
"Watch New Files": {
"main": [
[
{
"node": "Download New",
"type": "main",
"index": 0
}
]
]
},
"Cohere embeddings": {
"ai_embedding": [
[
{
"node": "Retrieve from Milvus",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Embeddings Cohere": {
"ai_embedding": [
[
{
"node": "Insert into Milvus",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Extract from File": {
"main": [
[
{
"node": "Insert into Milvus",
"type": "main",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Insert into Milvus",
"type": "ai_document",
"index": 0
}
]
]
},
"Retrieve from Milvus": {
"ai_tool": [
[
{
"node": "RAG Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "RAG Agent",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
cohereApigoogleDriveOAuth2ApimilvusApiopenAiApi
Pro
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About this workflow
This template creates a powerful Retrieval Augmented Generation (RAG) AI agent workflow in n8n. It monitors a specified Google Drive folder for new PDF files, extracts their content, generates vector embeddings using Cohere, and stores these embeddings in a Milvus vector…
Source: https://n8n.io/workflows/3848/ — original creator credit. Request a take-down →
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