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
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
How this works
This workflow empowers teams handling knowledge-intensive queries to deliver precise, context-aware responses from their document repositories, saving hours of manual research and reducing errors in customer support or internal consultations. It suits content managers, support specialists, or analysts who need an intelligent assistant that draws from company files without sifting through endless folders. The core step involves loading documents via Google Drive, generating embeddings with Cohere, and storing them in Milvus for rapid retrieval, enabling the RAG agent to fetch and synthesise relevant information during chat interactions.
Use this when you have unstructured documents in Google Drive that require on-demand querying, such as FAQs or reports, and want scalable vector search without building from scratch. Avoid it for real-time data streams or non-text assets like images, where specialised loaders would be needed. Common variations include swapping Google Drive for Dropbox triggers or integrating custom prompts in the agent for domain-specific tuning.
About this workflow
RAG AI Agent with Milvus and Cohere. Uses documentDefaultDataLoader, embeddingsCohere, chatTrigger, googleDriveTrigger. Chat trigger; 14 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →
Related workflows
Workflows that share integrations, category, or trigger type with this one. All free to copy and import.
This workflow acts as a 24/7 sales agent, engaging leads across WhatsApp, Instagram, Facebook, Telegram, and your website. It intelligently transcribes audio messages, answers questions using a knowle
• Create a Google Drive folder to watch. • Connect your Google Drive account in n8n and authorize access. • Point the Google Drive Trigger node to this folder (new/modified files trigger the flow).
Advanced Ai Demo Presented At Ai Developers 14 Meetup. Uses slack, stickyNote, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi. Chat trigger; 39 nodes.
Advanced Ai Demo (Presented At Ai Developers #14 Meetup). Uses slack, stickyNote, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi. Chat trigger; 39 nodes.
Workflow 2358. Uses slack, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi, documentDefaultDataLoader. Chat trigger; 39 nodes.