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": "A5R7XYSzrCJKlw9k",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Agent Milvus tool",
"tags": [
{
"id": "msnDWKHQmwMDxWQH",
"name": "Milvus",
"createdAt": "2025-04-16T12:48:14.539Z",
"updatedAt": "2025-04-16T12:48:14.539Z"
},
{
"id": "tnCpo8hq8uKrdASK",
"name": "AI",
"createdAt": "2025-04-16T12:47:57.976Z",
"updatedAt": "2025-04-16T12:47:57.976Z"
}
],
"nodes": [
{
"id": "cfe6264a-2be1-4d1e-974b-ee05ca8ae9ab",
"name": "When clicking \"Execute Workflow\"",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-280,
-40
],
"parameters": {},
"typeVersion": 1
},
{
"id": "c0665cc9-2bce-48db-a3bc-15baac68e569",
"name": "Fetch Essay List",
"type": "n8n-nodes-base.httpRequest",
"position": [
-20,
-40
],
"parameters": {
"url": "http://www.paulgraham.com/articles.html",
"options": {}
},
"typeVersion": 4.2
},
{
"id": "00bcdc0b-eb6d-41eb-ac0d-a6710d6232e4",
"name": "Extract essay names",
"type": "n8n-nodes-base.html",
"position": [
180,
-40
],
"parameters": {
"options": {},
"operation": "extractHtmlContent",
"extractionValues": {
"values": [
{
"key": "essay",
"attribute": "href",
"cssSelector": "table table a",
"returnArray": true,
"returnValue": "attribute"
}
]
}
},
"typeVersion": 1.2
},
{
"id": "523c319e-d1c7-4214-a725-dc557f6471a2",
"name": "Split out into items",
"type": "n8n-nodes-base.splitOut",
"position": [
380,
-40
],
"parameters": {
"options": {},
"fieldToSplitOut": "essay"
},
"typeVersion": 1
},
{
"id": "be155368-99f5-43b3-ba6c-50cccf2b72d2",
"name": "Fetch essay texts",
"type": "n8n-nodes-base.httpRequest",
"position": [
780,
-40
],
"parameters": {
"url": "=http://www.paulgraham.com/{{ $json.essay }}",
"options": {}
},
"typeVersion": 4.2
},
{
"id": "92af113c-dd71-4ddd-b50a-f5932392ed82",
"name": "Limit to first 3",
"type": "n8n-nodes-base.limit",
"position": [
580,
-40
],
"parameters": {
"maxItems": 3
},
"typeVersion": 1
},
{
"id": "1a1893c4-e8b2-454a-b49f-a0b0f3c01aca",
"name": "Extract Text Only",
"type": "n8n-nodes-base.html",
"position": [
1100,
-40
],
"parameters": {
"options": {},
"operation": "extractHtmlContent",
"extractionValues": {
"values": [
{
"key": "data",
"cssSelector": "body",
"skipSelectors": "img,nav"
}
]
}
},
"typeVersion": 1.2
},
{
"id": "d14ae606-f002-4fde-a896-bf1c7fa675b2",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-100,
-160
],
"parameters": {
"width": 1071.752021563343,
"height": 285.66037735849045,
"content": "## Scrape latest Paul Graham essays"
},
"typeVersion": 1
},
{
"id": "dfb0cb32-9d7c-4588-b75e-0b79231eb72a",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
1020,
-160
],
"parameters": {
"width": 625,
"height": 607,
"content": "## Load into Milvus vector database"
},
"typeVersion": 1
},
{
"id": "862a1a02-50e2-42af-9fa9-eb3a4f2ca463",
"name": "Recursive Character Text Splitter1",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
1440,
300
],
"parameters": {
"options": {},
"chunkSize": 6000
},
"typeVersion": 1
},
{
"id": "91ac110a-57db-44b1-b22f-d2a63f22f173",
"name": "Milvus Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreMilvus",
"position": [
1320,
-40
],
"parameters": {
"mode": "insert",
"options": {
"clearCollection": true
},
"milvusCollection": {
"__rl": true,
"mode": "list",
"value": "n8n_test",
"cachedResultName": "n8n_test"
}
},
"credentials": {
"milvusApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "456e917f-d466-4ec8-8df9-3774ba58151d",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
60,
360
],
"parameters": {
"options": {}
},
"typeVersion": 1.9
},
{
"id": "a5c5f308-097d-4fe0-92be-d717fd1e0b74",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-280,
360
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "dc352f07-335f-47cb-8270-32a4a0b87df7",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-460,
-200
],
"parameters": {
"width": 280,
"height": 180,
"content": "## Step 1\n1. Set up a Milvus server based on [this guide](https://milvus.io/docs/install_standalone-docker-compose.md). And then create a collection named `n8n_test`.\n2. Click this workflow to load scrape and load Paul Graham essays to Milvus collection.\n"
},
"typeVersion": 1
},
{
"id": "5c9e9871-c9c1-458e-b35c-eab87ac5ca26",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
1360,
180
],
"parameters": {
"options": {},
"jsonData": "={{ $('Extract Text Only').item.json.data }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "5b202001-525c-4481-a263-56b69c9b1bd8",
"name": "Milvus Vector Store as tool",
"type": "@n8n/n8n-nodes-langchain.vectorStoreMilvus",
"position": [
180,
560
],
"parameters": {
"mode": "retrieve-as-tool",
"toolName": "milvus_knowledge_base",
"toolDescription": "useful when you need to retrieve information",
"milvusCollection": {
"__rl": true,
"mode": "list",
"value": "n8n_test",
"cachedResultName": "n8n_test"
}
},
"credentials": {
"milvusApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "6b5b95c7-dde2-4c3f-952b-97a8f5c267c9",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-460,
260
],
"parameters": {
"width": 280,
"height": 120,
"content": "## Step 2\nStart to chat with the AI Agent with Milvus tool"
},
"typeVersion": 1
},
{
"id": "5ccfe636-2bb3-4026-98f0-57ba8d5780f0",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
1220,
200
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "982622e9-af05-4ee2-ae7d-166c47f75ce9",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
20,
560
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "abd97878-cce6-44a0-8bae-91536ea48b6b",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
200,
740
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "00d49aab-3200-44fc-a0fc-8f7f22998617",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-80,
300
],
"parameters": {
"color": 7,
"width": 574,
"height": 629,
"content": ""
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "8e6f0bb5-1fb5-48fc-8a1f-488362be4ef7",
"connections": {
"Fetch Essay List": {
"main": [
[
{
"node": "Extract essay names",
"type": "main",
"index": 0
}
]
]
},
"Limit to first 3": {
"main": [
[
{
"node": "Fetch essay texts",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Milvus Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Extract Text Only": {
"main": [
[
{
"node": "Milvus Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Fetch essay texts": {
"main": [
[
{
"node": "Extract Text Only",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Milvus Vector Store as tool",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Milvus Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Extract essay names": {
"main": [
[
{
"node": "Split out into items",
"type": "main",
"index": 0
}
]
]
},
"Split out into items": {
"main": [
[
{
"node": "Limit to first 3",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Milvus Vector Store as tool": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"When clicking \"Execute Workflow\"": {
"main": [
[
{
"node": "Fetch Essay List",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter1": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"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.
milvusApiopenAiApi
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How this works
This workflow empowers users to harness AI agents for intelligent querying against a personal knowledge base of essays, delivering precise, context-aware responses without manual data handling. It suits content creators, researchers, or educators seeking to analyse or retrieve insights from document collections efficiently. The core step involves fetching essays via HTTP requests, extracting and splitting their text, then loading them into a Milvus vector store for the agent to perform semantic searches and generate answers.
Use this workflow when building an event-driven system for on-demand AI interactions with textual archives, such as querying historical essays for thematic analysis. Avoid it for real-time processing of massive datasets, where simpler HTTP Request integrations might suffice without vector storage. Common variations include swapping the essay fetch for other document loaders or expanding the agent to incorporate additional tools like summarisation.
About this workflow
Agent Milvus tool. Uses manualTrigger, httpRequest, html, splitOut. Event-driven trigger; 21 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →
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