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 →
{
"meta": {
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "5cb0a836-f9a1-4f92-9326-cd82a392d0da",
"name": "Knowledge Base Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
220,
0
],
"parameters": {
"text": "={{ $json.chatInput }}",
"options": {
"systemMessage": "You are the AI assistant for an internal support team at a technology company specializing in advanced software solutions. Your task is to assist internal users by consulting the official product documentation stored in the company\u2019s knowledge base.\n\nAvailable references:\n\nproductDocs: Step-by-step guides, technical configurations, and official manuals extracted from the product\u2019s documentation.\n\nBehavior rules for answering questions:\nAlways consult the official product documentation first using the productDocs tool.\n\nRespond clearly and directly, explaining how to do what is requested.\n\nDo not filter by category unless explicitly asked by the user.\n\nDetect the language of each incoming message individually and respond in that language. Do not use prior conversation language or history to decide the response language.\n\nNever provide links, even if requested. If a user asks for a link, reply:\n\u201cI cannot provide links. If you need specific information, please let me know and I will help with the details.\u201d\n\nUse a professional, direct, and human tone.\n\nKeep answers between 2 and 4 lines unless the user requests more detail.\n\nDo not invent information that is not in the knowledge base.\n\nIf you give numbered steps or lists, number them sequentially (1, 2, 3...) without skipping or repeating numbers, even if the source content uses different numbering."
},
"promptType": "define"
},
"typeVersion": 1.9
},
{
"id": "56e6fb75-6a97-4466-9e7f-70710c2740d7",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
60,
240
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "e352c32e-7108-4a0d-b081-b2532d96d092",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
680,
380
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "74bbfb00-1a00-4131-a291-bce5b79628b4",
"name": "When clicking \"Execute Workflow\"",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-60,
-420
],
"parameters": {},
"typeVersion": 1
},
{
"id": "f720a4b0-6239-4a0b-bb61-1e43f78f8e40",
"name": "Simple Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
320,
220
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "94561d61-4a01-48b6-b114-dc4d47546ff3",
"name": "MongoDB Vector Search",
"type": "@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas",
"position": [
560,
220
],
"parameters": {
"mode": "retrieve-as-tool",
"options": {},
"toolName": "productDocs",
"mongoCollection": {
"__rl": true,
"mode": "list",
"value": "n8n-template",
"cachedResultName": "n8n-template"
},
"toolDescription": "retreive documentation",
"vectorIndexName": "data_index"
},
"credentials": {
"mongoDb": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "c473c33d-5681-4f3a-ac36-0d3012e7251f",
"name": "Document Section Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
740,
-260
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "doc_id",
"value": "={{ $json.documentId }}"
}
]
}
},
"jsonData": "={{ $json.content }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "321222cb-1daf-4be2-a6ca-1a03d24f670f",
"name": "Document Chunker",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
860,
-100
],
"parameters": {
"options": {
"splitCode": "markdown"
},
"chunkSize": 3000,
"chunkOverlap": 200
},
"typeVersion": 1
},
{
"id": "716519f5-cec1-4bfe-afbe-614fc23e74b5",
"name": "MongoDB Vector Store Inserter",
"type": "@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas",
"position": [
540,
-420
],
"parameters": {
"mode": "insert",
"options": {},
"mongoCollection": {
"__rl": true,
"mode": "list",
"value": "n8n-template",
"cachedResultName": "n8n-template"
},
"vectorIndexName": "data_index"
},
"credentials": {
"mongoDb": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "a49c19fc-f5f5-4381-b6ba-1bfc12b96135",
"name": "OpenAI Embeddings Generator",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
480,
-180
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "6de724d5-2941-4e72-af8b-302ca2cf2ca0",
"name": "Google Docs Importer",
"type": "n8n-nodes-base.googleDocs",
"position": [
200,
-420
],
"parameters": {
"operation": "get",
"documentURL": "https://docs.google.com/document/d/1gvgp71e9edob8WLqFIYCdzC7kUq3pLO37VKb-a-vVW4/edit?tab=t.0"
},
"credentials": {
"googleDocsOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 2
},
{
"id": "4f30bb21-72f0-4d13-b610-2ec218ad31b1",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-420,
-440
],
"parameters": {
"color": 5,
"content": "\uc774 \uc6cc\ud06c\ud50c\ub85c\ub97c \uc218\ub3d9\uc73c\ub85c \uc2e4\ud589\ud558\uc5ec Google Docs \uc81c\ud488 \ubb38\uc11c\ub97c MongoDB\uc5d0 \uac00\uc838\uc640 \uc778\ub371\uc2f1\ud558\uace0, \ubca1\ud130 \uc784\ubca0\ub529\uc744 \uc0ac\uc6a9\ud558\uc5ec \ube60\ub978 \uac80\uc0c9\uc744 \uac00\ub2a5\ud558\uac8c \ud558\uc138\uc694."
},
"typeVersion": 1
},
{
"id": "25fd33d5-041b-4f01-a46b-1bacabd88376",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
40,
0
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "f1f3fadd-d5e6-45df-b810-1616531dffcb",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-420,
40
],
"parameters": {
"color": 4,
"content": "\uc774 \uc6cc\ud06c\ud50c\ub85c\uc6b0\ub294 \uac80\uc0c9 \uc99d\uac15 \uc0dd\uc131(RAG)\uc744 \uc0ac\uc6a9\ud558\uc5ec MongoDB \ubca1\ud130 \uc2a4\ud1a0\uc5b4\ub97c \uac80\uc0c9\ud558\uace0, \ucee8\ud14d\uc2a4\ud2b8\ub97c \ud3ec\ud568\ud55c AI \uc751\ub2f5\uc744 \uc0dd\uc131\ud558\uc5ec \uc0ac\uc6a9\uc790 \uc9c8\ubb38\uc744 \ub2f5\ud569\ub2c8\ub2e4."
},
"typeVersion": 1
},
{
"id": "39eee95c-b332-4ae4-bde9-aaf0fe5e0546",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1060,
-380
],
"parameters": {
"height": 520,
"content": "\uac80\uc0c9 \uc778\ub371\uc2a4 \uc608\uc81c \n\n{\n \"mappings\": {\n \"dynamic\": false,\n \"fields\": {\n \"_id\": {\n \"type\": \"\ubb38\uc790\uc5f4\"\n },\n \"text\": {\n \"type\": \"\ubb38\uc790\uc5f4\"\n },\n \"embedding\": {\n \"type\": \"knnVector\",\n \"dimensions\": 1536,\n \"similarity\": \"\ucf54\uc0ac\uc778\"\n },\n \"source\": {\n \"type\": \"\ubb38\uc790\uc5f4\"\n },\n \"doc_id\": {\n \"type\": \"\ubb38\uc790\uc5f4\"\n }\n }\n }\n}"
},
"typeVersion": 1
}
],
"connections": {
"Simple Memory": {
"ai_memory": [
[
{
"node": "Knowledge Base Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Document Chunker": {
"ai_textSplitter": [
[
{
"node": "Document Section Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "MongoDB Vector Search",
"type": "ai_embedding",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Knowledge Base Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Google Docs Importer": {
"main": [
[
{
"node": "MongoDB Vector Store Inserter",
"type": "main",
"index": 0
}
]
]
},
"Knowledge Base Agent": {
"main": [
[]
]
},
"MongoDB Vector Search": {
"ai_tool": [
[
{
"node": "Knowledge Base Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Document Section Loader": {
"ai_document": [
[
{
"node": "MongoDB Vector Store Inserter",
"type": "ai_document",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Knowledge Base Agent",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Embeddings Generator": {
"ai_embedding": [
[
{
"node": "MongoDB Vector Store Inserter",
"type": "ai_embedding",
"index": 0
}
]
]
},
"When clicking \"Execute Workflow\"": {
"main": [
[
{
"node": "Google Docs Importer",
"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.
googleDocsOAuth2ApimongoDbopenAiApi
Pro
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About this workflow
4526. Uses agent, lmChatOpenAi, embeddingsOpenAi, memoryBufferWindow. Event-driven trigger; 15 nodes.
Source: https://github.com/n8nKOR/n8n-shared-workflow/blob/62a671327e906c22a40d290b339ff6d2373f8d75/workflows/n8nworkflows/ai/4526.json — original creator credit. Request a take-down →
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