Tushar Mishra This workflow corresponds to n8n.io template #6206 — 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": "hWRV5DnuYJoVsW0k",
"meta": {
"templateId": "5148",
"templateCredsSetupCompleted": true
},
"name": "ServiceNow Knowledge Chatbot",
"tags": [],
"nodes": [
{
"id": "1218186e-a93e-4e05-b47e-a395f28cf5f9",
"name": "Qdrant Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
816,
352
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "id",
"value": "rag_collection"
}
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2,
"alwaysOutputData": true
},
{
"id": "af14443b-ae01-48dc-8552-5ded7a27fce2",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
832,
640
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "660380c5-63da-4404-98e6-f9c0ee9aaa90",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
976,
784
],
"parameters": {
"options": {},
"chunkSize": 500,
"chunkOverlap": 50
},
"typeVersion": 1
},
{
"id": "49dbe387-751f-4a2e-8803-290bc2c06ec5",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
368,
240
],
"parameters": {
"color": 3,
"width": 840,
"height": 700,
"content": "## Data Ingestion\n**Add data to the semantic database"
},
"typeVersion": 1
},
{
"id": "45683271-af59-41d0-9e69-af721d566661",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
1456,
320
],
"parameters": {
"mode": "webhook",
"public": true,
"options": {},
"authentication": "basicAuth"
},
"credentials": {
"httpBasicAuth": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "af562588-2e8c-4c0b-b041-d6fc8c0affd0",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1936,
320
],
"parameters": {
"options": {
"systemMessage": "You are a helpful assistant. You have access to a tool to retrieve data from a semantic database to answer questions. Always provide arguments when you execute the tool. Always use tools for retrieval of the data from the semantic database first and then provide the answer. Answer must be specific and grounded in the articles provided. Always add the reference to the articles and Its Number"
}
},
"typeVersion": 2
},
{
"id": "de87b7bb-6fec-4d8f-a77a-25bc3a30a038",
"name": "Simple Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
1984,
560
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "16261539-5218-4df1-8b14-915dd3377167",
"name": "Qdrant Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
2256,
592
],
"parameters": {
"mode": "retrieve-as-tool",
"topK": 10,
"options": {},
"toolName": "retriever",
"toolDescription": "Retrieve data from a semantic database to answer questions",
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "rag_collection",
"cachedResultName": "rag_collection"
}
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "5919cc58-05f4-42c8-aada-3782a16574d9",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1248,
240
],
"parameters": {
"color": 4,
"width": 1200,
"height": 700,
"content": "## RAG Chatbot\n**Chat with your data"
},
"typeVersion": 1
},
{
"id": "3a3a2203-95ec-47f2-9dc9-ef651a6ab87a",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
576,
640
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "0e2f5f1e-7447-419b-854b-47ba008b36a7",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1808,
528
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "8d7912e1-2050-4fb6-a646-017457f24de8",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
2352,
800
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "add35fa0-dc1b-4c3e-af98-5144b1efec7b",
"name": "Get many table records",
"type": "n8n-nodes-base.serviceNow",
"position": [
656,
352
],
"parameters": {
"options": {
"sysparm_fields": [
"number",
"short_description",
"text"
],
"sysparm_exclude_reference_link": true
},
"resource": "tableRecord",
"operation": "getAll",
"returnAll": true,
"tableName": "kb_knowledge",
"authentication": "basicAuth"
},
"credentials": {
"serviceNowBasicApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "3bbd4aa4-965e-4f8b-a47c-983fc2980e15",
"name": "When clicking \u2018Execute workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
464,
352
],
"parameters": {},
"typeVersion": 1
}
],
"active": false,
"settings": {
"callerPolicy": "workflowsFromSameOwner",
"executionOrder": "v1",
"executionTimeout": -1
},
"versionId": "71ece873-347c-4e5c-a457-30957c143a51",
"connections": {
"Simple Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Qdrant Vector Store1": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Get many table records": {
"main": [
[
{
"node": "Qdrant Vector Store",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"When clicking \u2018Execute workflow\u2019": {
"main": [
[
{
"node": "Get many table records",
"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.
httpBasicAuthopenAiApiqdrantApiserviceNowBasicApi
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About this workflow
This part collects data from the ServiceNow Knowledge Article table, processes it into embeddings, and stores it in Qdrant. Trigger: When clicking ‘Execute workflow’
Source: https://n8n.io/workflows/6206/ — original creator credit. Request a take-down →
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