Niklas Hatje This workflow corresponds to n8n.io template #5011 — 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 →
{
"meta": {
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "83ed351e-90e8-458f-a01b-73001ef1800f",
"name": "Upload your file here",
"type": "n8n-nodes-base.formTrigger",
"position": [
220,
-120
],
"parameters": {
"options": {},
"formTitle": "Upload your data to test RAG",
"formFields": {
"values": [
{
"fieldType": "file",
"fieldLabel": "Upload your file(s)",
"requiredField": true,
"acceptFileTypes": ".pdf, .csv"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "26d63e24-2592-41f9-9b4b-edab81e99f21",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
860,
460
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "3a69c8a7-bf95-4de2-84b0-ae2cc3d2e4e7",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
660,
40
],
"parameters": {
"options": {},
"dataType": "binary"
},
"typeVersion": 1.1
},
{
"id": "0b42832b-c9e8-4627-b36c-94fc5e242b33",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-320,
-180
],
"parameters": {
"color": 4,
"width": 440,
"height": 300,
"content": "### Readme\nLoad your data into a vector database with the \ud83d\udcda **Load Data** flow, and then use your data as chat context with the \ud83d\udc15 **Retriever** flow.\n\n**Quick start**\n1. Click on the `Execute Workflow` button to run the \ud83d\udcda **Load Data** flow.\n2. Click on `Open Chat` button to run the \ud83d\udc15 **Retriever** flow. Then ask a question about content from your document(s)\n\n\nFor more info, check our [docs on RAG in n8n](https://docs.n8n.io/advanced-ai/rag-in-n8n/)"
},
"typeVersion": 1
},
{
"id": "f902ab8f-4620-4a95-86f7-c5857c4d6c4f",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
160,
-180
],
"parameters": {
"color": 7,
"width": 700,
"height": 460,
"content": "### \ud83d\udcda Load Data Flow"
},
"typeVersion": 1
},
{
"id": "0f4185ea-d7a9-44a9-a824-98f9dc2c2a5d",
"name": "Insert Data to Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
"position": [
400,
-120
],
"parameters": {
"mode": "insert",
"memoryKey": {
"__rl": true,
"mode": "list",
"value": "vector_store_key",
"cachedResultName": "vector_store_key"
}
},
"typeVersion": 1.2
},
{
"id": "ce86b41b-7e1b-458f-ab13-d6b187854ae8",
"name": "Query Data Tool",
"type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
"position": [
1300,
200
],
"parameters": {
"memoryKey": {
"__rl": true,
"mode": "list",
"value": "vector_store_key"
}
},
"typeVersion": 1.2
},
{
"id": "0039537b-558c-4fe8-9716-f8aa13676f4a",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1280,
-140
],
"parameters": {
"options": {}
},
"typeVersion": 2
},
{
"id": "2669a65e-f0f3-45aa-95c0-621b15a4fc67",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
1060,
-140
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "3d1b3f5a-bc35-4739-a618-9c85820d39a0",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
940,
-180
],
"parameters": {
"color": 7,
"width": 820,
"height": 520,
"content": "### \ud83d\udc15 2. Retriever Flow"
},
"typeVersion": 1
},
{
"id": "6553a758-c655-4196-88be-9d93187f9c7b",
"name": "Answer questions with a vector store",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [
1440,
60
],
"parameters": {
"description": "The data contains custom knowledge"
},
"typeVersion": 1.1
},
{
"id": "d43cf585-4192-4f53-9532-4677923289ba",
"name": "Expensive model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1060,
80
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1",
"cachedResultName": "gpt-4.1"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "18dcc49d-9ce0-4746-8135-7e4c77cfa501",
"name": "Cheap Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1600,
200
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
}
],
"connections": {
"Cheap Model": {
"ai_languageModel": [
[
{
"node": "Answer questions with a vector store",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Expensive model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Query Data Tool": {
"ai_tool": [
[]
],
"ai_vectorStore": [
[
{
"node": "Answer questions with a vector store",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Insert Data to Store",
"type": "ai_embedding",
"index": 0
},
{
"node": "Query Data Tool",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Insert Data to Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Upload your file here": {
"main": [
[
{
"node": "Insert Data to Store",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Answer questions with a vector store": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"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.
openAiApi
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About this workflow
This template shows how to use the Question and Answer tool to save costs in RAG use cases.
Source: https://n8n.io/workflows/5011/ — original creator credit. Request a take-down →
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