This workflow corresponds to n8n.io template #rag-starter-template — 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 →
{
"updatedAt": "2026-02-04T23:33:39.609Z",
"createdAt": "2026-01-23T23:04:15.366Z",
"id": "yUalubL2RY9cmka1",
"name": "Demo: RAG in n8n",
"active": false,
"isArchived": false,
"nodes": [
{
"parameters": {
"formTitle": "Upload your data to test RAG",
"formFields": {
"values": [
{
"fieldLabel": "Upload your file(s)",
"fieldType": "file",
"acceptFileTypes": ".pdf, .csv",
"requiredField": true
}
]
},
"options": {}
},
"type": "n8n-nodes-base.formTrigger",
"typeVersion": 2.2,
"position": [
-128,
0
],
"id": "f7a656ec-83fc-4ed2-a089-57a9def662b7",
"name": "Upload your file here"
},
{
"parameters": {
"dataType": "binary",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"typeVersion": 1.1,
"position": [
336,
240
],
"id": "94aecac0-03f9-4915-932b-d14a2576607b",
"name": "Default Data Loader"
},
{
"parameters": {
"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/).",
"height": 300,
"width": 440,
"color": 4
},
"type": "n8n-nodes-base.stickyNote",
"position": [
-656,
-64
],
"typeVersion": 1,
"id": "0d07742b-0b36-4c2e-990c-266cbe6e2d4d",
"name": "Sticky Note"
},
{
"parameters": {
"content": "### \ud83d\udcda Load Data Flow",
"height": 460,
"width": 1068,
"color": 7
},
"type": "n8n-nodes-base.stickyNote",
"position": [
-192,
-64
],
"typeVersion": 1,
"id": "d19d04f3-5231-4e47-bed7-9f24a4a8f582",
"name": "Sticky Note1"
},
{
"parameters": {
"mode": "insert",
"memoryKey": {
"__rl": true,
"value": "vector_store_key",
"mode": "list",
"cachedResultName": "vector_store_key"
}
},
"type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
"typeVersion": 1.2,
"position": [
512,
-16
],
"id": "bf50a11f-ca6a-4e04-a6d2-42fee272b260",
"name": "Insert Data to Store",
"disabled": true
},
{
"parameters": {
"mode": "retrieve-as-tool",
"toolName": "knowledge_base",
"toolDescription": "Use this knowledge base to answer questions from the user",
"memoryKey": {
"__rl": true,
"mode": "list",
"value": "vector_store_key"
}
},
"type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
"typeVersion": 1.2,
"position": [
1312,
208
],
"id": "09c0db62-5413-440e-8c13-fb6bb66d9b6a",
"name": "Query Data Tool"
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 2,
"position": [
1312,
-16
],
"id": "579aed76-9644-42d1-ac13-7369059ff1c2",
"name": "AI Agent"
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"typeVersion": 1.1,
"position": [
1088,
-16
],
"id": "9c30de61-935a-471f-ae88-ec5f67beeefc",
"name": "When chat message received"
},
{
"parameters": {
"content": "### \ud83d\udc15 2. Retriever Flow",
"height": 460,
"width": 680,
"color": 7
},
"type": "n8n-nodes-base.stickyNote",
"position": [
976,
-64
],
"typeVersion": 1,
"id": "28bc73a1-e64a-47bf-ac1c-ffe644894ea5",
"name": "Sticky Note2"
},
{
"parameters": {
"content": "### Embeddings\n\nThe Insert and Retrieve operation use the same embedding node.\n\nThis is to ensure that they are using the **exact same embeddings and settings**.\n\nDifferent embeddings might not work at all, or have unintended consequences.\n",
"height": 240,
"width": 320,
"color": 4
},
"type": "n8n-nodes-base.stickyNote",
"position": [
1040,
448
],
"typeVersion": 1,
"id": "0cf8c647-418c-4d1a-8952-766145afca72",
"name": "Sticky Note3"
},
{
"parameters": {
"model": "nomic-embed-text:latest"
},
"type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
"typeVersion": 1,
"position": [
800,
592
],
"id": "f425b6e3-1b80-40a9-8d5f-811c4613b33f",
"name": "Embeddings Ollama",
"credentials": {
"ollamaApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"model": "glm-4.7:cloud",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOllama",
"typeVersion": 1,
"position": [
1104,
224
],
"id": "6a472bb9-6d93-4a7c-b2d6-1b701ac8a897",
"name": "Ollama Chat Model",
"credentials": {
"ollamaApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"mode": "insert",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
"typeVersion": 1.3,
"position": [
160,
16
],
"id": "b647f5fd-67a6-4dfe-90cd-29238dbd95fe",
"name": "Postgres PGVector Store",
"credentials": {
"postgres": {
"name": "<your credential>"
}
}
}
],
"connections": {
"Upload your file here": {
"main": [
[
{
"node": "Postgres PGVector Store",
"type": "main",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Postgres PGVector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Query Data Tool": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Embeddings Ollama": {
"ai_embedding": [
[
{
"node": "Query Data Tool",
"type": "ai_embedding",
"index": 0
},
{
"node": "Postgres PGVector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Ollama Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Postgres PGVector Store": {
"main": [
[]
]
}
},
"settings": {
"executionOrder": "v1",
"availableInMCP": false
},
"staticData": null,
"meta": {
"templateId": "rag-starter-template",
"templateCredsSetupCompleted": true
},
"versionId": "e43c9354-34e4-42f1-99d5-b2bce2b3cb7e",
"activeVersionId": null,
"triggerCount": 0,
"shared": [
{
"updatedAt": "2026-01-23T23:04:15.366Z",
"createdAt": "2026-01-23T23:04:15.366Z",
"role": "workflow:owner",
"workflowId": "yUalubL2RY9cmka1",
"projectId": "aRJv9cLftn98cx8V"
}
],
"activeVersion": null,
"tags": []
}
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.
ollamaApipostgres
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
Demo: RAG in n8n. Uses formTrigger, documentDefaultDataLoader, vectorStoreInMemory, agent. Event-driven trigger; 13 nodes.
Source: https://github.com/ATHARVISM2804/n8n_workflow_main/blob/main/workflows/demo_-rag-in-n8n-.json — original creator credit. Request a take-down →
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