Davide Boizza This workflow corresponds to n8n.io template #7647 — we link there as the canonical source.
This workflow follows the Chainretrievalqa → Retrievervectorstore 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": "noMm1EH7z2ZrM2vt",
"name": "Complete RAG System with autoUpdate documents Using Qdrant",
"tags": [],
"nodes": [
{
"id": "40fb5a5b-cd99-4f4c-9a10-28bbb6bb56c4",
"name": "When clicking \u2018Test workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
32,
496
],
"parameters": {},
"typeVersion": 1
},
{
"id": "a86c5bdc-036e-4122-a4a8-24d831d72559",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
1424,
896
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "f7e9543c-1d4c-4e5a-bc5e-ba528f5c59c7",
"name": "Default Data Loader1",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
1568,
896
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "file_id",
"value": "={{ $('Get files').item.json.id }}"
},
{
"name": "file_name",
"value": "={{ $('Get files').item.json.name }}"
}
]
}
},
"dataType": "binary",
"binaryMode": "specificField"
},
"typeVersion": 1
},
{
"id": "38e08de0-2fc6-4a5e-a0c7-77388c979c65",
"name": "Create collection",
"type": "n8n-nodes-base.httpRequest",
"position": [
336,
368
],
"parameters": {
"url": "http:///collections/test_sparse",
"method": "PUT",
"options": {},
"jsonBody": "{\n \"vectors\": {\n \"size\": 1536,\n \"distance\": \"Cosine\" \n },\n \"sparse_vectors\": {\n \"text\": { }\n },\n \"shard_number\": 1, \n \"replication_factor\": 1, \n \"write_consistency_factor\": 1 \n}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "49bfc3c7-b697-4dfe-bfec-e1c7e681745a",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
1568,
1088
],
"parameters": {
"options": {},
"chunkSize": 500,
"chunkOverlap": 50
},
"typeVersion": 1
},
{
"id": "bf6b734b-6918-4a85-bdbd-272180be0574",
"name": "Loop Over Items",
"type": "n8n-nodes-base.splitInBatches",
"position": [
912,
640
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "fa9378dd-5c45-459f-b859-3788bf4d44fc",
"name": "Embeddings OpenAI2",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
992,
1840
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "c74ef2e9-3b29-4e75-87a4-4c8997d4ed63",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
1120,
1840
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "file_id",
"value": "={{ $('Get file').item.json.file_id }}"
},
{
"name": "file_name",
"value": "={{ $binary.data.fileName}}"
}
]
}
},
"dataType": "binary",
"binaryMode": "specificField"
},
"typeVersion": 1
},
{
"id": "930d03e9-15e2-42ef-aa32-c7bd611abfb9",
"name": "Recursive Character Text Splitter1",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
1104,
2048
],
"parameters": {
"options": {},
"chunkSize": 500,
"chunkOverlap": 50
},
"typeVersion": 1
},
{
"id": "fbbc9557-88fb-4051-a15a-8d0a4155ebc7",
"name": "Set file_id",
"type": "n8n-nodes-base.set",
"position": [
384,
1296
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "b413a226-0641-4ed8-9951-d17b6a6a9a4b",
"name": "file_id",
"type": "string",
"value": "={{ $json.id }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "d9fcd8ee-9580-4e0d-aa01-a973d4a28755",
"name": "Clear collection",
"type": "n8n-nodes-base.httpRequest",
"position": [
336,
640
],
"parameters": {
"url": "http:/YOUR_AWS_SECRET_KEY_HERE/delete",
"method": "POST",
"options": {},
"jsonBody": "{\n \"filter\": {}\n}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "c2850362-f822-4a42-9bce-0e3719a0cb4c",
"name": "Delete points by file_id",
"type": "n8n-nodes-base.httpRequest",
"position": [
784,
1184
],
"parameters": {
"url": "http:/YOUR_AWS_SECRET_KEY_HERE/delete",
"method": "POST",
"options": {},
"jsonBody": "={\n \"filter\": {\n \"must\": [\n {\n \"key\": \"metadata.file_id\",\n \"match\": { \"value\": \"{{$json.file_id}}\" }\n }\n ]\n }\n}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "7d3398ca-a2f8-4f2e-8b70-fdc6d4c58cc0",
"name": "Search files",
"type": "n8n-nodes-base.googleDrive",
"position": [
608,
640
],
"parameters": {
"filter": {
"driveId": {
"__rl": true,
"mode": "list",
"value": "My Drive",
"cachedResultUrl": "https://drive.google.com/drive/my-drive",
"cachedResultName": "My Drive"
},
"folderId": {
"__rl": true,
"mode": "list",
"value": "1RO5ByPhq2yvYLmbapTNC_kKdU5lZd4W5",
"cachedResultUrl": "https://drive.google.com/drive/folders/1RO5ByPhq2yvYLmbapTNC_kKdU5lZd4W5",
"cachedResultName": "Test Negozio"
}
},
"options": {},
"resource": "fileFolder"
},
"typeVersion": 3
},
{
"id": "f6b8f801-4079-4a47-9d9f-2beb3ffd8f18",
"name": "Wait 5 sec.",
"type": "n8n-nodes-base.wait",
"position": [
1872,
656
],
"parameters": {},
"typeVersion": 1.1
},
{
"id": "d2925096-3c92-41cb-ab22-eeb8a0b3d5bc",
"name": "Update file",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
1024,
1648
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "negozio-emporio-verde",
"cachedResultName": "negozio-emporio-verde"
}
},
"typeVersion": 1
},
{
"id": "6b53ea53-3fb2-4049-acbb-2c90e5e679ce",
"name": "Insert file",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
1488,
656
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "negozio-emporio-verde",
"cachedResultName": "negozio-emporio-verde"
}
},
"typeVersion": 1
},
{
"id": "6019beec-0f0a-4767-a1d4-048a7763bf21",
"name": "Get file",
"type": "n8n-nodes-base.googleDrive",
"position": [
784,
1648
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.file_id }}"
},
"options": {
"googleFileConversion": {
"conversion": {
"docsToFormat": "text/plain"
}
}
},
"operation": "download"
},
"typeVersion": 3
},
{
"id": "ee9f2b69-b28e-4c77-bbac-7a4c62748132",
"name": "Get files",
"type": "n8n-nodes-base.googleDrive",
"position": [
1184,
656
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.id }}"
},
"options": {
"googleFileConversion": {
"conversion": {
"docsToFormat": "text/plain"
}
}
},
"operation": "download"
},
"typeVersion": 3
},
{
"id": "21fdbbf6-2643-4918-8adb-8f57ee5e194e",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
0,
0
],
"parameters": {
"color": 3,
"width": 840,
"height": 220,
"content": "## Complete RAG System with autoUpdate documents Using Qdrant\n\nThis workflow implements a **Retrieval-Augmented Generation (RAG)** system that:\n\n* Stores vectorized documents in **Qdrant**,\n* Retrieves relevant content based on user input,\n* Generates AI answers using **Google Gemini**,\n* Automatically **update documents** (from Google Drive).\n"
},
"typeVersion": 1
},
{
"id": "a3c10de1-7559-4bf9-951b-0ccad7cf62c5",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
96,
2400
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "3e295eb8-7b41-4f9c-9489-5097442c9a00",
"name": "Question and Answer Chain",
"type": "@n8n/n8n-nodes-langchain.chainRetrievalQa",
"position": [
416,
2400
],
"parameters": {
"options": {}
},
"typeVersion": 1.5
},
{
"id": "43e4676f-ee0a-444c-9bed-0271a3392a19",
"name": "Google Gemini Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
304,
2640
],
"parameters": {
"options": {},
"modelName": "models/gemini-1.5-flash"
},
"typeVersion": 1
},
{
"id": "deba1a75-e1f3-4830-95f0-2f29503d5ab8",
"name": "Vector Store Retriever",
"type": "@n8n/n8n-nodes-langchain.retrieverVectorStore",
"position": [
544,
2688
],
"parameters": {
"topK": 5
},
"typeVersion": 1
},
{
"id": "eb2859b7-7c69-4376-a183-aa18cfd2ff34",
"name": "Qdrant Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
416,
2896
],
"parameters": {
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "negozio-emporio-verde",
"cachedResultName": "negozio-emporio-verde"
}
},
"typeVersion": 1.1
},
{
"id": "030a0e51-8d06-422e-bb8a-38805c2bf6e0",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
416,
3088
],
"parameters": {
"options": {}
},
"typeVersion": 1.2
},
{
"id": "f2659705-08a2-401c-b157-fe9fcb6ac41d",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
1888,
896
],
"parameters": {
"width": 520,
"height": 420,
"content": "Set as metadata:\n- FILE_ID from Google Drive\n- FILE_NAME from Google Drive\n\n```\n{\n \"source\": \"blob\",\n \"blobType\": \"text/plain\",\n \"loc\": {\n \"lines\": {\n \"from\": 1,\n \"to\": 15\n }\n },\n \"file_id\": \"xxxxxxxxxxxxxxxxxxxxxxxxxx\",\n \"file_name\": \"FAQ\"\n}\n```\n\n\n"
},
"typeVersion": 1
},
{
"id": "9af3ccb4-3056-496b-9cf1-9d40812c8356",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
240,
560
],
"parameters": {
"color": 4,
"width": 540,
"height": 460,
"content": "# STEP 2\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n## Documents vectorization with Qdrant and Google Drive\nChange:\n- QDRANTURL\n- COLLECTION"
},
"typeVersion": 1
},
{
"id": "b8eddec7-7228-4c2d-9fe0-eddc86018fa9",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
560,
288
],
"parameters": {
"color": 6,
"width": 880,
"height": 220,
"content": "# STEP 1\n\n## Create Qdrant Collection\nChange:\n- QDRANTURL\n- COLLECTION"
},
"typeVersion": 1
},
{
"id": "916c4619-3212-4027-87d5-832892e9db6e",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1408,
1808
],
"parameters": {
"width": 520,
"height": 420,
"content": "Set as metadata:\n- FILE_ID from Google Drive\n- FILE_NAME from Google Drive\n\n```\n{\n \"source\": \"blob\",\n \"blobType\": \"text/plain\",\n \"loc\": {\n \"lines\": {\n \"from\": 1,\n \"to\": 15\n }\n },\n \"file_id\": \"xxxxxxxxxxxxxxxxxxxxxxxxxx\",\n \"file_name\": \"FAQ\"\n}\n```\n\n\n"
},
"typeVersion": 1
},
{
"id": "a9cb8a81-b021-4a85-b0bd-0c60e0e2987c",
"name": "Update?",
"type": "n8n-nodes-base.googleDriveTrigger",
"position": [
64,
1296
],
"parameters": {
"event": "fileUpdated",
"options": {
"fileType": "all"
},
"pollTimes": {
"item": [
{
"mode": "everyHour"
}
]
},
"triggerOn": "specificFolder",
"folderToWatch": {
"__rl": true,
"mode": "list",
"value": "1RO5ByPhq2yvYLmbapTNC_kKdU5lZd4W5",
"cachedResultUrl": "https://drive.google.com/drive/folders/1RO5ByPhq2yvYLmbapTNC_kKdU5lZd4W5",
"cachedResultName": "Test Negozio"
}
},
"typeVersion": 1
},
{
"id": "dde138e9-7a07-4913-97d0-9f9106c9f1f2",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
640,
1104
],
"parameters": {
"color": 4,
"width": 400,
"height": 440,
"content": "# STEP 3\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n## Documents vectorization with Qdrant and Google Drive\nChange:\n- QDRANTURL\n- COLLECTION"
},
"typeVersion": 1
},
{
"id": "f438eace-d66c-4835-90d0-ac39e97ece38",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
80,
2240
],
"parameters": {
"color": 2,
"width": 580,
"height": 120,
"content": "# STEP 4\n\nTry RAG"
},
"typeVersion": 1
},
{
"id": "67fb6630-abdc-431e-8073-9fbd700a9d20",
"name": "Sticky Note8",
"type": "n8n-nodes-base.stickyNote",
"position": [
-784,
0
],
"parameters": {
"color": 7,
"width": 736,
"height": 736,
"content": "## MY NEW YOUTUBE CHANNEL\n\ud83d\udc49 [Subscribe to my new **YouTube channel**](https://youtube.com/@n3witalia). Here I\u2019ll share videos and Shorts with practical tutorials and **FREE templates for n8n**.\n\n[](https://youtube.com/@n3witalia)"
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"binaryMode": "separate",
"availableInMCP": false,
"executionOrder": "v1"
},
"versionId": "0def612b-079f-47e4-9f22-ec28957f9987",
"connections": {
"Update?": {
"main": [
[
{
"node": "Set file_id",
"type": "main",
"index": 0
}
]
]
},
"Get file": {
"main": [
[
{
"node": "Update file",
"type": "main",
"index": 0
}
]
]
},
"Get files": {
"main": [
[
{
"node": "Insert file",
"type": "main",
"index": 0
}
]
]
},
"Insert file": {
"main": [
[
{
"node": "Wait 5 sec.",
"type": "main",
"index": 0
}
]
]
},
"Set file_id": {
"main": [
[
{
"node": "Delete points by file_id",
"type": "main",
"index": 0
},
{
"node": "Get file",
"type": "main",
"index": 0
}
]
]
},
"Wait 5 sec.": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Search files": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "Get files",
"type": "main",
"index": 0
}
]
]
},
"Clear collection": {
"main": [
[
{
"node": "Search files",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Insert file",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Embeddings OpenAI2": {
"ai_embedding": [
[
{
"node": "Update file",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Update file",
"type": "ai_document",
"index": 0
}
]
]
},
"Default Data Loader1": {
"ai_document": [
[
{
"node": "Insert file",
"type": "ai_document",
"index": 0
}
]
]
},
"Qdrant Vector Store1": {
"ai_vectorStore": [
[
{
"node": "Vector Store Retriever",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Vector Store Retriever": {
"ai_retriever": [
[
{
"node": "Question and Answer Chain",
"type": "ai_retriever",
"index": 0
}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "Question and Answer Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Question and Answer Chain",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader1",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"When clicking \u2018Test workflow\u2019": {
"main": [
[
{
"node": "Create collection",
"type": "main",
"index": 0
},
{
"node": "Clear collection",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter1": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
}
}
}
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
This workflow implements a Retrieval-Augmented Generation (RAG) system that integrates Google Drive and Qdrant.
Source: https://n8n.io/workflows/7647/ — original creator credit. Request a take-down →
Related workflows
Workflows that share integrations, category, or trigger type with this one. All free to copy and import.
Api Schema Extractor. Uses manualTrigger, httpRequest, splitOut, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 88 nodes.
HTTP Request, Text Splitter Recursive Character Text Splitter, Document Default Data Loader +9
Wait Splitout. Uses manualTrigger, httpRequest, splitOut, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 88 nodes.
HTTP Request, Text Splitter Recursive Character Text Splitter, Document Default Data Loader +9
This workflow automates the process of discovering and extracting APIs from various services, followed by generating custom schemas. It works in three distinct stages: research, extraction, and schema
HTTP Request, Text Splitter Recursive Character Text Splitter, Document Default Data Loader +9
This comprehensive workflow bundle is designed as a powerful starter kit, enabling you to build a multi-functional AI assistant on Telegram. It seamlessly integrates AI-powered voice interactions, an
Telegram Trigger, Telegram, OpenAI +19
RAG_Ingest. Uses httpRequest, vectorStoreSupabase, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 73 nodes.
HTTP Request, Supabase Vector Store, Document Default Data Loader +4