Davide Boizza This workflow corresponds to n8n.io template #4400 — 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": "IwtOfHq5pZQNDAF0",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Complete RAG from PDF with Mistral OCR",
"tags": [],
"nodes": [
{
"id": "01a1b5b8-353f-4bfe-8bd0-c5dff1214c86",
"name": "Mistral Upload",
"type": "n8n-nodes-base.httpRequest",
"position": [
180,
520
],
"parameters": {
"url": "https://api.mistral.ai/v1/files",
"method": "POST",
"options": {},
"sendBody": true,
"contentType": "multipart-form-data",
"authentication": "predefinedCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "purpose",
"value": "ocr"
},
{
"name": "file",
"parameterType": "formBinaryData",
"inputDataFieldName": "data"
}
]
},
"nodeCredentialType": "mistralCloudApi"
},
"credentials": {
"mistralCloudApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "45267068-bdbc-45d3-bfe9-354cad73285b",
"name": "Mistral Signed URL",
"type": "n8n-nodes-base.httpRequest",
"position": [
500,
520
],
"parameters": {
"url": "=https://api.mistral.ai/v1/files/{{ $json.id }}/url",
"options": {},
"sendQuery": true,
"sendHeaders": true,
"authentication": "predefinedCredentialType",
"queryParameters": {
"parameters": [
{
"name": "expiry",
"value": "24"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "Accept",
"value": "application/json"
}
]
},
"nodeCredentialType": "mistralCloudApi"
},
"credentials": {
"mistralCloudApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "61416fe4-d5d9-4417-9142-461dd6a45fa4",
"name": "Mistral DOC OCR",
"type": "n8n-nodes-base.httpRequest",
"position": [
820,
520
],
"parameters": {
"url": "https://api.mistral.ai/v1/ocr",
"method": "POST",
"options": {},
"jsonBody": "={\n \"model\": \"mistral-ocr-latest\",\n \"document\": {\n \"type\": \"document_url\",\n \"document_url\": \"{{ $json.url }}\"\n },\n \"include_image_base64\": true\n}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "mistralCloudApi"
},
"credentials": {
"mistralCloudApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "c45ea050-c65b-48b2-b817-d651c3a0de8a",
"name": "When clicking \u2018Test workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-480,
-300
],
"parameters": {},
"typeVersion": 1
},
{
"id": "db931c73-916e-48ce-af17-cd5ab2e7c64d",
"name": "Loop Over Items",
"type": "n8n-nodes-base.splitInBatches",
"position": [
1540,
520
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "76daea49-34f2-4567-8547-4b3d89ce921c",
"name": "Refresh collection",
"type": "n8n-nodes-base.httpRequest",
"position": [
-140,
-300
],
"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"
}
]
}
},
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "ac7c469e-d0e8-4146-b323-f3948c3331fa",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
2320,
800
],
"parameters": {
"options": {
"stripNewLines": false
}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "67591961-ef29-4bef-9905-8be18d5e9814",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
2460,
760
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "54c38164-6492-42d6-8b44-e10bbfdcd807",
"name": "Token Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterTokenSplitter",
"position": [
2560,
940
],
"parameters": {
"chunkSize": 400,
"chunkOverlap": 40
},
"typeVersion": 1
},
{
"id": "661137fc-7afa-49a8-900c-a8c7fd63f557",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-480,
960
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "fe971655-b323-4ccf-a6c5-764d7cc3d8bc",
"name": "Question and Answer Chain",
"type": "@n8n/n8n-nodes-langchain.chainRetrievalQa",
"position": [
-160,
960
],
"parameters": {
"options": {}
},
"typeVersion": 1.5
},
{
"id": "8806cf11-83e8-4c75-ba96-71a62d1fb632",
"name": "Google Gemini Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
-220,
1160
],
"parameters": {
"options": {},
"modelName": "models/gemini-1.5-flash"
},
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "8012569d-5e29-4f36-89be-394ac4928194",
"name": "Vector Store Retriever",
"type": "@n8n/n8n-nodes-langchain.retrieverVectorStore",
"position": [
0,
1180
],
"parameters": {},
"typeVersion": 1
},
{
"id": "f33e13dd-2465-4971-bcbf-d2d9461f5453",
"name": "Qdrant Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
20,
1380
],
"parameters": {
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "ocr_mistral_test",
"cachedResultName": "ocr_mistral_test"
}
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "f864b8c1-8b4f-4116-812a-843aea0347ac",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
-100,
1520
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "48799279-d580-4c3f-ab39-e2da1bf1d577",
"name": "Code",
"type": "n8n-nodes-base.code",
"position": [
1140,
520
],
"parameters": {
"jsCode": "const data = $json.pages;\n\nreturn data.map(entry => ({\n json: {\n markdown: entry.markdown\n }\n}));"
},
"typeVersion": 2
},
{
"id": "5c68cd54-3841-46e0-9b2e-05a1188e92c0",
"name": "Wait",
"type": "n8n-nodes-base.wait",
"position": [
2860,
540
],
"parameters": {},
"typeVersion": 1.1
},
{
"id": "0853153e-33a3-44a8-ab22-c296b7aab892",
"name": "Qdrant Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
2380,
540
],
"parameters": {
"mode": "insert",
"options": {
"collectionConfig": ""
},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "ocr_mistral_test",
"cachedResultName": "ocr_mistral_test"
}
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "4123fa10-ec36-40cd-ad85-b8353479db28",
"name": "Loop Over Items1",
"type": "n8n-nodes-base.splitInBatches",
"position": [
540,
-300
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "16fadeb2-5612-421e-8da3-37bed966d966",
"name": "Execute Workflow",
"type": "n8n-nodes-base.executeWorkflow",
"position": [
1160,
-280
],
"parameters": {
"mode": "each",
"options": {
"waitForSubWorkflow": true
},
"workflowId": {
"__rl": true,
"mode": "list",
"value": "AdVUaHTE9Jk1KO72",
"cachedResultName": "Mistral OCR_subworkflow"
},
"workflowInputs": {
"value": {},
"schema": [],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": true
}
},
"typeVersion": 1.2
},
{
"id": "0f61b1ab-45a3-4167-a4f1-baeca03109bd",
"name": "When Executed by Another Workflow",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
-460,
520
],
"parameters": {
"inputSource": "passthrough"
},
"typeVersion": 1.1
},
{
"id": "034fc82c-b13a-4e72-ae86-97676d5b8867",
"name": "Edit Fields1",
"type": "n8n-nodes-base.set",
"position": [
860,
-280
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "ca7c30f2-444d-4551-988d-0f513e5ee4b1",
"name": "file_id",
"type": "string",
"value": "={{ $json.id }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "0c5d144c-77de-4d3b-a30b-3674ea858f3e",
"name": "Create collection",
"type": "n8n-nodes-base.httpRequest",
"position": [
-480,
-760
],
"parameters": {
"url": "http://QDRANTURL/collections/COLLECTION",
"method": "PUT",
"options": {},
"jsonBody": "{\n \"vectors\": {\n \"size\": 1536,\n \"distance\": \"Cosine\" \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"
}
]
}
},
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "5dab4a20-0daa-43f2-8878-eae8720aa50f",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-280,
-820
],
"parameters": {
"color": 6,
"width": 880,
"height": 220,
"content": "# STEP 1\n\n## Create Qdrant Collection\nChange:\n- QDRANTURL\n- COLLECTION"
},
"typeVersion": 1
},
{
"id": "b78c160a-0e73-4fa0-9015-d8b2a249fd33",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-540,
-400
],
"parameters": {
"color": 4,
"width": 620,
"height": 520,
"content": "# STEP 2\n\n\n\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": "8988f19e-259b-4e01-833a-f8ed2f89c7d6",
"name": "Summarization Chain",
"type": "@n8n/n8n-nodes-langchain.chainSummarization",
"position": [
1820,
140
],
"parameters": {
"options": {
"summarizationMethodAndPrompts": {
"values": {
"prompt": "Write a concise summary of the following (in italiano):\n\n\n\"{text}\"\n\n\nCONCISE SUMMARY:",
"combineMapPrompt": "Write a concise summary of the following (in italiano):\n\n\n\"{text}\"\n\n\nCONCISE SUMMARY:"
}
}
}
},
"typeVersion": 2
},
{
"id": "b7581380-5431-42f1-b4de-279faf4bdf16",
"name": "Google Gemini Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
1840,
320
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash-exp"
},
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "037896c0-7561-4ca1-9130-adc1175408b5",
"name": "Set page",
"type": "n8n-nodes-base.set",
"position": [
2000,
540
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "189f4944-a692-423c-bc6d-76747e1d04df",
"name": "text",
"type": "string",
"value": "={{ $json.markdown }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "06444ac1-382a-41df-8ff7-a1a5fea9e6ec",
"name": "Set summary",
"type": "n8n-nodes-base.set",
"position": [
2180,
140
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "189f4944-a692-423c-bc6d-76747e1d04df",
"name": "text",
"type": "string",
"value": "={{ $json.response.text }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "00bd51e2-a969-415a-9fca-f67bf9df96dc",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
1760,
20
],
"parameters": {
"width": 600,
"height": 680,
"content": "## STEP 3\nIf you want a \"light\" and faster rag with the main contents replace the \"Set page\" node with \"Summarization Chain\""
},
"typeVersion": 1
},
{
"id": "da71f8f9-bff9-48e1-b2e4-9fbbe7ac8924",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
320,
960
],
"parameters": {
"color": 2,
"width": 500,
"height": 120,
"content": "## STEP 4\nTest the RAG"
},
"typeVersion": 1
},
{
"id": "b0c57120-6b07-4823-9341-c3f42ca313f5",
"name": "Search PDFs",
"type": "n8n-nodes-base.googleDrive",
"position": [
200,
-300
],
"parameters": {
"filter": {
"folderId": {
"__rl": true,
"mode": "list",
"value": "1LWVo3yn_1bWQJsLskBIbWTGwlfObvtUK",
"cachedResultUrl": "https://drive.google.com/drive/folders/1LWVo3yn_1bWQJsLskBIbWTGwlfObvtUK",
"cachedResultName": "PDFs"
}
},
"options": {},
"resource": "fileFolder"
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 3
},
{
"id": "26990fbc-3315-42d5-948d-3b670e7d4f84",
"name": "Get PDF",
"type": "n8n-nodes-base.googleDrive",
"position": [
-140,
520
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.file_id }}"
},
"options": {},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 3
},
{
"id": "013e49fe-7ee2-4d24-b640-dda63fa034b3",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-540,
-1080
],
"parameters": {
"width": 1140,
"height": 140,
"content": "## Complete RAG system from PDF Documents with Mistral OCR, Qdrant and Gemini AI\n\nThis workflow is designed to process PDF documents using Mistral's OCR capabilities, store the extracted text in a Qdrant vector database, and enable Retrieval-Augmented Generation (RAG) for answering questions. "
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "45bfd9c5-ea80-49f9-bc3a-d2719a16b363",
"connections": {
"Code": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Wait": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Get PDF": {
"main": [
[
{
"node": "Mistral Upload",
"type": "main",
"index": 0
}
]
]
},
"Set page": {
"main": [
[
{
"node": "Qdrant Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Search PDFs": {
"main": [
[
{
"node": "Loop Over Items1",
"type": "main",
"index": 0
}
]
]
},
"Edit Fields1": {
"main": [
[
{
"node": "Execute Workflow",
"type": "main",
"index": 0
}
]
]
},
"Mistral Upload": {
"main": [
[
{
"node": "Mistral Signed URL",
"type": "main",
"index": 0
}
]
]
},
"Token Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "Set page",
"type": "main",
"index": 0
}
]
]
},
"Mistral DOC OCR": {
"main": [
[
{
"node": "Code",
"type": "main",
"index": 0
}
]
]
},
"Execute Workflow": {
"main": [
[
{
"node": "Loop Over Items1",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items1": {
"main": [
[],
[
{
"node": "Edit Fields1",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Mistral Signed URL": {
"main": [
[
{
"node": "Mistral DOC OCR",
"type": "main",
"index": 0
}
]
]
},
"Refresh collection": {
"main": [
[
{
"node": "Search PDFs",
"type": "main",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Qdrant Vector Store": {
"main": [
[
{
"node": "Wait",
"type": "main",
"index": 0
}
]
]
},
"Summarization Chain": {
"main": [
[
{
"node": "Set summary",
"type": "main",
"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
}
]
]
},
"Google Gemini Chat Model1": {
"ai_languageModel": [
[
{
"node": "Summarization Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Question and Answer Chain",
"type": "main",
"index": 0
}
]
]
},
"When Executed by Another Workflow": {
"main": [
[
{
"node": "Get PDF",
"type": "main",
"index": 0
}
]
]
},
"When clicking \u2018Test workflow\u2019": {
"main": [
[
{
"node": "Refresh collection",
"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.
googleDriveOAuth2ApigooglePalmApihttpHeaderAuthmistralCloudApiopenAiApiqdrantApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
This workflow is designed to process PDF documents using Mistral's OCR capabilities, store the extracted text in a Qdrant vector database, and enable Retrieval-Augmented Generation (RAG) for answering questions. Here’s how it functions:
Source: https://n8n.io/workflows/4400/ — original creator credit. Request a take-down →
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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
Code Extractfromfile. Uses manualTrigger, sort, httpRequest, compression. Event-driven trigger; 50 nodes.
HTTP Request, Compression, Edit Image +15