Dietmar This workflow corresponds to n8n.io template #7339 — we link there as the canonical source.
This workflow follows the Documentdefaultdataloader → Form Trigger recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"id": "rV1w47cZn1rsk7MP",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "PDF to Vector RAG System: Mistral OCR, Weaviate Database and MCP Server",
"tags": [],
"nodes": [
{
"id": "d9e90589-d6b6-4601-bac8-5009b765fa78",
"name": "Cohere Embeddings",
"type": "@n8n/n8n-nodes-langchain.embeddingsCohere",
"position": [
160,
336
],
"parameters": {
"modelName": "embed-multilingual-v3.0"
},
"typeVersion": 1
},
{
"id": "5e7c6668-64a4-4cc2-b519-ab75f07ecab5",
"name": "Document Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
-144,
336
],
"parameters": {
"options": {},
"textSplittingMode": "custom"
},
"typeVersion": 1.1
},
{
"id": "b808993d-a6b9-497f-88b5-271c16abc185",
"name": "Cohere Reranker",
"type": "@n8n/n8n-nodes-langchain.rerankerCohere",
"position": [
304,
336
],
"parameters": {},
"typeVersion": 1
},
{
"id": "117948ee-4be7-4869-b35b-d0c58a66fcd5",
"name": "MCP Knowledge Server",
"type": "@n8n/n8n-nodes-langchain.mcpTrigger",
"position": [
192,
-80
],
"parameters": {
"path": "c74c97f5-0197-45e3-b4dd-f3efbd4bab22",
"authentication": "headerAuth"
},
"typeVersion": 2
},
{
"id": "700afe32-2bcc-4f31-a680-cdce710861e2",
"name": "Search Knowledge Base",
"type": "@n8n/n8n-nodes-langchain.vectorStoreWeaviate",
"position": [
256,
128
],
"parameters": {
"mode": "retrieve-as-tool",
"options": {},
"useReranker": true,
"toolDescription": "Use this tool to search and retrieve information from the knowledge base containing various documents and resources",
"weaviateCollection": {
"__rl": true,
"mode": "list",
"value": "KnowledgeDocuments",
"cachedResultName": "KnowledgeDocuments"
},
"includeDocumentMetadata": false
},
"typeVersion": 1.3
},
{
"id": "4075cf1b-e9f6-44cc-b827-29fa5eb1ee97",
"name": "Upload Instructions",
"type": "n8n-nodes-base.stickyNote",
"position": [
-880,
-16
],
"parameters": {
"color": 5,
"width": 688,
"height": 304,
"content": "## Manual Document (PDF) Upload Section\nThis section allows users to upload PDF files to the knowledge base. The files will be processed by Mistrals OCR and stored in the vector database for later retrieval and search."
},
"typeVersion": 1
},
{
"id": "bf763fa9-03f4-4010-a6a9-63b6bb104fa1",
"name": "Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
-144,
480
],
"parameters": {
"options": {
"splitCode": "markdown"
},
"chunkSize": 600,
"chunkOverlap": 200
},
"typeVersion": 1
},
{
"id": "d1fe1c74-d9bc-4040-8446-47e90365c5f7",
"name": "Upload PDF",
"type": "n8n-nodes-base.formTrigger",
"position": [
-832,
112
],
"parameters": {
"options": {
"ignoreBots": true,
"buttonLabel": "Upload Document",
"appendAttribution": true
},
"formTitle": "Upload Documents to Knowledge Base",
"formFields": {
"values": [
{
"fieldType": "file",
"fieldLabel": "PDF File",
"multipleFiles": false,
"requiredField": true,
"acceptFileTypes": ".pdf"
}
]
},
"responseMode": "lastNode",
"formDescription": "Upload PDF files to the knowledge base for AI-powered search and retrieval"
},
"typeVersion": 2.2
},
{
"id": "1f04ecce-0277-4a88-9514-fec6b45ba1cf",
"name": "Extract Text from PDF",
"type": "n8n-nodes-base.mistralAi",
"position": [
-608,
112
],
"parameters": {
"options": {},
"binaryProperty": "file"
},
"retryOnFail": true,
"typeVersion": 1
},
{
"id": "53c82051-ce9b-432e-b90e-d5eb83483e49",
"name": "Prepare Document Data",
"type": "n8n-nodes-base.set",
"position": [
-384,
112
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "518ae17b-b486-4438-8151-f49afb3b68eb",
"name": "filename",
"type": "string",
"value": "={{ $('Upload PDF').item.json.file.filename }}"
},
{
"id": "a574ee4d-6341-4fd5-ac8e-9452eff70aa1",
"name": "content",
"type": "string",
"value": "={{ $json.extractedText }}"
},
{
"id": "metadata-source",
"name": "source",
"type": "string",
"value": "uploaded_pdf"
},
{
"id": "metadata-timestamp",
"name": "upload_timestamp",
"type": "string",
"value": "={{ new Date().toISOString() }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "d465c50a-87e9-4824-83ca-d5662630590c",
"name": "Store in Vector Database",
"type": "@n8n/n8n-nodes-langchain.vectorStoreWeaviate",
"position": [
-112,
112
],
"parameters": {
"mode": "insert",
"options": {},
"weaviateCollection": {
"__rl": true,
"mode": "list",
"value": ""
}
},
"typeVersion": 1.3
},
{
"id": "5a5e4028-3764-4bb2-8900-df77c0c47bde",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
112,
304
],
"parameters": {
"color": 4,
"width": 320,
"height": 288,
"content": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n## Embedding and Rerank\nYou can exchange the models, but you **must** use the same model for embedding and retrieval and **no switching** later on"
},
"typeVersion": 1
},
{
"id": "f0ecfbb5-e9ce-49fd-a921-672b12b7ef13",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
112,
-240
],
"parameters": {
"width": 336,
"height": 288,
"content": "## MCP Server Trigger\nYou can call this MCP Server as a tool in your AI Workflow"
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "8b3d2e49-d84f-4fca-bdda-298f3da3c15b",
"connections": {
"Upload PDF": {
"main": [
[
{
"node": "Extract Text from PDF",
"type": "main",
"index": 0
}
]
]
},
"Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Document Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Cohere Reranker": {
"ai_reranker": [
[
{
"node": "Search Knowledge Base",
"type": "ai_reranker",
"index": 0
}
]
]
},
"Document Loader": {
"ai_document": [
[
{
"node": "Store in Vector Database",
"type": "ai_document",
"index": 0
}
]
]
},
"Cohere Embeddings": {
"ai_embedding": [
[
{
"node": "Search Knowledge Base",
"type": "ai_embedding",
"index": 0
},
{
"node": "Store in Vector Database",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Extract Text from PDF": {
"main": [
[
{
"node": "Prepare Document Data",
"type": "main",
"index": 0
}
]
]
},
"Prepare Document Data": {
"main": [
[
{
"node": "Store in Vector Database",
"type": "main",
"index": 0
}
]
]
},
"Search Knowledge Base": {
"ai_tool": [
[
{
"node": "MCP Knowledge Server",
"type": "ai_tool",
"index": 0
}
]
]
}
}
}
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About this workflow
A comprehensive RAG (Retrieval-Augmented Generation) workflow that transforms PDF documents into searchable vector embeddings using advanced AI technologies. PDF Document Processing: Upload and extract text from PDF files using Mistral's OCR capabilities Vector Database Storage:…
Source: https://n8n.io/workflows/7339/ — original creator credit. Request a take-down →
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