{ "workflow": { "id": 4400, "name": "Build a PDF Document RAG System with Mistral OCR, Qdrant and Gemini AI", "views": 41620, "recentViews": 8, "totalViews": 41620, "createdAt": "2025-05-26T12:28:14.350Z", "description": "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: \n\nOnce configured, the workflow automates document ingestion, vectorization, and intelligent querying, enabling powerful RAG applications.\n\n---\n\n### **Benefits**\n\n* **End-to-End Automation**\n No manual interaction is needed: documents are read, processed, and made queryable with minimal setup.\n\n* **Scalable and Modular**\n The workflow uses subflows and batching, making it easy to scale and customize.\n\n* **Multi-Model Support**\n Combines Mistral for OCR, OpenAI for embeddings, and Gemini for intelligent answering—taking advantage of the strengths of each.\n\n* **Real-Time Q\\&A**\n With RAG integration, users can query document content through natural language and receive accurate responses grounded in the PDF data.\n\n* **Light or Full Mode**\n Users can choose to index full page content or only summarized text, optimizing for either performance or richness.\n\n---\n\n### **How It Works** \n\n1. **PDF Processing with Mistral OCR**: \n - The workflow starts by uploading a PDF file to Mistral's API, which performs OCR to extract text and metadata. \n - The extracted content is split into manageable chunks (e.g., pages or sections) for further processing. \n\n2. **Vector Storage in Qdrant**: \n - The extracted text is converted into embeddings using OpenAI's embedding model. \n - These embeddings are stored in a Qdrant vector database, enabling efficient similarity searches for RAG. \n\n3. **Question-Answering with RAG**: \n - When a user submits a question via a chat interface, the workflow retrieves relevant text chunks from Qdrant using vector similarity. \n - A language model (Google Gemini) generates answers based on the retrieved context, providing accurate and context-aware responses. \n\n4. **Optional Summarization**: \n - The workflow includes an optional summarization step using Google Gemini to condense the extracted text for faster processing or lighter RAG usage. \n\n---\n\n### **Set Up Steps** \nTo deploy this workflow in n8n, follow these steps: \n\n1. **Configure Qdrant Database**: \n - Replace `QDRANTURL` and `COLLECTION` in the \"Create collection\" and \"Refresh collection\" nodes with your Qdrant instance details. \n - Ensure the Qdrant collection is configured with the correct vector size (e.g., 1536 for OpenAI embeddings) and distance metric (e.g., Cosine). \n\n2. **Set Up Credentials**: \n - Add credentials for: \n - **Mistral Cloud API** (for OCR processing). \n - **OpenAI API** (for embeddings). \n - **Google Gemini API** (for chat and summarization). \n - **Google Drive** (if sourcing PDFs from Drive). \n - **Qdrant API** (for vector storage). \n\n3. **PDF Source Configuration**: \n - If using Google Drive, specify the folder ID in the \"Search PDFs\" node. \n - Alternatively, modify the workflow to accept PDFs from other sources (e.g., direct uploads or external APIs). \n\n4. **Customize Text Processing**: \n - Adjust chunk size and overlap in the \"Token Splitter\" node to optimize for your document type. \n - Choose between raw text or summarized content for RAG by toggling between the \"Set page\" and \"Summarization Chain\" nodes. \n\n5. **Test the RAG**: \n - Trigger the workflow manually or via a chat message to verify OCR, embedding, and Qdrant storage. \n - Use the \"Question and Answer Chain\" node to test query responses. \n\n6. **Optional Sub-Workflows**: \n - The workflow supports execution as a sub-workflow for batch processing (e.g., handling multiple PDFs). \n\n----\n### **Need help customizing?** \n[Contact me](mailto:info@n3w.it) for consulting and support or add me on [Linkedin](https://www.linkedin.com/in/davideboizza/). ", "workflow": { "id": "IwtOfHq5pZQNDAF0", "meta": { "instanceId": "a4bfc93e975ca233ac45ed7c9227d84cf5a2329310525917adaf3312e10d5462", "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": { "id": "credential-id", "name": "mistralCloudApi 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": { "id": "credential-id", "name": "mistralCloudApi 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": 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"genericCredentialType", "genericAuthType": "httpHeaderAuth", "headerParameters": { "parameters": [ { "name": "Content-Type", "value": "application/json" } ] } }, "credentials": { "httpHeaderAuth": { "id": "credential-id", "name": "httpHeaderAuth 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": { "id": "credential-id", "name": "openAiApi 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 ], "webhookId": "97d60090-1717-4195-bf27-2e3c3105b7f8", "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": { "id": "credential-id", "name": "googlePalmApi 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": { "id": "credential-id", "name": "qdrantApi 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": { "id": "credential-id", "name": "openAiApi 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 ], "webhookId": "1000b40d-5dc5-4795-9dd2-8a23653c2b49", "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": { "id": "credential-id", "name": "qdrantApi 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": { "id": "credential-id", "name": "httpHeaderAuth 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": { "id": "credential-id", "name": "googlePalmApi 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": { "id": "credential-id", "name": "googleDriveOAuth2Api 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": { "id": "credential-id", "name": "googleDriveOAuth2Api 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. 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