{ "workflow": { "id": 5023, "name": "Build a RAG system with automatic citations using Qdrant, Gemini & OpenAI", "views": 2588, "recentViews": 1, "totalViews": 2588, "createdAt": "2025-06-18T14:37:45.594Z", "description": "\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 **cites the document sources** (from Google Drive).\n\n---\n\n### **Workflow Steps**\n\n1. **Create Qdrant Collection**\n A REST API node creates a new collection in Qdrant with specified vector size (1536) and cosine similarity.\n\n2. **Load Files from Google Drive**\n The workflow lists all files in a Google Drive folder, downloads them as plain text, and loops through each.\n\n3. **Text Preprocessing & Embedding**\n\n * Documents are split into chunks (500 characters, with 50-character overlap).\n * Embeddings are created using **OpenAI embeddings** (`text-embedding-3-small` assumed).\n * Metadata (file name and ID) is attached to each chunk.\n\n4. **Store in Qdrant**\n All vectors, along with metadata, are inserted into the Qdrant collection.\n\n5. **Chat Input & Retrieval**\n\n * When a chat message is received, the question is embedded and matched against Qdrant.\n * Top 5 relevant document chunks are retrieved.\n * A Gemini model is used to generate the answer based on those sources.\n\n6. **Source Aggregation & Response**\n\n * File IDs and names are deduplicated.\n * The AI response is combined with a list of cited documents (filenames).\n * Final output:\n\n ```\n AI Response\n\n Sources: [\"Document1\", \"Document2\"]\n ```\n\n---\n\n### **Main Advantages**\n\n* **End-to-end Automation**: From document ingestion to chat response generation, fully automated with no manual steps.\n* **Scalable Knowledge Base**: Easy to expand by simply adding files to the Google Drive folder.\n* **Traceable Responses**: Each answer includes its source files, increasing transparency and trustworthiness.\n* **Modular Design**: Each step (embedding, storage, retrieval, response) is isolated and reusable.\n* **Multi-provider AI**: Combines OpenAI (for embeddings) and Google Gemini (for chat), optimizing performance and flexibility.\n* **Secure & Customizable**: Uses API credentials and configurable chunk size, collection name, etc.\n\n---\n\n\n### **How It Works** \n1. **Document Processing & Vectorization** \n - The workflow retrieves documents from a specified Google Drive folder. \n - Each file is downloaded, split into chunks (using a recursive text splitter), and converted into embeddings via OpenAI. \n - The embeddings, along with metadata (file ID and name), are stored in a Qdrant vector database under the collection `negozio-emporio-verde`. \n\n2. **Query Handling & Response Generation** \n - When a user submits a chat message, the workflow: \n - Embeds the query using OpenAI. \n - Retrieves the top 5 relevant document chunks from Qdrant. \n - Uses Google Gemini to generate a response based on the retrieved context. \n - Aggregates and deduplicates the source file names from the retrieved chunks. \n - The final output includes both the AI-generated response and a list of source documents (e.g., `Sources: [\"FAQ.pdf\", \"Policy.txt\"]`). \n\n--- \n\n### **Set Up Steps** \n1. **Configure Qdrant Collection** \n - Replace `QDRANTURL` and `COLLECTION` in the **\"Create collection\"** HTTP node to initialize the Qdrant collection with: \n - Vector size: `1536` (OpenAI embedding dimension). \n - Distance metric: `Cosine`. \n - Ensure the **\"Clear collection\"** node is configured to reset the collection if needed. \n\n2. **Google Drive & OpenAI Integration** \n - Link the Google Drive node to the target folder (`Test Negozio` in this example). \n - Verify OpenAI and Google Gemini API credentials are correctly set in their respective nodes. \n\n3. **Metadata & Output Customization** \n - Adjust the **\"Aggregate\"** and **\"Response\"** nodes if additional metadata fields are needed. \n - Modify the **\"Output\"** node to format the response (e.g., changing `Sources: {{...}}` to match your preferred style). \n\n4. **Testing** \n - Trigger the workflow manually to test document ingestion. \n - Use the chat interface to verify responses include accurate source attribution. \n\n**Note**: Replace placeholder values (e.g., `QDRANTURL`) with actual endpoints before deployment.\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": "OAvKQCYsly0DTlci", "meta": { "instanceId": "a4bfc93e975ca233ac45ed7c9227d84cf5a2329310525917adaf3312e10d5462", "templateCredsSetupCompleted": true }, "name": "Complete RAG System with Automatic Source Citations Using Qdrant", "tags": [], "nodes": [ { "id": "65d3e882-ef84-4b3b-88b0-bb69bbe6886f", "name": "When clicking ‘Test workflow’", "type": "n8n-nodes-base.manualTrigger", "position": [ -400, 220 ], "parameters": {}, "typeVersion": 1 }, { "id": "db531b5a-588a-4982-aa00-e565d6f5610b", "name": "Embeddings OpenAI1", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "position": [ 940, 480 ], "parameters": { "options": {} }, "credentials": { "openAiApi": { "id": "credential-id", "name": "openAiApi Credential" } }, "typeVersion": 1.1 }, { "id": "5a1606d8-d851-4fb1-a18a-a45f5de3adf2", "name": "Default Data Loader1", "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader", "position": [ 1080, 480 ], "parameters": { "options": { "metadata": { "metadataValues": [ { "name": "file_id", "value": "={{ $('Get file').item.json.id }}" }, { "name": "file_name", "value": "={{ $('Get file').item.json.name }}" } ] } }, "dataType": "binary", "binaryMode": "specificField" }, "typeVersion": 1 }, { "id": "9923f6be-c2a5-4498-9fd2-0ed74ccdab35", "name": "Qdrant Vector Store", "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant", "position": [ 1000, 240 ], "parameters": { "mode": "insert", "options": {}, "qdrantCollection": { "__rl": true, "mode": "list", "value": "negozio-emporio-verde", "cachedResultName": "negozio-emporio-verde" } }, "credentials": { "qdrantApi": { "id": "credential-id", "name": "qdrantApi Credential" } }, "typeVersion": 1 }, { "id": "db1f5e71-28ef-427e-8911-275ddea4e44f", "name": "Create collection", "type": "n8n-nodes-base.httpRequest", "position": [ -180, -140 ], "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": "e0936863-4ece-429c-bf32-49f75b5c8bf0", "name": "Recursive Character Text Splitter", "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter", "position": [ 1060, 660 ], "parameters": { "options": {}, "chunkSize": 500, "chunkOverlap": 50 }, "typeVersion": 1 }, { "id": "1311350d-75d3-4edc-8fa9-584a1c36da6d", "name": "Loop Over Items", "type": "n8n-nodes-base.splitInBatches", "position": [ 420, 220 ], "parameters": { "options": {} }, "typeVersion": 3 }, { "id": "1f9b623c-4e93-44da-8c9d-cfe9f5d5be9a", "name": "Wait", "type": "n8n-nodes-base.wait", "position": [ 1380, 240 ], "webhookId": "89248db2-59f2-4b61-9cd5-71406144efee", "parameters": {}, "typeVersion": 1.1 }, { "id": "22aacdc0-8ec5-42e2-b9a0-fb9e5a4aa23c", "name": "When chat message received", "type": "@n8n/n8n-nodes-langchain.chatTrigger", "position": [ -460, 1080 ], "webhookId": "c9460030-9e23-41c4-9fcd-6c4c7fadf784", "parameters": { "options": {} }, "typeVersion": 1.1 }, { "id": "1e132fba-00a5-414a-8f37-f34645f668ff", "name": "Question and Answer Chain", "type": "@n8n/n8n-nodes-langchain.chainRetrievalQa", "position": [ 120, 1280 ], "parameters": { "options": {} }, "typeVersion": 1.5 }, { "id": "badee75e-bca7-49e4-9095-c098cb7adcab", "name": "Google Gemini Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini", "position": [ 0, 1500 ], "parameters": { "options": {}, "modelName": "models/gemini-1.5-flash" }, "credentials": { "googlePalmApi": { "id": "credential-id", "name": "googlePalmApi Credential" } }, "typeVersion": 1 }, { "id": "ad8cec62-ce1d-4e58-8e40-9a7a9f4f93c7", "name": "Vector Store Retriever", "type": "@n8n/n8n-nodes-langchain.retrieverVectorStore", "position": [ 160, 1500 ], "parameters": { "topK": 5 }, "typeVersion": 1 }, { "id": "0c7d1804-4525-4117-a19d-f2d59195fd47", "name": "Qdrant Vector Store1", "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant", "position": [ 40, 1680 ], "parameters": { "options": {}, "qdrantCollection": { "__rl": true, "mode": "list", "value": "negozio-emporio-verde", "cachedResultName": "negozio-emporio-verde" } }, "credentials": { "qdrantApi": { "id": "credential-id", "name": "qdrantApi Credential" } }, "typeVersion": 1.1 }, { "id": "6cfe7e8a-e593-4e4e-a0be-3379dd85c748", "name": "Embeddings OpenAI", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "position": [ 20, 1880 ], "parameters": { "options": {} }, "credentials": { "openAiApi": { "id": "credential-id", "name": "openAiApi Credential" } }, "typeVersion": 1.2 }, { "id": "d9aa08b5-ed04-4506-9a2e-f994d4694f48", "name": "Embeddings OpenAI4", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "position": [ 100, 1140 ], "parameters": { "options": {} }, "credentials": { "openAiApi": { "id": "credential-id", "name": "openAiApi Credential" } }, "typeVersion": 1.2 }, { "id": "d0d32470-b1df-40ec-aac1-32e4c9cf2e4e", "name": "Merge1", "type": "n8n-nodes-base.merge", "position": [ 640, 1140 ], "parameters": { "mode": "combine", "options": {}, "combineBy": "combineAll" }, "typeVersion": 3.1 }, { "id": "2edbca72-220e-45a6-8295-830d58611549", "name": "Aggregate", "type": "n8n-nodes-base.aggregate", "position": [ 460, 940 ], "parameters": { "options": {}, "fieldsToAggregate": { "fieldToAggregate": [ { "fieldToAggregate": "document.metadata.file_id" }, { "fieldToAggregate": "document.metadata.file_name" } ] } }, "typeVersion": 1 }, { "id": "8d116bc2-73d8-4349-8b4e-8dc0aef7cfa3", "name": "Clear collection", "type": "n8n-nodes-base.httpRequest", "position": [ -140, 220 ], "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": { "id": "credential-id", "name": "httpHeaderAuth Credential" } }, "typeVersion": 4.2 }, { "id": "0d4d3bab-2370-4ced-8f5d-2977d360f72b", "name": "Get folder", "type": "n8n-nodes-base.googleDrive", "position": [ 120, 220 ], "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" }, "credentials": { "googleDriveOAuth2Api": { "id": "credential-id", "name": "googleDriveOAuth2Api Credential" } }, "typeVersion": 3 }, { "id": "e81ee92f-db50-4c62-aac7-7bc03e63cc1a", "name": "Get file", "type": "n8n-nodes-base.googleDrive", "position": [ 700, 240 ], "parameters": { "fileId": { "__rl": true, "mode": "id", "value": "={{ $json.id }}" }, "options": { "googleFileConversion": { "conversion": { "docsToFormat": "text/plain" } } }, "operation": "download" }, "credentials": { "googleDriveOAuth2Api": { "id": "credential-id", "name": "googleDriveOAuth2Api Credential" } }, "typeVersion": 3 }, { "id": "c9bdc00a-ca8c-4457-8720-e6dce07e6b22", "name": "chatInput", "type": "n8n-nodes-base.set", "position": [ -200, 1080 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "id": "282b9bed-e5fd-4edb-95d5-682bcc08e070", "name": "chatInput", "type": "string", "value": "={{ $json.chatInput }}" } ] } }, "typeVersion": 3.4 }, { "id": "b65ca747-e738-4b7a-ae02-83840ce0a460", "name": "Retrive sources", "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant", "position": [ 140, 940 ], "parameters": { "mode": "load", "topK": 5, "prompt": "={{ $json.chatInput }}", "options": {}, "qdrantCollection": { "__rl": true, "mode": "list", "value": "negozio-emporio-verde", "cachedResultName": "negozio-emporio-verde" } }, "credentials": { "qdrantApi": { "id": "credential-id", "name": "qdrantApi Credential" } }, "typeVersion": 1.1 }, { "id": "265e2613-0155-4585-8d5c-fcda26975585", "name": "Output", "type": "n8n-nodes-base.set", "position": [ 1060, 1140 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "id": "b296c92b-d9ee-4322-b972-d1069d01feb8", "name": "output", "type": "string", "value": "={{ $('Question and Answer Chain').item.json.response }}\n\nSources: {{ (JSON.stringify($json.unique_file_names)) }}," } ] } }, "typeVersion": 3.4 }, { "id": "9407fcaf-d90a-46bd-9b1f-b0493a0357c1", "name": "Response", "type": "n8n-nodes-base.code", "position": [ 880, 1140 ], "parameters": { "jsCode": "const item = $input.item.json;\n\n// Creiamo Set per rimuovere duplicati\nconst uniqueFileIds = Array.from(new Set(item.file_id));\nconst uniqueFileNames = Array.from(new Set(item.file_name));\n\n// Ritorniamo un item con i valori univoci\nreturn [\n {\n json: {\n unique_file_ids: uniqueFileIds,\n unique_file_names: uniqueFileNames\n }\n }\n];\n" }, "typeVersion": 2 }, { "id": "0b8a715f-36eb-49fd-895e-e8e76fdbb0c1", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [ -360, -520 ], "parameters": { "width": 840, "height": 220, "content": "## Complete RAG System with Automatic Source Citations 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 **cites the document sources** (from Google Drive).\n" }, "typeVersion": 1 }, { "id": "3220c323-a3d6-4855-ad05-2aaf8761771e", "name": "Sticky Note3", "type": "n8n-nodes-base.stickyNote", "position": [ 80, -200 ], "parameters": { "color": 6, "width": 880, "height": 220, "content": "# STEP 1\n\n## Create Qdrant Collection\nChange:\n- QDRANTURL\n- COLLECTION" }, "typeVersion": 1 }, { "id": "738835a3-888b-426e-a897-8ddc630b85bf", "name": "Sticky Note4", "type": "n8n-nodes-base.stickyNote", "position": [ -220, 120 ], "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": "70205511-2c4a-4f2f-8f68-97e3ede28851", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ 1200, 620 ], "parameters": { "color": 4, "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": "921497ff-1beb-4bd1-aecd-81b68c5a0357", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ 680, 1400 ], "parameters": { "color": 3, "width": 520, "height": 200, "content": "The final output is:\n\n\nRESPONSE\n\nSources: [\"FILENAME 1\", \"FILENAME 2\",...]\n\n" }, "typeVersion": 1 } ], "active": false, "pinData": {}, "settings": { "executionOrder": "v1" }, "versionId": "33d05a21-33dd-422a-ba58-3eafffc7d50a", "connections": { "Wait": { "main": [ [ { "node": "Loop Over Items", "type": "main", "index": 0 } ] ] }, "Merge1": { "main": [ [ { "node": "Response", "type": "main", "index": 0 } ] ] }, "Get file": { "main": [ [ { "node": "Qdrant Vector Store", "type": "main", "index": 0 } ] ] }, "Response": { "main": [ [ { "node": "Output", "type": "main", "index": 0 } ] ] }, "Aggregate": { "main": [ [ { "node": "Merge1", "type": "main", "index": 0 } ] ] }, "chatInput": { "main": [ [ { "node": "Question and Answer Chain", "type": "main", "index": 0 }, { "node": "Retrive sources", "type": "main", "index": 0 } ] ] }, "Get folder": { "main": [ [ { "node": "Loop Over Items", "type": "main", "index": 0 } ] ] }, "Loop Over Items": { "main": [ [], [ { "node": "Get file", "type": "main", "index": 0 } ] ] }, "Retrive sources": { "main": [ [ { "node": "Aggregate", "type": "main", "index": 0 } ] ] }, "Clear collection": { "main": [ [ { "node": "Get folder", "type": "main", "index": 0 } ] ] }, "Embeddings OpenAI": { "ai_embedding": [ [ { "node": "Qdrant Vector Store1", "type": "ai_embedding", "index": 0 } ] ] }, "Embeddings OpenAI1": { "ai_embedding": [ [ { "node": "Qdrant Vector Store", "type": "ai_embedding", "index": 0 } ] ] }, "Embeddings OpenAI4": { "ai_embedding": [ [ { "node": "Retrive sources", "type": "ai_embedding", "index": 0 } ] ] }, "Qdrant Vector Store": { "main": [ [ { "node": "Wait", "type": "main", "index": 0 } ] ] }, "Default Data Loader1": { "ai_document": [ [ { "node": "Qdrant Vector Store", "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 } ] ] }, "Question and Answer Chain": { "main": [ [ { "node": "Merge1", "type": "main", "index": 1 } ] ] }, "When chat message received": { "main": [ [ { "node": "chatInput", "type": "main", "index": 0 } ] ] }, "Recursive Character Text Splitter": { "ai_textSplitter": [ [ { "node": "Default Data Loader1", "type": "ai_textSplitter", "index": 0 } ] ] }, "When clicking ‘Test workflow’": { "main": [ [ { "node": "Clear collection", "type": "main", "index": 0 } ] ] } } }, "lastUpdatedBy": 29, "workflowInfo": { "nodeCount": 29, "nodeTypes": { "n8n-nodes-base.set": { "count": 2 }, "n8n-nodes-base.code": { "count": 1 }, "n8n-nodes-base.wait": { "count": 1 }, "n8n-nodes-base.merge": { "count": 1 }, "n8n-nodes-base.aggregate": { "count": 1 }, "n8n-nodes-base.stickyNote": { "count": 5 }, "n8n-nodes-base.googleDrive": { "count": 2 }, "n8n-nodes-base.httpRequest": { "count": 2 }, "n8n-nodes-base.manualTrigger": { "count": 1 }, "n8n-nodes-base.splitInBatches": { "count": 1 }, "@n8n/n8n-nodes-langchain.chatTrigger": { "count": 1 }, "@n8n/n8n-nodes-langchain.chainRetrievalQa": { "count": 1 }, "@n8n/n8n-nodes-langchain.embeddingsOpenAi": { "count": 3 }, "@n8n/n8n-nodes-langchain.vectorStoreQdrant": { "count": 3 }, 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