{ "workflow": { "id": 6994, "name": "Build a retrieval-based chatbot with Telegram, OpenAI and Google Drive PDF backup", "views": 1138, "recentViews": 0, "totalViews": 1138, "createdAt": "2025-08-05T06:25:33.284Z", "description": "# 📚 Telegram RAG Chatbot with PDF Document & Google Drive Backup\n- An upgraded Retrieval-Augmented Generation (RAG) chatbot built in **n8n** that lets users ask questions via Telegram and receive accurate answers from uploaded PDFs. It embeds documents using OpenAI and backs them up to Google Drive.\n\n## 👤 Who’s it for\n\nPerfect for:\n- Knowledge workers who want instant access to private documents\n- Support teams needing searchable SOPs and guides\n- Educators enabling course material Q&A for students\n- Individuals automating personal document search + cloud backup\n\n## ⚙️ How it works / What it does\n\n### 💬 Telegram Chat Handling\n1. **User sends a message** \n Triggered by the Telegram bot, the workflow checks if the message is text.\n\n2. **Text message → OpenAI RAG Agent** \n If the message is text, it's passed to a GPT-powered document agent. \n This agent:\n - Retrieves relevant info from embedded documents using semantic search\n - Returns a context-aware answer to the user\n\n3. **Send answer back** \n The bot sends the generated response back to the Telegram user.\n\n4. **Non-text input fallback** \n If the message is not text, the bot replies with a polite unsupported message.\n\n### 📄 PDF Upload and Embedding\n1. **User uploads PDFs manually** \n A manual trigger starts the embedding flow.\n\n2. **Default Data Loader** \n Reads and chunks the PDF(s) into text segments.\n\n3. **Insert to Vector Store (Embedding)** \n Text chunks are embedded using OpenAI and saved for retrieval.\n\n4. **Backup to Google Drive** \n The original PDF is uploaded to Google Drive for safekeeping.\n\n## 🛠️ How to set up\n\n1. **Telegram Bot**\n - Create via [BotFather](https://t.me/botfather)\n - Connect it to the Telegram Trigger node\n\n2. **OpenAI**\n - Use your OpenAI API key\n - Connect the Embeddings and Chat Model nodes (GPT-3.5/4)\n - Ensure both embedding and querying use the same Embedding node\n\n3. **Google Drive**\n - Set up credentials in n8n for your Google account\n - Connect the “Backup to Google Drive” node\n\n4. **PDF Ingestion**\n - Use the “Upload your PDF here” trigger\n - Connect it to the loader, embedder, and backup flow\n\n## ✅ Requirements\n\n- Telegram bot token\n- OpenAI API key (GPT + Embeddings)\n- n8n instance (self-hosted or cloud)\n- Google Drive integration\n- PDF files to upload\n\n## 🧩 How to customize the workflow\n\n| Feature | How to Customize |\n|-------------------------------|-------------------------------------------------------------------|\n| Auto-ingest from folders | Add Google Drive/Dropbox watchers for new PDFs |\n| Add file upload via Telegram | Extend Telegram bot to receive PDFs and run the embedding flow |\n| Track user questions | Log Telegram usernames and questions to a database |\n| Summarize documents | Add summarization step on upload |\n| Add Markdown or HTML support | Format replies for better Telegram rendering |\n\nBuilt with 💬 Telegram + 📄 PDF + 🧠 OpenAI Embeddings + ☁️ Google Drive + ⚡ n8n", "workflow": { "id": "bsT84L413PRtrNtZ", "meta": { "instanceId": "4a2e6764ba7a6bc9890d9225f4b21d570ce88fc9bd57549c89057fcee58fed0f", "templateId": "5010", "templateCredsSetupCompleted": true }, "name": "Telegram RAG Chatbot with PDF Document & Google Drive Backup", "tags": [ { "id": "ow6eIe95VK6fRkyw", "name": "Chatbot", "createdAt": "2025-08-05T06:23:11.231Z", "updatedAt": "2025-08-05T06:23:11.231Z" }, { "id": "JFZdpFVd2h3ZDZ7n", "name": "RAG", "createdAt": "2025-08-05T06:23:26.538Z", "updatedAt": "2025-08-05T06:23:26.538Z" }, { "id": "84SlSTthTSHRbFGM", "name": "Telegram", "createdAt": "2025-08-05T06:23:21.764Z", "updatedAt": "2025-08-05T06:23:21.764Z" } ], "nodes": [ { "id": "26d63e24-2592-41f9-9b4b-edab81e99f21", "name": "Embeddings OpenAI", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "position": [ 1760, 720 ], "parameters": { "options": {} }, "credentials": { "openAiApi": { "id": "credential-id", "name": "openAiApi Credential" } }, "typeVersion": 1.2 }, { "id": "3a69c8a7-bf95-4de2-84b0-ae2cc3d2e4e7", "name": "Default Data Loader", "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader", "position": [ 1232, 1112 ], "parameters": { "options": {}, "dataType": "binary" }, "typeVersion": 1.1 }, { "id": "0f4185ea-d7a9-44a9-a824-98f9dc2c2a5d", "name": "Insert Data to Store", "type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory", "position": [ 1136, 888 ], "parameters": { "mode": "insert", "memoryKey": { "__rl": true, "mode": "list", "value": "vector_store_key", "cachedResultName": "vector_store_key" } }, "typeVersion": 1.2 }, { "id": "ce86b41b-7e1b-458f-ab13-d6b187854ae8", "name": "Query Data Tool", "type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory", "position": [ 1664, 512 ], "parameters": { "mode": "retrieve-as-tool", "toolName": "knowledge_base", "memoryKey": { "__rl": true, "mode": "list", "value": "vector_store_key" }, "toolDescription": "Use this knowledge base to answer questions from the user" }, "typeVersion": 1.2 }, { "id": "d43cf585-4192-4f53-9532-4677923289ba", "name": "OpenAI Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 1536, 512 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4o-mini" }, "options": {} }, "credentials": { "openAiApi": { "id": "credential-id", "name": "openAiApi Credential" } }, "typeVersion": 1.2 }, { "id": "8d4c68cf-64d1-4b3a-bb19-2f003303c1df", "name": "Sticky Note3", "type": "n8n-nodes-base.stickyNote", "position": [ 1920, 688 ], "parameters": { "color": 4, "width": 320, "height": 224, "content": "### Embeddings\n\nThe Insert and Retrieve operation use the same embedding node.\n\nThis is to ensure that they are using the **exact same embeddings and settings**.\n\nDifferent embeddings might not work at all, or have unintended consequences.\n" }, "typeVersion": 1 }, { "id": "d4227342-0a19-420e-b088-2e37186ad074", "name": "Telegram Trigger", "type": "n8n-nodes-base.telegramTrigger", "position": [ 912, 696 ], "webhookId": "aac0aa6a-c86e-4b4d-8f81-daacfd20f2c8", "parameters": { "updates": [ "message" ], "additionalFields": {} }, "credentials": { "telegramApi": { "id": "credential-id", "name": "telegramApi Credential" } }, "typeVersion": 1.2 }, { "id": "7470655a-650a-48ca-98e0-b248cf99d18e", "name": "Is text message?", "type": "n8n-nodes-base.if", "position": [ 1224, 696 ], "parameters": { "options": {}, "conditions": { "options": { "version": 2, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "2439fbb6-c093-4b33-aabd-db08ebfd53b2", "operator": { "name": "filter.operator.equals", "type": "string", "operation": "equals" }, "leftValue": "", "rightValue": "" } ] } }, "typeVersion": 2.2 }, { "id": "fda67b3b-9844-40e4-aa53-252d2e36e667", "name": "Send respond to user", "type": "n8n-nodes-base.telegram", "position": [ 2064, 496 ], "webhookId": "bead9b9b-6410-4fe7-a36c-05bd069e3a02", "parameters": { "text": "={{ $json.output }}", "chatId": "={{ $('Telegram Trigger').item.json.message.chat.id }}", "additionalFields": {} }, "credentials": { "telegramApi": { "id": "credential-id", "name": "telegramApi Credential" } }, "typeVersion": 1.2 }, { "id": "62ae0117-0d2c-47dd-a772-7c4cd70885ec", "name": "Un-supported message type", "type": "n8n-nodes-base.telegram", "position": [ 1688, 896 ], "webhookId": "724418e9-e7ef-4aa2-8722-028683cadb2f", "parameters": { "text": "Sorry, I can’t read files or images right now. Just send me your question about uploaded document, and I’ll help you answer it!", "chatId": "={{ $('Telegram Trigger').item.json.message.chat.id }}", "additionalFields": {} }, "credentials": { "telegramApi": { "id": "credential-id", "name": "telegramApi Credential" } }, "typeVersion": 1.2 }, { "id": "0039537b-558c-4fe8-9716-f8aa13676f4a", "name": "Telegram document query agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 1552, 288 ], "parameters": { "text": "={{ $json.message.text }}", "options": { "systemMessage": "The output should not exceed 3000 characters after entities parsing." }, "promptType": "define" }, "typeVersion": 2 }, { "id": "0608a9d7-db7b-4a18-b8fb-26b936da919a", "name": "Sticky Note6", "type": "n8n-nodes-base.stickyNote", "position": [ 1104, 512 ], "parameters": { "width": 272, "height": 144, "content": "### 2. Is Text Message? \n**Description**: Checks whether the incoming Telegram message is a text message. If not, the workflow routes to an \"unsupported message type\" handler." }, "typeVersion": 1 }, { "id": "40c8b84f-ed8a-4fdc-b04c-d778a2fdea0e", "name": "Sticky Note5", "type": "n8n-nodes-base.stickyNote", "position": [ 544, 688 ], "parameters": { "width": 304, "height": 128, "content": "### 1. 📩 Telegram Trigger \n**Description**: Listens for incoming messages from the user via the connected Telegram bot. This is the entry point of the workflow." }, "typeVersion": 1 }, { "id": "91077637-5e75-4bb2-8419-235420bc5a96", "name": "Code", "type": "n8n-nodes-base.code", "position": [ 1224, 1288 ], "parameters": { "jsCode": "const data = $input.item.json;\nconst binaryData = $input.item.binary;\n\nlet output = [];\n\nObject.keys(binaryData)\n .filter(label => label.startsWith(\"CV_\"))\n .forEach(label => {\n output.push({\n json: data,\n binary: { data: binaryData[label] }\n });\n });\n\nreturn output;" }, "typeVersion": 2 }, { "id": "83ed351e-90e8-458f-a01b-73001ef1800f", "name": "Upload your PDF document here", "type": "n8n-nodes-base.formTrigger", "position": [ 912, 1140 ], "webhookId": "82848bc4-5ea2-4e5a-8bb6-3c09b94a8c5d", "parameters": { "options": {}, "formTitle": "Upload your data to test RAG", "formFields": { "values": [ { "fieldType": "file", "fieldLabel": "Upload your file(s)", "requiredField": true, "acceptFileTypes": ".pdf" } ] } }, "typeVersion": 2.2 }, { "id": "79a7f8b5-7af2-479c-883c-a4e02ce4bee8", "name": "Backup document(s) to Google Drive", "type": "n8n-nodes-base.googleDrive", "position": [ 1688, 1288 ], "parameters": { "name": "=document-{{ $now.toFormat(\"yyyyLLdd-HHmmss\") }}-{{$binary.data.fileName}}", "driveId": { "__rl": true, "mode": "list", "value": "My Drive", "cachedResultUrl": "https://drive.google.com/drive/my-drive", "cachedResultName": "My Drive" }, "options": {}, "folderId": { "__rl": true, "mode": "list", "value": "1ObNNVJFR2vcKqP8p-ZnX_eaZy4gBHgha", "cachedResultUrl": "https://drive.google.com/drive/folders/1ObNNVJFR2vcKqP8p-ZnX_eaZy4gBHgha", "cachedResultName": "SmartIT" } }, "credentials": { "googleDriveOAuth2Api": { "id": "credential-id", "name": "googleDriveOAuth2Api Credential" } }, "typeVersion": 3 }, { "id": "c8f73ac1-eb95-4fa0-a1d8-8b6f5befe885", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ -752, -96 ], "parameters": { "color": 7, "width": 1264, "height": 1856, "content": "# 📚 Telegram RAG Chatbot with PDF Document & Google Drive Backup\n- An upgraded Retrieval-Augmented Generation (RAG) chatbot built in **n8n** that lets users ask questions via Telegram and receive accurate answers from uploaded PDFs. It embeds documents using OpenAI and backs them up to Google Drive.\n\n## 👤 Who’s it for\n\nPerfect for:\n- Knowledge workers who want instant access to private documents\n- Support teams needing searchable SOPs and guides\n- Educators enabling course material Q&A for students\n- Individuals automating personal document search + cloud backup\n\n## ⚙️ How it works / What it does\n\n### 💬 Telegram Chat Handling\n1. **User sends a message** \n Triggered by the Telegram bot, the workflow checks if the message is text.\n\n2. **Text message → OpenAI RAG Agent** \n If the message is text, it's passed to a GPT-powered document agent. \n This agent:\n - Retrieves relevant info from embedded documents using semantic search\n - Returns a context-aware answer to the user\n\n3. **Send answer back** \n The bot sends the generated response back to the Telegram user.\n\n4. **Non-text input fallback** \n If the message is not text, the bot replies with a polite unsupported message.\n\n### 📄 PDF Upload and Embedding\n1. **User uploads PDFs manually** \n A manual trigger starts the embedding flow.\n\n2. **Default Data Loader** \n Reads and chunks the PDF(s) into text segments.\n\n3. **Insert to Vector Store (Embedding)** \n Text chunks are embedded using OpenAI and saved for retrieval.\n\n4. **Backup to Google Drive** \n The original PDF is uploaded to Google Drive for safekeeping.\n\n## 🛠️ How to set up\n\n1. **Telegram Bot**\n - Create via [BotFather](https://t.me/botfather)\n - Connect it to the Telegram Trigger node\n\n2. **OpenAI**\n - Use your OpenAI API key\n - Connect the Embeddings and Chat Model nodes (GPT-3.5/4)\n - Ensure both embedding and querying use the same Embedding node\n\n3. **Google Drive**\n - Set up credentials in n8n for your Google account\n - Connect the “Backup to Google Drive” node\n\n4. **PDF Ingestion**\n - Use the “Upload your PDF here” trigger\n - Connect it to the loader, embedder, and backup flow\n\n## ✅ Requirements\n\n- Telegram bot token\n- OpenAI API key (GPT + Embeddings)\n- n8n instance (self-hosted or cloud)\n- Google Drive integration\n- PDF files to upload\n\n## 🧩 How to customize the workflow\n\n| Feature | How to Customize |\n|-------------------------------|-------------------------------------------------------------------|\n| Auto-ingest from folders | Add Google Drive/Dropbox watchers for new PDFs |\n| Add file upload via Telegram | Extend Telegram bot to receive PDFs and run the embedding flow |\n| Track user questions | Log Telegram usernames and questions to a database |\n| Summarize documents | Add summarization step on upload |\n| Add Markdown or HTML support | Format replies for better Telegram rendering |\n\nBuilt with 💬 Telegram + 📄 PDF + 🧠 OpenAI Embeddings + ☁️ Google Drive + ⚡ n8n" }, "typeVersion": 1 }, { "id": "8ecf58dd-5beb-4f78-bd09-1238f25c623a", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ 704, 1360 ], "parameters": { "width": 464, "height": 80, "content": "### 1. Upload Your PDF Document Here \n- A manual execution trigger for uploading and processing PDF documents into the knowledge base." }, "typeVersion": 1 }, { "id": "2aefbbd3-1234-4843-bf34-430b229faa1f", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [ 1872, 1296 ], "parameters": { "width": 432, "height": 80, "content": "### 2.1 Backup Documents to Google Drive \n- Uploads a copy of the original PDF file to a connected Google Drive folder for safekeeping and future reference." }, "typeVersion": 1 }, { "id": "88a087f2-8656-4e82-b384-efdaf51ec021", "name": "Sticky Note4", "type": "n8n-nodes-base.stickyNote", "position": [ 1424, 176 ], "parameters": { "width": 560, "height": 96, "content": "### 3. Telegram Document Query Agent (GPT with RAG) \n- Sends the user’s text message to OpenAI’s Chat Model. Uses embeddings to retrieve relevant document chunks and generate a context-aware response using Retrieval-Augmented Generation." }, "typeVersion": 1 }, { "id": "38627375-43c0-47ad-87ab-a3ef94093c28", "name": "Sticky Note7", "type": "n8n-nodes-base.stickyNote", "position": [ 1360, 1120 ], "parameters": { "color": 4, "width": 496, "height": 96, "content": "### Default Data Loader \nExtracts and chunks text from the uploaded PDF documents to prepare them for semantic embedding." }, "typeVersion": 1 }, { "id": "8b2e116c-003f-4eb7-9cf1-30ac4cbd87d3", "name": "Sticky Note9", "type": "n8n-nodes-base.stickyNote", "position": [ 688, 896 ], "parameters": { "width": 352, "height": 112, "content": "### 2.2 Insert Data to Store (Embeddings) \nConverts document chunks into vector embeddings using OpenAI and inserts them into the vector store for future retrieval." }, "typeVersion": 1 }, { "id": "2abc9178-add2-4d8e-b395-cc9713ed4a2e", "name": "Sticky Note10", "type": "n8n-nodes-base.stickyNote", "position": [ 2432, 480 ], "parameters": { "width": 540, "height": 580, "content": "![Alt text](https://wisestackai.s3.ap-southeast-1.amazonaws.com/Screenshot+2025-08-05+at+1.18.12%E2%80%AFPM.png \"Optional title text\")" }, "typeVersion": 1 }, { "id": "1de83861-0a7d-4e0c-9ceb-beacbe84749b", "name": "Sticky Note8", "type": "n8n-nodes-base.stickyNote", "position": [ 2432, 1088 ], "parameters": { "width": 544, "height": 80, "content": "Sample document: https://ptgmedia.pearsoncmg.com/images/9780138203283/samplepages/9780138203283_Sample.pdf" }, "typeVersion": 1 } ], "active": false, "pinData": {}, "settings": {}, "versionId": "50ae16d0-7565-4f29-8f21-d769face925a", "connections": { "Code": { "main": [ [ { "node": "Backup document(s) to Google Drive", "type": "main", "index": 0 } ] ] }, "Query Data Tool": { "ai_tool": [ [ { "node": "Telegram document query agent", "type": "ai_tool", "index": 0 } ] ] }, "Is text message?": { "main": [ [ { "node": "Telegram document query agent", "type": "main", "index": 0 } ], [ { "node": "Un-supported message type", "type": "main", "index": 0 } ] ] }, "Telegram Trigger": { "main": [ [ { "node": "Is text message?", "type": "main", "index": 0 } ] ] }, "Embeddings OpenAI": { "ai_embedding": [ [ { "node": "Insert Data to Store", "type": "ai_embedding", "index": 0 }, { "node": "Query Data Tool", "type": "ai_embedding", "index": 0 } ] ] }, "OpenAI Chat Model": { "ai_languageModel": [ [ { "node": "Telegram document query agent", "type": "ai_languageModel", "index": 0 } ] ] }, "Default Data Loader": { "ai_document": [ [ { "node": "Insert Data to Store", "type": "ai_document", "index": 0 } ] ] }, "Insert Data to Store": { "main": [ [] ] }, "Send respond to user": { "main": [ [] ] }, "Telegram document query agent": { "main": [ [ { "node": "Send respond to user", "type": "main", "index": 0 } ] ] }, "Upload your PDF document here": { "main": [ [ { "node": "Insert Data to Store", "type": "main", "index": 0 }, { "node": "Code", "type": "main", "index": 0 } ] ] } } }, "lastUpdatedBy": 29, "workflowInfo": { "nodeCount": 24, "nodeTypes": { "n8n-nodes-base.if": { "count": 1 }, "n8n-nodes-base.code": { "count": 1 }, "n8n-nodes-base.telegram": { "count": 2 }, "n8n-nodes-base.stickyNote": { "count": 11 }, "n8n-nodes-base.formTrigger": { "count": 1 }, "n8n-nodes-base.googleDrive": { "count": 1 }, "@n8n/n8n-nodes-langchain.agent": { "count": 1 }, "n8n-nodes-base.telegramTrigger": { "count": 1 }, "@n8n/n8n-nodes-langchain.lmChatOpenAi": { "count": 1 }, "@n8n/n8n-nodes-langchain.embeddingsOpenAi": { "count": 1 }, "@n8n/n8n-nodes-langchain.vectorStoreInMemory": { "count": 2 }, "@n8n/n8n-nodes-langchain.documentDefaultDataLoader": { "count": 1 } } }, "status": "published", "user": { "name": "Trung Tran", "username": "trungtran", "bio": "Empowering small and medium businesses with smart automation and practical AI, no big tech team required. Youtube channel: youtube.com/@theStackExplorer", "verified": true, "links": [ "https://www.automatewith.me" ], "avatar": "https://gravatar.com/avatar/716d5835286b57fb642fb00972b432f63c72f3a0258e1f6a476ceb02829d21bd?r=pg&d=retro&size=200" }, "nodes": [ { "id": 20, "icon": "fa:map-signs", "name": "n8n-nodes-base.if", "codex": { "data": { "alias": [ "Router", "Filter", "Condition", "Logic", "Boolean", "Branch" ], "details": "The IF node can be used to implement binary conditional logic in your workflow. You can set up one-to-many conditions to evaluate each item of data being inputted into the node. That data will either evaluate to TRUE or FALSE and route out of the node accordingly.\n\nThis node has multiple types of conditions: Bool, String, Number, and Date & Time.", "resources": { "generic": [ { "url": "https://n8n.io/blog/learn-to-automate-your-factorys-incident-reporting-a-step-by-step-guide/", "icon": "🏭", "label": "Learn to Automate Your Factory's Incident Reporting: A Step by Step Guide" }, { "url": "https://n8n.io/blog/2021-the-year-to-automate-the-new-you-with-n8n/", "icon": "☀️", "label": "2021: The Year to Automate the New You with n8n" }, { "url": "https://n8n.io/blog/why-business-process-automation-with-n8n-can-change-your-daily-life/", "icon": "🧬", "label": "Why business process automation with n8n can change your daily life" }, { "url": "https://n8n.io/blog/create-a-toxic-language-detector-for-telegram/", "icon": "🤬", "label": "Create a toxic language detector for Telegram in 4 step" }, { "url": "https://n8n.io/blog/no-code-ecommerce-workflow-automations/", 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