Chatbot RAG de Telegram con documentos PDF y copia de seguridad de Google Drive

Avanzado

Este es unInternal Wiki, AI RAGflujo de automatización del dominio deautomatización que contiene 24 nodos.Utiliza principalmente nodos como If, Code, Telegram, FormTrigger, GoogleDrive. Construye un chatbot de recuperación usando Telegram, OpenAI y copias de seguridad de PDF de Google Drive

Requisitos previos
  • Bot Token de Telegram
  • Credenciales de API de Google Drive
  • Clave de API de OpenAI
Vista previa del flujo de trabajo
Visualización de las conexiones entre nodos, con soporte para zoom y panorámica
Exportar flujo de trabajo
Copie la siguiente configuración JSON en n8n para importar y usar este flujo de trabajo
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  },
  "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"
    },
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      "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": [
    {
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      "name": "Embeddings OpenAI",
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      "parameters": {
        "options": {}
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      "credentials": {
        "openAiApi": {
          "id": "PPSwAKeLQYgAPobT",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "3a69c8a7-bf95-4de2-84b0-ae2cc3d2e4e7",
      "name": "Cargador de Datos Predeterminado",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
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      ],
      "parameters": {
        "options": {},
        "dataType": "binary"
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      "typeVersion": 1.1
    },
    {
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      "parameters": {
        "mode": "insert",
        "memoryKey": {
          "__rl": true,
          "mode": "list",
          "value": "vector_store_key",
          "cachedResultName": "vector_store_key"
        }
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      "parameters": {
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        "toolName": "knowledge_base",
        "memoryKey": {
          "__rl": true,
          "mode": "list",
          "value": "vector_store_key"
        },
        "toolDescription": "Use this knowledge base to answer questions from the user"
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      "typeVersion": 1.2
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    {
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      "name": "Modelo de Chat OpenAI",
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          "mode": "list",
          "value": "gpt-4o-mini"
        },
        "options": {}
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      "credentials": {
        "openAiApi": {
          "id": "PPSwAKeLQYgAPobT",
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        "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"
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      "typeVersion": 1
    },
    {
      "id": "d4227342-0a19-420e-b088-2e37186ad074",
      "name": "Activador Telegram",
      "type": "n8n-nodes-base.telegramTrigger",
      "position": [
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      ],
      "webhookId": "aac0aa6a-c86e-4b4d-8f81-daacfd20f2c8",
      "parameters": {
        "updates": [
          "message"
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        "additionalFields": {}
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      "credentials": {
        "telegramApi": {
          "id": "paNoPvnV5Wzt4Lhv",
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    },
    {
      "id": "7470655a-650a-48ca-98e0-b248cf99d18e",
      "name": "¿Es mensaje de texto?",
      "type": "n8n-nodes-base.if",
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    {
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      "name": "Enviar respuesta al usuario",
      "type": "n8n-nodes-base.telegram",
      "position": [
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      ],
      "webhookId": "bead9b9b-6410-4fe7-a36c-05bd069e3a02",
      "parameters": {
        "text": "={{ $json.output }}",
        "chatId": "={{ $('Telegram Trigger').item.json.message.chat.id }}",
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      "credentials": {
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      "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!",
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        "additionalFields": {}
      },
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    },
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      "name": "Agente de consulta de documentos Telegram",
      "type": "@n8n/n8n-nodes-langchain.agent",
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      ],
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        "text": "={{ $json.message.text }}",
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          "systemMessage": "The output should not exceed 3000 characters after entities parsing."
        },
        "promptType": "define"
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      "position": [
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      "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
    },
    {
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        "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
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      "name": "Código",
      "type": "n8n-nodes-base.code",
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      "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;"
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      "typeVersion": 2
    },
    {
      "id": "83ed351e-90e8-458f-a01b-73001ef1800f",
      "name": "Sube tu documento PDF aquí",
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      "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": "Respaldar documento(s) en Google Drive",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
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      ],
      "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": "fC471es5gk5Mm900",
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        }
      },
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        "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"
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      "name": "Nota Adhesiva 1",
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      "position": [
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      "parameters": {
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        "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": "Nota Adhesiva 2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      "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
    },
    {
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      "position": [
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      "parameters": {
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        "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
    },
    {
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        "content": "### Default Data Loader  \nExtracts and chunks text from the uploaded PDF documents to prepare them for semantic embedding."
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    },
    {
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      "parameters": {
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        "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."
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        "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\")"
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      "position": [
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      "parameters": {
        "width": 544,
        "height": 80,
        "content": "Sample document: https://ptgmedia.pearsoncmg.com/images/9780138203283/samplepages/9780138203283_Sample.pdf"
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Preguntas frecuentes

¿Cómo usar este flujo de trabajo?

Copie el código de configuración JSON de arriba, cree un nuevo flujo de trabajo en su instancia de n8n y seleccione "Importar desde JSON", pegue la configuración y luego modifique la configuración de credenciales según sea necesario.

¿En qué escenarios es adecuado este flujo de trabajo?

Avanzado - Wiki interno, RAG de IA

¿Es de pago?

Este flujo de trabajo es completamente gratuito, puede importarlo y usarlo directamente. Sin embargo, tenga en cuenta que los servicios de terceros utilizados en el flujo de trabajo (como la API de OpenAI) pueden requerir un pago por su cuenta.

Información del flujo de trabajo
Nivel de dificultad
Avanzado
Número de nodos24
Categoría2
Tipos de nodos12
Descripción de la dificultad

Adecuado para usuarios avanzados, flujos de trabajo complejos con 16+ nodos

Autor
Trung Tran

Trung Tran

@trungtran

Empowering small and medium businesses with smart automation and practical AI, no big tech team required. Reach out: lets@automatewith.me

Enlaces externos
Ver en n8n.io

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Categorías

Categorías: 34