Chatbot RAG Telegram avec documents PDF et sauvegarde Google Drive

• •Token Bot Telegram
• •Informations d'identification Google Drive API
• •Clé API OpenAI

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        "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."
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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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        "content": "### 1. Upload Your PDF Document Here  \n- A manual execution trigger for uploading and processing PDF documents into the knowledge base."
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        "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."
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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."
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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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        "content": "Sample document: https://ptgmedia.pearsoncmg.com/images/9780138203283/samplepages/9780138203283_Sample.pdf"
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