This article provides a complete, practical walkthrough of the Telegram Splitout Automation Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   **Title:**  
   AI Document Assistant: Build a Smart Telegram Bot with n8n, Supabase & Google Gemini
   
   **Meta Description:**  
   Learn how to build a no-code AI-powered Telegram bot that understands PDFs using n8n, Supabase, and Google Gemini. Chat with your documents, get contextual answers, and integrate smart features like weather data—all without writing code.
   
   **Keywords:**  
   n8n workflow, AI Telegram bot, PDF chatbot, Supabase vector store, Google Gemini AI, Document QA chatbot, no-code AI, Telegram AI assistant, vector embeddings, chatbot using Supabase, document question answering, Google Gemini embeddings, n8n automation, Telegram Google Gemini bot
   
   **Third-Party APIs Used:**
   
   1. **Telegram Bot API** – Handles receiving user messages and sending AI-generated replies.
   2. **Google Gemini (PaLM) API** – Powers both conversational AI (text generation) and document embeddings (text-to-vector conversion).
   3. **Supabase API** – Stores and queries vector embeddings for document-based question-answering.
   4. **OpenWeatherMap API** – Provides up-to-date weather data upon user request (optional feature).
   
   ---
   
   **Short Article:**  
   
   # Build an AI-Powered Telegram Document Assistant: n8n + Supabase + Google Gemini
   
   What if you could chat with your documents just like you chat with a human assistant? Imagine uploading a PDF to Telegram and instantly asking it, “What are the main conclusions in section 2?”—and getting a rich, intelligent reply in seconds. With the combined power of n8n, Supabase, and Google Gemini, now you can.
   
   This no-code workflow converts a simple Telegram bot into a document-savvy AI assistant. Whether you're managing course materials, legal documents, or business reports, this self-building agent enables deep interaction with your documents—all from Telegram.
   
   ---
   
   ## 🤖 How It Works
   
   This smart AI assistant handles two primary scenarios:
   
   ### 1. 📥 Document Integration & Memory Building  
   When a user sends a PDF file to the Telegram bot:
   - The document is downloaded and parsed through n8n.
   - Text is extracted and split into manageable chunks.
   - Google Gemini's embedding model converts the text into vector representations.
   - These vectors are stored in **Supabase's vector-enabled PostgreSQL database**.
   - The document's contents are now searchable knowledge for future queries.
   
   The user gets a confirmation message like:  
   ✅ Document saved!  
   Feel free to start asking questions about it.
   
   ### 2. 💬 Conversational AI with Contextual Memory  
   Users can chat with the bot just like they would with ChatGPT—except now it "remembers" their documents:
   - Text messages sent to the bot are analyzed using Google Gemini’s chat model.
   - If a question seems based on an uploaded document, vector search is performed via Supabase to retrieve relevant chunks.
   - Gemini then crafts an accurate, well-formatted answer that uses both retrieved document snippets and general world knowledge.
   - Bonus: if the user asks for the weather in a specific city, the workflow queries the OpenWeatherMap API.
   
   All of this happens in a beautifully structured HTML-friendly format supported by Telegram—splitting up long answers into logical, multi-message chunks under Telegram’s character limit.
   
   ---
   
   ## 🧠 Under The Hood: Key Components
   
   This n8n workflow combines intelligent agents and robust integrations:
   - **n8n platform**: The no-code backbone that orchestrates triggers, logic, API calls, and AI interactions.
   - **Telegram Trigger + Router**: Sorts messages into text or document types.
   - **Google Gemini LLM**: Used for response generation and for outputting PDF embeddings.
   - **Supabase Vector Store**: Built using pgvector, it stores document embeddings and supports similarity search.
   - **Answer Formatting Module**: A custom Python script ensures answers are Telegram-compatible by cleaning unsupported HTML tags and splitting messages.
   - **Memory Buffer**: Maintains basic conversational continuity for AI responses.
   - **Optional Tool **– OpenWeatherMap: Adds current weather information based on the user’s input.
   
   ---
   
   ## 🛠 Developer-Free Setup: Just Plug & Play
   
   No need to write code or deploy a backend server.
   
   1. Ingest the template into your n8n instance.
   2. Connect your APIs: Telegram, Supabase, Google Gemini, and optionally OpenWeatherMap.
   3. Set up the Supabase table for embeddings (a helper SQL snippet is already included).
   4. Deploy and start chatting with your docs!
   
   ---
   
   ## 🚀 Use Cases
   
   - Students: Ask questions about course PDFs.
   - Lawyers: Reference contracts and legal docs.
   - Researchers: Interact with scientific papers.
   - Businesses: Search internal reports or onboarding materials.
   
   Imagine your team uploading a 50-page onboarding guide in PDF form to the bot and then asking questions like “Where is the benefits section?” or “What holidays do we get off?”—all through Telegram.
   
   ---
   
   ## 🌐 Conclusion
   
   This integration demonstrates the power of composable AI workflows. By connecting document understanding, vector search, and conversational AI—all within a Telegram bot interface—you offer users a seamless and intelligent experience.
   
   Best of all? It’s built using n8n’s drag-and-drop interface. No code. No infrastructure headaches. Just automation magic.
   
   Want to try it? Just bring your Supabase credentials, Gemini API key, and Telegram bot token—and let your AI start reading between the lines.
   
   ---

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.