This article provides a complete, practical walkthrough of the Stopanderror Telegram 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:  
   Build a Telegram Chatbot for PDF Q&A with OpenAI, Pinecone, and n8n
   
   Meta Description:  
   Learn how to set up a fully automated Telegram bot with n8n that accepts PDF documents, processes them using OpenAI embeddings, stores them in Pinecone, and answers user questions using Retrieval-Augmented Generation (RAG) techniques.
   
   Keywords:  
   n8n, Telegram bot, RAG, OpenAI embeddings, Pinecone, PDF chatbot, vector store, LangChain, document Q&A, Telegram automation, chatbot with memory
   
   Third-Party APIs Used:
   
   - Telegram Bot API  
   - OpenAI API  
   - Pinecone API  
   - Groq API (LLaMA 3 model via LangChain)
   
   Article:
   
   Chat With PDFs on Telegram Using RAG and n8n
   
   In today's era of generative AI, users increasingly seek personalized AI agents capable of understanding documents and answering questions based on their contents. Imagine sending a PDF directly to a Telegram bot and then querying its content like it was a live human reader. Thanks to n8n's powerful visual automation interface, this is no longer a dream—but a low-code reality.
   
   In this article, we break down an impressive n8n workflow that brings together Telegram, OpenAI, Pinecone, and Groq to create a Retrieval-Augmented Generation (RAG) chatbot that works seamlessly within Telegram.
   
   Overview of the Workflow
   
   This workflow performs two primary functions:
   
   1. Accepts documents (specifically PDFs) via Telegram, processes and segments them into text chunks, generates embeddings using OpenAI, and stores them in a Pinecone vector store.
   2. Handles natural language user queries, retrieves relevant document chunks, and generates context-aware answers using a LLaMA 3.1 model via Groq.
   
   Let’s dive into each stage of this automation.
   
   1. Telegram Message Trigger
   
   The flow begins with a Telegram Trigger node that listens for incoming messages. It checks whether the message contains a document. If it doesn’t, the user is assumed to have sent a query, triggering the RAG pipeline. If it is a PDF, the document ingestion process is initiated.
   
   2. Document Upload and Processing
   
   When a document upload is detected:
   
   - The Telegram API is used to fetch the file.
   - A custom JavaScript node ensures the file’s MIME type is set to application/pdf, and the filename ends with .pdf.
   - n8n’s LangChain integration kicks in with the Recursive Character Text Splitter node, which chunks the document (using parameters: chunk size: 3000, overlap: 200).
   - Those chunks are processed to generate embeddings using OpenAI's Embeddings API.
   
   3. Storing in Pinecone
   
   With the OpenAI-generated vectors ready, the next node sends these embeddings to Pinecone—a vector database optimized for high-speed similarity search.
   
   Finally, a Telegram message is sent to confirm the file has been processed, including how many pages were stored in Pinecone (retrieved from the document metadata).
   
   Example Response:  
   📄 12 pages saved on Pinecone
   
   4. Retrieval-Augmented Q&A Pipeline
   
   If the message was not a PDF, the user query moves through the retrieval Q&A pipeline:
   
   - A Retriever node fetches the most relevant document chunks from Pinecone based on OpenAI embeddings’ semantic similarity.
   - These are passed along to a retrieval-based QA Chain node, which includes a prompt to the language model to formulate the response.
   - The powerful Groq LLaMA 3.1 70B model is used to interpret the query in context and assemble a natural language answer.
   - Finally, the Telegram Response node sends the answer back to the user.
   
   The result? A chatbot that behaves like an intelligent document assistant right within Telegram.
   
   Error Handling
   
   The workflow is fortified with Stop and Error nodes that gracefully end execution and output messages if something fails along the way, such as an unsupported file type or embedding error.
   
   Why Use This Stack?
   
   - n8n provides a low-code interface and handles webhook listening, conditional logic, function scripting, and multi-app orchestration beautifully.
   - OpenAI delivers high-quality vector embeddings essential for meaningful semantic search.
   - Pinecone is optimized for low-latency vector similarity search at scale.
   - Groq hosts fast and expressive LLMs like Meta’s LLaMA-3.1, capable of generating nuanced and fluent responses.
   - Telegram offers a familiar, user-friendly environment where end-users can interact seamlessly.
   
   Potential Use Cases
   
   - Internal knowledge base search via chat
   - Academic PDF reader bots
   - Legal document Q&A assistants
   - Sales documentation search for clients
   
   Conclusion
   
   This n8n workflow is a prime example of the power that comes from integrating modern AI tools with classic messaging apps. With minimal code and no frontend development required, you can turn Telegram into a research assistant, librarian, or technical support bot—one PDF at a time.
   
   Whether you're automating client services, supporting documentation-heavy workflows, or simply experimenting with AI agents, this setup is a future-ready foundation for intelligent interactions.
   
   Ready to build your own RAG bot? With n8n, the no-code revolution puts AI in everyone’s hands.

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.