This article provides a complete, practical walkthrough of the Telegram Extractfromfile Automate Webhook 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: Building an Intelligent HR & IT Helpdesk Chatbot with Audio Transcription Using n8n and OpenAI
   
   Meta Description: Learn how to create a smart HR & IT helpdesk chatbot using n8n, OpenAI, PostgreSQL PGVector, and Telegram. This end-to-end tutorial includes audio-to-text transcription, vector search for internal policies, and conversational memory for contextual interactions.
   
   Keywords:
   n8n chatbot, HR automation, IT helpdesk automation, OpenAI transcription, AI agent, vector database, PGVector, Telegram bot, audio message bot, employee HR assistant, internal document vector search, AI workflow automation
   
   Third-Party APIs and Tools Used:
   
   - OpenAI API – for generating embeddings, audio transcription (Whisper), and answering questions via GPT-based models.
   - Telegram Bot API – for receiving and responding to user text and voice messages.
   - PostgreSQL with pgvector – for storing and querying vector embeddings from internal policy documents.
   
   —
   
   Article:
   
   Creating a Smart HR & IT Helpdesk Chatbot with Audio Transcription in n8n
   
   Workplace automation is evolving rapidly with the integration of AI and workflow orchestration tools. Businesses are increasingly looking for ways to personalize and speed up employee support — especially in HR and IT departments. In this guide, we explore how to construct a powerful, AI-driven helpdesk chatbot using n8n with support for both text and voice interactions via Telegram.
   
   This no-code/low-code workflow combines natural language processing, audio transcription, and vector search to answer employee queries about internal policies. Let’s break the solution down by steps and show how you can build a similar HR or IT assistant using OpenAI and PostgreSQL.
   
   —
   
   Step 1: Import and Extract Internal Policy Documents
   
   The workflow kicks off by fetching internal documentation such as employee handbooks or IT/HR policies using an HTTP Request node. In this example, we download a public PDF file hosted on AWS S3.
   
   Once the PDF is retrieved, the Extract from File node uses built-in functionality to parse and convert its contents from binary to plain text. This raw text is the foundational data that drives the chatbot’s responses.
   
   Pro Tip: You can replace the PDF link with a secure internal storage location where your organization hosts up-to-date policy documents.
   
   —
   
   Step 2: Convert Policy Text into Vector Embeddings
   
   With the text extracted, the next step is to convert these documents into a vector format for semantic search. Here's how it's done:
   
   - The text is split into smaller, manageable chunks using a Recursive Character Text Splitter.
   - Each chunk is passed through OpenAI’s Embeddings model to generate high-dimensional vector representations.
   - These vectors and their corresponding metadata are stored in a PostgreSQL database with pgvector support.
   
   Using a vector store like PGVector allows the system to perform similarity searches—matching employee queries with the most relevant policy information.
   
   This step ensures the chatbot retrieves highly accurate and context-relevant answers.
   
   —
   
   Step 3: Telegram Message Handling with Audio Support
   
   The chatbot is deployed via Telegram using a Telegram Trigger node in n8n. It listens for incoming messages and classifies them as:
   
   - Text: The message is passed directly to the chatbot AI agent.
   - Voice: The Telegram node extracts the audio file ID, retrieves the voice note, and passes it to OpenAI’s Whisper ASR (Automatic Speech Recognition) model for transcription. The result is treated like a regular text query.
   
   A Switch node ensures that unsupported message types (images, stickers, etc.) are met with a friendly fallback response, guiding the user to send either text or audio.
   
   This multi-format input pathway ensures your helpdesk assistant is conversationally versatile and accessible to more employees.
   
   —
   
   Step 4: Deploying an AI Agent to Power the Helpdesk
   
   The intelligent core of this solution is the AI Agent node, built with n8n’s integration with LangChain. The agent performs Retrieval-Augmented Generation via the previously created vector store. Here’s what makes it work:
   
   - The embedded HR/IT documents act as the "memory" of the assistant.
   - OpenAI’s Chat Completion API (e.g., GPT-4) turns vector matches into personalized responses.
   - The Postgres Chat Memory node tracks multi-turn conversations, maintaining context across sessions.
   
   This means employees can ask follow-up questions without repeating themselves — a crucial feature for creating human-like interactions with your virtual helpdesk agent.
   
   Examples of employee questions supported:
   
   - “What’s our policy on remote work?”
   - “How many vacation days do I have?”
   - “How do I reset my work email password?”
   - “Can I carry over unused PTO?”
   
   By grounding the AI's responses in official HR and IT documents, the chatbot stays relevant, accurate, and trustworthy.
   
   —
   
   Step 5: Returning Responses Back to Users
   
   After receiving a response from the AI agent, the final node in the workflow sends the message back to the user via Telegram. Whether the response came from a direct text input or a voice message transcription, the output is smooth and cohesive.
   
   In essence, this workflow turns a static repository of policies into a conversational interface accessible from a smartphone, empowering employees to resolve routine questions without waiting for HR or IT support staff.
   
   —
   
   Conclusion
   
   With powerful tools like n8n, OpenAI, and PGVector, creating an intelligent, multi-modal helpdesk assistant is more achievable than ever. This workflow demonstrates how to:
   
   - Extract knowledge from internal company documents.
   - Leverage embeddings and semantic search for accurate information retrieval.
   - Handle both text and voice inputs with fallback support.
   - Use AI agents for dynamic, natural language understanding.
   - Deploy on a popular communication platform like Telegram.
   
   By combining structured data and flexible AI, you can elevate your internal operations and give your employees the speedy support they need — one intelligent chat at a time.
   
   Ready to bring your documents to life? Build and deploy your own helpdesk chatbot with n8n today.

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.