This article provides a complete, practical walkthrough of the Telegram Code Create 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:**  
   Automating Resume Extraction and PDF Generation with n8n and OpenAI: A Complete Workflow Guide
   
   **Meta Description:**  
   Learn how to build a fully automated resume extractor using n8n, Telegram, OpenAI GPT-4, and Gotenberg. This workflow turns a resume PDF into structured JSON and outputs a professionally formatted PDF in minutes.
   
   **Keywords:**  
   n8n workflow, resume parser, PDF to JSON, OpenAI GPT-4, Telegram bot automation, generate resumes, Gotenberg, ChatGPT PDF parsing, resume automation, resume to structured data
   
   **Third-party APIs Used:**
   
   1. **Telegram Bot API** – To receive messages and resume PDF files from users.
   2. **OpenAI API (GPT-4 Turbo)** – To analyze and extract structured data from raw text.
   3. **Gotenberg API** – For converting structured HTML into PDF format.
   
   ---
   
   ## Automating Resume Extraction with GPT-4 and n8n: From File to Formatted PDF
   
   In today’s fast-paced recruitment environment, automating resume processing can save time and improve consistency — especially when updates are flowing in rapidly via various platforms. This article walks you through a smart n8n-based workflow that accepts resume PDFs via Telegram, extracts structured information using OpenAI's GPT-4 model, and finally delivers a clean, customized PDF version using HTML and Gotenberg.
   
   Let’s explore how this powerful automation works from end to end.
   
   ### How It Works: Step by Step Breakdown
   
   This resume extractor workflow consists of highly modular operations using n8n, a powerful and extendable automation tool. Here's a breakdown of each major component:
   
   ---
   
   ### 1. Receiving the File via Telegram
   
   The process begins with the **Telegram Trigger node**, allowing users to simply send a PDF resume to a bot. An authentication check restricts usage to specific authorized Telegram users. A second condition filters out the standard `/start` command, ensuring only uploaded documents are processed.
   
   ---
   
   ### 2. Extracting Content from the Resume PDF
   
   Once the document is submitted, the workflow:
   
   - Uses the **Telegram API** to download the file.
   - Processes it through the **Extract from File node** to extract raw, readable text content from the PDF file.
   
   ---
   
   ### 3. Parsing Resume Data with OpenAI and JSON Schema
   
   The extracted raw text is then handed over to the **OpenAI GPT-4 Turbo** via **LangChain’s LLM Chat Model**. A carefully crafted system prompt guides the model to extract:
   
   - Personal information
   - Employment history
   - Education
   - Projects
   - Volunteering
   - Technical skills
   - Foreign language proficiencies
   
   The output is expected to follow a strict **JSON schema**, enforced through LangChain’s **Structured Output Parser**. This ensures consistency and accuracy in the responses.
   
   To catch and fix minor format issues in the model output, an **Auto-fixing Output Parser** is placed before committing the data.
   
   ---
   
   ### 4. Transforming and Styling the Data as HTML
   
   With structured data available, multiple **Code nodes** handle the conversion of each section (education, employment, projects, et cetera) into clean HTML. These are then redirected into **Set nodes** with HTML formatting and merged into a single HTML block, ensuring that the final output is both organized and visually appealing.
   
   Each section is marked up with `<b>` and `<br/>` tags, giving a polished look when viewed as a PDF.
   
   ---
   
   ### 5. HTML to PDF Conversion Using Gotenberg
   
   HTML formatting complete, the payload undergoes:
   
   - Base64 encoding
   - Regeneration into a `.html` file using the **Convert to File node**
   - Conversion into PDF using **Gotenberg**, an open-source HTML-to-PDF and Office-to-PDF service.
   
   This highly efficient method ensures that the final PDF retains excellent formatting and structure.
   
   ---
   
   ### 6. Sending the Final Resume via Telegram
   
   Finally, the finished resume PDF is sent back to the user through Telegram using the same chat ID. The filename is dynamically generated based on the applicant’s name, ensuring a personal touch.
   
   ---
   
   ### Why This Workflow Matters
   
   This approach:
   
   ✅ Saves hours of manual data entry  
   ✅ Creates consistently formatted resume documents  
   ✅ Can be adapted for ATS systems or sourced into databases  
   ✅ Offers a real-time, user-friendly interface via Telegram  
   
   It’s a testament to what’s possible when integrating tools like n8n, OpenAI, and Gotenberg in a serverless or small-team automation stack.
   
   ---
   
   ### Final Thoughts
   
   This workflow is an excellent starter template for automating document analysis beyond just resumes. With a few tweaks, it could parse invoices, academic certificates, or even legal documents.
   
   If you're interested in extending this, consider:
   
   - Adding multilingual support
   - Storing the structured data in a database
   - Integrating with email or SaaS platforms like Notion, Airtable, or Slack
   
   Want to learn more? Check out the original creator’s [YouTube tutorial on Gotenberg integration](https://youtu.be/bo15xdjXf1Y?si=hFZMTfjzfSOLOLPK) or subscribe to [Workfloows](https://www.youtube.com/@workfloows) for more automation ideas.
   
   —
   
   **🚀 Automate smart. Work less. Output more.**

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.