Telegram Webhook Automation Webhook
This workflow can automatically receive research topics submitted by users and utilize Perplexity AI for in-depth information retrieval and content generation. Through a multi-step AI model processing, the workflow structures the research results and converts them into modern, responsive HTML webpages, beautified with Tailwind CSS. This process achieves full automation from topic research to webpage presentation, making it particularly suitable for content creators and researchers to quickly generate professional webpages, enhance work efficiency, and simplify the information integration and design process.
Workflow Name: “🔍🛠️ Perplexity Researcher to HTML Web Page”
Key Features and Highlights:
This workflow automatically receives user-submitted research topics and leverages the Perplexity AI research tool for in-depth information retrieval and content generation. Through a multi-step AI model processing pipeline, it structurally parses the research results and ultimately converts them into a modern, responsive single-page HTML webpage. The webpage is styled beautifully and efficiently using Tailwind CSS, supporting clear formatting such as headings, paragraphs, and citations, making it ready for immediate publishing or sharing.
Core Problem Solved:
It addresses the full-process automation challenge from topic research and content generation to webpage presentation. This eliminates the need for users to manually integrate information and design webpages, enabling a seamless, efficient workflow from research query to professional HTML content output.
Use Cases:
Ideal for content creators, researchers, market analysts, and educators who need to quickly transform research findings into professional web articles. Particularly suitable for automated workflows that require periodic generation of research reports, news updates, or knowledge sharing.
Main Workflow Steps:
- Receive user-submitted research topic requests via Webhook.
- Validate the topic and optimize the user’s query prompt using an LLM model.
- Perform in-depth content retrieval on the optimized topic using the Perplexity AI research tool.
- Parse the returned text from Perplexity to structurally extract core article elements, including categories, titles, metadata, content paragraphs, and tags.
- Use language models such as GPT-4o-mini to convert the structured content into standards-compliant HTML format, applying Tailwind CSS for page styling and responsive design.
- Generate the final HTML webpage content and return it via Webhook response to the frontend or subsequent processes.
- Support pushing summarized content to designated Telegram chat groups through a Telegram bot for instant information distribution.
Involved Systems and Services:
- Perplexity AI Research Tool (API calls)
- OpenAI GPT-4o-mini Language Model (multi-node text generation and parsing)
- n8n Built-in Nodes (Webhook, conditional logic, data transformation, no-op nodes, etc.)
- Telegram (message pushing)
- Tailwind CSS (webpage styling)
Target Users and Value Proposition:
This workflow is designed for users who need to rapidly generate professional research-oriented web content, such as content editors, researchers, data analysts, and automation operation teams. By leveraging high automation and intelligent content generation with format conversion, it significantly improves work efficiency, lowers the barrier to content creation, ensures accurate and well-structured output, and delivers visually appealing results—empowering knowledge dissemination and decision support.