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.
Tags
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.
Intelligent Conversational Agent Workflow
This intelligent dialogue agent workflow combines advanced language models with information retrieval tools, featuring contextual memory capabilities that allow it to respond to user chat messages in real time. By retaining recent conversation records and accessing external data sources, the workflow effectively addresses the issues of inaccurate responses and outdated information commonly found in traditional chatbots. It is suitable for various scenarios such as customer service, intelligent Q&A systems, and educational tutoring, enhancing the coherence and richness of conversations while providing users with a high-quality intelligent interaction experience.
E-mail Chatbot with Combined Semantic and Structured RAG Using Telegram and Pgvector
This workflow implements an intelligent email Q&A bot that allows users to interact with it via Telegram for quick inquiries about their personal emails. It combines semantic search with structured SQL queries, enabling it to understand natural language questions and accurately locate email content and time information, thereby providing precise answers. This system is particularly suitable for individuals and businesses that require efficient email management, enhancing the intelligence and convenience of email queries.
RAG Workflow For Stock Earnings Report Analysis
This workflow utilizes RAG technology to automatically process and analyze the quarterly financial reports of publicly listed companies in PDF format, generating structured financial analysis reports. It accurately extracts key information through semantic retrieval and large language models, intelligently generating detailed reports that include content such as revenue, costs, and profits, which are then automatically saved to Google Docs. This process significantly enhances the efficiency and accuracy of financial data insights, helping investment analysts, financial advisors, and others quickly obtain in-depth analysis results.
Voice RAG Chatbot with ElevenLabs and OpenAI
This workflow implements an intelligent chatbot based on voice interaction, integrating advanced speech synthesis, recognition technologies, and powerful language models. Users can ask questions via voice, and the system can retrieve relevant knowledge from the database in real-time and generate accurate, natural voice responses, significantly enhancing the accuracy and professionalism of voice Q&A. It is suitable for scenarios such as enterprise customer service, virtual shopping assistants, and knowledge base assistants, providing users with a convenient information retrieval experience.
My workflow
This workflow automatically identifies and extracts key parameters from OAuth2 authentication configurations, such as authorization URI, token URI, and audience information, using a powerful AI language model. It incorporates a confidence scoring mechanism to help users assess the reliability of the data. This significantly enhances the efficiency and accuracy of OAuth2 setup, addressing the complexity and error-proneness of manual querying processes. It is suitable for developers, IT operations personnel, and API integration platform managers, optimizing the process of obtaining OAuth2 authentication parameters.
AI Phone Agent with RetellAI
This workflow provides an intelligent phone agent solution that can automatically record and transcribe calls, extract call summaries, and utilize advanced AI technology for knowledge base Q&A. It supports automatic scheduling of customer appointments, streamlining the manual scheduling process and enhancing the efficiency of phone customer service and sales teams. This system can analyze call content in real-time, ensuring that key information is not overlooked, making it suitable for various types of businesses and improving the quality and responsiveness of customer service.
🔍🛠️ Tavily Search & Extract - Template
This workflow integrates Tavily's search and content extraction API with OpenAI's language model to achieve intelligent web information retrieval and content summarization. Users can input a topic in the chat window, and the system automatically filters highly relevant search results and extracts webpage content, ultimately generating a structured summary. This process addresses the issues of information overload and lack of structure found in traditional search methods, making it suitable for various scenarios such as research, business decision-making, and content creation, thereby enhancing the efficiency and quality of information acquisition.
Integrating AI with Open-Meteo API for Enhanced Weather Forecasting
This workflow integrates artificial intelligence with a weather API to provide an intelligent weather inquiry service. Users only need to enter the city name and the number of days for the query in the chat interface, and the system will automatically retrieve the city's latitude and longitude, as well as future weather information, offering accurate weather forecasts. It supports multi-turn dialogue memory, enhancing the user experience. This service is suitable for scenarios such as travel planning, education and training, and intelligent customer service, allowing users to quickly obtain the weather data they need, thereby assisting with daily travel and decision-making.