AI-Powered Web Scraping and API Data Retrieval Demonstration Workflow

This workflow automatically generates creative and educational children's English stories using AI technology, combining audio and illustrations to create multimedia content. It is triggered every 12 hours, automatically pushing the generated story text, audio, and images to a designated Telegram channel, eliminating the cumbersome steps of manual creation and distribution. It is suitable for educational institutions, parents, and content creators, enhancing the fun and interactivity of children's English learning. It achieves efficient production and precise sharing of story content.

This workflow builds an intelligent chatbot that can monitor updates to personal resumes and portfolios in real-time, providing instant Q&A services. By vectorizing and storing the resume content, and combining it with advanced AI models, it can accurately answer questions from recruiters or visitors. Additionally, the system automatically saves conversation history and sends daily summary reports, enhancing user experience and data analysis capabilities, making it highly suitable for job seekers and recruitment teams.

This workflow is a multimedia intelligent interactive robot that can automatically identify and process audio, video, images, and text messages on WhatsApp. By receiving user messages in real time, it intelligently sorts different types of content and utilizes advanced AI technology for analysis and response, significantly enhancing the customer interaction experience. It is suitable for various scenarios such as customer support, marketing interaction, and intelligent assistance, helping businesses achieve efficient automated communication.

This workflow achieves full-process automation of web information retrieval by automatically capturing webpage screenshots and utilizing AI for content analysis. First, it calls a screenshot API to generate a complete screenshot of the webpage. Then, AI is used to intelligently extract the core content from the screenshot. Finally, it integrates the webpage title, URL, and the generated description to output structured information. This approach overcomes the limitations of traditional text scraping, significantly enhancing the efficiency and quality of web content acquisition, making it suitable for various scenarios such as market research and content review.

This workflow allows users to engage in real-time conversations with AI through a locally deployed large language model, ensuring data security and privacy. Users can input text in the chat interface, and the system will utilize the powerful local model to generate intelligent responses, enhancing interaction efficiency. It is suitable for internal customer service in enterprises, model testing by researchers, and natural language processing tasks that require high response speed, helping users achieve a secure and convenient automated chat system.

This workflow automates the reading of local WAV format audio files and calls the Wit.ai speech recognition API for intelligent transcription, simplifying the process of converting speech to text. Through automation, it addresses the need for converting audio files to text, enhancing processing efficiency and accuracy. It is suitable for scenarios such as customer service and meeting management, significantly reducing labor costs and promoting intelligent office practices and data applications.

This workflow utilizes the Google Gemini multimodal visual language model to automatically generate structured titles and descriptions for input images, intelligently overlaying them as watermarks. The entire process includes steps such as image downloading, resizing, text generation, format parsing, and image editing, achieving intelligent understanding and automated annotation of visual content. This significantly enhances content production efficiency and image protection capabilities. It is applicable in various scenarios, including media publishing, social media management, and copyright protection.

This workflow enables flexible invocation and management of various large language models through the OpenRouter platform. Users can dynamically select models and input content simply by triggering chat messages, enhancing the efficiency of interactions. Its built-in chat memory function ensures contextual coherence, preventing information loss. This makes it suitable for scenarios such as intelligent customer service, content generation, and automated office tasks, greatly simplifying the integration and management of multiple models, making it ideal for AI developers and teams.