LLM Chaining Examples

This workflow utilizes artificial intelligence technology to automatically assign primary categories to WordPress blog posts, significantly enhancing content management efficiency. It addresses the time-consuming and error-prone issues of traditional manual categorization, making it suitable for content operators and website administrators, especially when managing a large number of articles. Users only need to manually trigger the process to retrieve all articles, which are then categorized through intelligent AI analysis. Finally, the categories are updated back to WordPress, streamlining the content organization process and improving the quality of the website's content and user experience.

This workflow integrates an intelligent assistant specifically designed to query the capitals of fictional countries. Users can obtain capital information for specific countries through simple natural language requests, or receive a list of all supported country names when they request "list." It combines language understanding and data mapping technologies, enabling quick and accurate responses to user inquiries, significantly enhancing the interactive experience. This is suitable for various scenarios, including game development, educational training, and role-playing.

This workflow aims to enhance the intelligence and accuracy of online searches. After the user inputs a research question, the system automatically generates the optimal search query and retrieves results through the Brave Web Search API. By leveraging advanced large language models, it conducts multi-dimensional semantic analysis and result re-ranking, ultimately outputting the top ten high-quality links and key information that closely match the user's needs. This process is suitable for scenarios such as academic research, market analysis, and media editing, effectively addressing the issues of imprecise traditional search queries and difficulties in information extraction.

This workflow is designed to automate the processing of YouTube videos by calling an API to extract video subtitles and using an AI language model to generate concise and clear content summaries. Users only need to provide the video link to quickly obtain the core information of the video, significantly enhancing information retrieval efficiency and saving time on watching and organizing. It is suitable for content creators, researchers, and professionals, helping them efficiently distill and utilize video materials to optimize their learning and work processes.

This workflow utilizes the OpenAI GPT-4 model to achieve understanding and generation of natural language. It can generate structured information based on user input and ensures the accuracy of output format and content through an automatic correction mechanism. It addresses the shortcomings of traditional language models in terms of data formatting and information accuracy, making it suitable for scenarios such as data organization, report generation, and content creation. It helps users efficiently extract and verify structured data, thereby enhancing work efficiency and reliability.

This workflow automatically detects and summarizes the AI model information used by all workflows in the current instance. It extracts the model IDs and names associated with each node and exports the results to Google Sheets. Through batch processing, users can quickly understand the model invocation status in a multi-workflow environment, avoiding the tediousness of manual checks and enhancing project management transparency and operational efficiency. It is suitable for automation engineers, team managers, and data analysts.

This workflow integrates the OpenAI intelligent assistant with custom tools, providing flexible intelligent interaction capabilities. Users can easily inquire about the capital information of fictional countries, supporting input of country names or retrieval of country lists, enhancing the practicality of the conversation. Additionally, the built-in time retrieval tool adds temporal context to the dialogue, making it suitable for various scenarios such as smart customer service and educational entertainment, thereby optimizing the efficiency and accuracy of data queries.

This workflow combines OpenAI assistants with vector storage technology to implement a document retrieval and question-answering function. It can accurately extract relevant content from a document library and generate text with citations. It supports Markdown formatting and HTML conversion, enhancing the readability and professionalism of the output content while ensuring the reliability of the generated information. This makes it suitable for various scenarios such as intelligent Q&A, content creation, enterprise knowledge management, and educational research.