Upload Video to Drive via Google Script

This workflow implements the automated processing of JSON formatted text data, capable of batch downloading files, performing text segmentation, and semantic vectorization. The generated vector embeddings are ultimately stored in the Qdrant vector database. By utilizing OpenAI's text embedding model, it enhances text semantic understanding and retrieval efficiency, making it suitable for scenarios such as intelligent question-answering systems, document indexing, and information recommendation. It provides an effective solution for the intelligent analysis of large-scale text data.

This workflow automates the creation and updating of data in the FileMaker database. Users only need to manually trigger it once to complete the addition, deletion, modification, and querying of records, significantly improving the efficiency of database management. It addresses the cumbersome issues of manual data entry and modification in traditional data management, making it suitable for business scenarios that require frequent updates of customer or product information. This reduces operational errors and time consumption, helping businesses achieve a more intelligent office workflow.

This workflow is manually triggered and automatically executes the creation of database tables, data setup, and query operations, simplifying the database management process. Users only need to click "Execute" to quickly complete table structure definition, data assignment, and data retrieval, enhancing efficiency and reducing human errors. It is suitable for scenarios such as database development and testing, as well as data initialization validation, helping technical teams efficiently build and query database tables while minimizing operational risks.

This workflow integrates AI models and databases to enable intelligent question-and-answer interactions in natural language. Users can easily send query requests, and the system converts natural language into SQL queries to retrieve accurate answers from the database. It also supports contextual memory to enhance the conversation experience. This tool reduces the difficulty of data access for non-professional users and improves data utilization efficiency. It is suitable for various scenarios such as enterprise data queries, customer support, and education and training, providing users with a convenient intelligent data interaction solution.

This workflow can automatically detect and upload new files received in Telegram chats to a designated Google Drive folder, eliminating the tedious process of manual downloading and uploading. It ensures that all important files are saved and backed up in a timely manner, enhancing the level of automation in file management. It is suitable for individual users and business teams that require automatic archiving and backup of Telegram files, significantly improving work efficiency and ensuring secure storage of files.

This workflow utilizes the Supabase database and OpenAI's text embedding technology to build an intelligent agent system that enables dynamic management of messages, tasks, statuses, and knowledge. Through semantic retrieval and contextual memory, the system can efficiently handle customer interactions, automatically update information, and enhance the efficiency of knowledge management and task management. It is suitable for scenarios such as intelligent customer service and knowledge base management, reducing manual intervention and achieving automated execution.

This workflow automatically creates multi-level nested folders in Google Drive recursively based on a path string input by the user, and returns the ID of the last-level folder. This process simplifies the cumbersome steps of manually creating folders layer by layer, avoids errors, and improves efficiency. It is suitable for both businesses and individuals to batch create folders for project or category management, as well as to build a standardized folder system in automated file archiving processes, ensuring clear and organized file management.

This workflow automates the generation and storage of sensor data. Every minute, it generates data that includes the sensor ID, a random humidity value, and a timestamp, and writes this information into a PostgreSQL database. It effectively addresses the need for real-time data collection and storage, eliminates the need for manual intervention, and enhances the automation and accuracy of data processing. This workflow is widely applicable in monitoring systems and smart home applications within the Internet of Things (IoT) environment.