Supabase Setup Postgres

This workflow implements the functionality of automatically importing CSV files into a Postgres database. Users can manually trigger the process to quickly read local CSV data, convert it into spreadsheet format, and automatically map fields for writing to the database, enhancing the efficiency and accuracy of data import. It simplifies the traditionally cumbersome operational steps and lowers the barrier for data processing, making it suitable for users such as data analysts and developers who need to regularly handle CSV data.

This workflow automatically detects newly uploaded files in a specified Google Drive folder, promptly shares them via email with designated recipients, and synchronizes the detailed metadata of the files into an Airtable database. Through this process, users can reduce the cumbersome tasks of manually searching for and sharing new files, thereby improving the efficiency and security of file sharing, ensuring centralized and traceable file management, which is suitable for businesses and teams to enhance work efficiency in remote collaboration.

This workflow implements automated management of bookmarks through the Raindrop API, including functionalities for creating, updating, and querying bookmarks. Users can easily create bookmark collections, dynamically update bookmark titles, and retrieve detailed information, thereby improving the efficiency and accuracy of bookmark management. It is suitable for positions in content management, information collection, and especially beneficial when frequently handling large amounts of online resources, as it effectively reduces errors caused by manual operations, saves time, and enhances management standardization.

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.

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 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 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 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.