With AI technology as the core underlying architecture, the entire platform's design revolves around how to better leverage the effectiveness of AI algorithms. At the data processing layer, specifically optimized for AI algorithms, efficient data storage and retrieval methods are adopted to reduce data loading time and improve algorithm execution efficiency. For example, for large-scale image data processing, distributed storage and parallel computing technologies are employed, enabling data to be quickly invoked by AI models for analysis, greatly enhancing the platform's readability and performance in handling complex AI tasks.

The platform boasts hundreds of plugins, covering various aspects such as functional extensions, data connectivity, and visual presentation. Developers can easily integrate plugins into their own applications to quickly expand functionality. For example, integrating a map plugin can provide real-time map display and navigation features for smart travel applications; integrating a payment plugin enables e-commerce applications to support multiple payment methods. Simultaneously, the platform supports the integration of external services, further enriching the application ecosystem. A list of popular plugins and application cases is displayed to intuitively showcase the functionality and application effects of the plugins.

The platform integrates advanced development frameworks such as RAG (Retrieval-Augmented Generation) and Agent. The RAG framework enables applications to combine retrieved information from external knowledge bases when generating content, producing more accurate and rich responses. For example, in intelligent writing applications, through the RAG framework, the application can retrieve relevant information from a vast amount of literature and materials to assist in generating high-quality articles. The Agent framework aids in developing intelligent interaction and decision-making applications, endowing intelligent agents with the ability to autonomously perceive the environment, make decisions, and execute tasks. Taking an intelligent logistics scheduling application as an example, an agent developed based on the Agent framework can autonomously plan the optimal logistics distribution route based on real-time traffic conditions, order information, vehicle status, and other factors. Relevant framework principle schematic diagrams can be inserted to help users understand the framework's working mechanism.