extract_ant_topic

This capability analyzes user queries and project requirements to recommend the most suitable AntV libraries such as G2, G6, or L7. It leverages a context-aware recommendation engine that utilizes metadata from the AntV ecosystem, ensuring that suggestions are relevant to the specific project context and user needs. This approach helps streamline the selection process, reducing the time spent on research.

This capability provides users with structured, step-by-step instructions for implementing various AntV libraries. It uses a guided workflow model that breaks down complex tasks into manageable steps, drawing from best practices and examples found in official AntV resources. This structured approach helps users avoid common pitfalls and accelerates the development process.

This capability assists users in troubleshooting and resolving issues encountered while working with AntV libraries. It leverages a knowledge base of common problems and their solutions, integrating directly with community forums and official documentation to provide users with up-to-date and relevant troubleshooting steps. This ensures that users can quickly find solutions without extensive searching.

This capability extracts and compiles best practices from a variety of AntV resources, including documentation, community discussions, and case studies. It utilizes a data aggregation approach that synthesizes information into actionable insights, helping users adopt proven strategies for their projects. This ensures that users are not only implementing features but are doing so in an optimal manner.

This capability generates code examples for various AntV components based on user input. It uses a template-based approach that pulls from a library of pre-defined examples and adapts them to fit the user's specific requirements. This allows users to quickly see how to implement features without having to write code from scratch.