From source code 2 LLM constraints:a semantic extractor for Python, SwiftUI, Lua (github.com)

A new tool called the Semantic Extractor has been announced, which integrates with Griffe to provide detailed insights into how programming frameworks should be used in languages like Python, SwiftUI, and Lua. While Griffe offers an overview of available APIs, the Semantic Extractor captures essential usage constraints—such as which decorators can be nested, signature transformations, and type validation rules—and serves this structured knowledge through an MCP (Minimal Communication Protocol) server. This enables language models (LLMs) to query specific constraints, ensuring that they generate correct and contextually appropriate code. The significance of the Semantic Extractor for the AI/ML community lies in its potential to enhance code generation capabilities of LLMs by supplying them with ground truth knowledge directly from the source code, rather than relying on potentially outdated documentation. By compiling framework source code into an easily queryable format, it helps eliminate guesswork when LLMs generate code. This innovation promises to streamline the development process and improve the accuracy of automated programming tools, ultimately bridging the gap between understanding framework capabilities and their practical application in software development.