Worked on the LightRAG repository to enhance robustness and responsiveness in October 2024, focusing on dynamic embedding dimension detection and asynchronous operations. Developed logic to automatically detect embedding dimensions, enabling seamless compatibility with various embedding models and reducing dimension-related errors during initialization. Migrated demo workflows from synchronous to asynchronous patterns using Python and asyncio, replacing blocking calls with non-blocking counterparts to improve responsiveness and integration with OpenAI-compatible workflows. Addressed potential embedding and asynchronous issues through targeted hotfixes, resulting in smoother demo execution and more stable behavior. Demonstrated skills in asynchronous programming, dynamic parameter handling, and robust Python engineering practices.