Month: 2025-11. Focused on delivering business-value enhancements to text classification within vllm-project/semantic-router by implementing an in-tree embedding similarity matching feature that augments the existing keyword-based classifier. The embedding-based approach enables semantic similarity checks to drive category assignments, improving classification accuracy and robustness of routing decisions for text input. Key work included integrating the embedding classifier into the in-tree pipeline, updating unit tests, and aligning classifier naming to ensure maintainability and compatibility with existing tooling. Impact: More accurate and stable categorization reduces misrouting, enabling downstream services to rely on higher-quality routing decisions and reducing manual intervention. This sets the foundation for future expansion of embedding-based and semantic-aware features across the router.