During the Qdrant Summer of Code 2024, Huong (Celine) Hoang enhanced the FastEmbed library by integrating cross-encoders for re-ranking tasks, expanding Qdrant's capabilities in building context-aware search applications. This involved developing a new input-output scheme to produce relevance scores rather than embeddings, a critical function for refining search results. The project focused on designing user-friendly class hierarchies, optimizing tokenization for ONNX models, and ensuring efficient model loading and integration without heavy dependencies like PyTorch. Huong overcame challenges related to model configurations and tokenization with mentorship from George Panchuk, emphasizing code readability and maintainability. Rigorous testing validated the ONNX models' performance against PyTorch counterparts. The enhancement, available in FastEmbed 0.4.0, facilitates applications like search engines and recommendation systems. Future improvements might include expanding model support, optimizing batch processing, and refining tokenization configurations. The internship significantly developed Huong's skills in model integration and user-friendly tool development, reinforcing her commitment to impactful tech solutions.