GitHub has introduced a new Copilot embedding model that significantly enhances code search capabilities in VS Code by improving retrieval quality, reducing memory usage, and increasing throughput. This model, designed to better understand the context of code and documentation, results in more accurate responses and faster search results, offering a 37.6% improvement in retrieval quality and a marked enhancement in code acceptance ratios for Java and C# developers. Utilizing advanced training techniques like contrastive learning with hard negatives, the model can effectively distinguish between nearly correct and correct code snippets, enhancing the user experience by minimizing "near misses" in search results. This development is part of a broader initiative to make AI coding assistants more reliable and efficient, with plans to expand training data and refine the negative mining pipeline for better quality results.