answer:尊敬的评审委员会， 我在此致信您，理由是我想申请卓越计划。以下是我申请该计划的理由及相关信息： 1.国际化培养相关课程经历： 在大学期间，我参加过一些与国际化培养相关的课程，其中包括国际商务、跨文化沟通和国际经济等课程。这些课程帮助我全面了解了国际化背景下的商务环境和文化差异，为我今后从事国际事务提供了坚实的基础。 2.报名课程原因： 我希望通过卓越计划报名课程，进一步提升自己在国际事务领域的能力。该计划提供的课程涵盖了国际关系、国际法、国际经济、外交与公共政策等多个领域，这些课程将帮助我深入了解国际事务的复杂性和多元性，提高我在国际事务领域的专业能力。 3.赴国际组织实习或从事高层次对外交流工作的意愿： 我对赴国际组织实习或从事高层次对外交流工作充满热情，并且拥有这样的意愿。我相信通过卓越计划的学习和培养，我将更加了解国际事务的具体运作和国际组织的运行机制。同时，我也相信我具备对外交流和合作的能力，能够在国际舞台上为促进各国间的友好关系和互利合作作出贡献。 总体而言，我希望通过卓越计划的学习和培养，进一步提升自己在国际事务领域的专业能力，并且为国际合作与交流做出自己的贡献。我相信我的经历和意愿使我成为该计划的有力候选人。 谢谢您抽出宝贵的时间阅读我的申请。 此致 敬礼

answer:Yes, I am aware of Amazon OpenSearch. It is an open-source search and analytics engine developed by Amazon Web Services (AWS). OpenSearch is based on the Elasticsearch project and offers a scalable and distributed search solution for various applications and use cases. It supports real-time data analysis, full-text searching, and log analytics among other functionalities.

answer:Yes, I am familiar with the KNN engine in OpenSearch. KNN (K-Nearest Neighbors) is a plugin in OpenSearch that enables efficient search and analysis of vector-based data. It is particularly useful for similarity search, where you want to find similar items or data points based on their characteristics or features. By leveraging approximate nearest neighbor algorithms, the KNN engine allows for fast and scalable similarity searches in high-dimensional vector spaces. It can be used for use cases such as recommendation systems, image and video analysis, anomaly detection, and more.

answer:The default KNN algorithm used by OpenSearch's KNN engine is the HNSW (Hierarchical Navigable Small World) algorithm. HNSW is an approximate nearest neighbor algorithm known for its efficiency and scalable performance in high-dimensional spaces. It constructs a hierarchy of navigable small-world graphs, enabling fast search and retrieval of nearest neighbors. HNSW is suitable for a wide range of use cases and provides good accuracy in approximate nearest neighbor search. However, OpenSearch also allows the use of other algorithms like Annoy and Faiss for KNN if you choose to customize and configure your KNN index accordingly.