Qdrant 1.14 introduces several enhancements aimed at improving performance and flexibility in vector search applications. The update features a Score-Boosting Reranker, which allows the blending of vector similarity with custom business logic to refine search outcomes, catering to specific needs like e-commerce promotions or prioritizing recent data in news searches. Incremental HNSW Indexing is introduced to efficiently handle new data by extending existing graphs rather than rebuilding them, reducing computational costs. The update also optimizes batch search operations by enhancing parallel processing capabilities, significantly speeding up query response times. Additionally, improvements in resource utilization, such as CPU and disk IO optimization, lead to faster processing and more predictable performance during large-scale data indexing. Memory usage has been optimized for handling large datasets, allowing for more efficient storage without additional hardware needs. Upgrading to this version is seamless with no major API changes, ensuring compatibility with existing client libraries.