We present a static token lookup methodology for text embedding generation that achieves 1.12 ms p50 latency for single text embeddings while maintaining 60.6 MTEB average score across 8 representative tasks, corresponding to 89% of contextual model quality. The Rust implementation delivers 50,000 requests per second throughput through static embedding lookup, optimized mean pooling, and zero-copy IEEE754 binary serialization. Evaluation demonstrates exceptional duplicate detection performance (90.1% AP), strong semantic similarity (76.1% Spearman correlation), and domain-specific performance ranging from 75% to 131% of baseline across specialized domains. The system enables real-time embedding applications where sub-5ms latency is critica

翻译：本文提出了一种用于文本嵌入生成的静态词元查找方法，该方法在单文本嵌入任务中实现了1.12毫秒的p50延迟，同时在8个代表性任务上保持60.6的MTEB平均得分，相当于上下文模型质量的89%。通过静态嵌入查找、优化的均值池化和零拷贝IEEE754二进制序列化技术，其Rust实现方案可达到每秒50,000次请求的吞吐量。评估结果表明，该方法在重复检测任务中表现出卓越性能（90.1% AP），具备强大的语义相似性识别能力（76.1% Spearman相关性），在专业领域中的性能达到基线模型的75%至131%。本系统为延迟要求低于5毫秒的实时嵌入应用提供了可行的解决方案。