KBLaM采用独特的向量化知识表示方法，其计算成本仅随知识库规模呈线性增长（O(n)），与传统上下文学习的平方级复杂度（O(n²)）形成鲜明对比。这一特性源于其将知识编码为固定维度的键值对，在模型推理时通过矩阵运算快速匹配相关向量。实验数据显示，处理包含百万级条目的知识库时，KBLaM的推理延迟仅增加23%，而传统方法可能产生300%以上的性能下降。这种可扩展性使其在企业文档管理、行业知识图谱等需要海量知识接入的场景具备实用价值。
This answer comes from the articleKBLaM: An Open Source Enhanced Tool for Embedding External Knowledge in Large ModelsThe
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