本篇指南基于 FlatBuffers 官方文档中 Python 语言章节，系统讲解如何在 Python 中读写 FlatBuffers 二进制数据：包括 Python 运行库在仓库中的位置、flatc --python 代码生成与项目集成方式、GetRootAsMonster 读取流程、AsNumpy() 向量零拷贝加速，以及文本解析的能力边界。

LLM 通过插件机制支持多种嵌入模型（参见 插件体系 ）。安装插件后，嵌入模型即可在命令行或 Python API 中使用，用来计算并存储内容的嵌入向量，然后对这批向量执行相似度搜索。 LLM 项目将这一整套能力拆分为四个相互衔接的层面，也正是本文的四个主体章节： ...

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Slicing bytes copies. On a small payload nobody notices, but slice a large packet or image buffer in a loop and the copies ...