What is a Vector Database? Powering Semantic Search & AI Applications

章节 1：传统数据库的困境与“语义鸿沟”

📝 本节摘要：

本节以一张山景日落的图片为例，探讨了传统关系型数据库在处理非结构化数据时的局限性。虽然可以通过存储二进制文件、元数据和人工标签来管理图片，但这种方式无法捕捉图片的“语义语境”（如色调风格或特定景观特征）。作者指出，计算机存储数据的方式与人类理解数据的方式之间存在断层，这被称为“语义鸿沟”，导致传统的结构化查询无法有效检索具有多维特征的非结构化数据。

[原文] [Speaker]: What is a vector database? Well, they say a picture is worth a thousand words. So let's start with one.

[译文] [Speaker]: 什么是向量数据库？嗯，常言道，一图胜千言。所以让我们先从一张图片开始。

[原文] [Speaker]: Now in case you can't tell, this is a picture of a sunset on a mountain vista. Beautiful.

[译文] [Speaker]: 既然你可能看不出来，这是一张山景日落的图片。很美。

[原文] [Speaker]: Now let's say this is a digital image and we want to store it. We want to put it into a database and we're going to use a traditional database here called a relational database.

[译文] [Speaker]: 现在假设这是一张数字图像，我们想要存储它。我们想把它放入数据库中，这里我们将使用一种传统的数据库，称为关系型数据库（Relational Database）。

[原文] [Speaker]: Now what can we store in that relational database of this picture? Well we can put the actual picture binary data into our database to start with,

[译文] [Speaker]: 那么关于这张图片，我们能在关系型数据库中存储什么呢？嗯，首先我们可以把实际的图片二进制数据（Binary Data）放入我们的数据库，

[原文] [Speaker]: so this is the actual image file but we can also store some other information as well like some basic metadata about the picture so that would be things like the file format and the date that it was created, stuff like that.

[译文] [Speaker]: 也就是实际的图像文件，但我们也可以存储一些其他信息，比如关于图片的一些基本元数据（Metadata），那会是像文件格式和创建日期之类的东西。

[原文] [Speaker]: And we can also add some manually added tags to this as well. So we could say, let's have tags for sunset and landscape and orange,

[译文] [Speaker]: 我们也可以给它添加一些人工标签。所以我们可以说，让我们加上“日落”、“风景”和“橙色”的标签，

[原文] [Speaker]: and that sort of gives us a basic way to be able to retrieve this image, but it kind of largely misses the images overall semantic context.

[译文] [Speaker]: 这某种程度上给了我们一种检索这张图片的基本方法，但它在很大程度上丢失了图片的整体语义语境（Semantic Context）。

[原文] [Speaker]: Like how would you query for images with similar color palettes for example using this information or images with landscapes of mountains in the background for example.

[译文] [Speaker]: 比如利用这些信息，你该如何查询具有相似调色板（Color Palettes）的图片，或者背景中有山脉景观的图片呢。

[原文] [Speaker]: Those concepts aren't really represented very well in these structured fields and that disconnect between how computers store data how humans understand it has a name. It's called the semantic gap.

[译文] [Speaker]: 那些概念在这些结构化字段中并没有得到很好的体现，这种计算机存储数据的方式与人类理解数据的方式之间的脱节有一个名字。它被称为语义鸿沟（Semantic Gap）。

[原文] [Speaker]: Now traditional database queries like select star where color equals orange, it kind of falls short because it doesn't really capture the nuanced multi-dimensional nature of unstructured data.

[译文] [Speaker]: 传统的数据库查询，比如“选择所有颜色等于橙色的项目”（SELECT * WHERE color = orange），之所以力不从心，是因为它无法真正捕捉非结构化数据（Unstructured Data）那细微的、多维的本质。