What is a Vector Database and why is it important for Generative AI?

Structured versus unstructured data

When you work with standard statistical models, you often work with structured data. This is numerical data that you can easily store in an Excel file with rows and columns. Language models, such as those used in AI, specialize in analyzing unstructured data. Unstructured data includes words, texts, but also images. Words and texts consist of letters that are linked together to give meaning and understand context. For images, pixels must be merged to form a visual whole. Both forms of data cannot easily be reduced to numbers, such as structured data. That is why a Vector Database is necessary to use.

The need for a Vector Database

To analyze and understand unstructured data, you need a Vector Database. Such a database is crucial for the functioning of language models. A Vector Database can be compared to a large dataset that is hidden behind a standard statistical model. It acts as a graph where all words are represented by coordinates, or vectors. Each coordinate in this graph represents a word.

How does a Vector Database work?

In a Vector Database, each word is given a specific location in a graph. For example, the coordinate 138.456 can represent a specific word. This makes unstructured data structured, because each word gets a fixed spot in the graph. Words that are close in meaning get coordinates that are close together. For example, the words “Paris” and “baguette” will be closer together than “bratwurst” because Paris and French bread have more in common.

Vector Database Applications

A Vector Database makes it possible to make unstructured data numerical. This facilitates interpretation by statistical models. In addition, it helps language models learn relationships between words. A language model such as ChatGPT, for example, has been trained by analyzing word vectors in order to make predictions and generate answers.

The importance of Vector Databases

Data forms the basis for every statistical model, including language models. A Vector Database is the brain behind these models, the foundation on which they work. The quality of a Vector Database determines how effective a language model is, how accurate it works and how useful it ultimately is in practice.

Conclusion

A Vector Database is an essential component for analyzing unstructured data, especially in the context of language models and AI. By representing words and other data with vectors, it becomes possible to understand and model complex relationships. This makes powerful applications possible, such as those we see in modern AI systems.

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