Vector Database Nedir ?

Giriş

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A vector database is specifically engineered to efficiently deal with vector data. So, what’s vector data? It represents data points in multi-dimensional space, a mathematical approach to defining real-world information.

Consider this, you have an assortment of images. Each of these images can be represented as a vector in a high-dimensional space where each dimension relates to some feature of the image (like color, shape, or texture). By comparing these vectors, we can find similar images. Sounds neat, right?

This capability is pivotal because it enables similarity search — a type of search where you’re fishing for things that are similar, not necessarily exact replicas. This is a game-changer in many domains, like recommendation systems and machine learning.

Technologies behind vector databases

1. Indexing

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The foundation of vector databases lies in data indexing. Through techniques like inverted indexing, vector databases can efficiently conduct similarity searches by grouping and indexing vector features. Furthermore, vector quantization techniques aid in mapping high-dimensional vectors to lower-dimensional spaces, resulting in reduced storage and computational requirements. By leveraging indexing techniques, vector databases enable efficient searching of vectors using various operations such as vector addition, similarity calculation, and clustering analysis.

2. Vector Quantization

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Furthermore, vector quantization techniques aid in mapping high-dimensional vectors to lower-dimensional spaces, resulting in reduced storage and computational requirements. By leveraging indexing techniques, vector databases enable efficient searching of vectors using various operations such as vector addition, similarity calculation, and clustering analysis.

3. Storage

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Regarding the storage aspect of vector databases, it is noteworthy that indexing techniques take precedence over the choice of underlying storage.