Vector Database: The Secret Behind Large Language Models Capabilities

What are Vector Databases and Why Are They Important for LLMs?

Have you ever wondered how language models like GPT-3, BERT, and others seem to understand and generate text with astonishing accuracy? The answer lies in their ability to represent words, sentences, and documents as dense numerical vectors, known as vector embeddings. These vector embeddings encode the semantic meaning and contextual information of the language, enabling LLMs to navigate and manipulate language data like never before.

In this blog, we will take you on an exciting journey through the world of Vector Databases, shedding light on their significance in modern language processing and machine learning. Whether you are a seasoned data scientist, a language enthusiast, or simply curious about the inner workings of these powerful models, this article is for you.

Table of Contents:

1. Vector Embedding

2. Why We Need a Vector Database?

3. How Does Vector Database Work?

4. Vector Index Creation Algorithms

5. Similarity Measurement Methods