Vector Databases: The Secret Behind the Power of Large Language Models What are vector databases and why are they important for LLM?

Have you ever wondered how language models like GPT-3, BERT, etc. understand and generate text with astonishing accuracy? The answer lies in their ability to represent words, sentences, and documents as dense numerical vectors called vector embeddings. These vector embeddings encode semantic and contextual information of language, allowing LL.M. to navigate and manipulate linguistic data in ways never before possible.

In this blog, we will take you on an exciting journey into the world of vector databases, illuminating their importance in modern language processing and machine learning. Whether you are an experienced data scientist, a language enthusiast, or just curious about the inner workings of these powerful models, this article is for you.

Table of contents:

Vector Embeddings
Why do we need a vector database?
How do vector databases work?
Vector index creation algorithm
Similarity measurement method

1. Vector embedding

Vector embeddings are a powerful way to represent data in artificial intelligence and natural language processing. It helps capture the essence of information, helps AI systems gain a deeper understanding of data and promotes long-term memory retention. Comprehension and recall are key factors when learning something new.

AI models, such as LLM, generate embeddings by converting data into low-dimensional vectors. This transformation is valuable because it simplifies data representation, especially when dealing with large numbers of features. The resulting embeddings encode every aspect of the data, enabling AI models to grasp complex relationships, detect patterns and discover hidden structures. Essentially, embeddings act as a bridge between the raw data and the AI ​​system's ability to make sense of it all.

2. Why do we need a vector database?

Using vector embeddings presents a unique set of challenges, especially when working with traditional scalar-based databases. These traditional databases struggle with the complexity and scale of vector data, which can hinder the extraction and real-time analysis of valuable insights. However, the solution to this problem lies in the use of vector databases, which are specifically designed to handle this type of data efficiently. By leveraging vector databases, organizations can unlock the full potential of their data