Introduction to the Course

Artificial intelligence (AI) is rapidly advancing and introducing many exciting innovations. One of the exciting areas is generative AI, which can create various types of content such as text, images, sound, and video. This is largely driven by advancements in large language models (LLMs), which can generate text closely resembling human writing.

To achieve this, LLMs must efficiently process and retrieve vast amounts of high-dimensional vector data. This is where vector databases come into play. Unlike traditional databases that store structured data, vector databases are optimized for managing and retrieving high-dimensional vectors generated from text, images, or other content. Learning about vector databases, we can understand how they help efficiently handle complex data representations, supporting LLMs in creating high-quality, human-like text.

In this course, we will learn about how vector databases function to generate text and why they are important for generative AI and LLMs. These advancements enhance the quality and efficiency of text generation and unlock possibilities for breakthroughs in various fields.

Course objectives

This course aims to comprehensively understand vector databases and their critical role in LLMs. By mastering these techniques, you can efficiently manage and manipulate the vast amounts of data required for LLMs, ensuring optimal performance and relevance in generative AI tasks. By the end of the course, you will have a solid foundation in using vector databases, enabling you to support and enhance the capabilities of LLMs in various applications.

Course structure

This course comprehensively explores Vector Databases and their applications for LLMs.

• The course starts with defining vector data, vector databases, and embeddings, and their roles in supporting LLMs.

• Next, we'll focus on the necessity of vector databases for LLMs, comparing them to traditional databases in terms of data storage, retrieval, scalability, and security. It also includes the internal architecture of vector databases, discussing query processing and indexing strategies for fast and efficient searches. Additionally, it covers approximate nearest neighbor search, a crucial concept for LLMs.

• Then, we'll introduce Chroma Vector DB, a vector database, showcasing its capabilities through code snippets demonstrating CRUD operations and similarity searches.

• Lastly, we'll explore the real-world applications of vector databases in various domains, such as recommender systems, image and video recognition, healthcare, natural language processing, and anomaly/fraud detection.

Prerequisites

To get the most out of this course, it is recommended that you have a good understanding of basics of linear algebra, database systems and machine learning algorithms. Proficiency in Python programming or another high-level programming language is also required to fully grasp the course material.

Intended audience

This course is designed for individuals with a basic understanding of natural language processing (NLP) and those who are new to the concept of vector databases or curious about how they enhance the performance of powerful language models (like LLMs). It is also suitable for those who are new to the concept of vector databases.