Fundamentals of Vector Databases, RAG, and Agents: The Future of Intelligent Information Systems

In this course, you will learn how to leverage Vector Databases to store and retrieve relevant information for Retrieval Augmented Generation (RAG) models. You will gain insights into enhancing the accuracy and trustworthiness of large language models by integrating them with Vector Databases as external knowledge bases, effectively mitigating hallucinations. By indexing data as dense vectors in Vector Databases, you will develop the skills to create powerful semantic search engines and recommendation systems that perform efficient similarity searches.

Additionally, the course will guide you through the principles of AI agent development. This allows for agents capable of perceiving their environment, making decisions, and taking actions such as querying APIs or databases to achieve specific goals related to data processing and analysis tasks. You will explore the implementation of multimodal applications that can understand and generate content across different modalities, including text, images, and audio, by combining Vector Databases with large language models and other AI components. Finally, you will learn to optimize the performance and scalability of AI systems by leveraging the advanced data management, fault tolerance, and query capabilities of Vector Databases.

What you’ll learn and how to apply it

• Understand the theory behind vector databases
• Implement Vectors (coding, document embedding, querying)
• Effectively leverage Large Language Model APIs (e.g. OpenAI, Claude3)
• Discover Retrieval-Augmented Generation
• Implement RAG (coding, e.g. OpenAI + chromadb)
• Agentic RAG (Theory)
• Agentic RAG (coding, e.g. llamaindex)
• Agent systems (Theory)
• Agent systems (coding, e.g. crewai)

This course is for you because

• You are a Data Scientist.
• You are a Data Engineer.
• You are a Machine Learning Engineer.
• You are a Software Engineer.
• You are a Full-Stack Developer.

Prerequisites

• Python data science libraries, e.g. Pandas, NumPy.
• The course requires you to set up an account at OpenAI to use the API. This is connected to some additional cost.

Course Materials

• GitHub link