From the course: Advanced RAG Applications with Vector Databases
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What you should know
From the course: Advanced RAG Applications with Vector Databases
What you should know
- [Instructror] This course assumes you have a conceptual understanding of vector databases, embedding models, and large language models. I also assume you understand the fundamentals of how to write Python code. Before we dive into the course, let's review some of these topics and how they're related to what we're about to learn here. We'll start with vector databases. A vector database is a tool that helps you work with unstructured data in the form of vectors. Fun fact, the name vector database is actually a misnomer. Vector databases are not real databases, but rather compute engines for vector data. Vector data is often referred to as a vector embedding in the context of Generative AI. And for the purpose of retrieval augmented generation, we will refer to vectors and embeddings as the same thing. These vectors are long series of numbers, typically hundreds or thousands of numbers. The reason we call them vector embeddings is because they're generated by deep neural networks…
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