From the course: Complete Guide to Data Lakes and Lakehouses

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Introduction to LLMs and vector embeddings: Llama

Introduction to LLMs and vector embeddings: Llama

From the course: Complete Guide to Data Lakes and Lakehouses

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Introduction to LLMs and vector embeddings: Llama

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- Now we are ready to start running queries and connecting systems to analyze the data in our lakehouse. But before we do that, let me do a quick walkthrough of Dremio. Imagine having the ability to query our PDF documents and receive precise, contextually accurate answers in seconds. The Sales Copilot will empower us to ask questions like, what are the key features of the EcoSprint vehicle models? In what color is the UrbanGlide model available? Which model is the best for someone that drives in the city? And more. Before we dive into the actual code, we will do a very quick exploration of the key components that make this possible, such as cutting edge technologies like large language models, vector embeddings, and vector databases. Then we will dive into the flow and architecture of our application. Let's start with large language models. Large language models, or LLMs, are advanced AI systems designed to understand and generate human-like text. They use deep learning techniques…

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