From the course: Defending and Deploying AI by Pearson

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Using vector databases

Using vector databases

From the course: Defending and Deploying AI by Pearson

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Using vector databases

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Let's go over what are vector databases. Vector databases are so popular nowadays in many modern AI applications, especially whenever you are leveraging Retrieval Augmented Generation or RAG and also in agentic implementations. But what are vector databases? At its core, vector databases, or a vector database, is a specialized system, a specialized database that is designed to store and manage data in the form of vector embeddings. And you already learned that vector embeddings are numerical representations of data like text, images, or audio. And these vectors capture the semantic meaning of the data, then enabling you to do similarity search or semantic search that go beyond traditional keyword matching. So this makes vector databases very powerful for tasks requiring context and relevance to be given to an AI model to then, of course, give you a better answer. Now, there are a whole bunch of different popular vector databases that are widely used in AI applications, agentic…

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