From the course: Advanced RAG Applications with Vector Databases

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Introduction to embeddings

Introduction to embeddings

From the course: Advanced RAG Applications with Vector Databases

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Introduction to embeddings

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- [Instructor] Now that we've wrapped up chunking, let's cover embeddings. Before vector embeddings, we didn't have a way to compare unstructured data. With embedding models, we do. Embedding models are machine learning models, almost always deep neural networks, that turn your text, images, videos, audio, whatever kind of data you have into vectors or vector embeddings. Vectors are the tools we use to quantitatively compare unstructured data. Remember that it's important to use the correct embedding models to embed whatever data you have. In most contexts, that refers to embedding models trained on your data type. For example, using ResNet50 for image embeddings, using Sentence Transformers for your text, or using Whisper for your audio. In this context, we are primarily concerned with embedding text. The rise in popularity of large language models late in 2022 and all of 2023 showed us that text is one of the most important mediums for AI to work with. As such, there are now…

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