From the course: Fundamentals of AI Engineering: Principles and Practical Applications
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Comparing embedding models
From the course: Fundamentals of AI Engineering: Principles and Practical Applications
Comparing embedding models
- [Instructor] Text embeddings have revolutionized how computers understand language. These mathematical representations allow machines to capture the meaning of words and sentences in a way that enables powerful applications like semantic search, question answering, and document classification. What's interesting is in the primary world of LLMs and AI engineering, you see a lot of comparisons around which LLMs to use but very few around which embeddings models to use. However, the choice of embeddings model can be incredibly powerful and is incredibly important, but with so many models available, how do you choose the right one? Today, we're going to build a practical framework for comparing embeddings models across several dimensions. First, open up notebook 04_03.ipynb. As always, in the upper right hand corner, make sure you've selected the correct virtual environment, which is the .venv virtual environment. I'm going to minimize some of these just to clean up my screen a bit. So…
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