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  3. Introduction to Embeddings with the OpenAI API

Exercise

Visualizing the embedded descriptions

Now that you've created embeddings from the product descriptions, it's time to explore them! You'll use t-SNE to reduce the number of dimensions in the embeddings data from 1,536 to two, which will make the data much easier to visualize.

You'll start with the products list of dictionaries you worked with in the last exercise, containing product information and the embeddings you created from the 'short_description'. As a reminder, here's a preview of products:

products = [
    {
        "title": "Smartphone X1",
        "short_description": "The latest flagship smartphone with AI-powered features and 5G connectivity.",
        "price": 799.99,
        "category": "Electronics",
        "features": [
            "6.5-inch AMOLED display",
            "Quad-camera system with 48MP main sensor",
            "Face recognition and fingerprint sensor",
            "Fast wireless charging"
        ],
        "embedding": [-0.014650369994342327, ..., 0.008677126839756966]
    },
    ...
]

matplotlib.pyplot and numpy have been imported as plt and np, respectively.

Instructions 1/3

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  • Create two lists by extracting information from products using list comprehensions: categories, containing the 'category' of each product, and embeddings, containing the embedded short description.