Enriching embeddings

Previously, when you embedded product information, you were limited to only embedding the product 'short_description', which captured some, but not all of the relevant product information available. In this exercise, you'll embed 'title', 'short_description', 'category', and 'features' to capture much more information.

Here's a reminder of the products list of dictionaries:

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"
        ]
    },
    ...
]

When combining the features into a single string, it should have the following structure:

Title: <product title>
Description: <product description>
Category: <product category>
Features: <feature 1>; <feature 2>; <feature 3>; ...

This exercise is part of the course

Introduction to Embeddings with the OpenAI API

View Course

Exercise instructions

Define a function called create_product_text() to combine the title, short_description, category, and features data into a single string with the desired structure.
Use create_product_text() to combine the features for each product in products, storing the results in a list.
Embed the text in product_texts.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Define a function to combine the relevant features into a single string
def create_product_text(product):
  return f"""Title: {____}
Description: {____}
Category: {____}
Features: {____}"""

# Combine the features for each product
product_texts = [____ for product in ____]

# Create the embeddings from product_texts
product_embeddings = ____

Edit and Run Code