Generate customer inquiry embeddings

For customer inquiries, we'll apply the same generate_embeddings function used for products. In your Jupyter Notebook, add a new cell to generate an embedding for a customer's query, using the text_description parameter instead of image_base64:

Jupyter Notebook

customer_query = "Hi! I'm looking for a red bag"
query_embedding = generate_embedding(text_description=customer_query)
query_embedding

This approach converts the text of the customer's query into a vector embedding, similar to how we processed product images.

The result is a numerical vector that represents the customer's query, ready for comparison against product embeddings using cosine similarity in the upcoming steps.

The output should look something like this:

Now that we have the vectors for the images in the product catalog and the vector embedding for the customer inquiry, let's calculate the cosine similarity in the next section.