Prompt Title: OpenAI endpoint for relevant notes.

Sure, I can provide you with a sample code for implementing an endpoint in Django that uses OpenAI's embeddings API to provide users with relevant notes for a meeting based on their previous notes. However, before we begin, please note that you'll need to sign up for an API key from OpenAI to use their embeddings API.

Here is an example code that uses the openai python package to call the embeddings API:

pythonCopy code
import openai
from django.http import JsonResponse

# Set up OpenAI API key
openai.api_key = "YOUR_API_KEY_HERE"

# Define the endpoint function
def relevant_notes(request):
    if request.method == 'POST':
        # Get the user's previous notes from the request body
        previous_notes = request.POST.get('previous_notes')

        # Call OpenAI's embeddings API to get the embeddings of the previous notes
        response = openai.Embedding.retrieve(
            model='text-davinci-002',
            texts=[previous_notes],
        )

        # Get the embeddings of the previous notes
        embeddings = response['data'][0]['embedding']

        # Use the embeddings to find the relevant notes for the meeting
        relevant_notes = find_relevant_notes(embeddings)

        # Return the relevant notes as a JSON response
        return JsonResponse({'relevant_notes': relevant_notes})
    else:
        return JsonResponse({'error': 'Invalid request method'})

# Function to find relevant notes based on embeddings
def find_relevant_notes(embeddings):
    # TODO: Implement your logic for finding relevant notes based on the embeddings
    pass

In the relevant_notes function, we first get the user's previous notes from the request body. We then call OpenAI's embeddings API to get the embeddings of the previous notes. Once we have the embeddings, we can use them to find the relevant notes for the meeting. The find_relevant_notes function is a placeholder function that you'll need to implement based on your own logic for finding relevant notes.

Note that in this example code, we're using the text-davinci-002 model for the embeddings. You can experiment with other models to see which one works best for your use case.

Also, be sure to properly secure your API key and configure your server's firewall to ensure that only authorized traffic can access the endpoint.