How can I allocate chat memory to the openai model

I'm working in django, I have a view where I call the openai api, and in the frontend I work with react, where I have a chatbot, I want the model to have a record of the data, like the chatgpt page does.

class chatbot(APIView):

    def post(self, request):
        chatbot_response = None
        if api_key is not None and request.method == 'POST':
            openai.api_key = api_key
            user_input = request.data.get('user_input')
            prompt = user_input

            response = openai.Completion.create(
                model = 'text-davinci-003',
             
                prompt = prompt,
                max_tokens=250,
                temperature=0.5
            )
           

            chatbot_response = response["choices"][0]["text"]
        if chatbot_response is not None:
            return Response({"response": chatbot_response}, status=status.HTTP_200_OK)
        else:
            return Response({'errors': {'error_de_campo': ['Promt vacio']}},
                                status=status.HTTP_404_NOT_FOUND)

I was planning to create a model and save the questions in the database, but I don't know how to integrate that information into the view, I'm worried about the token spending, I don't really know how it works. I hope someone can clarify these doubts for me. thank you so much.

How can I allocate chat memory to the openai model?

Save it in database

by saving into database, you can permanently save the history
by follow this

https://docs.djangoproject.com/en/4.2/topics/db/queries/

https://docs.djangoproject.com/en/4.0/intro/tutorial02/#database-setup

1. you need to create a database model containing User Identification & History

import jsonfield
from django.db import models


class ChatHistory(models.Model):
    owner_uid = models.CharField(max_length=100)
    chat = jsonfield.JSONField() # would be {USER, BOT} interaction

    def __str__(self):
        return self.name

2. Create A Get History Endpoint

from .models import ChatHistory
class chatbot(APIView):

    def post(self, request):
        chatbot_response = None
        if api_key is not None and request.method == 'POST':
            openai.api_key = api_key
            user_input = request.data.get('user_input')
            prompt = user_input

            response = openai.Completion.create(
                model = 'text-davinci-003',
             
                prompt = prompt,
                max_tokens=250,
                temperature=0.5
            )
           

            chatbot_response = response["choices"][0]["text"]
        if chatbot_response is not None:
            return Response({"response": chatbot_response}, status=status.HTTP_200_OK)
        else:
            return Response({'errors': {'error_de_campo': ['Promt vacio']}},
                                status=status.HTTP_404_NOT_FOUND)
    def get(self, request):
        uid = request.GET.get('uid')  # Assuming the uid is passed as a query parameter

        if uid is None:
            return Response({'errors': {'uid': ['UID parameter is required.']}}, status=status.HTTP_400_BAD_REQUEST)

        chat_history = ChatHistory.objects.filter(owner_uid=uid)
        serialized_data = ChatHistorySerializer(chat_history, many=True).data

        return Response({'data': serialized_data}, status=status.HTTP_200_OK)

Showing it In frontend?

well idk about your front-end code it's just a json data

you should managed to do it easily!

Worry About The Cost?

Well you shouldn't use text-davinci-3 for chatbot purpose in the firstplace!

text-davinci-3 is an InstructGPT model
Instruct models are optimized to follow single-turn instructions. Ada is the fastest model, while Davinci is the most powerful(and very expensive)

you should've used ChatCompletion with chatgpt-3.5-turbo instead

https://platform.openai.com/docs/api-reference/chat/create?lang=python

import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")

completion = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "user", "content": "Hello!"}
  ]
)

print(completion.choices[0].message)

Why? take a look at the pricing

gpt-3.5-turbo $0.002 / 1K tokens

Davinci $0.0200 / 1K tokens

The Cost Effectivness is 10 Times!