Basically I am trying to build a notes and mcq generator.
The workflow that I have had followed is :
First I have had generated the syllabus of a class(here class 8th) and then using that generated syllabus I have had generated notes and mcq questions.

So this is the code that I have had written ->

import os
import ast
from openai import OpenAI
from dotenv import load_dotenv

load_dotenv()

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

class_to_generate = "8th class"

def generate_syllabus(class_grade):
    response = client.chat.completions.create(
        model="gpt-3.5-turbo-16k",
        messages=[
            {"role": "system", "content": "You are a teacher."},
            {"role": "user", "content": f"Generate a list of subjects and chapters for {class_grade} grade in a dictionary format."}
        ],
        temperature=1,
        max_tokens=1024,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0
    )
    return response.choices[0].message.content

syllabus_str = generate_syllabus(class_to_generate)
syllabus = ast.literal_eval(syllabus_str)

def generate_notes(subject, chapter):
    response = client.chat.completions.create(
        model="gpt-3.5-turbo-16k",
        messages=[
            {"role": "system", "content": "You are a teacher."},
            {"role": "user", "content": f"Write notes (only key points) on {chapter} of {subject}."}
        ],
        temperature=1,
        max_tokens=1024,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0
    )
    return response.choices[0].message.content

def generate_mcqs(subject, chapter):
    response = client.chat.completions.create(
        model="gpt-3.5-turbo-16k",
        messages=[
            {"role": "system", "content": "You are a teacher."},
            {"role": "user", "content": f"Write MCQ questions on {chapter} of {subject}."}
        ],
        temperature=1,
        max_tokens=1024,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0
    )
    return response.choices[0].message.content

note = ""
mcq = ""

for subject, chapters in syllabus.items():
    for chapter in chapters:
        notes = generate_notes(subject, chapter)
        mcqs = generate_mcqs(subject, chapter)

        note += f"Subject: {subject}nChapter: {chapter}nNotes:n{notes}nn"
        mcq += f"Subject: {subject}nChapter: {chapter}nMCQs:n{mcqs}nn"

def split_text(text, max_tokens=2048):
    chunks = []
    while len(text) > 0:
        if len(text) > max_tokens:
            split_index = text[:max_tokens].rfind('n')
            if split_index == -1:
                split_index = max_tokens
        else:
            split_index = len(text)
        chunks.append(text[:split_index])
        text = text[split_index:]
    return chunks



def call_openai_function(prompt):
    chunk_size = 2000
    chunks = [prompt[i:i+chunk_size] for i in range(0, len(prompt), chunk_size)]
    notes_outputs = []
    mcqs_outputs = []

    for chunk in chunks:
        completion = client.chat.completions.create(
            model="gpt-3.5-turbo-0613",
            messages=[{"role": "user", "content": chunk}],
        )
        message_content = completion.choices[0].message.content
        if "Notes" in prompt:
            notes_outputs.append(message_content)
        if "MCQs" in prompt:
            mcqs_outputs.append(message_content)
    return notes_outputs, mcqs_outputs

notes_of_syllabus_prompt = f"Extract notes from the text:nn{note}"
mcqs_of_syllabus_prompt = f"Extract mcq questions from the text:nn{mcq}"

notes_of_syllabus, _ = call_openai_function(notes_of_syllabus_prompt)
_, mcqs_of_syllabus = call_openai_function(mcqs_of_syllabus_prompt)

output_json = {
    "Notes": notes_of_syllabus,
    "MCQS": mcqs_of_syllabus
}
print(output_json)

formatted_output_json = {
    "Notes": {
        "content": None,
        "role": "assistant",
        "function_call": {
            "arguments": f'{{n  "Notes": "{output_json["Notes"]}"n}}',
            "name": "fun"
        }
    },
    "MCQS": {
        "content": None,
        "role": "assistant",
        "function_call": {
            "arguments": f'{{n  "MCQS": "{output_json["MCQS"]}"n}}',
            "name": "fun"
        }
    }
}

print(formatted_output_json)

My final output is in format ->

{'Notes': {'content': None, 'role': 'assistant', 'function_call': {'arguments': '{n  "Notes": "['on method.\n- Quadratic equations involve variables raised .......

But I want my output to be in format ->

Example of expected format :

{
  "Mathematics": [
    {
      "chapter": "Rational Numbers",
      "topics": [
        {
          "topic": "Numbers",
          "notes": [
            "dcdkfd"
              ],
          "mcqs":[
            "dfvfdv"
                ]
            }
          ]
        }
      ]
}

I have had tried to use function calling but I still wasn’t able to solve it. Please can anyone help me with this question.