feat: Generate problem support for title variable

[shaohuzhang1]

feat: Generate problem support for title variable

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[shaohuzhang1]

The given code has two main issues:

1. Multiple replace functions: The original prompt.replace('{data}', paragraph.content) replaces all occurrences of the placeholder {data} with the paragraph's content. However, if the input prompt also contains a placeholder for '{title}', this line will replace both placeholders unnecessarily.

   # Incorrect usage due to double replacement
   Prompt = "I want you to generate problems based on {data} ({title})."
   Paragraph_content = "Sample Content"
   Title = "Sample Title"
   
   prompt_with_title = Prompt.replace('{data}', Paragraph_content)  # This replaces '{data}'
   final_prompt = prompt_with_title.replace('{title}', Title)       # This replaces '{title}' again

   Instead, separate replacements should be done to avoid unnecessary replacements and make it clearer that each variable serves a specific purpose when generating text.

2. String formatting in Python: If there is an intention to format strings using variables from paragraph.title, consider using named parameters instead of keyword arguments when creating objects like HumanMessage. In many languages like Python and some others, using named parameters can help improve readability by clearly indicating which part of the string corresponds to which parameter value.

   HumanMessage(content=prompt.replace('{data}', paragraph.content))

   becomes:

   HumanMessage(content='{originalPrompt}'.format(originalPrompt=prompt.format(data=paragraph.content)))

Here’s how these improvements could look after addressing the first issue alone:

Modified Code Block

try:
    ListenerManagement.update_status(QuerySet(Paragraph).filter(id=paragraph.id), TaskType.GENERATE_PROBLEM,
                                     State.STARTED)
    
    prompts_list = [
        {"message": prompt.replace('{data}', paragraph.content)},
        {"message": prompt.replace('{title}', paragraph.title)}
    ]

    for prompt_dict in prompts_list:
        res = llm_model.invoke([HumanMessage(**prompt_dict)])
    
        if (res.content is None) or (len(res.content) == 0):
                return
    
        problems = res.content.split('\n')

These changes ensure that each message generated uses only relevant placeholders defined in its respective dictionary key.