🤌 wtprompt: What the Prompt?

wtprompt is a lightweight, no-nonsense library designed to help you manage your LLM prompts efficiently.

Tired of cluttering your code with blocks of text? wtprompt keeps your code clean by loading prompts from files or JSON. Say goodbye to length issues and linting headaches.

🚀 Why wtprompt?

• ✅ Zero bloat – Need just prompts? No need for a full MLOps library.
• ✅ Jinja support – Use powerful templating syntax for dynamic prompts.
• ✅ Markdown-friendly – Ready for OpenAI-style Markdown prompting.
• ✅ Folder & JSON support – Load prompts from structured files or JSON.
• ✅ Prompt hashing – Track prompts with unique identifiers.
• ✅ Built-in preprocessing – Clean input text before inserting it into prompts.

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What's New in v0.1.2

• 🚀 Simplified Prompt Filling: You can now use fill_prompt directly with FolderPrompts:

  base_prompts.fill_prompt('fill_test', {'day': 'Monday', 'this_month': 'August'})

• ⚠ Breaking Change:
  fill_list and PromptGenerator are no longer accessible at the top level (wtprompt).
  Update your imports as follows:

  from wtprompt.fill import fill_list, PromptGenerator

  or drop them in favour of the simplified filling method.

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🛠 Installation

pip install wtprompt

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🔥 Quickstart

1️⃣ Load prompts from a folder

Store your prompts as .txt or .md files in a folder:

prompts/
  ├── fill_test.txt  # "Today is {{ day }} in the month of {{ this_month }}."
  ├── hello.txt
  ├── subfolder/
  │   ├── question.md

Then, load them:

from wtprompt import FolderPrompts

base_prompts = FolderPrompts("prompts")

# Retrieve a prompt
prompt = base_prompts.hello  # Loads "hello.txt"
prompt = base_prompts.subfolder.question  # Loads "subfolder/question.md"

2️⃣ Fill in values

Now you can directly fill prompts using fill_prompt:

filled_prompt = base_prompts.fill_prompt("fill_test", {"day": "Monday", "this_month": "August"})
print(filled_prompt)

➡️ Output:
"Today is Monday in the month of August."

For simpler cases, use fill_list:

filled_prompt = base_prompts.fill_list("fill_test", ["Monday", "August"])

Tip: fill_list is faster for basic substitutions, but Jinja allows more flexibility.

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📂 Folder-Based Prompt Management

FolderPrompts helps organize and manage prompts with structured storage.

Load prompts dynamically

my_prompts = FolderPrompts("prompts")

# Direct attribute access
prompt_text = my_prompts.prompt_name
prompt_text = my_prompts.subfolder.prompt_name

Save and load prompt reports

my_prompts.save_prompt_report("report.json")
my_prompts.load_from_prompt_report("report.json", strict=True)

Strict mode ensures that prompt hashes match.

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📄 JSON-Based Prompt Loading

Store prompts in a JSON file:

{
    "greeting": "Hello, {{ name }}!",
    "farewell": "Goodbye, {{ name }}."
}

Load and use them:

from wtprompt import JsonPrompts

prompts = JsonPrompts("prompts.json")
prompt = prompts.greeting  # Or prompts("greeting")

Note: JSON loading is not lazy. Use validate=True to check for errors.

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📝 Define Prompts in Code

For quick prototyping, define prompts manually:

from wtprompt import PromptLoader

prompts = PromptLoader()
prompts.add_prompt("greeting", "Hello, {{ name }}!")

# Retrieve the prompt and its hash
text, hash_id = prompts.get_prompt("greeting", get_hash=True)

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🔧 Text Preprocessing

Raw text can be messy. Use TextPreprocessor for basic cleaning:

from wtprompt.utils.preprocessor import TextPreprocessor

preprocessor = TextPreprocessor()

is_valid, cleaned_text = preprocessor.preprocess("   Hello!   ")
assert is_valid  # True

Customizable settings include:

• Whitespace handling: Trim spaces, limit consecutive spaces.
• Validation: Ensure text isn’t empty or gibberish.
• Truncation: Limit max length.
• Character filtering: Enforce ASCII, Unicode normalization.

Example:

def build_prompt(prompts, preprocessor, context, question):
    is_ok, context = preprocessor.preprocess(context)
    assert is_ok, "ERROR: Invalid context"
    return base_prompts.fill_list("prompt_name", [context, question])

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TL;DR

✔️ Store prompts in folders or JSON.
✔️ Use Jinja templating to insert dynamic values.
✔️ Apply preprocessing for cleaner input.

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📜 License & Contributions

Licensed under MIT. Contributions are welcome!