Using the Token Count for Debugging and Verification
 

• For understanding how many tokens your prompt uses, add a debugging print line after trimming. This can help in adjusting the max_allowed_tokens value:

  
  # Count tokens in the trimmed prompt
  token_count = count_tokens(full\_prompt)
  print("Token count:", token\_count)
      

• This print statement will show the token count, so you can see if further adjustments are needed.

 
Putting All Together in Your Application
 

• Ensure that any part of your application that builds or modifies the prompt ends by calling the trim\_prompt function. This will keep the token usage within the desired limits.
• If you have functions or classes that generate parts of the prompt separately, consider applying the trimming function before combining these parts for the final prompt.
• The overall flow in main.py might look like this:

  
  from token_utils import count_tokens, trim\_prompt
  
  def build_prompt(data):
      # Build the prompt by combining several pieces of data
      prompt = "Intro text. " + data.get("details", "")
      # Add more content as needed
      return prompt
  def main():
      # Generate the full prompt from data
      data = {"details": "Long context that might exceed token limits..."}
      full_prompt = build_prompt(data)
  # Set the maximum tokens allowed for compatibility with the language model
  max_allowed_tokens = 500
  full_prompt = trim_prompt(full_prompt, max_allowed\_tokens)
  
  # Optional: Print token count for debugging
  token_count = count_tokens(full\_prompt)
  print("Token count:", token\_count)
  
  # Continue with sending full\_prompt to the language model
  # Example: response = send_to_model(full\_prompt)
  # print(response)
  
  if name == "main":
      main()
      

  
  


• This structure will help reduce token usage by trimming extra text and making sure only the most relevant parts are sent to the model.

• In your project’s file explorer (within Lovable), create a new file named prompt\_manager.py. This file will handle token-related functions.
• Add the following code snippet in prompt\_manager.py. This simple function calculates token count by splitting text into words. In a real-world scenario, you might use a proper tokenizer library.

  
  def count\_tokens(text):
      # This is a simple example: count tokens by splitting on spaces.
      # For more accurate counts consider integrating a library.
      return len(text.split())
      

 
Integrating the Token Manager With Your Application
 

• Open your main application file where you handle large prompts (for example, main.py).
• At the top of that file, import the token counting function:

  
  from prompt_manager import count_tokens
      

• Where you process user input or large prompts, add code to calculate token usage. Insert the snippet below in the section where your prompt is prepared:

  
  user\_prompt = "Your large prompt goes here..."
  token_count = count_tokens(user\_prompt)
  print("Token count:", token\_count)
      

 
Modularizing Large Prompts Into Manageable Sections
 

• When dealing with very large prompts, split your text into logical segments stored in separate files. For example, create two new text files named prompt_intro.txt and prompt_body.txt using Lovable’s file creation tools.
• In these files, paste the corresponding parts of your prompt. Then in main.py, read in these files using code like:

  
  def load_prompt(file_name):
      with open(file\_name, 'r') as file:
          return file.read()
  
  intro_text = load_prompt("prompt_intro.txt")
  body_text = load_prompt("prompt_body.txt")
  full_prompt = intro_text + "\n" + body_text
  token_count = count_tokens(full_prompt)
  print("Total token count:", token_count)
      

  
  

 
Caching and Reusing Token Counts
 

• To avoid counting tokens repeatedly (which can be slow for very large texts), store the token count once computed.
• Add a caching mechanism in your prompt\_manager.py file. For example, add a simple dictionary-based cache:

  
  _token_cache = {}
  
  def get_token_count(text):
      if text in _token_cache:
          return _token_cache[text]
      count = count_tokens(text)
      _token_cache[text] = count
      return count
      

  
  


• Update your main.py file to call get_token_count instead of count_tokens directly:
  

  
  from prompt_manager import get_token_count
  
  token_count = get_token_count(full_prompt)
  print("Cached token count:", token_count)
      

  
  

 
Managing Dependencies Without a Terminal
 

• If you need a more advanced tokenizer (for example, one available via tiktoken), since Lovable does not provide a terminal, add the dependency installation directly in your code. At the top of your prompt\_manager.py file, include the following code snippet which programmatically installs the package if necessary:

  
  import importlib.util
  import sys
  import subprocess
  
  def install_and_import(package):
      if importlib.util.find_spec(package) is None:
          subprocess.check_call([sys.executable, "-m", "pip", "install", package])
      globals()[package] = importlib.import_module(package)
  install_and_import("tiktoken")
      

  
  


• This code checks if the package is installed; if not, it installs it. You can then use tiktoken for tokenizing in your functions.