prompt-groomer 0.1.0

A lightweight Python library for optimizing and cleaning LLM inputs

Prompt Groomer

A lightweight Python library for optimizing and cleaning LLM inputs. Reduce token usage, improve prompt quality, and lower API costs.

Overview

Prompt Groomer helps you clean and optimize prompts before sending them to LLM APIs. By removing unnecessary whitespace, duplicate characters, and other inefficiencies, you can:

• Reduce token usage and API costs
• Improve prompt quality and consistency
• Process inputs more efficiently

Status

This project is in early development. Features are being added iteratively.

Installation

# Using uv (recommended)
uv pip install prompt-groomer

# Using pip
pip install prompt-groomer

Quick Start

Build custom cleaning pipelines with a fluent API:

from prompt_groomer import Groomer, StripHTML, NormalizeWhitespace, TruncateTokens

# Define a cleaning pipeline
groomer = (
    Groomer()
    .pipe(StripHTML())
    .pipe(NormalizeWhitespace())
    .pipe(TruncateTokens(max_tokens=1000, strategy="middle_out"))
)

raw_input = "<div>  User input with <b>lots</b> of   spaces... </div>"
clean_prompt = groomer.run(raw_input)
# Output: "User input with lots of spaces..."

4 Core Modules

Prompt Groomer is organized into 4 specialized modules:

1. Cleaner - Clean Dirty Data

• StripHTML() - Remove HTML tags, convert to Markdown
• NormalizeWhitespace() - Collapse excessive whitespace
• FixUnicode() - Remove zero-width spaces and problematic Unicode

2. Compressor - Reduce Size

• TruncateTokens() - Smart truncation with sentence boundaries
  • Strategies: "head", "tail", "middle_out"
• Deduplicate() - Remove similar content (great for RAG)

3. Scrubber - Security & Privacy

• RedactPII() - Automatically redact emails, phones, IPs, credit cards, URLs, SSNs

4. Analyzer - Show Value

• CountTokens() - Track token savings and optimization impact

Complete Example

from prompt_groomer import (
    Groomer,
    # Cleaner
    StripHTML, NormalizeWhitespace, FixUnicode,
    # Compressor
    Deduplicate, TruncateTokens,
    # Scrubber
    RedactPII,
    # Analyzer
    CountTokens
)

original_text = """Your messy input here..."""

counter = CountTokens(original_text=original_text)

groomer = (
    Groomer()
    # Clean
    .pipe(StripHTML(to_markdown=True))
    .pipe(NormalizeWhitespace())
    .pipe(FixUnicode())
    # Compress
    .pipe(Deduplicate(similarity_threshold=0.85))
    .pipe(TruncateTokens(max_tokens=500, strategy="head"))
    # Secure
    .pipe(RedactPII(redact_types={"email", "phone"}))
    # Analyze
    .pipe(counter)
)

result = groomer.run(original_text)
print(counter.format_stats())  # Shows token savings

Examples

Check out the examples/ folder for detailed examples organized by module:

• cleaner/ - HTML cleaning, whitespace normalization, Unicode fixing
• compressor/ - Smart truncation, deduplication
• scrubber/ - PII redaction
• analyzer/ - Token counting and cost savings
• all_modules_demo.py - Complete demonstration

Development

This project uses uv for dependency management and make for common tasks.

# Install dependencies
make install

# Run tests
make test

# Format code
make format

License

MIT