LLM Cost Optimization Toolkit

A comprehensive collection of tools and strategies for reducing LLM API costs through intelligent token optimization.

🎯 Project Goal

Reduce LLM API costs by 50-80% through intelligent preprocessing, compression, and optimization techniques while maintaining output quality.

📁 Repository Structure

llm-cost-optimization/ ├── token-optimizer/ # Main pipeline implementation │ ├── pipeline.py # Core orchestrator with verbose mode │ ├── scripts/ # Individual optimization stages │ │ ├── 01_spell_check.py │ │ ├── 02_abbreviations.py │ │ ├── 03_token_aware.py │ │ └── 04_ml_paraphrase.py │ ├── config/ # Configuration files │ ├── utils/ # Utilities (diff viewer, etc.) │ └── examples/ # Usage examples ├── docs/ # Research and documentation │ ├── ideas-brainstorm.md │ ├── text-normalization-approaches.md │ └── implementation-plan.md ├── requirements.txt # Python dependencies ├── setup_project.sh # One-click setup script └── todo.md # Project task tracking

🚀 Quick Start

1. Initial Setup

```bash

Clone and setup

cd llm-cost-optimization ./setup_project.sh

Activate virtual environment

source venv/bin/activate ```

2. Basic Usage

```bash

Navigate to token optimizer

cd token-optimizer

Process text

echo "Please could you help me understand this?" | python pipeline.py

Output: "help understand this?"

Process file with verbose mode

python pipeline.py input.txt --verbose

Analyze optimization potential

python pipeline.py document.txt --analyze ```

3. Advanced Features

```bash

Use specific stages only

python pipeline.py input.txt --stages abbreviations token_aware

Different tokenizer models

sed -i 's/model: "gpt2"/model: "gpt-4"/' config/pipeline_config.yaml

Run the demo

./demo.sh ```

🛠️ Features

Token Optimizer Pipeline

• Modular Architecture: 4 independent stages that can be mixed and matched
• Smart Token Counting: Actual token measurement for your target LLM
• ML Compression: Uses small models (T5-small) for intelligent paraphrasing
• Beautiful Verbose Mode: Visual diffs, statistics, and progress tracking
• Configurable: YAML configuration for easy customization

Optimization Techniques

1. Spell Correction: Fix typos that waste tokens
2. Smart Abbreviations: Context-aware replacements
3. Token-Aware Optimization: Only changes that actually save tokens
4. ML Paraphrasing: Intelligent compression using small language models

📊 Performance

Typical results on real-world prompts: - Token Reduction: 30-60% - Processing Time: <500ms (without ML), <2s (with ML) - Quality Preservation: 95%+ semantic similarity

🔧 Configuration

Edit token-optimizer/config/pipeline_config.yaml:

```yaml tokenizer: model: "gpt2" # or "gpt-4", "claude", etc.

pipeline: spellcheck: enabled: true abbreviations: enabled: true tokenaware: enabled: true mlparaphrase: enabled: true model: "t5-small" maxlength_ratio: 0.8 ```

📚 Documentation

• Ideas & Brainstorming - Comprehensive strategies list
• Research Topics - Academic and industry research
• Text Normalization Approaches - All possible methods
• Implementation Plan - Technical implementation guide
• Todo List - Project tasks and progress

🤝 Contributing

This project is designed for parallel development. Check todo.md for available tasks.

Development Workflow

1. Pick a task from todo.md
2. Create a new branch
3. Implement and test
4. Update documentation
5. Submit PR

🧪 Testing

Quick Test

```bash

Run basic tests

./run_tests.sh ```

Full Test Suite

```bash

Unit tests with coverage

make ci-test

All CI checks (format, lint, type, test)

make ci

Individual checks

make ci-format # Format code make ci-lint # Run linters make ci-typecheck # Type checking ```

Test Structure

tests/ ├── conftest.py # Shared fixtures and helpers ├── unit/ # Fast, isolated unit tests │ ├── test_spell_check.py │ ├── test_abbreviations.py │ ├── test_token_aware.py │ └── test_pipeline.py └── integration/ # End-to-end integration tests └── test_full_pipeline.py

Writing Tests

```python

Use provided fixtures

def testexample(sampletext, abbreviationsdict, testhelpers): result = process(sampletext) assert testhelpers.calculatereduction(sampletext, result) > 30 ```

📈 Roadmap

• [x] Core pipeline implementation
• [x] Verbose mode with visual diffs
• [ ] Web interface
• [ ] Real-time optimization API
• [ ] Custom model training
• [ ] Integration plugins (VSCode, CLI tools)

📄 License

MIT License - See LICENSE file for details

🙏 Acknowledgments

• Microsoft Research for LLMLingua
• Hugging Face for transformers library
• Rich library for beautiful terminal UI