deus-llm-token-stats-guru 0.3.1

Advanced LLM token analysis and statistics toolkit for various data formats

Deus LLM Token Stats Guru

Advanced LLM token analysis and statistics toolkit for various data formats.

Features

• Accurate Token Counting: Uses OpenAI's tiktoken library for precise token counts
• Recursive Processing: Automatically finds and processes all CSV files in directories
• Multiple Encoding Models: Supports different OpenAI models (gpt-4, gpt-3.5-turbo, etc.)
• Dual CLI Interface: Two convenient command-line tools
• Comprehensive Output: Detailed statistics including file sizes, row counts, and processing times
• JSON Export: Results can be exported to JSON format
• Type Safety: Full type hints and modern Python features
• Extensible: Designed for future support of multiple data formats beyond CSV

Installation

pip install deus-llm-token-stats-guru

Quick Start

Command Line Usage

# Count tokens in current directory
deus-llm-token-guru .

# Alternative command
llm-token-stats ./data

# Count tokens using specific model
deus-llm-token-guru /path/to/csv/files --model gpt-3.5-turbo

# Save results to JSON file
llm-token-stats /path/to/csv/files --output results.json

# Enable debug logging
deus-llm-token-guru /path/to/csv/files --debug --log-file debug.log

# Quiet mode (suppress progress output)
llm-token-stats ./data --quiet

Python API Usage

from deus_llm_token_stats_guru import CSVTokenCounter
from pathlib import Path

# Initialize counter
counter = CSVTokenCounter(encoding_model="gpt-4")

# Count tokens in a single CSV file
result = counter.count_tokens_in_csv(Path("data.csv"))
print(f"Total tokens: {result['total_tokens']:,}")

# Process entire directory
summary = counter.count_tokens_in_directory(Path("./csv_files"))
print(f"Processed {summary['total_files']} files")
print(f"Total tokens: {summary['total_tokens']:,}")

Available Commands

The package provides two CLI commands:

• deus-llm-token-guru: Main command name
• llm-token-stats: Alternative shorter command

Both commands have identical functionality.

API Reference

CSVTokenCounter

Main class for counting tokens in CSV files.

Methods

• __init__(encoding_model: str = "gpt-4"): Initialize with specific encoding model
• count_tokens_in_text(text: str) -> int: Count tokens in a text string
• count_tokens_in_csv(file_path: Path) -> CountResult: Count tokens in single CSV file
• count_tokens_in_directory(directory: Path) -> CountSummary: Process all CSV files in directory

Type Definitions

class CountResult(TypedDict):
    file_path: str
    total_tokens: int
    row_count: int
    column_count: int
    encoding_model: str
    file_size_bytes: int

class CountSummary(TypedDict):
    total_files: int
    total_tokens: int
    total_rows: int
    total_file_size_bytes: int
    encoding_model: str
    file_results: List[CountResult]
    processing_time_seconds: float

Examples

Basic File Processing

from deus_llm_token_stats_guru import CSVTokenCounter
import pandas as pd
from pathlib import Path

# Create sample data
data = {
    'title': ['AI Introduction', 'Machine Learning'],
    'content': [
        'Artificial intelligence is transforming industries',
        'ML enables computers to learn from data'
    ]
}

# Save to CSV
df = pd.DataFrame(data)
df.to_csv('articles.csv', index=False)

# Count tokens
counter = CSVTokenCounter()
result = counter.count_tokens_in_csv(Path('articles.csv'))

print(f"File: articles.csv")
print(f"Tokens: {result['total_tokens']:,}")
print(f"Rows: {result['row_count']}")
print(f"Columns: {result['column_count']}")

Directory Processing with Different Models

from deus_llm_token_stats_guru import CSVTokenCounter
from pathlib import Path

models = ["gpt-4", "gpt-3.5-turbo"]
directory = Path("./data")

for model in models:
    counter = CSVTokenCounter(encoding_model=model)
    summary = counter.count_tokens_in_directory(directory)

    print(f"Model: {model}")
    print(f"Total tokens: {summary['total_tokens']:,}")
    print(f"Processing time: {summary['processing_time_seconds']:.2f}s")
    print()

Cost Estimation

from deus_llm_token_stats_guru import CSVTokenCounter

counter = CSVTokenCounter(encoding_model="gpt-4")
summary = counter.count_tokens_in_directory("./training_data")

# Estimate OpenAI API costs (example rates)
gpt4_cost_per_1k = 0.03  # USD per 1K tokens
total_cost = (summary['total_tokens'] / 1000) * gpt4_cost_per_1k
print(f"Estimated GPT-4 cost: ${total_cost:.2f}")

CLI Output Format

The CLI tools output JSON with the following structure:

{
  "summary": {
    "total_files": 3,
    "total_tokens": 15420,
    "total_rows": 150,
    "total_file_size_mb": 2.1,
    "encoding_model": "gpt-4",
    "processing_time_seconds": 0.85
  },
  "file_details": [
    {
      "file_path": "/path/to/file1.csv",
      "tokens": 5140,
      "rows": 50,
      "columns": 4,
      "size_mb": 0.7
    }
  ]
}

Environment Setup

1. Create Virtual Environment

python -m venv .venv

2. Activate Virtual Environment

# Linux/macOS
source .venv/bin/activate

# Windows
.venv\Scripts\activate

3. Install Package

pip install --upgrade pip
pip install deus-llm-token-stats-guru

Development

Local Installation

git clone https://github.com/yourusername/deus-llm-token-stats-guru.git
cd deus-llm-token-stats-guru
pip install -e .

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=src --cov-report=term-missing

# Run specific test file
pytest tests/unit/test_core.py

Code Quality

# Format code
ruff format src/ tests/

# Lint code
ruff check src/ tests/

# Type checking
mypy src/

Building Package

# Build package
python setup.py sdist bdist_wheel

# Check package
twine check dist/*

# Test installation
pip install dist/*.whl

Supported Models

The package supports all OpenAI tiktoken encoding models:

• gpt-4 (default)
• gpt-3.5-turbo
• text-davinci-003
• text-davinci-002
• code-davinci-002
• Custom encodings via tiktoken

Performance

• Processes ~1000 rows/second on typical hardware
• Memory usage scales with CSV file size
• Supports files with millions of rows
• Recursive directory scanning with progress tracking

Error Handling

The package includes comprehensive error handling:

• FileProcessingError: Issues reading or processing CSV files
• EncodingError: Problems with tiktoken encoding
• ConfigurationError: Invalid configuration or paths

Use Cases

Training Data Analysis

# Analyze ML training datasets
deus-llm-token-guru ./training_data --model gpt-4 --output analysis.json

Cost Planning

# Estimate API costs for large datasets
llm-token-stats ./production_data --model gpt-3.5-turbo --output cost_analysis.json

Batch Processing

# Process multiple directories
from deus_llm_token_stats_guru import CSVTokenCounter

counter = CSVTokenCounter()
directories = ["./data1", "./data2", "./data3"]

for dir_path in directories:
    summary = counter.count_tokens_in_directory(dir_path)
    print(f"{dir_path}: {summary['total_tokens']:,} tokens")

Future Roadmap

• Support for additional data formats (JSON, XML, TXT)
• Integration with more LLM providers
• Advanced analytics and reporting features
• Web interface for easy visualization

Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make changes with tests
4. Run quality checks: ruff check && mypy src/ && pytest
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

v0.1.0

• Initial release
• Core CSV token counting functionality
• Dual CLI interface (deus-llm-token-guru and llm-token-stats)
• Support for multiple encoding models
• Comprehensive test suite
• JSON output format
• Python API with full type hints