tokenc 0.1.1

Python SDK for The Token Company API - Compress LLM inputs to reduce costs

tokenc - Python SDK for The Token Company

A Python client library for compressing LLM inputs to reduce token usage, lower costs, and speed up AI applications.

Installation

pip install tokenc

Quick Start

from tokenc import TokenClient

# Initialize the client
client = TokenClient(api_key="your-api-key")

# Compress text
response = client.compress_input(
    input="Your long text here that needs compression...",
    aggressiveness=0.5
)

print(f"Compressed text: {response.output}")
print(f"Original tokens: {response.original_input_tokens}")
print(f"Compressed tokens: {response.output_tokens}")
print(f"Tokens saved: {response.tokens_saved}")
print(f"Compression ratio: {response.compression_ratio:.2f}x")

Features

• 🚀 Simple and intuitive API
• 🎯 Type-safe with dataclasses
• 🔧 Flexible compression settings
• 📊 Built-in compression metrics
• ⚡ Context manager support
• 🛡️ Comprehensive error handling

Usage Examples

Basic Compression

from tokenc import TokenClient

client = TokenClient(api_key="your-api-key")

response = client.compress_input(
    input="This is a long text that contains lots of unnecessary filler words and redundant information that can be compressed.",
    aggressiveness=0.5
)

print(response.output)

Advanced Compression with Settings

from tokenc import TokenClient, CompressionSettings

client = TokenClient(api_key="your-api-key")

# Create custom compression settings
settings = CompressionSettings(
    aggressiveness=0.7,
    max_output_tokens=100,
    min_output_tokens=50
)

response = client.compress_input(
    input="Your text here...",
    compression_settings=settings
)

print(f"Compression percentage: {response.compression_percentage:.1f}%")

Using as Context Manager

from tokenc import TokenClient

with TokenClient(api_key="your-api-key") as client:
    response = client.compress_input(
        input="Your text here...",
        aggressiveness=0.6
    )
    print(response.output)
# Session automatically closed

Different Compression Levels

from tokenc import TokenClient

client = TokenClient(api_key="your-api-key")

text = "Your long text here..."

# Light compression - preserve most content
light = client.compress_input(input=text, aggressiveness=0.2)

# Moderate compression - balanced approach
moderate = client.compress_input(input=text, aggressiveness=0.5)

# Aggressive compression - maximum savings
aggressive = client.compress_input(input=text, aggressiveness=0.8)

Compression Aggressiveness Levels

• 0.1-0.3: Light compression - Removes obvious filler words
• 0.4-0.6: Moderate compression - Balanced approach (recommended)
• 0.7-0.9: Aggressive compression - Maximum cost savings

API Reference

TokenClient

__init__(api_key: str, base_url: str = ..., timeout: int = 30)

Initialize the Token Company API client.

Parameters:

• api_key (str): Your API key for authentication
• base_url (str, optional): Base URL for the API
• timeout (int, optional): Request timeout in seconds

compress_input(...) -> CompressResponse

Compress text input for optimized LLM inference.

Parameters:

• input (str): The text to compress
• model (str, optional): Model to use (default: "bear-1")
• aggressiveness (float, optional): Compression intensity 0.0-1.0 (default: 0.5)
• max_output_tokens (int, optional): Maximum token count for output
• min_output_tokens (int, optional): Minimum token count for output
• compression_settings (CompressionSettings, optional): Custom settings object

Returns:

• CompressResponse: Object containing compressed output and metadata

Raises:

• AuthenticationError: Invalid API key
• InvalidRequestError: Invalid request parameters
• RateLimitError: Rate limit exceeded
• APIError: Other API errors

CompressionSettings

Dataclass for compression configuration.

Attributes:

• aggressiveness (float): Compression intensity 0.0-1.0
• max_output_tokens (int | None): Optional maximum output tokens
• min_output_tokens (int | None): Optional minimum output tokens

CompressResponse

Dataclass for compression results.

Attributes:

• output (str): The compressed text
• output_tokens (int): Token count of compressed output
• original_input_tokens (int): Token count of original input
• compression_time (float): Processing duration in seconds

Properties:

• tokens_saved (int): Number of tokens saved
• compression_ratio (float): Ratio of original to compressed tokens
• compression_percentage (float): Percentage reduction in tokens

Error Handling

from tokenc import TokenClient, AuthenticationError, InvalidRequestError, RateLimitError, APIError

client = TokenClient(api_key="your-api-key")

try:
    response = client.compress_input(input="Your text...")
except AuthenticationError:
    print("Invalid API key")
except InvalidRequestError as e:
    print(f"Invalid request: {e}")
except RateLimitError:
    print("Rate limit exceeded, please wait")
except APIError as e:
    print(f"API error: {e}")

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/yourusername/tokenc.git
cd tokenc

# Install in development mode
pip install -e ".[dev]"

Running Tests

pytest tests/

Code Formatting

black tokenc/

License

MIT License - see LICENSE file for details

Support

For issues and questions:

• GitHub Issues: https://github.com/yourusername/tokenc/issues
• Documentation: https://thetokencompany.com/docs

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.