LLM Token Analytics Library - Wiki Documentation

Overview

The LLM Token Analytics Library is a robust Python library designed for analyzing Large Language Model (LLM) token usage patterns, retrieving provider data, and running comprehensive pricing simulations. This project serves as a valuable tool for developers, data scientists, and researchers who are interested in optimizing LLM usage costs and understanding usage dynamics.

Primary Use Cases and Target Audience

• Developers looking to integrate LLM token analytics into their applications.
• Data Scientists seeking to analyze and visualize LLM token usage patterns.
• Researchers studying the economic implications of different pricing mechanisms for LLMs.

Key Features and Capabilities

• Monte Carlo simulations for pricing mechanism comparisons.
• REST API client for remote simulation execution and data retrieval.
• Data collection from various LLM providers.
• Customizable pricing mechanisms and simulation configurations.
• Full end-to-end data collection and optimization workflows.

Architecture

System Design and Architecture

The architecture of the LLM Token Analytics Library is designed to facilitate modularity and reusability. It consists of core components that interact seamlessly to provide a comprehensive analytics solution.

Core Components and Their Interactions

• API Server: A Flask-based server that handles incoming requests for running simulations and data analysis.
• Simulation Engine: The core logic that executes various pricing simulations.
• Data Collection Module: Interfaces with LLM providers to gather usage data.
• Visualization Module: Generates visual representations of the simulation results and usage data.

Technology Stack and Dependencies

• Programming Languages: Python, JavaScript
• Frameworks: Flask (for API server), Dash (for dashboard visualization)
• Data Processing: Pandas, NumPy
• Statistical Analysis: SciPy, Statsmodels
• Visualization: Plotly, Matplotlib, Seaborn
• Databases: Supports local file-based storage; can be extended for cloud storage

Design Patterns Used

• MVC (Model-View-Controller): Separates data handling, user interface, and application logic for cleaner code organization.
• Singleton: Used for managing API client instances to ensure a single point of access.

Getting Started

Prerequisites

• System Requirements
  • Python 3.9 or higher
  • Basic understanding of Python and command-line usage
• Required Software and Tools
  • Python package manager (pip)
  • Virtual environment manager (optional but recommended)

Installation

1. Clone the repository:

   git clone https://github.com/aanshshah/llm_token_analytics_lib.git
   cd llm_token_analytics_lib

2. Install the library and dependencies:

   pip install -e .

3. (Optional) Install provider dependencies for data collection:

   pip install llm-token-analytics[providers]

4. Set up your environment variables for API keys (if applicable):

   export OPENAI_API_KEY="your-key"
   export ANTHROPIC_API_KEY="your-key"
   export GOOGLE_CLOUD_PROJECT="your-project"

Verification Steps

To verify the installation, run the basic simulation example:

python examples/01_basic_simulation.py

Quick Start

Basic Usage Example

To get started quickly, run the basic simulation:

python examples/01_basic_simulation.py

Common Workflows

• Running a Monte Carlo simulation: Use 01_basic_simulation.py.
• Interacting with the API: Start the API server and then run 02_api_client.py.
• Collecting usage data: Ensure API keys are set and run 03_data_collection.py.

Usage Guide

Detailed Usage Instructions

Each example script demonstrates a specific functionality: - Basic Simulation: Run the script to see how different pricing mechanisms perform. - API Client: Interacts with the API for remote simulations and retrieves results. - Data Collection: Gathers real-time usage data from LLM providers.

Command-Line Interface

• Each script can be executed directly via the command line.
• Arguments and configurations can be adjusted within the scripts for different scenarios.

Configuration Options

Configuration files, such as .env and config.yaml, can be used to set environment variables and application settings.

Examples for Common Scenarios

Refer to the examples/ directory for practical scripts demonstrating common use cases.

API Documentation

Public APIs and Interfaces

• /simulation: Endpoint to run simulations.
• /analysis: Endpoint for retrieving analysis results.
• /health: Endpoint to check the health status of the API server.

Function/Method Documentation

Refer to the source code in the app/routes/ directory for detailed method-level documentation.

Data Models and Schemas

The API expects JSON requests and responses structured according to the specifications defined in the codebase.

Request/Response Formats

Refer to the API documentation within the source code for the exact formats expected.

Development

Setting Up Development Environment

• Clone the repository and follow the installation instructions.
• Set up a virtual environment for isolated package management.

Building from Source

Run the following command to build the project:

python setup.py install

Running Tests

To run the tests, use:

pytest

Contributing

Contribution Guidelines

• Please fork the repository and submit a pull request.
• Ensure your code is well-documented and follows the project’s coding standards.

Code Style and Standards

Follow PEP 8 for Python code styling and ensure all changes are tested.

Pull Request Process

• Open a pull request with a detailed description of your changes.
• Ensure all tests pass before submission.

Deployment

Deployment Options

• The library can be deployed as a standalone API server or integrated into existing applications.

Production Configuration

• Configure the API server for production use, including setting up a WSGI server (e.g., Gunicorn).

Performance Optimization

• Optimize database queries and caching strategies for high-load scenarios.

Security Considerations

• Secure API keys and sensitive data using environment variables.
• Implement rate limiting and authentication for the API server.

Troubleshooting

Common Issues and Solutions

• Issue: Library does not install correctly.
  • Solution: Ensure you have Python 3.9+ and all dependencies are correctly specified.
• Issue: API server fails to start.
  • Solution: Check for port conflicts and ensure all required environment variables are set.

FAQ

• Q: How can I contribute to the library?
  • A: Please refer to the contributing section for details.
• Q: Where can I find more examples?
  • A: Check the examples/ directory for usage demonstrations.

Debug Tips

• Use logging to debug issues in the API server or simulation scripts.
• Test individual components separately to isolate issues.

Where to Get Help

For further assistance, please raise an issue on the GitHub repository or contact the maintainers.

Additional Resources

• GitHub Repository
• Documentation
• Community Forum
• Contributing Guidelines

This documentation aims to provide comprehensive guidance to developers and users of the LLM Token Analytics Library, ensuring they can utilize its features effectively and contribute to its growth.