Track & Analyze Your OpenAI API Usage: A Guide to llm.report

Are you looking for a way to better understand your OpenAI API usage? Do you want tools to analyze costs, log requests, and improve your prompts? Then look no further: llm.report is here to help.

What is llm.report?

llm.report is an open-source logging and analytics platform designed specifically for OpenAI. It allows you to track and analyze how you are using the OpenAI API. And gain insights into cost, token usage, and prompt performance. This helps improve your AI applications.

While it is not actively maintained you can start using it locally by following these steps.

Key Benefits of Using llm.report

• Cost Analysis: See exactly how much you're spending on your OpenAI API usage.
• Prompt Improvement: Analyze request/response and discover issues with your prompts.
• Open Source: With llm.report you have full control over your data.

Unleash the Power of llm.report Features

llm.report comes packed with awesome things to help you master your OpenAI usage.

Gain Deep Insights Into Your OpenAI API Analytics

Understand where your money is going. llm.report provides a no-code solution to analyze your OpenAI API costs and token usage. Pinpoint your highest spending area and optimize.

Supercharge Your Prompts with Detailed Logs

Optimize your prompts using detailed logs of your OpenAI API requests and responses. Analyze the logs, identify bottlenecks, and tune your prompts for better performance and accuracy.

Understand User Behavior with User Analytics

Calculate the cost per user for your AI application to gain valuable insights to user behavior. Use this data to refine your offers, and optimize your AI application for maximum profitability.

Setting Up llm.report: A Quick Guide

Here's how to get llm.report up and running on your local machine:

1. Clone the Repository:

   git clone https://github.com/dillionverma/llm.report.git
   

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2. Navigate to the directory:

   cd llm.report
   

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3. Install Dependencies:

   yarn
   

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4. Configure Environment Variables:

   cp .env.example .env
   

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   Generate using:

   openssl rand -base64 32
   

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5. Quickstart with Docker: If you have Docker and Docker Compose installed, this is the easiest way to get started:

   yarn dx
   

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Open http://localhost:3000 in your browser to access the llm.report interface.

Tech Stack Powering llm.report

llm.report is built using a modern and robust tech stack, including:

• Next.js: For a smooth user experience.
• Typescript: Provides code maintainability.
• Tailwind: Ensure a sleek and responsive design.
• Postgres: For reliable data storage.

Contribute to llm.report

llm.report thrives on community contributions! Have an idea for a new feature or found a bug? Here's how you can help:

• Open an Issue: Report bugs.
• Submit a Pull Request: Add new features or fix existing issues.

Maximize Your OpenAI Investment with Effective API Logging

llm.report provides invaluable tools for optimizing your OpenAI API usage. Start tracking, analyzing, and improving your prompts today. While maintanence is inactive you can test the project locally to better use the OpenAI API.