Track OpenAI API Usage with This Open-Source Logging Tool: llm.report Tutorial
Are you looking for a way to better track and analyze your spending on OpenAI's API? Do you want to improve your prompts and optimize your AI application's performance, all while maintaining control over your data? Look no further than llm.report!
llm.report is an open-source logging and analytics platform designed specifically for OpenAI. It allows you to log your ChatGPT API requests, analyze costs, and optimize your prompts—all within a self-hosted environment. While the project is no longer actively maintained, it still offers valuable insights and functionalities for those seeking greater control over their OpenAI usage data.
Key Benefits of Using llm.report for OpenAI API Tracking
- Cost Analysis: Track your OpenAI API expenses and token usage without relying on third-party platforms.
- Prompt Improvement: Analyze API requests and responses to identify areas for prompt optimization.
- User Insights: Calculate the cost per user for your AI application, enabling better resource allocation.
- Data Privacy: Because the platform is self-hosted, you maintain complete control over your data.
Dive Deeper into llm.report Features
llm.report packs a set of features that directly address common pain points in managing OpenAI API usage. Let's explore some of the key offerings in more detail:
1. OpenAI API Analytics: Get a bird's-eye view of how your application is consuming OpenAI resources. The no-code analytical tools within llm.report can show you cost trends.
2. Log Analysis: Delve into past interactions with the OpenAI API. Detailed logging of requests and responses enables you to pinpoint the prompts that provided the most useful outputs.
3. User-Level Analysis: Identify who your heaviest AI users are and what costs are associated with your power users. Doing so will provide invaluable insight for improving efficiency.
Self-Hosted Installation: Your Path to OpenAI Usage Insights
Setting up llm.report on your own infrastructure gives you complete autonomy over your data. Here’s a simplified guide on how to get started:
-
Clone the Repository: Start by cloning the llm.report repository from GitHub.
git clone https://github.com/dillionverma/llm.report.git -
Navigate to the Directory: Change into the newly created directory.
cd llm.report -
Install Dependencies: Install the necessary dependencies using Yarn.
yarn -
Configure Environment Variables: Copy the example environment file and configure your settings, including generating a
NEXTAUTH_SECRET.cp .env.example .env -
Launch with Docker: For a quick start, use Docker and Docker Compose. This launches a local Postgres instance.
yarn dx
Once the setup is complete, access the platform in your browser at http://localhost:3000.
Tech Stack: The Foundation of llm.report
llm.report leverages a modern tech stack, including:
- Next.js: Robust framework for building web applications.
- Typescript: Adds type safety.
- Tailwind: Utility-first CSS framework to streamline styling.
- Postgres: Reliable database for storing logs and analytics.
Contributing to the Project
While the original developers are no longer actively maintaining llm.report, the open-source nature of the project grants everyone the opportunity to contribute. If you encounter a bug or have an idea for improvement, consider submitting a pull request.
Final Thoughts: Taking Control of Your OpenAI Costs and Prompt Engineering
By using llm.report, you gain invaluable insights into your OpenAI API usage, allowing you to make data-driven decisions to optimize costs and improve prompt effectiveness. While active development has ceased, the tool remains a powerful and valuable resource for developers seeking transparency when using OpenAI's language models.
