Source URL: https://simonwillison.net/2024/Dec/2/datasette-llm-usage/
Source: Simon Willison’s Weblog
Title: datasette-llm-usage
Feedly Summary: datasette-llm-usage
I released the first alpha of a Datasette plugin to help track LLM usage by other plugins, with the goal of supporting token allowances – both for things like free public apps that stop working after a daily allowance, plus free previews of AI features for paid-account-based projects such as Datasette Cloud.
It’s using the usage features I added in LLM 0.19.
The alpha doesn’t do much yet – it will start getting interesting once I upgrade other plugins to depend on it.
Design notes so far in issue #1.
Tags: llm, datasette-cloud, plugins, ai, llms, datasette, generative-ai, projects
AI Summary and Description: Yes
Summary: The text discusses the release of an alpha Datasette plugin designed to track the usage of Large Language Models (LLMs), with an emphasis on managing token allowances for both free public apps and paid services. This development is particularly relevant for professionals in the AI and cloud sectors, as it ties into resource management and usage monitoring for AI applications.
Detailed Description: The provided text outlines a significant milestone in the development of a Datasette plugin, which is intended to enhance the management and tracking of LLM usage within applications. Here are the key points of relevance:
– **Release of Datasette Plugin**: The alpha version of the plugin allows for the tracking of LLM usage, helping developers manage how tokens are consumed across various applications.
– **Token Allowances**: This functionality is crucial for applications with different monetization models:
– **Free Public Applications**: Can now automatically restrict usage based on a daily token allowance.
– **Paid-Account Based Projects**: Provides the option for limited previews of AI features, which can engage users before requiring payment.
– **Dependency Management**: The plugin’s capabilities will expand as other plugins begin to rely on this usage tracking feature, hinting at a collaborative development approach that may enhance overall functionality and user experience.
– **Future Development**: There are plans to enrich the plugin with more features, which could add further layers of resource management for developers using LLMs in their projects.
These developments underscore the increasing importance of managing AI resource usage within cloud environments, as well as the need for tools that facilitate better compliance and governance in AI applications. The plugin’s emphasis on tracking usage can potentially lead to more secure and efficient management of AI services, making it pertinent for security and compliance professionals to monitor such innovations.