JSON is all you need: Easily monitor LLM apps with structlog
Jun 06, 2024 - ploomber.io
The article discusses the use of `structlog`, a Python logging library, for improving the observability of Language Learning Model (LLM) applications. The author argues that LLM apps are no different from regular applications and do not require specialized tools for monitoring. `Structlog` allows users to generate JSON logs and maintain thread-local context during a web request, which can be used to build a robust monitoring system for an LLM application. The article provides a detailed guide on how to install and configure `structlog`, and how to use it to trace requests and log errors.
The author also demonstrates how to analyze the generated logs using DuckDB, an embedded analytical database. By storing logs as JSON objects, users can better understand the usage of their applications. The author concludes by stating that there is no need for fancy LLM monitoring frameworks; logging as JSON objects using `structlog` is sufficient.
Key takeaways:
`structlog` is a powerful Python logging library that can be used to monitor LLM applications, without the need for specialized tools.
One of the key features of `structlog` is its ability to keep thread-local context, which helps trace the lifetime of a web request.
Logs can be stored as JSON objects, making it easier to analyze and understand app usage.
Tools like DuckDB can be used to analyze the logs, helping to identify and resolve issues in the application.
