The article also provides examples of how to use `superopenai`, including how to initialize it, how to use it with the `openai` client, and how to use the `Logger` object for aggregate statistics. It also discusses caching, compatibility with other libraries, and how to use `superopenai` in the context of building and testing a RAG pipeline with langchain. The article concludes by inviting contributions to the open-source project, suggesting potential areas for development such as porting to other languages, adding retries and detailed function tracing, and integrating with third-party logging services.
Key takeaways:
- Superopenai is a minimal library designed for logging and caching LLM requests and responses during development, providing visibility and facilitating rapid iteration.
- It logs prompts, responses, latency, cost, and token usage, and caches LLM requests and responses when the request is identical and temperature=0.
- Superopenai is not intended for production apps, but for development, allowing developers to look at their logs locally rather than setting up a remote observability tool.
- The library is compatible with other third-party libraries like langchain, llama-index, instructor, guidance, Dspy, and more. It also supports streaming and async, and allows for nested loggers.