-
-
Notifications
You must be signed in to change notification settings - Fork 501
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Support LLM monitoring in Ruby SDK. #2405
Labels
Comments
Thanks for the request @khaile! |
sl0thentr0py
added
Waiting for: Product Owner
and removed
Waiting for: Product Owner
labels
Sep 18, 2024
Hi @sl0thentr0py , I would love to see if we can support for langchainrb! |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Describe the idea
Support LLM monitoring in Ruby SDK.
Why do you think it's beneficial to most of the users
Implementing LLM monitoring in the Ruby SDK allows developers to gain deeper insights into the performance and behavior of large language models in their applications. Users can track the health of their models, detect anomalies, and ensure optimal functioning, which can lead to enhanced user experiences and increased trust in AI-driven features. By providing detailed monitoring, users can make informed decisions on how to iterate on their models, improving accuracy and responsiveness while minimizing downtime.
Possible implementation
To implement LLM monitoring in the Ruby SDK, we can follow a structured approach:
Integrate Monitoring Hooks: Introduce SDK hooks that enable developers to easily add monitoring functionalities at critical points in the model's lifecycle, including initialization, inference, and error handling.
Capture Metrics: Create predefined metrics for automatic capture, such as response times, error rates, input/output token counts, and model latency. Additionally, allow users to define custom metrics relevant to their use cases.
Anomaly Detection: Implement real-time algorithms to analyze captured metrics for anomalies, such as unexpected spikes in error rates or response times. Notify developers when anomalies are detected.
Dashboard and Visualization: Develop a user-friendly dashboard that visually displays collected metrics and anomalies, providing insights into model performance over time to help developers identify trends and improvement areas.
Documentation and Examples: Offer comprehensive documentation and practical examples for enabling and using LLM monitoring within the SDK, including step-by-step guides, code snippets, and best practices for seamless integration.
Community Feedback Loop: Create a feedback mechanism for users to share their experiences and suggest improvements, such as a dedicated forum or feedback section in the documentation, allowing the SDK to evolve based on real-world usage.
The text was updated successfully, but these errors were encountered: