How to instrument multi-modal pipelines in HoneyHive
HoneyHive’s tracing capabilities extend beyond text-based data, allowing you to capture and analyze multi-modal information in your AI applications. This guide focuses on instrumenting functions that handle multi-modal data, particularly those that return S3 URLs pointing to images, audio, or other non-text assets.
Multi-modal tracing is crucial for applications that process various types of data, such as:
By tracing these functions, you can gain insights into how your application handles different data types and how they impact your AI pipeline’s performance and accuracy.
trace
Decorator for Multi-modal DataTo instrument functions that return S3 URLs for multi-modal data, you’ll use the same trace
decorator as with text-based functions. Here’s how to set it up:
trace
decorator:To make your traces more informative, you can add metadata about the multi-modal data:
Here are examples of tracing different types of multi-modal data:
Include relevant metadata: Add information about the data type, format, size, and any processing steps to provide context.
Use consistent naming conventions: For S3 URLs, use a consistent structure to make it easier to analyze and group related assets.
Consider privacy and data protection: Ensure that your S3 URLs and metadata don’t contain sensitive information.
Link related traces: If a multi-modal process involves multiple steps, use consistent identifiers in your metadata to link related traces.