Very cool, gotta try this out! Curious, how does it handle blob data (e.g., images) -> Is there a way to display the image blobs in the widget too? Kind of like how Hugging Face displays Lance blobs in their dataset viewer: https://huggingface.co/datasets/lance-format/handwriting-ocr
Really cool stuff! Looking at the schema of the financial dataset, though, it's really flat, and it doesn't really leverage the power of the BAML schema format (which excels at representing nested data with fewer tokens than JSON). I suspect that the performance difference with and without the BAMLAdapter would have been more stark if the data model being extracted was more nested.
Great work though, and here's to more experiments of this nature with nested data extraction. Kudos to the BAML team for coming up with such an awesome format, and kudos to the DSPy maintainers for designing such a hackable, usable framework!
The semantic routing step with intent classification and query rewriting was fascinating. The fact that multiple indexes were used is also understated. Is there anywhere one can explore the internals?
Either way, thanks for sharing!