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timmarkhuff
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Mar 12, 2026
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| """Example of constructing an edge endpoint configuration programmatically.""" | |||
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This is temporary, I'll get rid of this before merging.
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Getting rid of it is too easy, grab the most important snippets and put them in docs in a md file, we auto push those to a webpage
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brandon-wada
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This looks good so far, we should discuss more what the calls to update the config on the remote machine should look like
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| """Example of constructing an edge endpoint configuration programmatically.""" | |||
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Getting rid of it is too easy, grab the most important snippets and put them in docs in a md file, we auto push those to a webpage
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Nit, this is a little redundant since it includes all objects that could be importet
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Moves the edge endpoint config schema from
edge-endpointinto the Python SDK so that users can programmatically build and validate edge configurations.What's new
groundlight.edgesubmodule with Pydantic models ported fromedge-endpoint/app/core/configs.py:EdgeEndpointConfig(renamed fromRootEdgeConfig) -- top-level config withadd_detector()for ergonomic constructionEdgeInferenceConfig-- per-detector inference behavior (frozen/immutable)GlobalConfig,DetectorConfig-- supporting modelsDEFAULT,EDGE_ANSWERS_WITH_ESCALATION,NO_CLOUD,DISABLEDdisable_cloud_escalationrequiresalways_return_edge_prediction, duplicate detector ID checks, inference config cross-references) is preservedUsage
Design decisions
EdgeInferenceConfigis frozen to prevent accidental mutation of shared presetsadd_detector()accepts a detector ID string or aDetectorobject (matching SDK conventions likesubmit_image_query)add_detector()-- no separate registration step neededdetectorsis a list,edge_inference_configsis a dict keyed by name)namefield onEdgeInferenceConfigis excluded from serialization (it serves as the dict key)Follow-up work
edge-endpointto import these models from the SDK instead of defining them locallyEdgeEndpointConfigto a running edge endpoint