diff --git a/src/groundlight/client.py b/src/groundlight/client.py index 8ea90bec..ff5a4f5a 100644 --- a/src/groundlight/client.py +++ b/src/groundlight/client.py @@ -370,39 +370,46 @@ def create_detector( # noqa: PLR0913 gl = Groundlight() - # Create a basic binary detector detector = gl.create_detector( - name="dog-on-couch-detector", query="Is there a dog on the couch?", - confidence_threshold=0.9, patience_time=30.0 + # Create a basic binary detector + detector = gl.create_detector( + name="dog-on-couch-detector", + query="Is there a dog on the couch?", + confidence_threshold=0.9, + patience_time=30.0 ) - # Create a detector with metadata detector = gl.create_detector( - name="door-monitor", query="Is the door open?", metadata={"location": - "front-entrance", "building": "HQ"}, confidence_threshold=0.95 + # Create a detector with metadata + detector = gl.create_detector( + name="door-monitor", + query="Is the door open?", + metadata={"location": "front-entrance", "building": "HQ"}, + confidence_threshold=0.95 ) - # Create a detector in a specific group detector = gl.create_detector( - name="vehicle-monitor", query="Are there vehicles are in the parking lot?", - group_name="parking-monitoring", patience_time=60.0 + # Create a detector in a specific group + detector = gl.create_detector( + name="vehicle-monitor", + query="Are there vehicles are in the parking lot?", + group_name="parking-monitoring", + patience_time=60.0 ) - :param name: A short, descriptive name for the detector. This name should be unique within - your account and help identify the detector's purpose. - :param query: The question that the detector will answer about images. For binary - classification, this should be a yes/no question (e.g. "Is there a person in - the image?"). - :param group_name: Optional name of a group to organize related detectors together. If not - specified, the detector will be placed in the default group. - :param confidence_threshold: A value between 0.5 and 1 that sets the minimum confidence - level required for the ML model's predictions. If confidence is - below this threshold, the query may be sent for human review. - :param patience_time: The maximum time in seconds that Groundlight will attempt to generate - a confident prediction before falling back to human review. Defaults to - 30 seconds. - :param pipeline_config: Advanced usage only. Configuration string needed to instantiate a - specific prediction pipeline for this detector. - :param metadata: A dictionary or JSON string containing custom key/value pairs to associate - with the detector (limited to 1KB). This metadata can be used to store - additional information like location, purpose, or related system IDs. You - can retrieve this metadata later by calling `get_detector()`. + :param name: A short, descriptive name for the detector. This name should be unique within your account + and help identify the detector's purpose. + :param query: The question that the detector will answer about images. For binary classification, + this should be a yes/no question (e.g. "Is there a person in the image?"). + :param group_name: Optional name of a group to organize related detectors together. If not specified, + the detector will be placed in the default group. + :param confidence_threshold: A value between 0.5 and 1 that sets the minimum confidence level required + for the ML model's predictions. If confidence is below this threshold, + the query may be sent for human review. + :param patience_time: The maximum time in seconds that Groundlight will attempt to generate a + confident prediction before falling back to human review. Defaults to 30 seconds. + :param pipeline_config: Advanced usage only. Configuration string needed to instantiate a specific + prediction pipeline for this detector. + :param metadata: A dictionary or JSON string containing custom key/value pairs to associate with + the detector (limited to 1KB). This metadata can be used to store additional + information like location, purpose, or related system IDs. You can retrieve this + metadata later by calling `get_detector()`. :return: The created Detector object """