Webhooks
How to use Aquarium webhooks to communicate with your own services, including automating labeling service integrations.
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{
event: str, // the event-type that triggered the call
project: str; // the name of the project that the event originated in
[entity]: Dict; // a json representation of the entity that the event refers to, if any
}{
event: str, // the event-type that triggered the call
project: str; // the name of the project that the event originated in
[entity]: Dict; // a json representation of the entity that the event refers to, if any
}{
event: "dataset-complete",
project: str,
dataset: {
id: str,
archived: bool,
created_at: str,
updated_at: str,
frame_count: int,
label_count: int,
data_url: str[],
embeddings_url?: str[],
dataflow_status: str
dataflow_status_postprocess: str,
}
}{
event: "dataset-complete",
project: str,
inference_set: {
id: str,
archived: bool,
base_dataset: str,
created_at: str,
updated_at: str,
data_url: str[],
frame_count: int,
label_count: int,
data_url: str[],
embeddings_url?: str[],
cached_metrics?: float[][][],
dataflow_status: str
dataflow_status_postprocess: str,
}
}{
event: "dataset-failed",
project: str,
dataset: {
id: str,
archived: bool,
created_at: str,
updated_at: str,
frame_count: int,
label_count: int,
data_url: str[],
embeddings_url?: str[],
dataflow_status: str
dataflow_status_postprocess: str,
}
}{
event: "dataset-failed",
project: str,
inference_set: {
id: str,
archived: bool,
base_dataset: str,
created_at: str,
updated_at: str,
data_url: str[],
frame_count: int,
label_count: int,
data_url: str[],
embeddings_url?: str[],
cached_metrics?: float[][][],
dataflow_status: str
dataflow_status_postprocess: str,
}
}{
event: "issues-created",
project: str,
issues: [{
id: str,
issue_name: str,
creation_type: "auto" | "manual",
element_type: "frame" | "crop",
num_elements: int,
originating_inference_set_name?: str // the inference set that was used to generate this issue's element, usually applicable to auto-created issues
}]
}{
event: "issue-updated",
project: str,
issue_update: {
id: str,
update_type: "add" | "remove" | "move" | "elt_status" | "issue_status",
num_elements_added?: int, // if update_type == "add"
num_elements_removed?: int, // if update_type == "remove"
num_elements_moved?: int, // if update_type == "move"
num_elements_already_present?: int, // if update_type == "move"
original_issue_name?: int, // if update_type == "move", the issue the elements were originally in
new_elements_status?: str, // if update_type == "elt_status"
num_elements_updated?: int, // if update_type == "elt_status"
previous_issue_status?: str, // if update_type == "issue_status"
new_issue_status?: str // if update_type == "issue_status"
}
}{
event: "issue-exported",
project: str,
issue: {
id: str,
elements: [{
dataset: str,
inference_set: str,
issue_name: str,
element_id: str,
element_type: "frame" | "crop",
frame_id: str,
frame_data: {
coordinate_frames: [{
coordinate_frame_id: str,
coordinate_frame_metadata: Optional[Dict],
coordinate_frame_type: str,
}],
custom_metrics: {
[custom_metric_type]: int[][] | float,
},
date_captured: str,
device_id: str,
geo_data: {
[coordinate_field]: float,
},
label_data: [{
attributes: {
confidence: float,
...
},
label: str,
label_coorindate_frame: str,
label_type: str,
linked_labels: str[],
uuid: str,
}],
sensor_data: [{
coordinate_frame: str,
data_urls: {
image_url: str,
},
date_captured: str,
sensor_id: str,
sensor_metadata: Dict,
sensor_type: str,
}],
task_id: str,
[user__metadata_field]: str | int | float | bool,
},
}]
}
}