Allow specifying an ID when submitting an image query (#271)

Adds optional parameter `image_query_id` to methods which allow
submitting an image query. Adds tests for new parameter. Updates
generated code based on new spec.

---------

Co-authored-by: Auto-format Bot <[email protected]>

Author: CoreyEWood

Makefile

@@ -49,10 +49,13 @@ test-4edge: install  ## Run tests against the prod API via the edge-endpoint (ne
 	${PYTEST} ${PROFILING_ARGS} ${TEST_ARGS} ${EDGE_FILTERS} test
 
 test-local: install  ## Run tests against a localhost API (needs GROUNDLIGHT_API_TOKEN and a local API server)
-	GROUNDLIGHT_ENDPOINT="http://localhost:8000/" ${PYTEST} ${TEST_ARGS} ${CLOUD_FILTERS} test
+	GROUNDLIGHT_ENDPOINT="http://localhost:8000/" $(MAKE) test
 
 test-integ: install  ## Run tests against the integ API server (needs GROUNDLIGHT_API_TOKEN)
-	GROUNDLIGHT_ENDPOINT="https://api.integ.groundlight.ai/" ${PYTEST} ${TEST_ARGS} ${CLOUD_FILTERS} test
+	GROUNDLIGHT_ENDPOINT="https://api.integ.groundlight.ai/" $(MAKE) test
+
+test-dev: install ## Run tests against a dev API server (needs GROUNDLIGHT_API_TOKEN and properly configured dns-hostmap)
+	GROUNDLIGHT_ENDPOINT="https://api.dev.groundlight.ai/" $(MAKE) test
 
 test-docs: install  ## Run the example code and tests in our docs against the prod API (needs GROUNDLIGHT_API_TOKEN)
 	${PYTEST} --markdown-docs ${TEST_ARGS} docs README.md

docs/docs/building-applications/7-edge.md

@@ -14,8 +14,8 @@ and for communicating with the Groundlight cloud service.
 
 To use the edge endpoint, simply configure the Groundlight SDK to use the edge endpoint's URL instead of the cloud endpoint.
 All application logic will work seamlessly and unchanged with the Groundlight Edge Endpoint, except some ML answers will
-return much faster locally.  The only visible difference is that image queries answered at the edge endpoint will have the prefix `iqe_` instead of `iq_` for image queries answered in the cloud.  `iqe_` stands for "image query edge".  Edge-originated
-image queries will not appear in the cloud dashboard.
+return much faster locally.  Image queries answered at the edge endpoint will not appear in the cloud dashboard unless
+specifically configured to do so, in which case the edge prediction will not be reflected on the image query in the cloud.
 
 ## Configuring the Edge Endpoint
 

generated/README.md

@@ -3,7 +3,7 @@ Groundlight makes it simple to understand images. You can easily create computer
 
 This Python package is automatically generated by the [OpenAPI Generator](https://openapi-generator.tech) project:
 
-- API version: 0.18.1
+- API version: 0.18.2
 - Package version: 1.0.0
 - Build package: org.openapitools.codegen.languages.PythonClientCodegen
 

generated/docs/BinaryClassificationResult.md

@@ -6,7 +6,7 @@ Name | Type | Description | Notes
 ------------ | ------------- | ------------- | -------------
 **label** | **str** |  | 
 **confidence** | **float** |  | [optional] 
-**source** | **str** | Source is optional to support edge v0.2 | [optional] 
+**source** | **str** |  | [optional] 
 **any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
 
 [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)

generated/docs/CountingResult.md

@@ -6,7 +6,7 @@ Name | Type | Description | Notes
 ------------ | ------------- | ------------- | -------------
 **count** | **int** |  | 
 **confidence** | **float** |  | [optional] 
-**source** | **str** | Source is optional to support edge v0.2 | [optional] 
+**source** | **str** |  | [optional] 
 **greater_than_max** | **bool** |  | [optional] 
 **any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
 

generated/docs/ImageQueriesApi.md

@@ -248,7 +248,7 @@ Name | Type | Description  | Notes
 
 
 
- Submit an image query against a detector.  You must use `\"Content-Type: image/jpeg\"` for the image data. For example:  ```Bash $ curl https://api.groundlight.ai/device-api/v1/image-queries?detector_id=det_abc123 \\     --header \"Content-Type: image/jpeg\" \\     --data-binary @path/to/filename.jpeg ```  
+ Submit an image query against a detector.  You must use `\"Content-Type: image/jpeg\"` or similar (image/png, image/webp, etc) for the image data. For example: ```Bash $ curl https://api.groundlight.ai/device-api/v1/image-queries?detector_id=det_abc123 \\     --header \"Content-Type: image/jpeg\" \\     --data-binary @path/to/filename.jpeg ``` 
 
 ### Example
 
@@ -283,6 +283,7 @@ with groundlight_openapi_client.ApiClient(configuration) as api_client:
     api_instance = image_queries_api.ImageQueriesApi(api_client)
     detector_id = "detector_id_example" # str | Choose a detector by its ID.
     human_review = "human_review_example" # str | If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident. (optional)
+    image_query_id = "image_query_id_example" # str | The ID to assign to the created image query. (optional)
     inspection_id = "inspection_id_example" # str | Associate the image query with an inspection. (optional)
     metadata = "metadata_example" # str | A dictionary of custom key/value metadata to associate with the image query (limited to 1KB). (optional)
     patience_time = 3.14 # float | How long to wait for a confident response. (optional)
@@ -299,7 +300,7 @@ with groundlight_openapi_client.ApiClient(configuration) as api_client:
     # example passing only required values which don't have defaults set
     # and optional values
     try:
-        api_response = api_instance.submit_image_query(detector_id, human_review=human_review, inspection_id=inspection_id, metadata=metadata, patience_time=patience_time, want_async=want_async, body=body)
+        api_response = api_instance.submit_image_query(detector_id, human_review=human_review, image_query_id=image_query_id, inspection_id=inspection_id, metadata=metadata, patience_time=patience_time, want_async=want_async, body=body)
         pprint(api_response)
     except groundlight_openapi_client.ApiException as e:
         print("Exception when calling ImageQueriesApi->submit_image_query: %s\n" % e)
@@ -312,6 +313,7 @@ Name | Type | Description  | Notes
 ------------- | ------------- | ------------- | -------------
  **detector_id** | **str**| Choose a detector by its ID. |
  **human_review** | **str**| If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident. | [optional]
+ **image_query_id** | **str**| The ID to assign to the created image query. | [optional]
  **inspection_id** | **str**| Associate the image query with an inspection. | [optional]
  **metadata** | **str**| A dictionary of custom key/value metadata to associate with the image query (limited to 1KB). | [optional]
  **patience_time** | **float**| How long to wait for a confident response. | [optional]

generated/docs/LabelValue.md

@@ -9,9 +9,9 @@ Name | Type | Description | Notes
 **annotations_requested** | **[bool, date, datetime, dict, float, int, list, str, none_type]** |  | [readonly] 
 **created_at** | **datetime** |  | [readonly] 
 **detector_id** | **int, none_type** |  | [readonly] 
+**source** | **bool, date, datetime, dict, float, int, list, str, none_type** |  | [readonly] 
 **text** | **str, none_type** | Text annotations | [readonly] 
 **rois** | [**[ROI], none_type**](ROI.md) |  | [optional] 
-**source** | **bool, date, datetime, dict, float, int, list, str, none_type** |  | [optional] [readonly] 
 **any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
 
 [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)

generated/docs/MultiClassificationResult.md

@@ -6,7 +6,7 @@ Name | Type | Description | Notes
 ------------ | ------------- | ------------- | -------------
 **label** | **str** |  | 
 **confidence** | **float** |  | [optional] 
-**source** | **str** | Source is optional to support edge v0.2 | [optional] 
+**source** | **str** |  | [optional] 
 **any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
 
 [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)

generated/groundlight_openapi_client/__init__.py

@@ -5,7 +5,7 @@
 
     Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language.  # noqa: E501
 
-    The version of the OpenAPI document: 0.18.1
+    The version of the OpenAPI document: 0.18.2
     Contact: [email protected]
     Generated by: https://openapi-generator.tech
 """

generated/groundlight_openapi_client/api/actions_api.py

@@ -3,7 +3,7 @@
 
     Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language.  # noqa: E501
 
-    The version of the OpenAPI document: 0.18.1
+    The version of the OpenAPI document: 0.18.2
     Contact: [email protected]
     Generated by: https://openapi-generator.tech
 """