New Answer-Modes section

Author: tyler-romero

docs/docs/answer-modes/1-binary-detectors.md

@@ -0,0 +1,60 @@
+# [BETA] Multi-Class Detectors
+
+If you want to classify images into multiple categories, you can create a multi-class detector.
+
+```python
+from groundlight import ExperimentalApi
+gl_exp = ExperimentalApi()
+
+# highlight-start
+class_names = ["Golden Retriever", "Labrador Retriever", "German Shepherd", "Other"]
+detector = gl_exp.create_multiclass_detector(
+    name="dog-breed-detector",
+    query="What kind of dog is this?",
+    class_names=class_names,
+)
+# highlight-end
+```
+
+:::tip
+We recommend adding an "Other" class to your multi-class detector to handle cases where the image does not belong to any of the pre-defined classes.
+:::
+
+<!-- :::note
+Multi-Class Detectors are available on [Business and Enterprise plans](https://www.groundlight.ai/pricing).
+::: -->
+
+## Submit an Image Query to a Multi-Class Detector
+
+Now that you have created a multi-class detector, you can submit an image query to it.
+
+```python notest
+from groundlight import ExperimentalApi
+gl_exp = ExperimentalApi()
+
+detector = gl_exp.get_detector_by_name("dog-breed-detector")
+
+# highlight-start
+# Classify the breed of a dog in an image
+image_query = gl_exp.submit_image_query(detector, "path/to/image.jpg")
+# highlight-end
+
+print(f"Result: {image_query.result.label}")
+print(f"Confidence: {image_query.result.confidence}")
+```
+
+Multi-class detectors return a `label` attribute in the result object, which contains the predicted class label. The `label` attribute will be one of the class names provided when creating the detector. The `confidence` attribute represents the confidence level in the predicted class, which is a value between `1/len(class_names)` and 1.
+
+## Add a label to a Multi-Class Detector
+
+To provide ground truth labels for multi-class detectors, you can specify the label of the correct class.
+
+```python notest
+from groundlight import ExperimentalApi
+gl_exp = ExperimentalApi()
+
+# highlight-start
+# Add a multi-class label to the image query from the previous example
+gl_exp.add_label(image_query, label="German Shepherd")
+# highlight-end
+```

docs/docs/answer-modes/2-multi-class-detectors.md

@@ -1,7 +1,4 @@
-# Detector Answer Modes
-Groundlight offers several detector modalities to suit different computer vision tasks. While previous examples have focused on binary classification, this guide will walk you through using counting and multi-class detectors. Let's explore how these different modes can be used via the Groundlight SDK.
-
-## Counting Detectors
+# Counting Detectors
 
 Counting detectors are used to count the number of objects in an image. Groundlight's counting detectors also return bounding boxes around the objects they count.
 
@@ -30,7 +27,7 @@ The `confidence_threshold` parameter sets the minimum confidence level required
 Counting Detectors are available on [Business and Enterprise plans](https://www.groundlight.ai/pricing).
 :::
 
-### Submit an Image Query to a Counting Detector
+## Submit an Image Query to a Counting Detector
 
 Now that you have created a counting detector, you can submit an image query to it.
 
@@ -95,7 +92,7 @@ def draw_bounding_boxes(image_path, rois):
 ```
 :::
 
-### Add a label to a Counting Detector
+## Add a label to a Counting Detector
 
 The Groundlight API allows you to add labels to image queries, including Region of Interest (ROI) data.
 When adding a label to a counting detector, if you include ROIs, the number of ROIs should match
@@ -115,67 +112,4 @@ roi3 = gl_exp.create_roi("car", (0.6, 0.5), (0.8, 0.9))
 rois = [roi1, roi2, roi3]
 gl_exp.add_label(image_query, label=len(rois), rois=rois)
 # highlight-end
-```
-
-## [BETA] Multi-Class Detectors
-
-If you want to classify images into multiple categories, you can create a multi-class detector.
-
-```python
-from groundlight import ExperimentalApi
-gl_exp = ExperimentalApi()
-
-# highlight-start
-class_names = ["Golden Retriever", "Labrador Retriever", "German Shepherd", "Other"]
-detector = gl_exp.create_multiclass_detector(
-    name="dog-breed-detector",
-    query="What kind of dog is this?",
-    class_names=class_names,
-)
-# highlight-end
-```
-
-:::tip
-We recommend adding an "Other" class to your multi-class detector to handle cases where the image does not belong to any of the pre-defined classes.
-:::
-
-<!-- :::note
-Multi-Class Detectors are available on [Business and Enterprise plans](https://www.groundlight.ai/pricing).
-::: -->
-
-### Submit an Image Query to a Multi-Class Detector
-
-Now that you have created a multi-class detector, you can submit an image query to it.
-
-```python notest
-from groundlight import ExperimentalApi
-gl_exp = ExperimentalApi()
-
-detector = gl_exp.get_detector_by_name("dog-breed-detector")
-
-# highlight-start
-# Classify the breed of a dog in an image
-image_query = gl_exp.submit_image_query(detector, "path/to/image.jpg")
-# highlight-end
-
-print(f"Result: {image_query.result.label}")
-print(f"Confidence: {image_query.result.confidence}")
-```
-
-Multi-class detectors return a `label` attribute in the result object, which contains the predicted class label. The `label` attribute will be one of the class names provided when creating the detector. The `confidence` attribute represents the confidence level in the predicted class, which is a value between `1/len(class_names)` and 1.
-
-### Add a label to a Multi-Class Detector
-
-To provide ground truth labels for multi-class detectors, you can specify the label of the correct class.
-
-```python notest
-from groundlight import ExperimentalApi
-gl_exp = ExperimentalApi()
-
-# highlight-start
-# Add a multi-class label to the image query from the previous example
-gl_exp.add_label(image_query, label="German Shepherd")
-# highlight-end
-```
-
-<!-- TODO: text, object detection modes -->
+```

docs/docs/guide/5-detector-modes.md docs/docs/answer-modes/3-counting-detectors.md

@@ -0,0 +1,4 @@
+{
+    "label": "Answer Modes",
+    "position": 3
+}

docs/docs/answer-modes/_category_.json

@@ -0,0 +1,9 @@
+# Detector Answer Modes
+
+Groundlight offers several detector modalities to suit different computer vision tasks. While previous examples have focused on binary classification, this guide will walk you through using counting and multi-class detectors. Let's explore how these different modes can be used via the Groundlight SDK.
+
+1. [Binary Detectors](1-binary-detectors.md)
+2. [Multi-class Detectors](2-multi-class-detectors.md)
+3. [Counting Detectors](3-counting-detectors.md)
+
+<!-- TODO: text, object detection modes -->

docs/docs/answer-modes/answer-modes.md

@@ -73,7 +73,7 @@ image_query = gl.get_image_query(id=image_query.id)
 # highlight-end
 ```
 
-See this [guide](./8-async-queries.md) for more information on ImageQueries submitted asynchronously.
+See this [guide](./7-async-queries.md) for more information on ImageQueries submitted asynchronously.
 
 ### (Advanced) Get the first available answer, regardless of confidence
 `ask_ml` evaluates an image with Groundlight and returns the first answer Groundlight can provide, agnostic of confidence. There is no wait period when using this method. It is called `ask_ml` because our machine learning models are earliest on our escalation ladder and thus always the fastest to respond.

docs/docs/guide/4-submitting-image-queries.md

@@ -1,4 +1,4 @@
 {
   "label": "Guide",
-  "position": 3
+  "position": 4
 }

docs/docs/guide/6-managing-confidence.md docs/docs/guide/5-managing-confidence.md

@@ -6,12 +6,11 @@ On the following pages, we'll guide you through the process of building applicat
 - **[Grabbing images](2-grabbing-images.md)**: Understand the intricacies of how to submit images from various input sources to Groundlight.
 - **[Working with detectors](3-working-with-detectors.md)**: Learn how to create, configure, and use detectors in your Groundlight-powered applications.
 - **[Submitting image queries](4-submitting-image-queries.md)**: Submit images to Groundlight for analysis and retrieve the results.
-- **[Detector answer modes](5-detector-modes.md)**: Answer counting and multi-classification queries with Groundlight.
-- **[Confidence levels](6-managing-confidence.md)**: Master how to control the trade-off of latency against accuracy by configuring the desired confidence level for your detectors.
-- **[Handling errors](7-handling-errors.md)**: Understand how to handle and troubleshoot HTTP errors (`ApiException`) that may occur while using Groundlight.
-- **[Asynchronous queries](8-async-queries.md)**: Groundlight makes it easy to submit asynchronous queries. Learn how to submit queries asynchronously and retrieve the results later.
-- **[Using Groundlight on the edge](9-edge.md)**: Discover how to deploy Groundlight in edge computing environments for improved performance and reduced latency.
-- **[Alerts](10-alerts.md)**: Learn how to set up alerts to notify you via text (SMS) or email when specific conditions are met in your visual applications.
+- **[Confidence levels](5-managing-confidence.md)**: Master how to control the trade-off of latency against accuracy by configuring the desired confidence level for your detectors.
+- **[Handling errors](6-handling-errors.md)**: Understand how to handle and troubleshoot HTTP errors (`ApiException`) that may occur while using Groundlight.
+- **[Asynchronous queries](7-async-queries.md)**: Groundlight makes it easy to submit asynchronous queries. Learn how to submit queries asynchronously and retrieve the results later.
+- **[Using Groundlight on the edge](8-edge.md)**: Discover how to deploy Groundlight in edge computing environments for improved performance and reduced latency.
+- **[Alerts](9-alerts.md)**: Learn how to set up alerts to notify you via text (SMS) or email when specific conditions are met in your visual applications.
 
 
 By exploring these resources and sample applications, you'll be well on your way to building powerful visual applications using Groundlight's computer vision and natural language capabilities.

docs/docs/guide/7-handling-errors.md docs/docs/guide/6-handling-errors.md

@@ -1,5 +1,5 @@
 {
     "label": "Alternative Deployment Options",
-    "position": 5,
+    "position": 6,
     "collapsed": false
 }

docs/docs/guide/8-async-queries.md docs/docs/guide/7-async-queries.md

@@ -1,4 +1,4 @@
 {
     "label": "Sample Applications",
-    "position": 4
+    "position": 5
   }