Improve user messaging and documentation for Cross-Region Inference on Amazon Bedrock

docs/source/users/bedrock.md

@@ -57,4 +57,17 @@ Once the model is fine-tuned, it will have its own `arn`, as shown below:
 
 As seen above, you may click on `Purchase provisioned throughput` to buy inference units with which to call the custom model's API. Enter the model's `arn` in Jupyter AI's Language model user interface to use the provisioned model.
 
+Amazon Bedrock now permits cross-region inference, where a model hosted in a different region than that of the user may be specified, see the [inference profiles documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html). Such models have IDs prefixed by a region identifier such as `us.meta.llama3-2-1b-instruct-v1:0`, for example. To use this feature, simply enter the Inference profile ID for the cross-region model instead of the ARN.
+
+<img src="../_static/bedrock-cross-region-inference.png"
+    width="75%"
+    alt='Screenshot of Bedrock cross-region inference usage.'
+    class="screenshot" />
+
+In summary:
+1. Bedrock Base models: All available models will already be available in the drop down model list. The above interface also allows use of base model IDs or ARNs, though this is unnecessary as they are in the dropdown list.
+2. Bedrock Custom models: If you have fine-tuned a Bedrock base model you may use the ARN for this custom model. Make sure to enter the correct provider information, such as `amazon`, `anthropic`, `cohere`, `meta`, `mistral` (always in lower case).
+3. Provisioned Models: These are models that run on dedicated endpoints. Users can purchase Provisioned Throughput Model Units to get faster throughput. These may be base or custom models. Enter the ARN for these models in the Model ID field.
+4. Cross-region Inference: Use the Inference profile ID for the cross-region model instead of the ARN.
+
 [(Return to Chat Interface page for Bedrock)](index.md#amazon-bedrock-usage)

docs/source/users/index.md

@@ -349,14 +349,20 @@ You may now select a chosen Bedrock model from the drop-down menu box title `Com
 
 If your provider requires an API key, please enter it in the box that will show for that provider. Make sure to click on `Save Changes` to ensure that the inputs have been saved.
 
-Bedrock also allows custom models to be trained from scratch or fine-tuned from a base model. Jupyter AI enables a custom model to be called in the chat panel using its `arn` (Amazon Resource Name). The interface is shown below:
+Bedrock also allows custom models to be trained from scratch or fine-tuned from a base model. Jupyter AI enables a custom model to be called in the chat panel using its `arn` (Amazon Resource Name, ARN). The interface is shown below:
 
 <img src="../_static/bedrock-chat-custom-model-arn.png"
     width="75%"
     alt='Screenshot of the Jupyter AI chat panel where the custom model is selected using model arn.'
     class="screenshot" />
 
-For detailed workflows, see [Using Amazon Bedrock with Jupter AI](bedrock.md).
+Note that:
+1. Bedrock Base models: All available models will already be available in the drop down model list. The above interface also allows use of base model IDs or ARNs, though this is unnecessary as they are in the dropdown list.
+2. Bedrock Custom models: If you have fine-tuned a Bedrock base model you may use the ARN for this custom model. Make sure to enter the correct provider information, such as `amazon`, `anthropic`, `cohere`, `meta`, `mistral` (always in lower case).
+3. Provisioned Models: These are models that run on dedicated endpoints. Users can purchase Provisioned Throughput Model Units to get faster throughput. These may be base or custom models. Enter the ARN for these models in the Model ID field.
+4. Cross-region Inference: Amazon Bedrock now permits cross-region inference, where a model hosted in a different region than that of the user may be specified, see the [inference profiles documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html). Such models have IDs prefixed by a region identifier such as `us.meta.llama3-2-1b-instruct-v1:0`, for example. To use this feature, simply enter the Inference profile ID for the cross-region model instead of the ARN.
+
+For detailed workflows, including the creation and use of custom models, see [Using Amazon Bedrock with Jupter AI](bedrock.md).
 
 
 ### SageMaker endpoints usage

packages/jupyter-ai-magics/jupyter_ai_magics/partner_providers/aws.py

@@ -124,8 +124,9 @@ class BedrockCustomProvider(BaseProvider, ChatBedrock):
         ),
     ]
     help = (
-        "Specify the ARN (Amazon Resource Name) of the custom/provisioned model as the model ID. For more information, see the [Amazon Bedrock model IDs documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html).\n\n"
-        "The model provider must also be specified below. This is the provider of your foundation model *in lowercase*, e.g. `amazon`, `anthropic`, `meta`, or `mistral`."
+        "- For Cross-Region Inference use the appropriate `Inference profile ID` (Model ID with a region prefix, e.g., `us.meta.llama3-2-11b-instruct-v1:0`). See the [inference profiles documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html). \n"
+        "- For custom/provisioned models, specify the model ARN (Amazon Resource Name) as the model ID. For more information, see the [Amazon Bedrock model IDs documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html).\n\n"
+        "The model provider must also be specified below. This is the provider of your foundation model *in lowercase*, e.g., `amazon`, `anthropic`, `cohere`, `meta`, or `mistral`."
     )
     registry = True