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Amazon Bedrock

Available since 25.3

To use Amazon Bedrock as an AI provider in Team Edition, set it up like this:

1. In Team Edition, go to Window -> Preferences -> AI -> Model configurations
2. Click + and choose Amazon Bedrock as the engine to create a profile
3. Select AWS Credentials as the authorization type
4. Choose a credentials method:
   • Access/secret keys: In the AWS console, create or use an IAM user with permission to use Bedrock. Generate an Access Key ID and Secret Access Key, and enter them in Team Edition.
   • AWS profile: Choose a profile configured in your shared AWS credentials files.
5. Select your Region
6. Choose a model from the Model/Inference list
7. Click Test connection to verify that the credentials and model work correctly
8. Apply the changes

Engine settings

Setting	Description	default
Authorization Type	Select AWS Credentials to configure AWS credentials in Team Edition, or Integrated AWS Cloud to use credentials from a configured AWS Cloud.
Credentials	When AWS Credentials is selected: - Access/secret keys - AWS profile - Default credentials - Session credentials use access and secret keys from the current AWS cloud session. When Integrated AWS Cloud is selected, use credentials from a configured AWS Cloud.
Model/Inference	Choose the AI model. Use Load model list to refresh the available models. You can also type a model name manually.
Show available inferences	Fetches and displays the models available to your AWS account in the selected region.	false
Context size	A larger number allows the AI to use more data for better answers but may slow down response time.	20000
Temperature	Control AI's creativity from 0.0 (more precise) to 0.9 (more diverse). Note that higher temperature can lead to less predictable results.	0.0
Request timeout (sec)	Maximum time Team Edition waits for a response from the AI engine before the request fails.	30
Write AWS Bedrock queries to debug log	Logs your AI requests. For more details on logging, see Log Viewer.	false