Amazon Bedrock
Since 25.3
This feature is available in Lite, Enterprise, and Ultimate editions only.
To use Amazon Bedrock as an AI provider in DBeaver, set it up like this:
- In DBeaver, go to Window -> Preferences -> AI -> Model configurations
- Click + and choose Amazon Bedrock as the engine to create a profile
- Select AWS Credentials as the authorization type
- 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 DBeaver.
- AWS profile: Choose a profile configured in your shared AWS credentials files.
- Select your Region
- Choose a model from the Model/Inference list
- Click Test connection to verify that the credentials and model work correctly
- Apply the changes
Engine settings¶
| Setting | Description | default |
|---|---|---|
| Authorization Type | Select AWS Credentials to configure AWS credentials in DBeaver, 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 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 DBeaver 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 |