Create Grounded Modellverbindungen with Amazon Bedrock RAG capability

Create Grounding durch Wissensdatenbank Modellverbindungen using the native RAG (Retrieval Augmented Generation) capability from Amazon Bedrock to generate accurate and contextually relevant information referenced from Amazon Wissensdatenbank.

We are now offering you the option to create Modellverbindungen using the Amazon Bedrock-RAG capability. A search query on RAG retrieves relevant chunks of content from large-data-sets which are relevant and accurate to the provided context. After retrieving the relevant information, the model uses it to generate a response.

A few things to consider

  • To use the Amazon Bedrock-RAG capability, you would first create a Knowledge Base in Amazon Bedrock before creating the Grounding durch Wissensdatenbank Modellverbindung.

    See: Create Amazon Wissensdatenbank

  • To use additional models from Amazon Bedrock that are not available for selection when creating a Modellverbindung, you would have to procure the Model ID and the Model ARN from the supported models of Amazon Bedrock.

    See: Add Amazon Bedrock models from AWS-Dienste

Work flow for using Amazon Bedrock RAG capability for creating Modellverbindungen

Vorbereitungen

The Automation Admin requires these roles and permissions to create and manage Modellverbindungen for their business organization.
  • Role: AAE_Basic, Automation Admin custom role
  • Permission: Attended Bot Runner
  • Settings: AI Data Management must be enabled by the Automation Admin and the check box selected for Allow users to disable logs on AI Skills. Allow users with the Bot Creator license to disable data logging when using AI Skills to enable the Data logging toggle in the KI-Fähigkeiten screen.

See Rollen und Berechtigungen für KI-Werkzeuge for the Automation Admin custom role permissions.

Other requirements:

  • As mentioned earlier, you would first create an Amazon Wissensdatenbank to create a Grounding durch Wissensdatenbank Modellverbindung and use it successfully in an KI-Fähigkeit.
  • If you want to store authentication details in a credential vault, have that information handy. See Sicherer Anmeldedatenspeicher mit dem Credential Vault.
  • To test a Modellverbindung, you must be connected to a Bot-Agent 22.60.10 and later. As part of the test, you would have to run the Bot on your desktop. Hence ensure the Bot-Agent is configured to your user. For this task, if you have to switch connection to a different Control Room, see: Umschalten der Geräteregistrierung zwischen Control Room-Instanzen.
  • You would need access to the Recorder package and the KI-Fähigkeiten package to test the connection successfully. A test Prompt would be executed to test the Modellverbindung.

Prozedur

  1. In your Control Room environment, navigate to AI > Model connections > Create model connection.
  2. In the Create model connection screen you would configure these Connection settings:
    Create Grounded Modellverbindungen with Amazon Bedrock RAG capability
    You can manually enter the model name in the Choose a model or create a custom one field. The name you enter will be used for creating the Modellverbindung.
    1. Model connection name: Provide a name for easy identification of the Modellverbindung.
    2. Description (optional): Add a meaningful short description defining the connection.
    3. Choose a vendor: Choose a foundational model vendor from the supported list of vendors. For creating a Grounding durch Wissensdatenbank Modellverbindung with Amazon Bedrock, you would select Amazon Bedrock from the drop-down list.
    4. Choose a type: Choose Grounding durch Wissensdatenbank for using the RAG capability.
    5. Choose a model or create a custom one: Choose a model from the drop-down list of validated models from Amazon Bedrock.
      Additionally, we also support other models available from Amazon Bedrock, which are not available in the drop-down list. You can use either the Model ID or Model ARN to add to the list. See: Add Amazon Bedrock models from AWS-Dienste.
      For a complete list of supported models for each foundational model vendor, see .
    6. Click Next to proceed to the Authentication details section.
  3. In the Authentication details section, configure these settings:
    1. Region: Choose the region where your selected model is deployed for authenticating the Modellverbindung. You can also add a region that is not available in the drop-down list by referring to the list in Amazon Bedrock. Enter in this format to get the region added to the list. For example: us-east-1.
      For a list of supported deployment regions for Amazon Bedrock models, see Supported regions and models for Amazon Bedrock knowledge bases .
    2. Knowledge base ID: Provide the Knowledge Base ID you procured from Amazon Bedrock. .
    3. Access Key: This AWS access key serves as your unique identifier within the AWS ecosystem. It is a fundamental part of the authentication process, allowing AWS-Dienste to recognize and validate your access.
    4. Secret Access Key: This key is the confidential counterpart to your Access Key ID. This key is used to sign requests made to AWS, enhancing security by ensuring that only authorized individuals or systems can access your AWS resources.
    5. Session Token (optional): This is an optional field. In addition to the above information, you can include a Session token, which is a temporary, time-bound token used when working with temporary security credentials. It provides an added layer of security, particularly in scenarios where temporary access is required, such as when using temporary security credentials.
    6. After setting up the authentication details, confirm and click Next to proceed to the Test connection section to test the Modellverbindung.
    Anmerkung: For details on setting up the Access Key, Secret Access Key, and Session Token for Amazon Bedrock, see: Amazon Bedrock: Aktion „Authentifizieren“.
  4. Click Test connection to make sure all connection details have been defined correctly and check if the connection is working.
    This is a desktop operation using a Bot-Agent. Use Bot-Agent 22.60.10 and later for successful testing.
    • If the connection works as expected, the system will process the request and you will get a system generated success message.
    • If the connection does not work as expected, you will get a system-generated message stating the reason for the connection failure. For example, if you have not downloaded the supported foundational model package to your workspace, you would get an error message. You would have to download the package and then retest the Modellverbindung.
    • If the testing a Modellverbindung is unsuccessful or if you leave the task incomplete, the Modellverbindung will not get saved and you will have to restart the process of creating the Modellverbindung.
  5. Click Next to proceed to the Invite roles section to begin assigning custom roles to users.
    The Automation Admin would create custom roles and assign the Modellverbindungen to the role, which can then be assigned to users. Only users assigned to this custom role can use this Modellverbindung.
  6. Assign access to the Pro Developer via custom role (using RBAC), for using this Modellverbindung to create an KI-Fähigkeit.
  7. Click Create model connection to complete creating the Modellverbindung.
    After successfully creating the Modellverbindung, the Pro Developer would use it to create an KI-Fähigkeit

    See: Erstellen Sie KI-Fähigkeiten mit Grounding durch Wissensdatenbank Modellverbindungen

    .

Nächste Maßnahme

After creating and testing the Modellverbindung, you would assign it to the Pro Developers, who would use this connection to create KI-Fähigkeiten.

See Erstellen Sie KI-Fähigkeiten mit Grounding durch Wissensdatenbank Modellverbindungen.

Anmerkung: When you create or test an KI-Fähigkeit in the AI Skill screen, the success or failure details along with the model responses can be viewed in these navigation screens:
  • Administration > AI governance > AI Prompt log
  • Administration > AI governance > Event log
  • Administration > Audit log

See KI-Governance.

As the next step in your sequence of tasks, go to Erstellen Sie KI-Fähigkeiten mit Grounding durch Wissensdatenbank Modellverbindungen and create an KI-Fähigkeit and connect to a Grounding durch Wissensdatenbank Modellverbindung to eventually use it in an automation.