Setting Up Users with BIACM Application Roles

本文介绍如何在WebLogic控制台中创建用户,并通过Enterprise Manager将这些用户关联到应用程序角色,实现权限管理。

I. Create Users in Weblogic Console:

 

1.    Login to weblogicconsole (i.e. http://localhost:7001/console)using your weblogic admin user (i.e. Administrator/Admin123)

2.    Select ‘SecurityRealms’ in Domain Structure pane:

3.    Select myrealm inthe Summary of Security Realms page:

4.    Select the “Usersand Groups” tab in the Settings for myrealm page:

5.    Click “New” buttonin the Users tab:

6.    In the “Create a New User” dialog, enter the following info and click “OK”:

       Name                 (i.e. biacm_roletest)

       Password          (i.e. welcome1)

       ConfirmPassword     (i.e. welcome1)

II. Associate Users to Application Roles in Enterprise Manager:

 

1.    Login to EnterpriseManager (i.e. http://localhost:7001/em)using your weblogic admin user (i.e. Administrator /Admin123)

2.    Right clickWebLogic Domain -> bifoundation_domain and select Security -> ApplicationRoles:

3.    Enter info inSearch pane as follows and invoke search:

       SelectApplication Stripe to Search:  obi

       RoleName:  BIA

4.    In table ofmatching Role Names, hover over the Role Name column header and select sortAscending icon:

5.     Click on BIA_ADMINISTRATOR_DUTY role name:

6.    Click the Edit button:

7.    In Edit dialog, click Add button:

8.    Fill biacm_roletest into Principal Name field and select type user.

9.    Click OK:

10. Smile, you’re done !

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Google Cloud Tools¶ Google Cloud tools make it easier to connect your agents to Google Cloud’s products and services. With just a few lines of code you can use these tools to connect your agents with: Any custom APIs that developers host in Apigee. 100s of prebuilt connectors to enterprise systems such as Salesforce, Workday, and SAP. Automation workflows built using application integration. Databases such as Spanner, AlloyDB, Postgres and more using the MCP Toolbox for databases. Google Cloud Tools Apigee API Hub Tools¶ ApiHubToolset lets you turn any documented API from Apigee API hub into a tool with a few lines of code. This section shows you the step by step instructions including setting up authentication for a secure connection to your APIs. Prerequisites Install ADK Install the Google Cloud CLI. Apigee API hub instance with documented (i.e. OpenAPI spec) APIs Set up your project structure and create required files project_root_folder | `-- my_agent |-- .env |-- __init__.py |-- agent.py `__ tool.py Create an API Hub Toolset¶ Note: This tutorial includes an agent creation. If you already have an agent, you only need to follow a subset of these steps. Get your access token, so that APIHubToolset can fetch spec from API Hub API. In your terminal run the following command gcloud auth print-access-token # Prints your access token like 'ya29....' Ensure that the account used has the required permissions. You can use the pre-defined role or assign the following permissions:roles/apihub.viewer apihub.specs.get (required) apihub.apis.get (optional) apihub.apis.list (optional) apihub.versions.get (optional) apihub.versions.list (optional) apihub.specs.list (optional) Create a tool with . Add the below to APIHubToolsettools.py If your API requires authentication, you must configure authentication for the tool. The following code sample demonstrates how to configure an API key. ADK supports token based auth (API Key, Bearer token), service account, and OpenID Connect. We will soon add support for various OAuth2 flows. from google.adk.tools.openapi_tool.auth.auth_helpers import token_to_scheme_credential from google.adk.tools.apihub_tool.apihub_toolset import APIHubToolset # Provide authentication for your APIs. Not required if your APIs don't required authentication. auth_scheme, auth_credential = token_to_scheme_credential( "apikey", "query", "apikey", apikey_credential_str ) sample_toolset_with_auth = APIHubToolset( name="apihub-sample-tool", description="Sample Tool", access_token="...", # Copy your access token generated in step 1 apihub_resource_name="...", # API Hub resource name auth_scheme=auth_scheme, auth_credential=auth_credential, ) For production deployment we recommend using a service account instead of an access token. In the code snippet above, use and provide your security account credentials instead of the token.service_account_json=service_account_cred_json_str For apihub_resource_name, if you know the specific ID of the OpenAPI Spec being used for your API, use . If you would like the Toolset to automatically pull the first available spec from the API, use `projects/my-project-id/locations/us-west1/apis/my-api-id/versions/version-id/specs/spec-id``projects/my-project-id/locations/us-west1/apis/my-api-id` Create your agent file Agent.py and add the created tools to your agent definition: from google.adk.agents.llm_agent import LlmAgent from .tools import sample_toolset root_agent = LlmAgent( model='gemini-2.0-flash', name='enterprise_assistant', instruction='Help user, leverage the tools you have access to', tools=sample_toolset.get_tools(), ) Configure your `__init__.py` to expose your agent from . import agent Start the Google ADK Web UI and try your agent: # make sure to run `adk web` from your project_root_folder adk web Then go to http://localhost:8000 to try your agent from the Web UI. 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Run: gcloud config set project <project-id> gcloud auth application-default login gcloud auth application-default set-quota-project <project-id> Set up your project structure and create required files project_root_folder |-- .env `-- my_agent |-- __init__.py |-- agent.py `__ tools.py When running the agent, make sure to run adk web in project_root_folder Use Integration Connectors¶ Connect your agent to enterprise applications using Integration Connectors. Prerequisites To use a connector from Integration Connectors, you need to provision Application Integration in the same region as your connection by clicking on "QUICK SETUP" button. Google Cloud Tools Go to Connection Tool template from the template library and click on "USE TEMPLATE" button. Google Cloud Tools Fill the Integration Name as ExecuteConnection (It is mandatory to use this integration name only) and select the region same as the connection region. Click on "CREATE". 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Update your fileagent.py from google.adk.agents.llm_agent import LlmAgent from .tools import connector_tool root_agent = LlmAgent( model='gemini-2.0-flash', name='connector_agent', instruction="Help user, leverage the tools you have access to", tools=connector_tool.get_tools(), ) Configure your `__init__.py` to expose your agent from . import agent Start the Google ADK Web UI and try your agent. # make sure to run `adk web` from your project_root_folder adk web Then go to http://localhost:8000, and choose my_agent agent (same as the agent folder name) Use App Integration Workflows¶ Use existing Application Integration workflow as a tool for your agent or create a new one. 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Update your fileagent.py from google.adk.agents.llm_agent import LlmAgent from .tools import integration_tool, connector_tool root_agent = LlmAgent( model='gemini-2.0-flash', name='integration_agent', instruction="Help user, leverage the tools you have access to", tools=integration_tool.get_tools(), ) Configure your `__init__.py` to expose your agent from . import agent Start the Google ADK Web UI and try your agent. # make sure to run `adk web` from your project_root_folder adk web Then go to http://localhost:8000, and choose my_agent agent (same as the agent folder name) Toolbox Tools for Databases¶ MCP Toolbox for Databases is an open source MCP server for databases. It was designed with enterprise-grade and production-quality in mind. It enables you to develop tools easier, faster, and more securely by handling the complexities such as connection pooling, authentication, and more. Google’s Agent Development Kit (ADK) has built in support for Toolbox. For more information on getting started or configuring Toolbox, see the documentation. GenAI Toolbox Configure and deploy¶ Toolbox is an open source server that you deploy and manage yourself. For more instructions on deploying and configuring, see the official Toolbox documentation: Installing the Server Configuring Toolbox Install client SDK¶ ADK relies on the python package to use Toolbox. Install the package before getting started:toolbox-langchain pip install toolbox-langchain langchain Loading Toolbox Tools¶ Once you’ve Toolbox server is configured and up and running, you can load tools from your server using the ADK: from google.adk.tools.toolbox_tool import ToolboxTool toolbox = ToolboxTool("https://127.0.0.1:5000") # Load a specific set of tools tools = toolbox.get_toolset(toolset_name='my-toolset-name'), # Load single tool tools = toolbox.get_tool(tool_name='my-tool-name'), root_agent = Agent( ..., tools=tools # Provide the list of tools to the Agent ) Advanced Toolbox Features¶ Toolbox has a variety of features to make developing Gen AI tools for databases. For more information, read more about the following features: Authenticated Parameters: bind tool inputs to values from OIDC tokens automatically, making it easy to run sensitive queries without potentially leaking data Authorized Invocations: restrict access to use a tool based on the users Auth token OpenTelemetry: get metrics and tracing from Toolbox with OpenTelemetry帮我画一张思维导图
05-02
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