Maven script to deploy MDS (Metadata Services) artifacts in a SOA 12.2.1.4

To create a Maven script to deploy MDS (Metadata Services) artifacts in a SOA 12.2.1.4 environment, you need to use the oracle-maven-sync configuration and Oracle's oracle-maven-plugin to manage the deployment. Below is a sample pom.xml setup and a script to achieve this.

Click here for the above step which is a prerequisite

Prerequisites

1. Make sure the Oracle SOA 12.2.1.4 Maven plugin is installed in your local repository or is accessible through a corporate repository.
2. Your environment should have Oracle WebLogic and SOA Suite 12.2.1.4 configured properly.
3. Oracle MDS repository should be set up and accessible.

Maven pom.xml Configuration

Here’s a sample pom.xml file for deploying an MDS artifact using Maven:

xml
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>com.example</groupId>
    <artifactId>soa-mds-deployment</artifactId>
    <version>1.0-SNAPSHOT</version>
    <packaging>pom</packaging>

    <properties>
        <!-- Update with your SOA and WebLogic version -->
        <oracle.soa.version>12.2.1.4</oracle.soa.version>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
    </properties>

    <dependencies>
        <dependency>
            <groupId>oracle.soa.common</groupId>
            <artifactId>oracle-soa-maven-plugin</artifactId>
            <version>${oracle.soa.version}</version>
        </dependency>
    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>oracle.soa.common</groupId>
                <artifactId>oracle-soa-maven-plugin</artifactId>
                <version>${oracle.soa.version}</version>
                <configuration>
                    <!-- Configuration for the SOA MDS deployment -->
                    <action>deploy</action>
                    <repositoryName>mds-soa</repositoryName>
                    <sourcePath>src/main/resources/mds/</sourcePath>
                    <serverURL>t3://<admin-server-host>:<admin-server-port></serverURL>
                    <username>weblogic</username>
                    <password>your_weblogic_password</password>
                    <partition>soa-infra</partition>
                </configuration>
            </plugin>
        </plugins>
    </build>

    <profiles>
        <profile>
            <id>soa-mds-deploy</id>
            <build>
                <plugins>
                    <plugin>
                        <groupId>oracle.soa.common</groupId>
                        <artifactId>oracle-soa-maven-plugin</artifactId>
                        <executions>
                            <execution>
                                <goals>
                                    <goal>deploy</goal>
                                </goals>
                            </execution>
                        </executions>
                        <configuration>
                            <!-- MDS repository configuration -->
                            <repositoryName>mds-soa</repositoryName>
                            <serverURL>t3://<admin-server-host>:<admin-server-port></serverURL>
                            <username>weblogic</username>
                            <password>your_weblogic_password</password>
                            <partition>soa-infra</partition>
                            <sourcePath>src/main/resources/mds/</sourcePath>
                        </configuration>
                    </plugin>
                </plugins>
            </build>
        </profile>
    </profiles>

</project>

Folder Structure

Ensure your project directory is structured like this:

css
.
├── pom.xml
└── src
    └── main
        └── resources
            └── mds
                └── your_mds_artifacts

Place your MDS artifacts (e.g., .xml or .wsdl files) in the src/main/resources/mds/ folder.

Maven Command

To deploy the MDS artifacts, use the following command:

bash
mvn clean install -Psoa-mds-deploy

Key Points

1. repositoryName: The MDS repository name (mds-soa) should match the target repository configured in your SOA environment.
2. serverURL: Replace <admin-server-host> and <admin-server-port> with your WebLogic Admin server’s host and port.
3. username/password: Use the WebLogic Admin credentials to authenticate the deployment.
4. sourcePath: Specify the folder containing your MDS artifacts.

This script configures a Maven build to deploy MDS artifacts to your SOA 12.2.1.4 environment. If you encounter specific errors during deployment, check the logs on the Admin server to ensure correct configurations.

Installation of Oracle SOA 12.2.1.4 Maven plugin

 To install the Oracle SOA 12.2.1.4 Maven plugin, follow the steps below. The Oracle SOA Maven plugin is not hosted in a public repository like Maven Central, so it needs to be installed manually from the Oracle installation directory or configured in a local repository.

Step 1: Locate the Oracle SOA Maven Plugin

The Oracle SOA Suite installation directory contains a script that generates a pom.xml file and installs the necessary SOA Maven artifacts into your local Maven repository. This is usually found in your Oracle Middleware home directory.

The typical path to the Maven sync script is:

ruby

<ORACLE_HOME>/oracle_common/plugins/maven/com/oracle/maven/oracle-maven-sync.jar

For Example on my Server this file is located in following directory





C:\Oracle\Middleware\Oracle_Home\oracle_common\plugins\maven\com\oracle\maven\oracle-maven-sync\12.2.1

Step 2: Execute the oracle-maven-sync Script

1. Open a terminal or command prompt.

2. Navigate to the directory containing oracle-maven-sync.jar.

   bash
   cd C:\Oracle\Middleware\Oracle_Home\oracle_common\plugins\maven\com\oracle\maven\oracle-maven-sync\12.2.1

3. Run the Maven sync command to install the SOA Maven plugin and dependencies:

   bash
   mvn install:install-file -DpomFile=oracle-maven-sync.xml -Dfile=oracle-maven-sync.jar
   

   Alternatively, you can use the oracle-maven-sync script:

   bash
   java -jar oracle-maven-sync.jar -f

This command installs all the necessary SOA artifacts, including the oracle-soa-maven-plugin into your local Maven repository (~/.m2).

Step 3: Verify Installation

After running the command, verify that the artifacts have been installed in your local Maven repository. Check under the com/oracle/soa/oracle-soa-maven-plugin directory inside the .m2 folder:

ruby
~/.m2/repository/com/oracle/soa/oracle-soa-maven-plugin

You should see subdirectories like 12.2.1.4, containing the plugin JAR files and associated pom.xml files.

Step 4: Update the Maven pom.xml

Once the plugin is installed locally, update your pom.xml to reference it:

xml

<plugin>
    <groupId>com.oracle.soa</groupId>
    <artifactId>oracle-soa-maven-plugin</artifactId>
    <version>12.2.1.4</version>
</plugin>

Additional Configuration (Optional)

If you need to use this plugin in a shared environment (e.g., CI/CD pipeline or team development), consider deploying it to a shared Maven repository like Nexus or Artifactory. Here’s how to do that:

1. Install the plugin to your shared repository:

   bash
   mvn deploy:deploy-file -DgroupId=com.oracle.soa \
                          -DartifactId=oracle-soa-maven-plugin \
                          -Dversion=12.2.1.4 \
                          -Dpackaging=jar \
                          -Dfile=<ORACLE_HOME>/soa/plugins/maven/oracle-soa-maven-plugin-12.2.1.4.jar \
                          -DpomFile=<ORACLE_HOME>/soa/plugins/maven/oracle-soa-maven-plugin-12.2.1.4.pom \
                          -DrepositoryId=<repository_id> \
                          -Durl=<repository_url>
   

2. Configure your pom.xml to point to the shared repository:

xml

<repositories>
    <repository>
        <id>shared-repo</id>
        <url>http://<repository_url>/repository/maven-public/</url>
    </repository>
</repositories>

Healthcare Information Extraction Using Amazon Bedrock using advanced NLP with Titan or Claude Models

Healthcare Information Extraction Using Amazon Bedrock

Client: Leading Healthcare Provider

Project Overview:
This project was developed for a healthcare client to automate the extraction of critical patient information from unstructured medical records using advanced Natural Language Processing (NLP) capabilities offered by Amazon Bedrock. The primary objective was to streamline the processing of patient case narratives, reducing the manual effort needed to identify key data points such as patient demographics, symptoms, medical history, medications, and recommended treatments.

Key Features Implemented:

1. Automated Text Analysis: Utilized Amazon Bedrock's NLP models to analyze healthcare use cases, automatically identifying and extracting relevant clinical details.
2. Customizable Information Extraction: Implemented the solution to support specific healthcare entities (e.g., patient name, age, symptoms, medications) using customizable extraction models.
3. Seamless Integration: Integrated with existing systems using Java-based AWS SDK, enabling the healthcare provider to leverage the extracted information for clinical decision support and reporting.
4. Real-time Data Processing: Enabled the client to process patient case records in real-time, accelerating the review of patient documentation and improving overall efficiency.

Amazon Bedrock provides access to foundational models for Natural Language Processing (NLP), which can be used for various applications, such as extracting relevant information from text documents. Below is the implementation design with Amazon Bedrock with Java to analyze patient healthcare use cases. For this example, I will illustrate how to structure a solution that utilizes AWS SDK for Java to interact with Bedrock and apply language models like Titan or Claude (depending on the model availability).

Prerequisites

1. AWS SDK for Java: Make sure you have included the necessary dependencies for interacting with Amazon Bedrock.
2. Amazon Bedrock Access: Ensure that your AWS credentials and permissions are configured to access Amazon Bedrock.
3. Java 11 or Higher: Recommended to use a supported version of Java.

Step 1: Include Maven Dependencies

First, add the necessary dependencies in your pom.xml to include the AWS SDK for Amazon Bedrock.

xml

<dependency>
    <groupId>software.amazon.awssdk</groupId>
    <artifactId>bedrock</artifactId>
    <version>2.20.0</version>
</dependency>

Step 2: Set Up AWS SDK Client

Next, create a client to connect to Amazon Bedrock using the BedrockClient provided by the AWS SDK.

java code
import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrock.BedrockClient;
import software.amazon.awssdk.services.bedrock.model.*;

public class BedrockHelper {

    public static BedrockClient createBedrockClient() {
        return BedrockClient.builder()
                .region(Region.US_EAST_1)  // Set your AWS region
                .credentialsProvider(ProfileCredentialsProvider.create())
                .build();
    }
}

Step 3: Define a Method to Extract Information

Create a method that will interact with Amazon Bedrock, pass the healthcare use case text, and get relevant information back.

java

import software.amazon.awssdk.services.bedrock.model.InvokeModelRequest;
import software.amazon.awssdk.services.bedrock.model.InvokeModelResponse;

public class HealthcareUseCaseProcessor {

    private BedrockClient bedrockClient;

    public HealthcareUseCaseProcessor(BedrockClient bedrockClient) {
        this.bedrockClient = bedrockClient;
    }

    public String extractRelevantInformation(String useCaseText) {
        InvokeModelRequest request = InvokeModelRequest.builder()
                .modelId("titan-chat-b7") // Replace with the relevant model ID
                .body("{ \"text\": \"" + useCaseText + "\" }")
                .build();

        InvokeModelResponse response = bedrockClient.invokeModel(request);
        return response.body();  // The response will contain the extracted information
    }
}

Step 4: Analyze Patient Healthcare Use Cases

This example uses a test healthcare use case to demonstrate the interaction.

java
public class BedrockApp {
    public static void main(String[] args) {
        BedrockClient bedrockClient = BedrockHelper.createBedrockClient();
        HealthcareUseCaseProcessor processor = new HealthcareUseCaseProcessor(bedrockClient);

        // Sample healthcare use case text
        String healthcareUseCase = "Patient John Doe, aged 45, reported symptoms of chest pain and dizziness. "
                + "Medical history includes hypertension and type 2 diabetes. "
                + "Prescribed medication includes Metformin and Atenolol. "
                + "Referred for an ECG and follow-up with a cardiologist.";

        // Extract relevant information
        String extractedInfo = processor.extractRelevantInformation(healthcareUseCase);

        // Print the extracted information
        System.out.println("Extracted Information: " + extractedInfo);
    }
}

Step 5: Handling the Extracted Information

The extractRelevantInformation method uses Amazon Bedrock’s language models to identify key data points. Depending on the model and the request format, you may want to parse and analyze the output JSON.

For example, if the output JSON has a specific structure, you can use libraries like Jackson or Gson to parse the data:

java
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;

public void processResponse(String jsonResponse) {
    ObjectMapper mapper = new ObjectMapper();
    try {
        JsonNode rootNode = mapper.readTree(jsonResponse);
        JsonNode patientName = rootNode.get("patient_name");
        JsonNode age = rootNode.get("age");
        System.out.println("Patient Name: " + patientName.asText());
        System.out.println("Age: " + age.asText());
    } catch (Exception e) {
        e.printStackTrace();
    }
}

Points to Consider

1. Model Selection: Choose the correct model that suits your use case, such as those specialized in entity extraction or text classification.
2. Region Availability: Amazon Bedrock is available in specific regions. Make sure you are using the right region.
3. API Limits: Be aware of any rate limits or quotas for invoking models on Amazon Bedrock.