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WCM Content AI Analysis

Learn how to configure the AI analysis feature for WCM Content in a traditional, on-premise deployment. You can also learn steps for configuring a content AI provider to be used for AI analysis. The AI analysis for a WCM Content feature is available in HCL Digital Experience 9.5 Container Update CF213 and later.

Note

OpenAI ChatGPT is the supported content AI provider in CF213 and later. Custom AI implementation is supported in CF214 and later.

Content AI provider overview

OpenAI ChatGPT overview

OpenAI is the AI research and deployment company that offers ChatGPT. When you sign up with ChatGPT, it provides API access through an API key. After signing up at https://platform.openai.com/playground, you can create a personal account with limited access or a corporate account. You can use the playground to experiment with the API also. A highlight of the API is that it accepts natural language commands similar to the ChatGPT chatbot.

For privacy and API availability and other conditions, see the OpenAI website or contact the OpenAI team.

Configure the engine task for enabling content AI analysis

To enable content AI analysis:

  1. Connect to DX Core and run the following config engine task:

    /opt/HCL/wp_profile/ConfigEngine/ConfigEngine.sh action-configure-wcm-content-ai-service -DContentAIProvider=CUSTOM -DCustomAIClassName={CustomerAIClass} -DContentAIProviderAPIKey={APIKey} -DWasPassword=wpsadmin -DPortalAdminPwd=wpsadmin

    Note

    • Possible values for the ContentAIProvider parameter are OPEN_AI or CUSTOM.
    • If the ContentAIProvider value is set as OPEN_AI, the value set for the parameter CustomAIClassName is ignored.
    • If ContentAIProvider value is set as CUSTOM, set the custom content AI provider implementation class in the CustomAIClassName parameter . For example, enter com.ai.sample.CustomerAI. Refer to Configuring AI Class for Custom Content AI Provider for more information about how to implement a custom content AI provider class.
    • Depending on the ContentAIProvider value, set the correct API key of the respective provider in the ContentAIProviderAPIKey parameter.

  2. Validate that all the required configurations are added.

    1. Log in to the WebSphere Application Server console.

    2. Verify that the AI_CLASS configuration property is added in WCM Config Service by going to Custom Properties: click Resource > Resource Environment > Resource Environment Providers > WCM_WCMConfigService > Custom Properties. Possible values for AI_CLASS are:

      • com.hcl.workplace.wcm.restv2.ai.ChatGPTAnalyzerService (if ContentAIProvider value is set as OPEN_AI)
      • Value set in parameter CustomAIClassName (if ContentAIProvider value is set as CUSTOM)

    3. Log in to the DX Portal.

    4. Verify that the Credential Vault with the Vault slot Name ai.auth is configured using the AI content provider's API key by clicking Administration > Security > Credential Vault > Manage System Vault Slot.

Configuring an AI class for a custom content AI provider

Only administrators can configure an AI class to use a custom content AI provider.

  1. Write the Custom Content AI Provider class by implementing the com.hcl.workplace.wcm.restv2.ai.IAIGeneration interface.

    1. Create the JAR file.

    2. Put the JAR file either in a custom-shared library or in the /opt/HCL/wp_profile/PortalServer/sharedLibrary folder.

    3. Restart JVM.

    The following example of a custom content AI provider class can be used to call custom AI services for AI analysis.

    package com.ai.sample;

import java.util.ArrayList;
import java.util.List;
import com.hcl.workplace.wcm.restv2.ai.IAIGeneration;
import com.ibm.workplace.wcm.rest.exception.AIGenerationException;

public class CustomerAI implements IAIGeneration {

    @Override
    public String generateSummary(List<String> values) throws AIGenerationException {
        // Call the custom AI Service to get the custom AI generated summary
        return "AIAnalysisSummary";
    }

    @Override
    public List<String> generateKeywords(List<String> values) throws AIGenerationException {
        // Call the custom AI Service to get the custom AI generated keywords
        List<String> keyWordList = new ArrayList<String>();
        keyWordList.add("keyword1");
        return keyWordList;
    }

    @Override
    public Sentiment generateSentiment(List<String> values) throws AIGenerationException {
        // Call the custom AI Service to get the custom AI generated sentiment
        return Sentiment.POSITIVE;
    }

}

  2. Run the following config engine task:

    /opt/HCL/wp_profile/ConfigEngine/ConfigEngine.sh action-configure-wcm-content-ai-service -DContentAIProvider=CUSTOM -DCustomAIClassName={CustomerAIClass} -DContentAIProviderAPIKey={APIKey} -DWasPassword=wpsadmin -DPortalAdminPwd=wpsadmin

Config engine task for disabling content AI analysis

To disable content AI analysis:

  1. Connect to DX Core and run the following config engine task:

    /opt/HCL/wp_profile/ConfigEngine/ConfigEngine.sh action-remove-wcm-content-ai-service -DWasPassword=wpsadmin -DPortalAdminPwd=wpsadmin

  2. Validate that all the required configurations are deleted.

    1. Log in to the WebSphere Application Server console.
    2. Verify that the AI_CLASS configuration property is deleted from WCM Config Service.
    3. Log in to the DX Portal.
    4. Verify that the Credential Vault with the Vault slot name ai.auth is deleted.

Custom configurations for AI analysis

If AI analysis-related configurations require customization, log in to the WebSphere Application Server console for customizing any of the custom properties in the WCM Config Service. Click Resource > Resource Environment > Resource Environment Providers > WCM_WCMConfigService > Custom Properties.

OpenAI ChatGPT specific custom configurations

  1. OPENAI_MODEL: The currently supported AI model is text-davinci-003. However, AI model can be overriden by overriding this property.
  2. OPENAI_MAX_TOKENS: Set positive integer values between 1 and 2048 for GPT-3 models like text-davinci-003. It specifies the maximum number of tokens that the model can output in its response.
  3. OPENAI_TEMPERATURE: Set positive float values ranging from 0.0 to 1.0. This parameter in OpenAI's GPT-3 API controls the randomness and creativity of the generated text. Higher values produce more diverse and random output. Lower values produce more focused and deterministic output.

After enabling the content AI analysis in DX deployment, use the WCM REST V2 AI Analysis API to call the AI analyzer APIs of the configured content AI provider.