WCM Content AI Analysis

This topic describes how to enable AI analysis for a WCM Content in Kubernetes Deployment. This also discusses steps on how to configure a content AI provider to be used for AI analysis. The AI analysis for a WCM Content feature is available from HCL Digital Experience 9.5 Container Update CF213 and higher.

Note

OpenAI ChatGPT is the supported content AI provider from CF213 and higher. Custom AI implementation is supported from CF214 and higher.

Content AI Provider Overview

OpenAI ChatGPT Overview

OpenAI is the AI research and deployment company behind ChatGPT. When you sign up with ChatGPT, it provides API access via 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. The playground can be used to experiment with the API as well. 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.

Enabling AI Analysis in Helm Chart

The content-ai configurations are available in the Helm Chart values.yaml file as contentAI.

The administrator can enable AI analysis for content in the configuration section of the core as shown in the following example.

Note

When upgrading from older CF versions (for example, CF212) to CF213 or higher to enable AI analysis, the following configuration can be done during helm upgrade.

# Application configuration
configuration:
  # Application specific configuration for Core
  core:
    # Settings for content AI 
    contentAI:
      # Configures if content AI is enabled/disabled
      enabled: true
      # Settings for checking content AI provider. When enabled is true, provider will be used 
      # and possible values are 'OPEN_AI' or 'CUSTOM'
      provider: "CUSTOM"
      className: "com.ai.sample.CustomerAI"

For enabling AI analysis for content, set enabled as true inside the contentAI section. It is mandatory to specify the content AI provider to be used in the provider property. Possible values for the provider are OPEN_AI or CUSTOM.

Configuring AI Class for Custom Content AI Provider

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

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

Create the JAR file.
Put the JAR file either at a custom-shared library or in /opt/HCL/wp_profile/PortalServer/sharedLibrary.
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;
  }

}

Configure the Content AI Provider class in the helm chart and run helm upgrade.

Configuring Custom Secret or API Key of Content AI Provider

The API key or custom secret of a content AI provider can be configured in the security section of the core as shown in the following example.

# Security related configuration, e.g. default credentials
security:
  # Security configuration for Core
  core:
    # Security configuration for AI APIs: 
    # API key for AI Provider
    contentAIProviderAPIKey: ""
    # Provide a secret name that will be used to set AI API Keys
    customContentAISecret: "sample-core-content-ai-secret"

Important

Once AI analysis is enabled, it is mandatory for the administrator to specify the content AI provider's API key in contentAIProviderAPIKey property.

If a custom secret is used instead of an API key directly in the values.yaml file, then the custom secret must be created using the content AI provider's API key. You must then reference the secret name in customContentAISecret property and you can leave the contentAIProviderAPIKey blank.

For more information, see Guidelines for Configuring Credentials from Secrets.

Disabling AI Analysis in Helm Chart

The content-ai configurations are available in the Helm Chart values.yaml file as contentAI.

The administrator can disable AI analysis for content in the configuration section of the core as shown in the following example.

# Application configuration
configuration:
  # Application specific configuration for Core
  core:
    # Settings for content AI 
    contentAI:
      # Configures if content AI is enabled/disabled
      enabled: false
      # Settings for checking content AI provider. When contentAI enabled is true, provider will be used.
      provider: ""

For disbaling AI analysis for content, set enabled as false inside the contentAI section. Specifying the provider is not required to disable AI analysis.

Custom Configurations for AI Analysis

To customize AI analysis-related configurations, log in to WAS console for customizing any of the custom properties in the WCM Config Service (Resource > Resource Environment > Resource Environment Providers > WCM_WCMConfigService > Custom Properties).

OpenAI ChatGPT specific custom configurations

OPENAI_MODEL: Currently supported AI model is text-davinci-003. However, the model can be overidden by overriding this property.
OPENAI_MAX_TOKENS: Set any 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.
OPENAI_TEMPERATURE: Set any 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, with higher values producing more diverse and random output and lower values producing 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.