Development & Testing Services

import io.quarkiverse.langchain4j.openai.runtime.devui.OpenAiImagesJsonRPCService;
import io.quarkiverse.langchain4j.openai.runtime.devui.OpenAiModerationModelsJsonRPCService;
import io.quarkiverse.langchain4j.openai.runtime.config.LangChain4jOpenAiConfig;
import jakarta.inject.Inject;
import jakarta.json.JsonObject;
import jakarta.json.JsonArray;

/**
 * JSON-RPC service for testing OpenAI image generation in Dev UI.
 *
 * This service provides an interactive interface in the Quarkus Dev UI for
 * testing DALL-E models with different configurations, sizes, qualities,
 * and prompts without writing application code.
 *
 * Access via: Quarkus Dev UI → Extensions → LangChain4j OpenAI → Image Generation
 *
 * Usage:
 *     Navigate to Dev UI in development mode (http://localhost:8080/q/dev)
 *     Find the LangChain4j OpenAI extension card
 *     Click to access image generation testing interface
 */
public class OpenAiImagesJsonRPCService {

    /**
     * Runtime configuration injected by CDI.
     *
     * Provides access to both default and named OpenAI configurations
     * defined in application.properties.
     */
    @Inject
    LangChain4jOpenAiConfig config;

    /**
     * Generate an image using OpenAI DALL-E models.
     *
     * Parameters:
     *     configuration - Configuration name to use ("default" or named config)
     *     modelName - Model to use (e.g., "dall-e-3", "dall-e-2")
     *     size - Image size (e.g., "1024x1024", "1792x1024", "1024x1792")
     *     prompt - Text prompt describing the desired image
     *     quality - Image quality ("standard" or "hd", DALL-E 3 only)
     *
     * Returns:
     *     JsonObject with fields:
     *       - prompt (String): The original prompt
     *       - url (String): URL to the generated image (if responseFormat is "url")
     *       - base64Data (String): Base64-encoded image (if responseFormat is "b64_json")
     *       - revisedPrompt (String): OpenAI's refined version of the prompt
     *
     * Example Response:
     *     {
     *       "prompt": "A sunset over mountains",
     *       "url": "https://oaidalleapiprodscus.blob.core.windows.net/private/...",
     *       "base64Data": null,
     *       "revisedPrompt": "A breathtaking sunset over majestic mountains..."
     *     }
     *
     * Throws:
     *     RuntimeException if image generation fails or API key is invalid
     *
     * The service builds a QuarkusOpenAiImageModel using the specified
     * configuration and model settings, generates the image, and returns
     * the result in a structured JSON format for display in the Dev UI.
     */
    public JsonObject generate(
        String configuration,
        String modelName,
        String size,
        String prompt,
        String quality
    );
}

/**
 * JSON-RPC service for testing OpenAI content moderation in Dev UI.
 *
 * This service provides an interactive interface in the Quarkus Dev UI for
 * testing OpenAI's content moderation API with different configurations and
 * content. It helps developers understand how moderation works and test
 * content safety policies before implementation.
 *
 * Access via: Quarkus Dev UI → Extensions → LangChain4j OpenAI → Content Moderation
 *
 * Usage:
 *     Navigate to Dev UI in development mode (http://localhost:8080/q/dev)
 *     Find the LangChain4j OpenAI extension card
 *     Click to access content moderation testing interface
 */
public class OpenAiModerationModelsJsonRPCService {

    /**
     * Runtime configuration injected by CDI.
     *
     * Provides access to both default and named OpenAI configurations
     * defined in application.properties.
     */
    @Inject
    LangChain4jOpenAiConfig config;

    /**
     * Moderate content using OpenAI moderation models.
     *
     * Parameters:
     *     configuration - Configuration name to use ("default" or named config)
     *     modelName - Model to use (e.g., "omni-moderation-latest", "text-moderation-latest")
     *     prompt - Content text to moderate
     *
     * Returns:
     *     JsonObject with fields:
     *       - flagged (boolean): Whether any policy violations were detected
     *       - categories (JsonArray): Array of moderation category results
     *
     *     Each category object in the array contains:
     *       - name (String): Category name (e.g., "sexual", "hate", "violence")
     *       - flagged (boolean): Whether this specific category was flagged
     *       - score (double): Confidence score (0.0 to 1.0) for this category
     *
     * Example Response:
     *     {
     *       "flagged": false,
     *       "categories": [
     *         { "name": "sexual", "flagged": false, "score": 0.00012 },
     *         { "name": "hate", "flagged": false, "score": 0.00008 },
     *         { "name": "hate-threatening", "flagged": false, "score": 0.00001 },
     *         { "name": "self-harm", "flagged": false, "score": 0.00003 },
     *         { "name": "violence", "flagged": false, "score": 0.00015 },
     *         { "name": "violence-graphic", "flagged": false, "score": 0.00004 },
     *         { "name": "sexual-minors", "flagged": false, "score": 0.00002 }
     *       ]
     *     }
     *
     * Supported Categories:
     *   - sexual: Sexual content
     *   - hate: Hateful content
     *   - hate-threatening: Hateful content that includes violence or threats
     *   - self-harm: Content promoting or describing self-harm
     *   - violence: Violent content
     *   - violence-graphic: Graphic violent content
     *   - sexual-minors: Sexual content involving minors
     *
     * Throws:
     *     RuntimeException if moderation fails or API key is invalid
     *
     * The service builds a moderation model using the specified configuration,
     * moderates the content, and returns detailed category scores and flagging
     * results in a structured JSON format for display in the Dev UI.
     */
    public JsonObject moderate(
        String configuration,
        String modelName,
        String prompt
    );
}

# application.properties
quarkus.langchain4j.openai.api-key=sk-default-key

# Named configuration for creative images
quarkus.langchain4j.openai.creative.api-key=sk-creative-key
quarkus.langchain4j.openai.creative.image-model.model-name=dall-e-3
quarkus.langchain4j.openai.creative.image-model.quality=hd
quarkus.langchain4j.openai.creative.image-model.style=vivid

# Named configuration for strict moderation
quarkus.langchain4j.openai.strict.api-key=sk-strict-key
quarkus.langchain4j.openai.strict.moderation-model.model-name=omni-moderation-latest

{
  "prompt": "A futuristic city at night",
  "url": "https://oaidalleapiprodscus.blob.core.windows.net/...",
  "base64Data": null,
  "revisedPrompt": "A stunning futuristic city skyline at night with neon lights..."
}

{
  "flagged": true,
  "categories": [
    { "name": "sexual", "flagged": false, "score": 0.00012 },
    { "name": "hate", "flagged": true, "score": 0.89234 },
    { "name": "hate-threatening", "flagged": false, "score": 0.12345 },
    { "name": "self-harm", "flagged": false, "score": 0.00003 },
    { "name": "violence", "flagged": false, "score": 0.00015 },
    { "name": "violence-graphic", "flagged": false, "score": 0.00004 },
    { "name": "sexual-minors", "flagged": false, "score": 0.00002 }
  ]
}

import io.quarkiverse.langchain4j.openai.QuarkusOpenAiImageModel;
import dev.langchain4j.model.image.ImageModel;
import dev.langchain4j.data.image.Image;
import dev.langchain4j.model.output.Response;

ImageModel imageModel = QuarkusOpenAiImageModel.builder()
    .configName("creative")  // Use named config tested in Dev UI
    .build();

Response<Image> response = imageModel.generate("A futuristic city at night");
String imageUrl = response.content().url();

import dev.langchain4j.model.moderation.ModerationModel;
import dev.langchain4j.model.moderation.Moderation;
import dev.langchain4j.model.output.Response;
import jakarta.inject.Inject;

@ApplicationScoped
public class ModerationService {
    @Inject
    ModerationModel moderationModel;  // Uses default config

    public boolean isSafe(String content) {
        Response<Moderation> response = moderationModel.moderate(content);
        return !response.content().flagged();
    }
}