Replicate AI, powered by Java 21 ☕ & Spring Boot 3 🍃

Welcome to the Replicate AI Integration project! 🎉

This project provides a streamlined Java 21-based framework to effortlessly interact with and create predictions for * Replicate*'s powerful AI models. Built with Spring Boot, it enables quick and modular access to various AI services, making integration a breeze for developers.

The project allows downloading of Replicate AI models' JSONSchemas for requests and responses through the official API, and dynamically creates a Maven module to enable developers to interact with the AI models using strongly typed Java code.

Furthermore, it supports Spring Boot AutoConfiguration, allowing efficient and powerful integration with Spring Boot applications to enable easy access to Replicate's AI offerings—just add your API key!

🚀 Quick Start

The quickest way to get off the ground is depending on spring-boot-starter-replicate. The module, provided an API key to Replicate, handles all configuration of the underlying modules to ensure a smooth start.

<dependency>
    <groupId>io.graversen</groupId>
    <artifactId>spring-boot-starter-replicate</artifactId>
    <version>0.0.8</version>
</dependency>

Add your Replicate API key to your application.yml file.

replicate:
  token: r8...

... And you're good to go!

Replicate Models

By default, this project supports a number of common AI models for text completion using large language models and text-to-image models for image generation. Specifically, the following models are currently supported out of the box:

Black Forest Labs Flux

• flux-2-dev
• flux-2-flex
• flux-2-pro
• flux-2-max

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Tip

It is possible to build this project to support different AI models that are not included by default for simplicity reasons. Please see the section describing the replicate-tools module.

📂 Module Overview

Below is a more focused description regarding each discrete module of this project.

replicate-client

Core module for handling API requests and responses from Replicate. Use this if you only want to create predictions with Java code in the simplest possible manner.

replicate-models

Auto-generated models from Replicate’s JSONSchemas, providing strongly typed Java interfaces for AI models. The models supported are generated using replicate-tools. From this repository, the following packages are supported:

• default: Default models as described above.
• openai: A specialized module consisting only of OpenAI models.
• flux: A specialized module consisting only of the Flux family of AI models.

replicate-tools

Python project to interact with Replicate API services to get all JSONSchemas of supplied models.

Example Usage

Install:

python -m pip install --upgrade pip
pip install -r replicate-tools/requirements.txt

Run Python script with your supplied REPLICATE_MODELS.

Tip

The models follow a owner / model-name convention, just fetch it from the Replicate URL. For example: https://replicate.com/black-forest-labs/flux-dev → black-forest-labs/flux-dev

REPLICATE_API_TOKEN="r8..." REPLICATE_MODELS="meta/meta-llama-3-8b-instruct,black-forest-labs/flux-dev" python download_replicate_schemas.py

After this, you are able to build the replicate-models module using Maven.

mvn compile

Tip

If you decide to use AI models that are more complex than simple text completion or text-to-image generators, note that you may depend on just replicate-client and replicate-models with your custom set of models, to enable easy, strongly typed access to the Replicate API.

spring-boot-starter-replicate

Auto-configures Replicate integration, provided an API token. Through this, you will have access to more advanced encapsulations to create images and manage conversations.

📈 Examples

Example – Image generation with Flux 2 Pro

@Slf4j
@Component
@RequiredArgsConstructor
public class Flux2Example {
    private final ReplicateFacade replicateFacade;

    /**
     * Create one image using flux2-dev
     */
    public void runExample() {
        final var createImagePrediction = CreateImagePrediction2.builder()
                .prompt("A photo of a happy sheep grazing on a beautiful summer day")
                .aspectRatio(AspectRatios.RATIO_5_BY_4)
                .build();

        replicateFacade.createPrediction(FluxModels.FLUX_2_PRO, createImagePrediction);
    }

    @EventListener(value = PredictionUpdatedEvent.class, condition = "#event.getStatus().name() == 'SUCCEEDED'")
    public void onPredictionUpdated(PredictionUpdatedEvent event) {
        log.info(
                "Completed prediction '{}' with model '{}', view it here: {}",
                event.getId(),
                event.getModel(),
                ReplicateUrl.fromPrediction(event.getId())
        );
    }
}

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Note

This project is still in the early stages of development.