Category: GenAI

Spring AI’s 0.8.0-SNAPSHOT release includes support for Image Generation using Dall-E 2/3 or Stability. This was added on January 24th and has not yet been documented but a video by Craig Walls describes how to use the new functionality.

I thought that an interesting example to try would be to combine ChatGPT with Dall-E. This way I can take a restricted range of parameters for an image (ie mood, animal, activity) and ask ChatGPT to expand this into a detailed prompt, which I can then use to generate an image. The idea here is to take user input for the prompt but to restrict what they can specify, maybe through some dropdowns. Another way of doung this would be to use ChatGPT to check freeform user input, but this seems to be simpler.

The example was pretty easy to put together. I used the org.springframework.ai.openai.OpenAiChatClient class to communicate with chatGPT followed by the org.springframework.ai.image.ImageClient class to generate the image using Dall-E 3. A simple Controller took some GET parameters and placed them into a prompt template:

I want to generate amusing images.
These images should feature an animal. The animal chosen is {animal}.
The animal in question should be {activity}.
The picture should make the user feel {mood}.

This template prompt could be changed to further restrict or specify the sort of image being produced through prompt engineering.

There’s a fair amount of Spring magic tying things together – in particular a @Configuration class that sets up the OpenAIImageClient, since auto-configuration is not yet available. The Controller method is as follows:

@GetMapping("safeimagegen")
public String restrictedImageGeneration(
@RequestParam(name = "animal") String animal,
@RequestParam(name = "activity") String activity,
@RequestParam(name = "mood") String mood) {

    PromptTemplate promptTemplate = new PromptTemplate(imagePrompt);
    Message message = promptTemplate.createMessage(Map.of("animal", animal, "activity", activity, "mood", mood));

    Prompt prompt = new Prompt(List.of(message));

    logger.info(prompt.toString());
    ChatResponse response = chatClient.call(prompt);
    String generatedImagePrompt = response.getResult().toString();
    logger.info("AI responded: generatedImagePrompt);
    ImageOptions imageOptions = ImageOptionsBuilder.builder().withModel("dall-e-3")
                .build();

    ImagePrompt imagePrompt = new ImagePrompt(generatedImagePrompt, imageOptions);
    ImageResponse imageResponse = imageClient.call(imagePrompt);
    String imageUrl = imageResponse.getResult().getOutput().getUrl();
    return "redirect:"+imageUrl;

}

This is not a particularly sophisticated piece of code, but it does show how simple it is to get SpringAI examples working.

I submitted a request for a picture of an aligator rollerblading, and set the mood as “joyful”. ChatGPT then generated a detailed prompt:

The image features a cheerful green gator. He’s wearing a pair of shiny, multicolored rollerblades that sparkle as they catch the light. His eyes are wide with excitement, and his mouth is stretched in a wide, friendly grin, revealing his white teeth. He’s standing in a beautiful park with green trees and flowers in the background, and there’s a clear blue sky overhead. He’s waving at the viewer as if inviting them to join him in his rollerblading adventure, adding to the joyful and playful vibe of the image.

And then the browser was redirected to the image:

On Tuesday, after a long day working in Leeds, I came home and decided to play with SpringAI, trying to see if I could set up a retrieval-augmented generation example. It took me just over an hour to get something running.

The documentation for SpringAI feels a little shinier and more solid than that for LangChain4j. Both projects have similar aims, providing abstractions for working with common AI tools and both are explicitly inspired by the LangChain project.

As with LangChain4j, there were issues caused by rapid changes in the project’s APIs. I started work with an example built against OpenAI Azure. It was simple enough to switch this to working against OpenAI, requiring just a change in Spring dependencies and a few properties – Spring magic did the rest. The main problem was updating the code from 0.2.0-SNAPSHOT to 0.8.0-SNAPSHOT (I’d not realised how old the example I’d started with was).

The actual code itself is, once again very simple. When the application receives a query, it uses the SpringAI org.springframework.ai.reader.JsonReader class to load a document – in this case one about bikes from the original project – and divides it into chunks. Each of these chunks are run through a org.springframework.ai.embedding.EmbeddingClient, which produces a vector describing that chunk, and these are placed in a org.springframework.ai.vectorstore.SimpleVectorStore. Once I’d found the updated classes, the APIs were all very straightforward to work with.

An incoming query is then compared against the document database to find likely matches – these are then compiled into a SystemQuery template, which contains a natural-language prompt explaining the LLMs role in this application (You’re assisting with questions about products in a bicycle catalog). The SystemQuery is sent by the application alongside the specific UserQuery, which contains the user’s submitted question.

The responses from the ChatGPT4 model combined the user query with the document, producing obviously relevant responses in natural language. For example:

The SwiftRide Hybrid’s largest size (L) is suitable for riders with a height of 175 – 186 cm (5’9″ – 6’1″). If the person is taller than 6’1″, this bike may not be the best fit.

Playing around with this was not cheap – the RAG method sends a lot of data to OpenAI, and was burning through $0.10-$0.16 worth of tokens in each query. I also managed to hit my account’s rate limit of 10000 per minute playing with this. I’m not sure how feasible using the OpenAI model in production would be.

Notes and follow-ups

• I need to put some of the code into github to share.
• I’m fascinated by how part of the application is a natural-language prompt to tell ChatGPT how to respond. Programming LLMs is spooky, very close to asking a person to pretend they’re doing a role.
• In production, this sort of application would require a lot of protection – some of which would use natural language instructions, but there are also models specifically for this role.
• The obvious improvement here is to use a local model and see how effective that is.