Creating custom AI providers using AIChatProvider

Suppose you are using an AI model or service not currently supported by Brightspot (like alternatives to Amazon Bedrock or Google Vertex). In that case, this guide will show you how to implement your own custom provider using the AIChatProvider interface.

The AIChatProvider interface allows you to integrate custom AI chat providers. This document provides a guide and code examples for implementing the interface.

1. Implementing AIChatProvider

To create a custom AI provider, implement the AIChatProvider interface and override its methods.

Example implementation:

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@Recordable.DisplayName("my-custom-provider")
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public class MyCustomAIProvider extends Record implements AIChatProvider {
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    @ToolUi.ValueGeneratorClass(AIChatProviderModelIdValueGenerator.class)
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    private String model;
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    public String getModel() {
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        return model;
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    }
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    public void setModel(String model) {
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        this.model = model;
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    }
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    @Override
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    public Set<String> getModelNames() {
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        // Return a set of supported model names.
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        return Set.of("model-v1", "model-v2");
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    }
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    @Override
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    public void invokeLlm(AIChatRequest request) {
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        // Simulate invoking an LLM synchronously.
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        String prompt = getPrompt(request);
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        String response = "Simulated response for: " + prompt;
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        request.getResponse().setText(response);
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        request.getChat().save();
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    }
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    @Override
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    public CompletableFuture<?> invokeLlmAsync(AIChatRequest request) {
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        // Simulate invoking an LLM asynchronously.
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        return CompletableFuture.runAsync(() -> {
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            String prompt = getPrompt(request);
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            String response = "Simulated async response for: " + prompt;
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            request.getResponse().setText(response);
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            request.getChat().save();
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        });
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    }
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    @Override
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    public Message templatedContextMessage(Prompt prompt, Message contextMessage) {
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        // Create a templated context message based on the prompt and context.
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        Message message = new Message();
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        message.setText(prompt.getText());
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        message.setTemplatedText("User question: " + prompt.getText() + " with context: " + contextMessage.getText());
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        return message;
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    }
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    private String getPrompt(AIChatRequest request) {
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        // Simulate prompt generation
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        return request.getConversationHistory().stream()
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            .map(m -> m.getUser().toString() + " " + m.getTemplatedText())
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            .collect(Collectors.joining("\n"));
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    }
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}

2. Method details and providers

getModelNames

The getModelNames method returns a set of supported model names. These names are tied to the @ToolUi.ValueGeneratorClass annotation, which enables the models to appear as selectable options in the UI.

Key Integration:

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@ToolUi.ValueGeneratorClass(AIChatProviderModelIdValueGenerator.class)
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private String model;

• Annotation purpose—The @ToolUi.ValueGeneratorClass annotation ensures that the values returned by getModelNames populate the dropdown or selection options in the UI for the model field.
• User interaction—In the UI, users can choose from the set of models defined by the getModelNames method.

Example usage:

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AIChatProvider provider = new MyCustomAIProvider();
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System.out.println("Supported models: " + provider.getModelNames());

Output:

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Supported models: [model-v1, model-v2]

invokeLlm

Invoke the LLM synchronously and populate the response in AIChatRequest.

Example usage:

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AIChatRequest request = AIChatRequest.newBuilder()
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    .newChat()
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    .withUserPrompt("Hello, AI!")
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    .build();
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AIChatProvider provider = new MyCustomAIProvider();
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provider.invokeLlm(request);
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System.out.println("LLM Response: " + request.getResponse().getText());

Output:

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LLM Response: Simulated response for: Hello, AI!

invokeLlmAsync

Invoke the LLM asynchronously and handle the response with a CompletableFuture.

Example Usage:

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AIChatRequest request = AIChatRequest.newBuilder()
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    .newChat()
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    .withUserPrompt("Hello, async AI!")
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    .build();
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AIChatProvider provider = new MyCustomAIProvider();
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CompletableFuture<?> future = provider.invokeLlmAsync(request);
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future.thenRun(() -> {
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    System.out.println("Async LLM Response: " + request.getResponse().getText());
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});

Output:

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Async LLM Response: Simulated async response for: Hello, async AI!

templatedContextMessage

The templatedContextMessage method allows you to format and customize how the context is inserted into a prompt. This ensures the AI receives structured and relevant information to generate accurate responses.

Example usage:

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Prompt prompt = new Prompt("What are some good Italian restaurants?");
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Message contextMessage = new Message();
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contextMessage.setText("I am in downtown San Francisco and prefer outdoor seating.");
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AIChatProvider provider = new MyCustomAIProvider();
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Message templatedMessage = provider.templatedContextMessage(prompt, contextMessage);
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System.out.println("Text: " + templatedMessage.getText());
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System.out.println("TemplatedText: " + templatedMessage.getTemplatedText());

Output:

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Text: What are some good Italian restaurants?
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TemplatedText: User question: What are some good Italian restaurants? Context: I am in downtown San Francisco and prefer outdoor seating.

3. Additional Notes

• Thread safety—Ensure thread safety if your implementation involves shared resources.
• Asynchronous handling—Handle CompletableFuture cancellations gracefully in invokeLlmAsync.
• Custom templating—Customize templatedContextMessage based on your AI provider's requirements and templating logic.

With this guide and examples, you can implement your own custom AI provider using the AIChatProvider interface.