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Using AI Services in Spring Boot Application
LangChain4j Spring Boot starter
greatly simplifies using AI Services in Spring Boot applications.
@SystemMessage
Now, let’s look at a more complicated example.
We’ll force the LLM reply using slang 😉
This is usually achieved by providing instructions in the SystemMessage.
interface Friend {@SystemMessage("You are a good friend of mine. Answer using slang.")String chat(String userMessage);
}Friend friend = AiServices.create(Friend.class, model);String answer = friend.chat("Hello"); // Hey! What's up?
In this example, we have added the @SystemMessage annotation with a system prompt we want to use.
This will be converted into a SystemMessage behind the scenes and sent to the LLM along with the UserMessage.
System Message Provider
System messages can also be defined dynamically with the system message provider:
Friend friend = AiServices.builder(Friend.class).chatLanguageModel(model).systemMessageProvider(chatMemoryId -> "You are a good friend of mine. Answer using slang.").build();
As you can see, you can provide different system messages based on a chat memory ID (user or conversation).
@UserMessage
Now, let’s assume the model we use does not support system messages,
or maybe we just want to use UserMessage for that purpose.
interface Friend {@UserMessage("You are a good friend of mine. Answer using slang. {{it}}")String chat(String userMessage);
}Friend friend = AiServices.create(Friend.class, model);String answer = friend.chat("Hello"); // Hey! What's shakin'?
We have replaced the @SystemMessage annotation with @UserMessage
and specified a prompt template with the variable it to refer to the only method argument.
Additionally, it’s possible to annotate the String userMessage with @V
and assign a custom name to the prompt template variable:
interface Friend {@UserMessage("You are a good friend of mine这篇关于大模型实战-【Langchain4J中Using AI Services in Spring Boot Application②】的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
