dev.langchain4j.adaptiverag.AnswerGrader Maven / Gradle / Ivy

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package dev.langchain4j.adaptiverag;

import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.model.input.Prompt;
import dev.langchain4j.model.input.structured.StructuredPrompt;
import dev.langchain4j.model.input.structured.StructuredPromptProcessor;
import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.model.output.structured.Description;
import dev.langchain4j.service.AiServices;
import dev.langchain4j.service.SystemMessage;
import lombok.Value;

import java.time.Duration;
import java.util.function.Function;

@Value(staticConstructor="of")
public class AnswerGrader implements Function {
    /**
     * Binary score to assess answer addresses question.
     */
    public static class Score {

        @Description("Answer addresses the question, 'yes' or 'no'")
        public String binaryScore;
    }

    @StructuredPrompt("User question: \\n\\n {question} \\n\\n LLM generation: {generation}")
    @Value(staticConstructor="of")
    public static class Arguments {
        String question;
        String generation;
    }

    interface Service {

        @SystemMessage("You are a grader assessing whether an answer addresses / resolves a question \\n \n" +
                "     Give a binary score 'yes' or 'no'. Yes' means that the answer resolves the question.")
        Score invoke(String userMessage);
    }

    String openApiKey;

    @Override
    public Score apply(Arguments args) {
        ChatLanguageModel chatLanguageModel = OpenAiChatModel.builder()
                .apiKey( openApiKey )
                .modelName( "gpt-3.5-turbo-0125" )
                .timeout(Duration.ofMinutes(2))
                .logRequests(true)
                .logResponses(true)
                .maxRetries(2)
                .temperature(0.0)
                .maxTokens(2000)
                .build();


        Service service = AiServices.create(Service.class, chatLanguageModel);

        Prompt prompt = StructuredPromptProcessor.toPrompt(args);

        return service.invoke(prompt.text());
    }

}