feat: add question rewriter function object

work on #6

adaptive-rag/src/main/java/dev/langchain4j/adaptiverag/QuestionRewriter.java

@@ -0,0 +1,59 @@
+package dev.langchain4j.adaptiverag;
+
+import dev.langchain4j.model.chat.ChatLanguageModel;
+import dev.langchain4j.model.input.Prompt;
+import dev.langchain4j.model.input.PromptTemplate;
+import dev.langchain4j.model.openai.OpenAiChatModel;
+import dev.langchain4j.service.AiServices;
+import dev.langchain4j.service.SystemMessage;
+import lombok.Value;
+
+import java.time.Duration;
+import java.util.function.Function;
+
+import static org.bsc.langgraph4j.utils.CollectionsUtils.mapOf;
+
+@Value(staticConstructor="of")
+public class QuestionRewriter implements Function<String, String> {
+
+    private final String openApiKey;
+
+    interface LLMService {
+
+        @SystemMessage(
+                "You a question re-writer that converts an input question to a better version that is optimized \n" +
+                "for vectorstore retrieval. Look at the input and try to reason about the underlying semantic intent / meaning.")
+        String invoke(String question);
+    }
+
+
+//    private QuestionRewriter( String openApiKey ) {
+//        this.openApiKey = openApiKey;
+//    }
+
+    @Override
+    public String apply(String question) {
+
+        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();
+
+
+        LLMService service = AiServices.create(LLMService.class, chatLanguageModel);
+
+        PromptTemplate template = PromptTemplate.from("Here is the initial question: \n\n {{question}} \n Formulate an improved question.");
+
+        Prompt prompt = template.apply( mapOf( "question", question ) );
+
+        return service.invoke( prompt.text() );
+    }
+
+
+}