Switch to LangChain4j

Author: pbernet

build.sbt

@@ -36,9 +36,6 @@ libraryDependencies ++= Seq(
   "org.apache.pekko" %% "pekko-http-spray-json" % pekkoHTTPVersion,
   "org.apache.pekko" %% "pekko-http-xml" % pekkoHTTPVersion,
 
-  // JSON (un)marshalling in Java examples
-  "org.json" % "json" % "20240303",
-
   "io.circe" %% "circe-core" % circeVersion,
   "io.circe" %% "circe-generic" % circeVersion,
   "io.circe" %% "circe-parser" % circeVersion,
@@ -147,6 +144,12 @@ libraryDependencies ++= Seq(
   "org.apache.pekko" %% "pekko-testkit" % pekkoVersion % Test,
   "org.assertj" % "assertj-core" % "3.25.3" % Test,
 
+  "org.apache.poi" % "poi-ooxml" % "5.3.0",
+  "dev.langchain4j" % "langchain4j-anthropic" % "1.0.0-beta2",
+  "dev.langchain4j" % "langchain4j-open-ai" % "1.0.0-beta2",
+
+
+
   // https://docs.gatling.io/reference/integrations/build-tools/sbt-plugin/
   "io.gatling" % "gatling-core" % gatlingVersion,
   "io.gatling.highcharts" % "gatling-charts-highcharts" % gatlingVersion,

src/main/scala/tools/AnthropicCompletions.java

@@ -0,0 +1,94 @@
+package tools;
+
+import dev.langchain4j.data.message.SystemMessage;
+import dev.langchain4j.data.message.UserMessage;
+import dev.langchain4j.model.anthropic.AnthropicChatModel;
+import dev.langchain4j.model.chat.ChatLanguageModel;
+import dev.langchain4j.model.chat.response.ChatResponse;
+import org.apache.commons.lang3.tuple.ImmutablePair;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.time.Duration;
+
+/**
+ * Access Anthropic Claude models with LangChain4j
+ * <br>
+ * Doc:
+ * https://docs.anthropic.com/claude/reference/getting-started-with-the-api
+ * https://github.com/langchain4j/langchain4j
+ */
+public class AnthropicCompletions {
+    private static final Logger LOGGER = LoggerFactory.getLogger(AnthropicCompletions.class);
+    public static final String API_KEY = "***";
+    public static final String MODEL_NAME = "claude-3-7-sonnet-20250219";
+
+    private final ChatLanguageModel model;
+    private final String moviePlotContext;
+
+    public AnthropicCompletions() {
+        this(CompletionsUtil.DEFAULT_TRANSLATOR_SYSTEM_MESSAGE);
+    }
+
+    private AnthropicCompletions(String moviePlotContext) {
+        this.model = AnthropicChatModel.builder()
+                .apiKey(API_KEY)
+                .modelName(MODEL_NAME)
+                .temperature(0.2)
+                .cacheSystemMessages(true)
+                .cacheTools(true)
+                .maxTokens(8192)
+                .maxRetries(10)
+                .timeout(Duration.ofMinutes(5))
+                .build();
+        this.moviePlotContext = moviePlotContext;
+    }
+
+    /**
+     * Creates a new AnthropicCompletions instance with movie context information.
+     *
+     * @param movieTitle       The title of the movie
+     * @param movieReleaseYear The release year of the movie
+     * @return A new AnthropicCompletions instance with the resolved movie plot context
+     */
+    public static AnthropicCompletions withContext(String movieTitle, int movieReleaseYear) {
+        ChatLanguageModel contextModel = AnthropicChatModel.builder()
+                .apiKey(API_KEY)
+                .modelName(MODEL_NAME)
+                .temperature(0.1)
+                .maxTokens(1024)
+                .build();
+
+        String contextPrompt = CompletionsUtil.createMovieContextPrompt(movieTitle, movieReleaseYear);
+        ChatResponse response = contextModel.chat(
+                UserMessage.from(contextPrompt)
+        );
+
+        String movieContext = response.aiMessage().text();
+        LOGGER.info("Generated movie context with: {} for: {} ({}):\n{}", MODEL_NAME, movieTitle, movieReleaseYear, movieContext);
+
+        return new AnthropicCompletions(movieContext);
+    }
+
+    public ImmutablePair<String, Integer> runCompletions(String prompt) {
+        ChatResponse response = model.chat(
+                SystemMessage.systemMessage(moviePlotContext),
+                UserMessage.from(prompt)
+        );
+        return new ImmutablePair<>(response.aiMessage().text(), response.tokenUsage().totalTokenCount());
+    }
+
+    public static void main(String[] args) {
+        String toTranslate = CompletionsUtil.createTranslationPrompt("This is fun.", "English", "German");
+
+        ImmutablePair<String, Integer> result = new AnthropicCompletions().runCompletions(toTranslate);
+        CompletionsUtil.logCompletionResult(result.getLeft(), result.getRight(), "Translation");
+
+        ImmutablePair<String, Integer> resultWithContext = AnthropicCompletions.withContext("The Hangover", 2009).runCompletions(toTranslate);
+        CompletionsUtil.logCompletionResult(resultWithContext.getLeft(), resultWithContext.getRight(), "Translation with context");
+    }
+
+    public String toString() {
+        return "Anthropic: " + MODEL_NAME;
+    }
+}

src/main/scala/tools/CompletionsUtil.java

@@ -0,0 +1,65 @@
+package tools;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/**
+ * Utility class for common functionality used by different LLM completion implementations
+ */
+public class CompletionsUtil {
+    private static final Logger LOGGER = LoggerFactory.getLogger(CompletionsUtil.class);
+
+    /**
+     * Default system message for translation tasks
+     */
+    public static final String DEFAULT_TRANSLATOR_SYSTEM_MESSAGE = "You are a translator";
+
+    /**
+     * Creates a translation prompt
+     *
+     * @param text           Text to translate
+     * @param sourceLanguage Source language
+     * @param targetLanguage Target language
+     * @return Formatted translation prompt
+     */
+    public static String createTranslationPrompt(String text, String sourceLanguage, String targetLanguage) {
+        return String.format("Translate the following subtitle text from %s to %s: %s",
+                sourceLanguage, targetLanguage, text);
+    }
+
+    /**
+     * Creates a movie context prompt
+     *
+     * @param movieTitle       The title of the movie
+     * @param movieReleaseYear The release year of the movie
+     * @return Formatted movie context prompt
+     */
+    public static String createMovieContextPrompt(String movieTitle, int movieReleaseYear) {
+        return String.format(
+                "Find movie metadata about the movie '%s' released in %d\n" +
+                        "If you don't know this movie or if the title or release year is ambiguous: Respond with \"N/A\", do not apologize." +
+                        "Structure of the response:\n" +
+                        "Plot summary: [Max 100 words]" +
+                        "Locations: [Max 3 key locations]" +
+                        "Key characters: [Max 5 main character names, no actor names]" +
+                        "Themes: [Max 3 main themes]" +
+                        "Notable scenes: [Max 3 brief descriptions of memorable scenes]",
+                movieTitle, movieReleaseYear);
+    }
+
+    /**
+     * Logs completion results
+     *
+     * @param result     The completion result
+     * @param tokenCount The token count
+     * @param context    Additional context information (optional)
+     */
+    public static void logCompletionResult(String result, int tokenCount, String context) {
+        if (context != null && !context.isEmpty()) {
+            LOGGER.info("{}: {}", context, result);
+        } else {
+            LOGGER.info("Result: {}", result);
+        }
+        LOGGER.info("Total tokens: {}", tokenCount);
+    }
+}