(improvement)[build] Use Spotless to customize the code formatting (#1750)

common/src/main/java/dev/langchain4j/model/embedding/S2OnnxEmbeddingModel.java

@@ -13,10 +13,10 @@ import java.util.Objects;
 /**
  * An embedding model that runs within your Java application's process. Any BERT-based model (e.g.,
  * from HuggingFace) can be used, as long as it is in ONNX format. Information on how to convert
- * models into ONNX format can be found <a
- * href="https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model">here</a>.
- * Many models already converted to ONNX format are available <a
- * href="https://huggingface.co/Xenova">here</a>. Copy from
+ * models into ONNX format can be found <a href=
+ * "https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model">here</a>. Many
+ * models already converted to ONNX format are available
+ * <a href="https://huggingface.co/Xenova">here</a>. Copy from
  * dev.langchain4j.model.embedding.OnnxEmbeddingModel.
  */
 public class S2OnnxEmbeddingModel extends AbstractInProcessEmbeddingModel {
@@ -28,9 +28,8 @@ public class S2OnnxEmbeddingModel extends AbstractInProcessEmbeddingModel {
         if (shouldReloadModel(pathToModel, vocabularyPath)) {
             synchronized (S2OnnxEmbeddingModel.class) {
                 if (shouldReloadModel(pathToModel, vocabularyPath)) {
-                    URL resource =
-                            AbstractInProcessEmbeddingModel.class.getResource(
-                                    "/bert-vocabulary-en.txt");
+                    URL resource = AbstractInProcessEmbeddingModel.class
+                            .getResource("/bert-vocabulary-en.txt");
                     if (StringUtils.isNotBlank(vocabularyPath)) {
                         try {
                             resource = Paths.get(vocabularyPath).toUri().toURL();
@@ -56,15 +55,14 @@ public class S2OnnxEmbeddingModel extends AbstractInProcessEmbeddingModel {
     }
 
     private static boolean shouldReloadModel(String pathToModel, String vocabularyPath) {
-        return cachedModel == null
-                || !Objects.equals(cachedModelPath, pathToModel)
+        return cachedModel == null || !Objects.equals(cachedModelPath, pathToModel)
                 || !Objects.equals(cachedVocabularyPath, vocabularyPath);
     }
 
     static OnnxBertBiEncoder loadFromFileSystem(Path pathToModel, URL vocabularyFile) {
         try {
-            return new OnnxBertBiEncoder(
-                    Files.newInputStream(pathToModel), vocabularyFile, PoolingMode.MEAN);
+            return new OnnxBertBiEncoder(Files.newInputStream(pathToModel), vocabularyFile,
+                    PoolingMode.MEAN);
         } catch (IOException e) {
             throw new RuntimeException(e);
         }

common/src/main/java/dev/langchain4j/model/openai/OpenAiChatModel.java

@@ -60,8 +60,8 @@ import static java.util.Collections.singletonList;
 
 /**
  * Represents an OpenAI language model with a chat completion interface, such as gpt-3.5-turbo and
- * gpt-4. You can find description of parameters <a
- * href="https://platform.openai.com/docs/api-reference/chat/create">here</a>.
+ * gpt-4. You can find description of parameters
+ * <a href="https://platform.openai.com/docs/api-reference/chat/create">here</a>.
  */
 @Slf4j
 public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
@@ -88,32 +88,13 @@ public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
     private final List<ChatModelListener> listeners;
 
     @Builder
-    public OpenAiChatModel(
-            String baseUrl,
-            String apiKey,
-            String organizationId,
-            String modelName,
-            Double temperature,
-            Double topP,
-            List<String> stop,
-            Integer maxTokens,
-            Double presencePenalty,
-            Double frequencyPenalty,
-            Map<String, Integer> logitBias,
-            String responseFormat,
-            Boolean strictJsonSchema,
-            Integer seed,
-            String user,
-            Boolean strictTools,
-            Boolean parallelToolCalls,
-            Duration timeout,
-            Integer maxRetries,
-            Proxy proxy,
-            Boolean logRequests,
-            Boolean logResponses,
-            Tokenizer tokenizer,
-            Map<String, String> customHeaders,
-            List<ChatModelListener> listeners) {
+    public OpenAiChatModel(String baseUrl, String apiKey, String organizationId, String modelName,
+            Double temperature, Double topP, List<String> stop, Integer maxTokens,
+            Double presencePenalty, Double frequencyPenalty, Map<String, Integer> logitBias,
+            String responseFormat, Boolean strictJsonSchema, Integer seed, String user,
+            Boolean strictTools, Boolean parallelToolCalls, Duration timeout, Integer maxRetries,
+            Proxy proxy, Boolean logRequests, Boolean logResponses, Tokenizer tokenizer,
+            Map<String, String> customHeaders, List<ChatModelListener> listeners) {
 
         baseUrl = getOrDefault(baseUrl, OPENAI_URL);
         if (OPENAI_DEMO_API_KEY.equals(apiKey)) {
@@ -123,21 +104,11 @@ public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
 
         timeout = getOrDefault(timeout, ofSeconds(60));
 
-        this.client =
-                OpenAiClient.builder()
-                        .openAiApiKey(apiKey)
-                        .baseUrl(baseUrl)
-                        .organizationId(organizationId)
-                        .callTimeout(timeout)
-                        .connectTimeout(timeout)
-                        .readTimeout(timeout)
-                        .writeTimeout(timeout)
-                        .proxy(proxy)
-                        .logRequests(logRequests)
-                        .logResponses(logResponses)
-                        .userAgent(DEFAULT_USER_AGENT)
-                        .customHeaders(customHeaders)
-                        .build();
+        this.client = OpenAiClient.builder().openAiApiKey(apiKey).baseUrl(baseUrl)
+                .organizationId(organizationId).callTimeout(timeout).connectTimeout(timeout)
+                .readTimeout(timeout).writeTimeout(timeout).proxy(proxy).logRequests(logRequests)
+                .logResponses(logResponses).userAgent(DEFAULT_USER_AGENT)
+                .customHeaders(customHeaders).build();
         this.modelName = getOrDefault(modelName, GPT_3_5_TURBO);
         this.temperature = getOrDefault(temperature, 0.7);
         this.topP = topP;
@@ -146,14 +117,10 @@ public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
         this.presencePenalty = presencePenalty;
         this.frequencyPenalty = frequencyPenalty;
         this.logitBias = logitBias;
-        this.responseFormat =
-                responseFormat == null
-                        ? null
-                        : ResponseFormat.builder()
-                                .type(
-                                        ResponseFormatType.valueOf(
-                                                responseFormat.toUpperCase(Locale.ROOT)))
-                                .build();
+        this.responseFormat = responseFormat == null ? null
+                : ResponseFormat.builder()
+                        .type(ResponseFormatType.valueOf(responseFormat.toUpperCase(Locale.ROOT)))
+                        .build();
         this.strictJsonSchema = getOrDefault(strictJsonSchema, false);
         this.seed = seed;
         this.user = user;
@@ -183,61 +150,44 @@ public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
     }
 
     @Override
-    public Response<AiMessage> generate(
-            List<ChatMessage> messages, List<ToolSpecification> toolSpecifications) {
+    public Response<AiMessage> generate(List<ChatMessage> messages,
+            List<ToolSpecification> toolSpecifications) {
         return generate(messages, toolSpecifications, null, this.responseFormat);
     }
 
     @Override
-    public Response<AiMessage> generate(
-            List<ChatMessage> messages, ToolSpecification toolSpecification) {
-        return generate(
-                messages, singletonList(toolSpecification), toolSpecification, this.responseFormat);
+    public Response<AiMessage> generate(List<ChatMessage> messages,
+            ToolSpecification toolSpecification) {
+        return generate(messages, singletonList(toolSpecification), toolSpecification,
+                this.responseFormat);
     }
 
     @Override
     public ChatResponse chat(ChatRequest request) {
         Response<AiMessage> response =
-                generate(
-                        request.messages(),
-                        request.toolSpecifications(),
-                        null,
+                generate(request.messages(), request.toolSpecifications(), null,
                         getOrDefault(
                                 toOpenAiResponseFormat(request.responseFormat(), strictJsonSchema),
                                 this.responseFormat));
-        return ChatResponse.builder()
-                .aiMessage(response.content())
-                .tokenUsage(response.tokenUsage())
-                .finishReason(response.finishReason())
-                .build();
+        return ChatResponse.builder().aiMessage(response.content())
+                .tokenUsage(response.tokenUsage()).finishReason(response.finishReason()).build();
     }
 
-    private Response<AiMessage> generate(
-            List<ChatMessage> messages,
-            List<ToolSpecification> toolSpecifications,
-            ToolSpecification toolThatMustBeExecuted,
+    private Response<AiMessage> generate(List<ChatMessage> messages,
+            List<ToolSpecification> toolSpecifications, ToolSpecification toolThatMustBeExecuted,
             ResponseFormat responseFormat) {
 
-        if (responseFormat != null
-                && responseFormat.type() == JSON_SCHEMA
+        if (responseFormat != null && responseFormat.type() == JSON_SCHEMA
                 && responseFormat.jsonSchema() == null) {
             responseFormat = null;
         }
 
-        ChatCompletionRequest.Builder requestBuilder =
-                ChatCompletionRequest.builder()
-                        .model(modelName)
-                        .messages(toOpenAiMessages(messages))
-                        .topP(topP)
-                        .stop(stop)
-                        .maxTokens(maxTokens)
-                        .presencePenalty(presencePenalty)
-                        .frequencyPenalty(frequencyPenalty)
-                        .logitBias(logitBias)
-                        .responseFormat(responseFormat)
-                        .seed(seed)
-                        .user(user)
-                        .parallelToolCalls(parallelToolCalls);
+        ChatCompletionRequest.Builder requestBuilder = ChatCompletionRequest.builder()
+                .model(modelName).messages(toOpenAiMessages(messages)).topP(topP).stop(stop)
+                .maxTokens(maxTokens).presencePenalty(presencePenalty)
+                .frequencyPenalty(frequencyPenalty).logitBias(logitBias)
+                .responseFormat(responseFormat).seed(seed).user(user)
+                .parallelToolCalls(parallelToolCalls);
 
         if (!(baseUrl.contains(ZHIPU))) {
             requestBuilder.temperature(temperature);
@@ -257,40 +207,33 @@ public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
         Map<Object, Object> attributes = new ConcurrentHashMap<>();
         ChatModelRequestContext requestContext =
                 new ChatModelRequestContext(modelListenerRequest, attributes);
-        listeners.forEach(
-                listener -> {
-                    try {
-                        listener.onRequest(requestContext);
-                    } catch (Exception e) {
-                        log.warn("Exception while calling model listener", e);
-                    }
-                });
+        listeners.forEach(listener -> {
+            try {
+                listener.onRequest(requestContext);
+            } catch (Exception e) {
+                log.warn("Exception while calling model listener", e);
+            }
+        });
 
         try {
             ChatCompletionResponse chatCompletionResponse =
                     withRetry(() -> client.chatCompletion(request).execute(), maxRetries);
 
-            Response<AiMessage> response =
-                    Response.from(
-                            aiMessageFrom(chatCompletionResponse),
-                            tokenUsageFrom(chatCompletionResponse.usage()),
-                            finishReasonFrom(
-                                    chatCompletionResponse.choices().get(0).finishReason()));
+            Response<AiMessage> response = Response.from(aiMessageFrom(chatCompletionResponse),
+                    tokenUsageFrom(chatCompletionResponse.usage()),
+                    finishReasonFrom(chatCompletionResponse.choices().get(0).finishReason()));
 
-            ChatModelResponse modelListenerResponse =
-                    createModelListenerResponse(
-                            chatCompletionResponse.id(), chatCompletionResponse.model(), response);
-            ChatModelResponseContext responseContext =
-                    new ChatModelResponseContext(
-                            modelListenerResponse, modelListenerRequest, attributes);
-            listeners.forEach(
-                    listener -> {
-                        try {
-                            listener.onResponse(responseContext);
-                        } catch (Exception e) {
-                            log.warn("Exception while calling model listener", e);
-                        }
-                    });
+            ChatModelResponse modelListenerResponse = createModelListenerResponse(
+                    chatCompletionResponse.id(), chatCompletionResponse.model(), response);
+            ChatModelResponseContext responseContext = new ChatModelResponseContext(
+                    modelListenerResponse, modelListenerRequest, attributes);
+            listeners.forEach(listener -> {
+                try {
+                    listener.onResponse(responseContext);
+                } catch (Exception e) {
+                    log.warn("Exception while calling model listener", e);
+                }
+            });
 
             return response;
         } catch (RuntimeException e) {
@@ -305,14 +248,13 @@ public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
             ChatModelErrorContext errorContext =
                     new ChatModelErrorContext(error, modelListenerRequest, null, attributes);
 
-            listeners.forEach(
-                    listener -> {
-                        try {
-                            listener.onError(errorContext);
-                        } catch (Exception e2) {
-                            log.warn("Exception while calling model listener", e2);
-                        }
-                    });
+            listeners.forEach(listener -> {
+                try {
+                    listener.onError(errorContext);
+                } catch (Exception e2) {
+                    log.warn("Exception while calling model listener", e2);
+                }
+            });
 
             throw e;
         }
@@ -328,8 +270,8 @@ public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
     }
 
     public static OpenAiChatModelBuilder builder() {
-        for (OpenAiChatModelBuilderFactory factory :
-                loadFactories(OpenAiChatModelBuilderFactory.class)) {
+        for (OpenAiChatModelBuilderFactory factory : loadFactories(
+                OpenAiChatModelBuilderFactory.class)) {
             return factory.get();
         }
         return new OpenAiChatModelBuilder();

common/src/main/java/dev/langchain4j/model/openai/OpenAiChatModelName.java

@@ -3,9 +3,8 @@ package dev.langchain4j.model.openai;
 public enum OpenAiChatModelName {
     GPT_3_5_TURBO("gpt-3.5-turbo"), // alias
     @Deprecated
-    GPT_3_5_TURBO_0613("gpt-3.5-turbo-0613"),
-    GPT_3_5_TURBO_1106("gpt-3.5-turbo-1106"),
-    GPT_3_5_TURBO_0125("gpt-3.5-turbo-0125"),
+    GPT_3_5_TURBO_0613("gpt-3.5-turbo-0613"), GPT_3_5_TURBO_1106(
+            "gpt-3.5-turbo-1106"), GPT_3_5_TURBO_0125("gpt-3.5-turbo-0125"),
 
     GPT_3_5_TURBO_16K("gpt-3.5-turbo-16k"), // alias
     @Deprecated
@@ -13,22 +12,18 @@ public enum OpenAiChatModelName {
 
     GPT_4("gpt-4"), // alias
     @Deprecated
-    GPT_4_0314("gpt-4-0314"),
-    GPT_4_0613("gpt-4-0613"),
+    GPT_4_0314("gpt-4-0314"), GPT_4_0613("gpt-4-0613"),
 
     GPT_4_TURBO_PREVIEW("gpt-4-turbo-preview"), // alias
-    GPT_4_1106_PREVIEW("gpt-4-1106-preview"),
-    GPT_4_0125_PREVIEW("gpt-4-0125-preview"),
+    GPT_4_1106_PREVIEW("gpt-4-1106-preview"), GPT_4_0125_PREVIEW("gpt-4-0125-preview"),
 
     GPT_4_32K("gpt-4-32k"), // alias
-    GPT_4_32K_0314("gpt-4-32k-0314"),
-    GPT_4_32K_0613("gpt-4-32k-0613"),
+    GPT_4_32K_0314("gpt-4-32k-0314"), GPT_4_32K_0613("gpt-4-32k-0613"),
 
     @Deprecated
     GPT_4_VISION_PREVIEW("gpt-4-vision-preview"),
 
-    GPT_4_O("gpt-4o"),
-    GPT_4_O_MINI("gpt-4o-mini");
+    GPT_4_O("gpt-4o"), GPT_4_O_MINI("gpt-4o-mini");
 
     private final String stringValue;
 

common/src/main/java/dev/langchain4j/model/zhipu/ChatCompletionModel.java

@@ -1,9 +1,7 @@
 package dev.langchain4j.model.zhipu;
 
 public enum ChatCompletionModel {
-    GLM_4("glm-4"),
-    GLM_3_TURBO("glm-3-turbo"),
-    CHATGLM_TURBO("chatglm_turbo");
+    GLM_4("glm-4"), GLM_3_TURBO("glm-3-turbo"), CHATGLM_TURBO("chatglm_turbo");
 
     private final String value;
 

common/src/main/java/dev/langchain4j/model/zhipu/ZhipuAiChatModel.java

@@ -27,8 +27,8 @@ import static java.util.Collections.singletonList;
 
 /**
  * Represents an ZhipuAi language model with a chat completion interface, such as glm-3-turbo and
- * glm-4. You can find description of parameters <a
- * href="https://open.bigmodel.cn/dev/api">here</a>.
+ * glm-4. You can find description of parameters
+ * <a href="https://open.bigmodel.cn/dev/api">here</a>.
  */
 public class ZhipuAiChatModel implements ChatLanguageModel {
 
@@ -41,15 +41,8 @@ public class ZhipuAiChatModel implements ChatLanguageModel {
     private final ZhipuAiClient client;
 
     @Builder
-    public ZhipuAiChatModel(
-            String baseUrl,
-            String apiKey,
-            Double temperature,
-            Double topP,
-            String model,
-            Integer maxRetries,
-            Integer maxToken,
-            Boolean logRequests,
+    public ZhipuAiChatModel(String baseUrl, String apiKey, Double temperature, Double topP,
+            String model, Integer maxRetries, Integer maxToken, Boolean logRequests,
             Boolean logResponses) {
         this.baseUrl = getOrDefault(baseUrl, "https://open.bigmodel.cn/");
         this.temperature = getOrDefault(temperature, 0.7);
@@ -57,18 +50,14 @@ public class ZhipuAiChatModel implements ChatLanguageModel {
         this.model = getOrDefault(model, ChatCompletionModel.GLM_4.toString());
         this.maxRetries = getOrDefault(maxRetries, 3);
         this.maxToken = getOrDefault(maxToken, 512);
-        this.client =
-                ZhipuAiClient.builder()
-                        .baseUrl(this.baseUrl)
-                        .apiKey(apiKey)
-                        .logRequests(getOrDefault(logRequests, false))
-                        .logResponses(getOrDefault(logResponses, false))
-                        .build();
+        this.client = ZhipuAiClient.builder().baseUrl(this.baseUrl).apiKey(apiKey)
+                .logRequests(getOrDefault(logRequests, false))
+                .logResponses(getOrDefault(logResponses, false)).build();
     }
 
     public static ZhipuAiChatModelBuilder builder() {
-        for (ZhipuAiChatModelBuilderFactory factories :
-                loadFactories(ZhipuAiChatModelBuilderFactory.class)) {
+        for (ZhipuAiChatModelBuilderFactory factories : loadFactories(
+                ZhipuAiChatModelBuilderFactory.class)) {
             return factories.get();
         }
         return new ZhipuAiChatModelBuilder();
@@ -80,15 +69,13 @@ public class ZhipuAiChatModel implements ChatLanguageModel {
     }
 
     @Override
-    public Response<AiMessage> generate(
-            List<ChatMessage> messages, List<ToolSpecification> toolSpecifications) {
+    public Response<AiMessage> generate(List<ChatMessage> messages,
+            List<ToolSpecification> toolSpecifications) {
         ensureNotEmpty(messages, "messages");
 
         ChatCompletionRequest.Builder requestBuilder =
                 ChatCompletionRequest.builder().model(this.model).maxTokens(maxToken).stream(false)
-                        .topP(topP)
-                        .toolChoice(AUTO)
-                        .messages(toZhipuAiMessages(messages));
+                        .topP(topP).toolChoice(AUTO).messages(toZhipuAiMessages(messages));
 
         if (!isNullOrEmpty(toolSpecifications)) {
             requestBuilder.tools(toTools(toolSpecifications));
@@ -96,17 +83,15 @@ public class ZhipuAiChatModel implements ChatLanguageModel {
 
         ChatCompletionResponse response =
                 withRetry(() -> client.chatCompletion(requestBuilder.build()), maxRetries);
-        return Response.from(
-                aiMessageFrom(response),
-                tokenUsageFrom(response.getUsage()),
+        return Response.from(aiMessageFrom(response), tokenUsageFrom(response.getUsage()),
                 finishReasonFrom(response.getChoices().get(0).getFinishReason()));
     }
 
     @Override
-    public Response<AiMessage> generate(
-            List<ChatMessage> messages, ToolSpecification toolSpecification) {
-        return generate(
-                messages, toolSpecification != null ? singletonList(toolSpecification) : null);
+    public Response<AiMessage> generate(List<ChatMessage> messages,
+            ToolSpecification toolSpecification) {
+        return generate(messages,
+                toolSpecification != null ? singletonList(toolSpecification) : null);
     }
 
     public static class ZhipuAiChatModelBuilder {