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(improvement)(build) Add spotless during the build process. (#1639)

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lexluo09
2024-09-07 00:36:17 +08:00
committed by GitHub
parent ee15a88b06
commit 5f59e89eea
986 changed files with 15609 additions and 12706 deletions
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25
common/src/main/java/dev/langchain4j/model/embedding/S2OnnxEmbeddingModel.java
View File

@@ -11,12 +11,13 @@ import java.nio.file.Paths;
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
* 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 dev.langchain4j.model.embedding.OnnxEmbeddingModel.
* 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 {
private static volatile OnnxBertBiEncoder cachedModel;
@@ -27,7 +28,9 @@ 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();
@@ -53,19 +56,17 @@ 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
);
Files.newInputStream(pathToModel), vocabularyFile, PoolingMode.MEAN);
} catch (IOException e) {
throw new RuntimeException(e);
}
}
}
}

190
common/src/main/java/dev/langchain4j/model/openai/OpenAiChatModel.java
View File

@@ -44,8 +44,9 @@ import static java.util.Collections.emptyList;
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>.
* 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>.
*/
@Slf4j
public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
@@ -67,31 +68,33 @@ public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
private final Integer maxRetries;
private final Tokenizer tokenizer;
private final List<ModelListener<ChatLanguageModelRequest, ChatLanguageModelResponse>> listeners;
private final List<ModelListener<ChatLanguageModelRequest, ChatLanguageModelResponse>>
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,
Integer seed,
String user,
Duration timeout,
Integer maxRetries,
Proxy proxy,
Boolean logRequests,
Boolean logResponses,
Tokenizer tokenizer,
Map<String, String> customHeaders,
List<ModelListener<ChatLanguageModelRequest, ChatLanguageModelResponse>> 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,
Integer seed,
String user,
Duration timeout,
Integer maxRetries,
Proxy proxy,
Boolean logRequests,
Boolean logResponses,
Tokenizer tokenizer,
Map<String, String> customHeaders,
List<ModelListener<ChatLanguageModelRequest, ChatLanguageModelResponse>> listeners) {
baseUrl = getOrDefault(baseUrl, OPENAI_URL);
if (OPENAI_DEMO_API_KEY.equals(apiKey)) {
@@ -101,20 +104,21 @@ 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;
@@ -141,31 +145,34 @@ 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);
}
@Override
public Response<AiMessage> generate(List<ChatMessage> messages, ToolSpecification toolSpecification) {
public Response<AiMessage> generate(
List<ChatMessage> messages, ToolSpecification toolSpecification) {
return generate(messages, singletonList(toolSpecification), toolSpecification);
}
private Response<AiMessage> generate(List<ChatMessage> messages,
List<ToolSpecification> toolSpecifications,
ToolSpecification toolThatMustBeExecuted
) {
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);
private Response<AiMessage> generate(
List<ChatMessage> messages,
List<ToolSpecification> toolSpecifications,
ToolSpecification toolThatMustBeExecuted) {
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);
if (!(baseUrl.contains(ZHIPU))) {
requestBuilder.temperature(temperature);
}
@@ -181,36 +188,37 @@ public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
ChatLanguageModelRequest modelListenerRequest =
createModelListenerRequest(request, messages, toolSpecifications);
listeners.forEach(listener -> {
try {
listener.onRequest(modelListenerRequest);
} catch (Exception e) {
log.warn("Exception while calling model listener", e);
}
});
listeners.forEach(
listener -> {
try {
listener.onRequest(modelListenerRequest);
} 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()));
ChatLanguageModelResponse modelListenerResponse = createModelListenerResponse(
chatCompletionResponse.id(),
chatCompletionResponse.model(),
response
);
listeners.forEach(listener -> {
try {
listener.onResponse(modelListenerResponse, modelListenerRequest);
} catch (Exception e) {
log.warn("Exception while calling model listener", e);
}
});
ChatLanguageModelResponse modelListenerResponse =
createModelListenerResponse(
chatCompletionResponse.id(), chatCompletionResponse.model(), response);
listeners.forEach(
listener -> {
try {
listener.onResponse(modelListenerResponse, modelListenerRequest);
} catch (Exception e) {
log.warn("Exception while calling model listener", e);
}
});
return response;
} catch (RuntimeException e) {
@@ -222,13 +230,14 @@ public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
error = e;
}
listeners.forEach(listener -> {
try {
listener.onError(error, null, modelListenerRequest);
} catch (Exception e2) {
log.warn("Exception while calling model listener", e2);
}
});
listeners.forEach(
listener -> {
try {
listener.onError(error, null, modelListenerRequest);
} catch (Exception e2) {
log.warn("Exception while calling model listener", e2);
}
});
throw e;
}
}
@@ -243,7 +252,8 @@ 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();
@@ -261,4 +271,4 @@ public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
return this;
}
}
}
}

2
common/src/main/java/dev/langchain4j/model/zhipu/ChatCompletionModel.java
View File

@@ -15,4 +15,4 @@ public enum ChatCompletionModel {
public String toString() {
return this.value;
}
}
}

54
common/src/main/java/dev/langchain4j/model/zhipu/ZhipuAiChatModel.java
View File

@@ -26,8 +26,9 @@ import static dev.langchain4j.spi.ServiceHelper.loadFactories;
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>.
* 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>.
*/
public class ZhipuAiChatModel implements ChatLanguageModel {
@@ -49,24 +50,25 @@ public class ZhipuAiChatModel implements ChatLanguageModel {
Integer maxRetries,
Integer maxToken,
Boolean logRequests,
Boolean logResponses
) {
Boolean logResponses) {
this.baseUrl = getOrDefault(baseUrl, "https://open.bigmodel.cn/");
this.temperature = getOrDefault(temperature, 0.7);
this.topP = topP;
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();
@@ -78,36 +80,36 @@ 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));
ChatCompletionRequest.Builder requestBuilder =
ChatCompletionRequest.builder().model(this.model).maxTokens(maxToken).stream(false)
.topP(topP)
.toolChoice(AUTO)
.messages(toZhipuAiMessages(messages));
if (!isNullOrEmpty(toolSpecifications)) {
requestBuilder.tools(toTools(toolSpecifications));
}
ChatCompletionResponse response = withRetry(() -> client.chatCompletion(requestBuilder.build()), maxRetries);
ChatCompletionResponse response =
withRetry(() -> client.chatCompletion(requestBuilder.build()), maxRetries);
return Response.from(
aiMessageFrom(response),
tokenUsageFrom(response.getUsage()),
finishReasonFrom(response.getChoices().get(0).getFinishReason())
);
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 {
public ZhipuAiChatModelBuilder() {
}
public ZhipuAiChatModelBuilder() {}
}
}