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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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190
common/src/main/java/dev/langchain4j/model/openai/OpenAiChatModel.java
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@@ -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;
}
}
}
}