openai scala client
Version 1.1.0
這是一個毫無意義的異步scala客戶端，用於OpenAI API，支持所有可用的端點和參數，包括流式傳輸，最新的聊天完成，視覺和語音例程（如下所示），以單個便捷的服務為OpenAiservice提供。受支持的電話是：
請注意，為了與OpenAI API命名保持一致，服務功能名稱與API端點標題/描述與Camelcase完全匹配。另外，我們的目標是通過最少的依賴關係進行自我限制，因此我們最終僅使用兩個libs play-ahc-ws-standalone和play-ws-standalone-json （位於頂級）。此外，如果需要依賴注射，我們也使用scala-guice lib。
沒有時間閱讀冗長的教程？當然，我們聽到你的聲音！查看示例，以查看如何在實踐中使用LIB。
除OpenAI API外，該庫還支持與API兼容的提供商（請參閱示例），例如：
有關背景信息，請閱讀有關媒介上LIB/客戶端的文章。
還要嘗試使用我們的Scala客戶端以獲取Pinecone矢量數據庫，或一起使用兩個客戶端！該演示項目顯示瞭如何生成和存儲OpenAI嵌入（帶有text-embedding-ada-002型號）中的Pinecone並之後查詢它們。 OpenAI + Pinecone組合通常用於自主AI代理，例如Babyagi和autogpt。
✔️重要：這是一個“社區維護”的圖書館，因此，與Openai Company沒有關係。
當前支持的Scala版本為2.12、2.13和3 。
要安裝庫，請在您的build.sbt中添加以下依賴關係
"io.cequence" %% "openai-scala-client" % "1.1.0"
或to pom.xml （如果使用maven）
<dependency>
<groupId>io.cequence</groupId>
<artifactId>openai-scala-client_2.12</artifactId>
<version>1.1.0</version>
</dependency>
如果需要流支持，請使用"io.cequence" %% "openai-scala-client-stream" % "1.1.0" 。
OPENAI_SCALA_CLIENT_API_KEY和OPENAI_SCALA_CLIENT_ORG_ID （如果有一個）I.獲取OpenAiservice
首先，您需要提供隱式執行上下文以及Akka材料器，例如
implicit val ec = ExecutionContext .global
implicit val materializer = Materializer ( ActorSystem ())然後，您可以通過以下方式之一獲得服務。
Config部分中定義） val service = OpenAIServiceFactory () val config = ConfigFactory .load( " path_to_my_custom_config " )
val service = OpenAIServiceFactory (config) val service = OpenAIServiceFactory (
apiKey = " your_api_key " ,
orgId = Some ( " your_org_id " ) // if you have one
) val service = OpenAIServiceFactory .forAzureWithApiKey(
resourceName = " your-resource-name " ,
deploymentId = " your-deployment-id " , // usually model name such as "gpt-35-turbo"
apiVersion = " 2023-05-15 " , // newest version
apiKey = " your_api_key "
)OpenAICoreService支持listModels ， createCompletion ， createChatCompletion和createEmbeddings調用 - 例如，在端口8000上運行的FastChat服務提供 val service = OpenAICoreServiceFactory ( " http://localhost:8000/v1/ " )OpenAIChatCompletionService提供僅提供createChatCompletion val service = OpenAIChatCompletionServiceFactory .forAzureAI(
endpoint = sys.env( " AZURE_AI_COHERE_R_PLUS_ENDPOINT " ),
region = sys.env( " AZURE_AI_COHERE_R_PLUS_REGION " ),
accessToken = sys.env( " AZURE_AI_COHERE_R_PLUS_ACCESS_KEY " )
)openai-scala-anthropic-client lib和ANTHROPIC_API_KEY val service = AnthropicServiceFactory .asOpenAI()openai-scala-google-vertexai-client lib和VERTEXAI_LOCATION + VERTEXAI_PROJECT_ID val service = VertexAIServiceFactory .asOpenAI()GROQ_API_KEY" val service = OpenAIChatCompletionServiceFactory ( ChatProviderSettings .groq)
// or with streaming
val service = OpenAIChatCompletionServiceFactory .withStreaming( ChatProviderSettings .groq)GROK_API_KEY" val service = OpenAIChatCompletionServiceFactory ( ChatProviderSettings .grok)
// or with streaming
val service = OpenAIChatCompletionServiceFactory .withStreaming( ChatProviderSettings .grok)FIREWORKS_API_KEY" val service = OpenAIChatCompletionServiceFactory ( ChatProviderSettings .fireworks)
// or with streaming
val service = OpenAIChatCompletionServiceFactory .withStreaming( ChatProviderSettings .fireworks)OCTOAI_TOKEN val service = OpenAIChatCompletionServiceFactory ( ChatProviderSettings .octoML)
// or with streaming
val service = OpenAIChatCompletionServiceFactory .withStreaming( ChatProviderSettings .octoML)TOGETHERAI_API_KEY val service = OpenAIChatCompletionServiceFactory ( ChatProviderSettings .togetherAI)
// or with streaming
val service = OpenAIChatCompletionServiceFactory .withStreaming( ChatProviderSettings .togetherAI)CEREBRAS_API_KEY val service = OpenAIChatCompletionServiceFactory ( ChatProviderSettings .cerebras)
// or with streaming
val service = OpenAIChatCompletionServiceFactory .withStreaming( ChatProviderSettings .cerebras)MISTRAL_API_KEY val service = OpenAIChatCompletionServiceFactory ( ChatProviderSettings .mistral)
// or with streaming
val service = OpenAIChatCompletionServiceFactory .withStreaming( ChatProviderSettings .mistral) val service = OpenAIChatCompletionServiceFactory (
coreUrl = " http://localhost:11434/v1/ "
)或與流媒體
val service = OpenAIChatCompletionServiceFactory .withStreaming(
coreUrl = " http://localhost:11434/v1/ "
)createCompletionStreamed和createChatCompletionStreamed （需要openai-scala-client-stream -tream lib） import io . cequence . openaiscala . service . StreamedServiceTypes . OpenAIStreamedService
import io . cequence . openaiscala . service . OpenAIStreamedServiceImplicits . _
val service : OpenAIStreamedService = OpenAIServiceFactory .withStreaming()類似地用於聊天完成服務
import io . cequence . openaiscala . service . OpenAIStreamedServiceImplicits . _
val service = OpenAIChatCompletionServiceFactory .withStreaming(
coreUrl = " https://api.fireworks.ai/inference/v1/ " ,
authHeaders = Seq (( " Authorization " , s " Bearer ${sys.env( " FIREWORKS_API_KEY " )} " ))
)或僅當需要流式傳輸時
val service : OpenAIChatCompletionStreamedServiceExtra =
OpenAIChatCompletionStreamedServiceFactory (
coreUrl = " https://api.fireworks.ai/inference/v1/ " ,
authHeaders = Seq (( " Authorization " , s " Bearer ${sys.env( " FIREWORKS_API_KEY " )} " ))
)openai-scala-guice Lib） class MyClass @ Inject () ( openAIService : OpenAIService ) {...}ii。調用功能
OpenAiservice提供了每個呼叫的完整文檔及其各自的輸入和設置。由於所有呼叫都是異步的，因此他們返回Future包含的響應。
有一個新的項目OpenAi-Scala-Calient-examples，您可以在其中找到很多現成的示例！
service.listModels.map(models =>
models.foreach(println)
) service.retrieveModel( ModelId .text_davinci_003).map(model =>
println(model.getOrElse( " N/A " ))
) val text = """ Extract the name and mailing address from this email:
|Dear Kelly,
|It was great to talk to you at the seminar. I thought Jane's talk was quite good.
|Thank you for the book. Here's my address 2111 Ash Lane, Crestview CA 92002
|Best,
|Maya
""" .stripMargin
service.createCompletion(text).map(completion =>
println(completion.choices.head.text)
) val text = """ Extract the name and mailing address from this email:
|Dear Kelly,
|It was great to talk to you at the seminar. I thought Jane's talk was quite good.
|Thank you for the book. Here's my address 2111 Ash Lane, Crestview CA 92002
|Best,
|Maya
""" .stripMargin
service.createCompletion(
text,
settings = CreateCompletionSettings (
model = ModelId .gpt_4o,
max_tokens = Some ( 1500 ),
temperature = Some ( 0.9 ),
presence_penalty = Some ( 0.2 ),
frequency_penalty = Some ( 0.2 )
)
).map(completion =>
println(completion.choices.head.text)
) val source = service.createCompletionStreamed(
prompt = " Write me a Shakespeare poem about two cats playing baseball in Russia using at least 2 pages " ,
settings = CreateCompletionSettings (
model = ModelId .text_davinci_003,
max_tokens = Some ( 1500 ),
temperature = Some ( 0.9 ),
presence_penalty = Some ( 0.2 ),
frequency_penalty = Some ( 0.2 )
)
)
source.map(completion =>
println(completion.choices.head.text)
).runWith( Sink .ignore)為此，您需要使用OpenAIServiceStreamedFactory中的openai-scala-client-stream stream lib。
val createChatCompletionSettings = CreateChatCompletionSettings (
model = ModelId .gpt_4o
)
val messages = Seq (
SystemMessage ( " You are a helpful assistant. " ),
UserMessage ( " Who won the world series in 2020? " ),
AssistantMessage ( " The Los Angeles Dodgers won the World Series in 2020. " ),
UserMessage ( " Where was it played? " ),
)
service.createChatCompletion(
messages = messages,
settings = createChatCompletionSettings
).map { chatCompletion =>
println(chatCompletion.choices.head.message.content)
} val messages = Seq (
SystemMessage ( " You are a helpful assistant. " ),
UserMessage ( " What's the weather like in San Francisco, Tokyo, and Paris? " )
)
// as a param type we can use "number", "string", "boolean", "object", "array", and "null"
val tools = Seq (
FunctionSpec (
name = " get_current_weather " ,
description = Some ( " Get the current weather in a given location " ),
parameters = Map (
" type " - > " object " ,
" properties " - > Map (
" location " - > Map (
" type " - > " string " ,
" description " - > " The city and state, e.g. San Francisco, CA "
),
" unit " - > Map (
" type " - > " string " ,
" enum " - > Seq ( " celsius " , " fahrenheit " )
)
),
" required " - > Seq ( " location " )
)
)
)
// if we want to force the model to use the above function as a response
// we can do so by passing: responseToolChoice = Some("get_current_weather")`
service.createChatToolCompletion(
messages = messages,
tools = tools,
responseToolChoice = None , // means "auto"
settings = CreateChatCompletionSettings ( ModelId .gpt_3_5_turbo_1106)
).map { response =>
val chatFunCompletionMessage = response.choices.head.message
val toolCalls = chatFunCompletionMessage.tool_calls.collect {
case (id, x : FunctionCallSpec ) => (id, x)
}
println(
" tool call ids : " + toolCalls.map(_._1).mkString( " , " )
)
println(
" function/tool call names : " + toolCalls.map(_._2.name).mkString( " , " )
)
println(
" function/tool call arguments : " + toolCalls.map(_._2.arguments).mkString( " , " )
)
} val messages = Seq (
SystemMessage ( " Give me the most populous capital cities in JSON format. " ),
UserMessage ( " List only african countries " )
)
val capitalsSchema = JsonSchema . Object (
properties = Map (
" countries " - > JsonSchema . Array (
items = JsonSchema . Object (
properties = Map (
" country " - > JsonSchema . String (
description = Some ( " The name of the country " )
),
" capital " - > JsonSchema . String (
description = Some ( " The capital city of the country " )
)
),
required = Seq ( " country " , " capital " )
)
)
),
required = Seq ( " countries " )
)
val jsonSchemaDef = JsonSchemaDef (
name = " capitals_response " ,
strict = true ,
structure = schema
)
service
.createChatCompletion(
messages = messages,
settings = DefaultSettings .createJsonChatCompletion(jsonSchemaDef)
)
.map { response =>
val json = Json .parse(messageContent(response))
println( Json .prettyPrint(json))
}createChatCompletions或createChatFunCompletions之前，預期的使用代幣，這可以幫助您選擇適當的模型並降低成本。這是一個實驗功能，可能對所有模型都不起作用。需要openai-scala-count-tokens Lib。一個示例如何計算消息令牌：
import io . cequence . openaiscala . service . OpenAICountTokensHelper
import io . cequence . openaiscala . domain .{ AssistantMessage , BaseMessage , FunctionSpec , ModelId , SystemMessage , UserMessage }
class MyCompletionService extends OpenAICountTokensHelper {
def exec = {
val model = ModelId .gpt_4_turbo_2024_04_09
// messages to be sent to OpenAI
val messages : Seq [ BaseMessage ] = Seq (
SystemMessage ( " You are a helpful assistant. " ),
UserMessage ( " Who won the world series in 2020? " ),
AssistantMessage ( " The Los Angeles Dodgers won the World Series in 2020. " ),
UserMessage ( " Where was it played? " ),
)
val tokenCount = countMessageTokens(model, messages)
}
}涉及函數時如何計數消息令牌的一個示例：
import io . cequence . openaiscala . service . OpenAICountTokensHelper
import io . cequence . openaiscala . domain .{ BaseMessage , FunctionSpec , ModelId , SystemMessage , UserMessage }
class MyCompletionService extends OpenAICountTokensHelper {
def exec = {
val model = ModelId .gpt_4_turbo_2024_04_09
// messages to be sent to OpenAI
val messages : Seq [ BaseMessage ] =
Seq (
SystemMessage ( " You are a helpful assistant. " ),
UserMessage ( " What's the weather like in San Francisco, Tokyo, and Paris? " )
)
// function to be called
val function : FunctionSpec = FunctionSpec (
name = " getWeather " ,
parameters = Map (
" type " - > " object " ,
" properties " - > Map (
" location " - > Map (
" type " - > " string " ,
" description " - > " The city to get the weather for "
),
" unit " - > Map ( " type " - > " string " , " enum " - > List ( " celsius " , " fahrenheit " ))
)
)
)
val tokenCount = countFunMessageTokens(model, messages, Seq (function), Some (function.name))
}
} ✔️很重要：使用服務完成後，您應該通過致電service.close將其關閉。否則，基礎資源/線程將不會發布。
iii。使用適配器
OpenAiserViceadapters提供了OpenAI服務的適配器（聊天，核心或完整）。適配器用於在多個服務之間分配負載，在瞬態錯誤，路由上重試或提供其他功能。有關更多詳細信息，請參見示例。
請注意，適配器可以任意合併/堆疊。
val adapters = OpenAIServiceAdapters .forFullService
val service1 = OpenAIServiceFactory ( " your-api-key1 " )
val service2 = OpenAIServiceFactory ( " your-api-key2 " )
val service = adapters.roundRobin(service1, service2) val adapters = OpenAIServiceAdapters .forFullService
val service1 = OpenAIServiceFactory ( " your-api-key1 " )
val service2 = OpenAIServiceFactory ( " your-api-key2 " )
val service = adapters.randomOrder(service1, service2) val adapters = OpenAIServiceAdapters .forFullService
val rawService = OpenAIServiceFactory ()
val service = adapters.log(
rawService,
" openAIService " ,
logger.log
) val adapters = OpenAIServiceAdapters .forFullService
implicit val retrySettings : RetrySettings = RetrySettings (maxRetries = 10 ).constantInterval( 10 .seconds)
val service = adapters.retry(
OpenAIServiceFactory (),
Some (println(_)) // simple logging
) class MyCompletionService @ Inject () (
val actorSystem : ActorSystem ,
implicit val ec : ExecutionContext ,
implicit val scheduler : Scheduler
)( val apiKey : String )
extends RetryHelpers {
val service : OpenAIService = OpenAIServiceFactory (apiKey)
implicit val retrySettings : RetrySettings =
RetrySettings (interval = 10 .seconds)
def ask ( prompt : String ) : Future [ String ] =
for {
completion < - service
.createChatCompletion(
List ( MessageSpec ( ChatRole . User , prompt))
)
.retryOnFailure
} yield completion.choices.head.message.content
} val adapters = OpenAIServiceAdapters .forFullService
// OctoAI
val octoMLService = OpenAIChatCompletionServiceFactory (
coreUrl = " https://text.octoai.run/v1/ " ,
authHeaders = Seq (( " Authorization " , s " Bearer ${sys.env( " OCTOAI_TOKEN " )} " ))
)
// Anthropic
val anthropicService = AnthropicServiceFactory .asOpenAI()
// OpenAI
val openAIService = OpenAIServiceFactory ()
val service : OpenAIService =
adapters.chatCompletionRouter(
// OpenAI service is default so no need to specify its models here
serviceModels = Map (
octoMLService - > Seq ( NonOpenAIModelId .mixtral_8x22b_instruct),
anthropicService - > Seq (
NonOpenAIModelId .claude_2_1,
NonOpenAIModelId .claude_3_opus_20240229,
NonOpenAIModelId .claude_3_haiku_20240307
)
),
openAIService
) val adapters = OpenAIServiceAdapters .forCoreService
val service = adapters.chatToCompletion(
OpenAICoreServiceFactory (
coreUrl = " https://api.fireworks.ai/inference/v1/ " ,
authHeaders = Seq (( " Authorization " , s " Bearer ${sys.env( " FIREWORKS_API_KEY " )} " ))
)
)Wen Scala 3？
2023年2月。你是對的；我們選擇了最短的一個月:)完畢！
我有一個超時例外。如何更改超時設置？
您可以通過將timeouts傳遞到OpenAIServiceFactory來做到這一點，或者，如果您使用自己的配置文件，則可以簡單地將其添加為：
openai-scala-client {
timeouts {
requestTimeoutSec = 200
readTimeoutSec = 200
connectTimeoutSec = 5
pooledConnectionIdleTimeoutSec = 60
}
}
我有一個例外，例如com.typesafe.config.ConfigException$UnresolvedSubstitution: openai-scala-client.conf @ jar:file:.../io/cequence/openai-scala-client_2.13/0.0.1/openai-scala-client_2.13-0.0.1.jar!/openai-scala-client.conf: 4: Could not resolve substitution to a value: ${OPENAI_SCALA_CLIENT_API_KEY} 。我應該怎麼辦？
設置Env。變量OPENAI_SCALA_CLIENT_API_KEY 。如果您在這裡沒有一個登記冊。
一切看起來都很酷。我想與您聊天有關您的研發？
只需通過[email protected]向我們發送電子郵件。
根據MIT許可條款，該庫可用並以開源為開源。
該項目是開源的，歡迎任何貢獻或反饋（在此）。
該圖書館的開發得到了 - cequence.io的支持 - The future of contracting
由彼得·班達（Peter Banda）創建和維護。
