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OpenAI Scala Client ?

This is a no-nonsense async Scala client for OpenAI API supporting all the available endpoints and params including streaming, the newest chat completion, vision, and voice routines (as defined here), provided in a single, convenient service called OpenAIService. The supported calls are:

Models: listModels, and retrieveModel
Completions: createCompletion
Chat Completions: createChatCompletion, createChatFunCompletion (deprecated), and createChatToolCompletion
Edits: createEdit (deprecated)
Images: createImage, createImageEdit, and createImageVariation
Embeddings: createEmbeddings
Batches: createBatch, retrieveBatch, cancelBatch, and listBatches
Audio: createAudioTranscription, createAudioTranslation, and createAudioSpeech
Files: listFiles, uploadFile, deleteFile, retrieveFile, and retrieveFileContent
Fine-tunes: createFineTune, listFineTunes, retrieveFineTune, cancelFineTune, listFineTuneEvents, listFineTuneCheckpoints, and deleteFineTuneModel
Moderations: createModeration
Assistants: createAssistant, listAssistants, retrieveAssistant, modifyAssistant, and deleteAssistant
Threads: createThread, retrieveThread, modifyThread, and deleteThread
Thread Messages: createThreadMessage, retrieveThreadMessage, modifyThreadMessage, listThreadMessages, retrieveThreadMessageFile, and listThreadMessageFiles
Runs: createRun, createThreadAndRun, listRuns, retrieveRun, modifyRun, submitToolOutputs, and cancelRun
Run Steps: listRunSteps, and retrieveRunStep
Vector Stores: createVectorStore, listVectorStores, retrieveVectorStore, modifyVectorStore, and deleteVectorStore
Vector Store Files: createVectorStoreFile, listVectorStoreFiles, retrieveVectorStoreFile, and deleteVectorStoreFile
Vector Store File Batches: createVectorStoreFileBatch, retrieveVectorStoreFileBatch, cancelVectorStoreFileBatch, and listVectorStoreBatchFiles

Note that in order to be consistent with the OpenAI API naming, the service function names match exactly the API endpoint titles/descriptions with camelcase. Also, we aimed the lib to be self-contained with the fewest dependencies possible therefore we ended up using only two libs play-ahc-ws-standalone and play-ws-standalone-json (at the top level). Additionally, if dependency injection is required we use scala-guice lib as well.

No time to read a lengthy tutorial? Sure, we hear you! Check out the examples to see how to use the lib in practice.

In addition to the OpenAI API, this library also supports API-compatible providers (see examples) such as:

Azure OpenAI - cloud-based, utilizes OpenAI models but with lower latency
Azure AI - cloud-based, offers a vast selection of open-source models
Anthropic - cloud-based, a major competitor to OpenAI, features proprietary/closed-source models such as Claude3 - Haiku, Sonnet, and Opus. New: now with cache support!
Google Vertex AI - cloud-based, features proprietary/closed-source models such as Gemini 1.5 Pro and flash
Groq - cloud-based provider, known for its superfast inference with LPUs
Grok ( New) - cloud-based provider from x.AI
Fireworks AI - cloud-based provider
OctoAI - cloud-based provider
TogetherAI - cloud-based provider
Cerebras - cloud-based provider, superfast (akin to Groq)
Mistral - cloud-based, leading open-source LLM company
Deepseek ( New) - cloud-based provider from China
Ollama - runs locally, serves as an umbrella for open-source LLMs including LLaMA3, dbrx, and Command-R
FastChat - runs locally, serves as an umbrella for open-source LLMs such as Vicuna, Alpaca, and FastChat-T5

For background information read an article about the lib/client on Medium.

Also try out our Scala client for Pinecone vector database, or use both clients together! This demo project shows how to generate and store OpenAI embeddings (with text-embedding-ada-002 model) into Pinecone and query them afterward. The OpenAI + Pinecone combo is commonly used for autonomous AI agents, such as babyAGI and AutoGPT.

✔️ Important: this is a "community-maintained" library and, as such, has no relation to OpenAI company.

Installation

The currently supported Scala versions are 2.12, 2.13, and 3.

To install the library, add the following dependency to your build.sbt

"io.cequence" %% "openai-scala-client" % "1.1.0"

or to pom.xml (if you use maven)

<dependency>
    <groupId>io.cequence</groupId>
    <artifactId>openai-scala-client_2.12</artifactId>
    <version>1.1.0</version>
</dependency>

If you want streaming support, use "io.cequence" %% "openai-scala-client-stream" % "1.1.0" instead.

Config

Env. variables: OPENAI_SCALA_CLIENT_API_KEY and optionally also OPENAI_SCALA_CLIENT_ORG_ID (if you have one)
File config (default): openai-scala-client.conf

Usage ?‍?

I. Obtaining OpenAIService

First you need to provide an implicit execution context as well as akka materializer, e.g., as

  implicit val ec = ExecutionContext.global
  implicit val materializer = Materializer(ActorSystem())

Then you can obtain a service in one of the following ways.

Default config (expects env. variable(s) to be set as defined in Config section)

  val service = OpenAIServiceFactory()

Custom config

  val config = ConfigFactory.load("path_to_my_custom_config")
  val service = OpenAIServiceFactory(config)

Without config

  val service = OpenAIServiceFactory(
     apiKey = "your_api_key",
     orgId = Some("your_org_id") // if you have one
  )

For Azure with API Key

  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"
  )

Minimal OpenAICoreService supporting listModels, createCompletion, createChatCompletion, and createEmbeddings calls - provided e.g. by FastChat service running on the port 8000

  val service = OpenAICoreServiceFactory("http://localhost:8000/v1/")

OpenAIChatCompletionService providing solely createChatCompletion

Azure AI - e.g. Cohere R+ model

  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")
  )

Anthropic - requires openai-scala-anthropic-client lib and ANTHROPIC_API_KEY

  val service = AnthropicServiceFactory.asOpenAI()

Google Vertex AI - requires openai-scala-google-vertexai-client lib and VERTEXAI_LOCATION + VERTEXAI_PROJECT_ID

  val service = VertexAIServiceFactory.asOpenAI()

Groq - requires GROQ_API_KEY"

  val service = OpenAIChatCompletionServiceFactory(ChatProviderSettings.groq)
  // or with streaming
  val service = OpenAIChatCompletionServiceFactory.withStreaming(ChatProviderSettings.groq)

Grok - requires GROK_API_KEY"

  val service = OpenAIChatCompletionServiceFactory(ChatProviderSettings.grok)
  // or with streaming
  val service = OpenAIChatCompletionServiceFactory.withStreaming(ChatProviderSettings.grok)

Fireworks AI - requires FIREWORKS_API_KEY"

  val service = OpenAIChatCompletionServiceFactory(ChatProviderSettings.fireworks)
  // or with streaming
  val service = OpenAIChatCompletionServiceFactory.withStreaming(ChatProviderSettings.fireworks)

Octo AI - requires OCTOAI_TOKEN

  val service = OpenAIChatCompletionServiceFactory(ChatProviderSettings.octoML)
  // or with streaming
  val service = OpenAIChatCompletionServiceFactory.withStreaming(ChatProviderSettings.octoML)

TogetherAI requires TOGETHERAI_API_KEY

  val service = OpenAIChatCompletionServiceFactory(ChatProviderSettings.togetherAI)
  // or with streaming
  val service = OpenAIChatCompletionServiceFactory.withStreaming(ChatProviderSettings.togetherAI)

Cerebras requires CEREBRAS_API_KEY

  val service = OpenAIChatCompletionServiceFactory(ChatProviderSettings.cerebras)
  // or with streaming
  val service = OpenAIChatCompletionServiceFactory.withStreaming(ChatProviderSettings.cerebras)

Mistral requires MISTRAL_API_KEY

  val service = OpenAIChatCompletionServiceFactory(ChatProviderSettings.mistral)
  // or with streaming
  val service = OpenAIChatCompletionServiceFactory.withStreaming(ChatProviderSettings.mistral)

Ollama

  val service = OpenAIChatCompletionServiceFactory(
    coreUrl = "http://localhost:11434/v1/"
  )

or with streaming

  val service = OpenAIChatCompletionServiceFactory.withStreaming(
    coreUrl = "http://localhost:11434/v1/"
  )

Note that services with additional streaming support - createCompletionStreamed and createChatCompletionStreamed provided by OpenAIStreamedServiceExtra (requires openai-scala-client-stream lib)

  import io.cequence.openaiscala.service.StreamedServiceTypes.OpenAIStreamedService
  import io.cequence.openaiscala.service.OpenAIStreamedServiceImplicits._

  val service: OpenAIStreamedService = OpenAIServiceFactory.withStreaming()

similarly for a chat-completion service

  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")}"))
  )

or only if streaming is required

  val service: OpenAIChatCompletionStreamedServiceExtra =
    OpenAIChatCompletionStreamedServiceFactory(
      coreUrl = "https://api.fireworks.ai/inference/v1/",
      authHeaders = Seq(("Authorization", s"Bearer ${sys.env("FIREWORKS_API_KEY")}"))
   )

Via dependency injection (requires openai-scala-guice lib)

  class MyClass @Inject() (openAIService: OpenAIService) {...}

II. Calling functions

Full documentation of each call with its respective inputs and settings is provided in OpenAIService. Since all the calls are async they return responses wrapped in Future.

There is a new project openai-scala-client-examples where you can find a lot of ready-to-use examples!

List models

  service.listModels.map(models =>
    models.foreach(println)
  )

Retrieve model

  service.retrieveModel(ModelId.text_davinci_003).map(model =>
    println(model.getOrElse("N/A"))
  )

Create completion

  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)
  )

Create completion with a custom setting

  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)
  )

Create completion with streaming and a custom setting

  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)

For this to work you need to use OpenAIServiceStreamedFactory from openai-scala-client-stream lib.

Create chat completion

  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)
  }

Create chat completion for functions

  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(", ")
    )
  }

Create chat completion with json output

  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))
    }

Count expected used tokens before calling createChatCompletions or createChatFunCompletions, this helps you select proper model and reduce costs. This is an experimental feature and it may not work for all models. Requires openai-scala-count-tokens lib.

An example how to count message tokens:

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)
  }
}

An example how to count message tokens when a function is involved:

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))
  }
}

✔️ Important: After you are done using the service, you should close it by calling service.close. Otherwise, the underlying resources/threads won't be released.

III. Using adapters

Adapters for OpenAI services (chat completion, core, or full) are provided by OpenAIServiceAdapters. The adapters are used to distribute the load between multiple services, retry on transient errors, route, or provide additional functionality. See examples for more details.

Note that the adapters can be arbitrarily combined/stacked.

Round robin load distribution

  val adapters = OpenAIServiceAdapters.forFullService

  val service1 = OpenAIServiceFactory("your-api-key1")
  val service2 = OpenAIServiceFactory("your-api-key2")

  val service = adapters.roundRobin(service1, service2)

Random order load distribution

  val adapters = OpenAIServiceAdapters.forFullService

  val service1 = OpenAIServiceFactory("your-api-key1")
  val service2 = OpenAIServiceFactory("your-api-key2")

  val service = adapters.randomOrder(service1, service2)

Logging function calls

  val adapters = OpenAIServiceAdapters.forFullService

  val rawService = OpenAIServiceFactory()
  
  val service = adapters.log(
    rawService,
    "openAIService",
    logger.log
  )

Retry on transient errors (e.g. rate limit error)

  val adapters = OpenAIServiceAdapters.forFullService

  implicit val retrySettings: RetrySettings = RetrySettings(maxRetries = 10).constantInterval(10.seconds)

  val service = adapters.retry(
    OpenAIServiceFactory(),
    Some(println(_)) // simple logging
  )

Retry on a specific function using RetryHelpers directly

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
}

Route chat completion calls based on models

  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
    )

Chat-to-completion adapter

    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")}"))
      )
    )

FAQ ?

Wen Scala 3?

~~Feb 2023. You are right; we chose the shortest month to do so :)~~ Done!
I got a timeout exception. How can I change the timeout setting?

You can do it either by passing the timeouts param to OpenAIServiceFactory or, if you use your own configuration file, then you can simply add it there as:

openai-scala-client {
    timeouts {
        requestTimeoutSec = 200
        readTimeoutSec = 200
        connectTimeoutSec = 5
        pooledConnectionIdleTimeoutSec = 60
    }
}

I got an exception like 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}. What should I do?

Set the env. variable OPENAI_SCALA_CLIENT_API_KEY. If you don't have one register here.
It all looks cool. I want to chat with you about your research and development?

Just shoot us an email at [email protected].