Wrapper around OpenAI large language models.

To use you should have the openai package installed, with the OPENAI_API_KEY environment variable set.

To use with Azure you should have the openai package installed, with the AZURE_OPENAI_API_KEY, AZURE_OPENAI_API_INSTANCE_NAME, AZURE_OPENAI_API_DEPLOYMENT_NAME and AZURE_OPENAI_API_VERSION environment variable set.

Remarks

Any parameters that are valid to be passed to openai.createCompletion can be passed through modelKwargs, even if not explicitly available on this class.

Example

const model = new OpenAI({
modelName: "gpt-4",
temperature: 0.7,
maxTokens: 1000,
maxRetries: 5,
});

const res = await model.call(
"Question: What would be a good company name for a company that makes colorful socks?\nAnswer:"
);
console.log({ res });

Hierarchy

Constructors

Properties

CallOptions: OpenAICallOptions
ParsedCallOptions: Omit<OpenAICallOptions, never>
batchSize: number = 20

Batch size to use when passing multiple documents to generate

caller: AsyncCaller

The async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.

frequencyPenalty: number = 0

Penalizes repeated tokens according to frequency

maxTokens: number = 256

Maximum number of tokens to generate in the completion. -1 returns as many tokens as possible given the prompt and the model's maximum context size.

modelName: string = "gpt-3.5-turbo-instruct"

Model name to use

n: number = 1

Number of completions to generate for each prompt

presencePenalty: number = 0

Penalizes repeated tokens

streaming: boolean = false

Whether to stream the results or not. Enabling disables tokenUsage reporting

temperature: number = 0.7

Sampling temperature to use

topP: number = 1

Total probability mass of tokens to consider at each step

verbose: boolean

Whether to print out response text.

azureOpenAIApiDeploymentName?: string

Azure OpenAI API deployment name to use for completions when making requests to Azure OpenAI. This is the name of the deployment you created in the Azure portal. e.g. "my-openai-deployment" this will be used in the endpoint URL: https://{InstanceName}.openai.azure.com/openai/deployments/my-openai-deployment/

azureOpenAIApiInstanceName?: string

Azure OpenAI API instance name to use when making requests to Azure OpenAI. this is the name of the instance you created in the Azure portal. e.g. "my-openai-instance" this will be used in the endpoint URL: https://my-openai-instance.openai.azure.com/openai/deployments/{DeploymentName}/

azureOpenAIApiKey?: string

API key to use when making requests to Azure OpenAI.

azureOpenAIApiVersion?: string

API version to use when making requests to Azure OpenAI.

azureOpenAIBasePath?: string

Custom endpoint for Azure OpenAI API. This is useful in case you have a deployment in another region. e.g. setting this value to "https://westeurope.api.cognitive.microsoft.com/openai/deployments" will be result in the endpoint URL: https://westeurope.api.cognitive.microsoft.com/openai/deployments/{DeploymentName}/

bestOf?: number

Generates bestOf completions server side and returns the "best"

callbacks?: Callbacks
logitBias?: Record<string, number>

Dictionary used to adjust the probability of specific tokens being generated

metadata?: Record<string, unknown>
modelKwargs?: Record<string, any>

Holds any additional parameters that are valid to pass to openai.createCompletion that are not explicitly specified on this class.

name?: string
openAIApiKey?: string

API key to use when making requests to OpenAI. Defaults to the value of OPENAI_API_KEY environment variable.

organization?: string
stop?: string[]

List of stop words to use when generating

tags?: string[]
timeout?: number

Timeout to use when making requests to OpenAI.

user?: string

Unique string identifier representing your end-user, which can help OpenAI to monitor and detect abuse.

Accessors

Methods

  • Assigns new fields to the dict output of this runnable. Returns a new runnable.

    Parameters

    • mapping: RunnableMapLike<Record<string, unknown>, Record<string, unknown>>

    Returns RunnableSequence<any, any>

  • Default implementation of batch, which calls invoke N times. Subclasses should override this method if they can batch more efficiently.

    Parameters

    Returns Promise<string[]>

    An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set

  • Parameters

    Returns Promise<(string | Error)[]>

  • Parameters

    Returns Promise<(string | Error)[]>

  • Parameters

    • prompt: string
    • Optional options: string[] | OpenAICallOptions
    • Optional callbacks: Callbacks

    Returns Promise<string>

    Deprecated

    Use .invoke() instead. Will be removed in 0.2.0. Convenience wrapper for generate that takes in a single string prompt and returns a single string output.

  • Run the LLM on the given prompts and input, handling caching.

    Parameters

    • prompts: string[]
    • Optional options: string[] | OpenAICallOptions
    • Optional callbacks: Callbacks

    Returns Promise<LLMResult>

  • This method takes prompt values, options, and callbacks, and generates a result based on the prompts.

    Parameters

    • promptValues: BasePromptValueInterface[]

      Prompt values for the LLM.

    • Optional options: string[] | OpenAICallOptions

      Options for the LLM call.

    • Optional callbacks: Callbacks

      Callbacks for the LLM call.

    Returns Promise<LLMResult>

    An LLMResult based on the prompts.

  • Parameters

    • Optional suffix: string

    Returns string

  • Parameters

    Returns Promise<number>

  • Get the identifying parameters for the model

    Returns Omit<CompletionCreateParams, "prompt"> & {
        model_name: string;
    } & ClientOptions

  • This method takes an input and options, and returns a string. It converts the input to a prompt value and generates a result based on the prompt.

    Parameters

    Returns Promise<string>

    A string result based on the prompt.

  • Pick keys from the dict output of this runnable. Returns a new runnable.

    Parameters

    • keys: string | string[]

    Returns RunnableSequence<any, any>

  • Create a new runnable sequence that runs each individual runnable in series, piping the output of one runnable into another runnable or runnable-like.

    Type Parameters

    • NewRunOutput

    Parameters

    • coerceable: RunnableLike<string, NewRunOutput>

      A runnable, function, or object whose values are functions or runnables.

    Returns RunnableSequence<BaseLanguageModelInput, Exclude<NewRunOutput, Error>>

    A new runnable sequence.

  • Parameters

    • text: string

      Input text for the prediction.

    • Optional options: string[] | OpenAICallOptions

      Options for the LLM call.

    • Optional callbacks: Callbacks

      Callbacks for the LLM call.

    Returns Promise<string>

    A prediction based on the input text.

    ⚠️ Deprecated ⚠️

    Use .invoke() instead. Will be removed in 0.2.0.

    This feature is deprecated and will be removed in the future.

    It is not recommended for use.

    This method is similar to call, but it's used for making predictions based on the input text.

  • Parameters

    • messages: BaseMessage[]

      A list of messages for the prediction.

    • Optional options: string[] | OpenAICallOptions

      Options for the LLM call.

    • Optional callbacks: Callbacks

      Callbacks for the LLM call.

    Returns Promise<BaseMessage>

    A predicted message based on the list of messages.

    Deprecated

    Use .invoke() instead. Will be removed in 0.2.0.

    This method takes a list of messages, options, and callbacks, and returns a predicted message.

  • Stream output in chunks.

    Parameters

    Returns Promise<IterableReadableStream<string>>

    A readable stream that is also an iterable.

  • Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run. The jsonpatch ops can be applied in order to construct state.

    Parameters

    Returns AsyncGenerator<RunLogPatch, any, unknown>

  • Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.

    Parameters

    Returns AsyncGenerator<string, any, unknown>

  • Bind lifecycle listeners to a Runnable, returning a new Runnable. The Run object contains information about the run, including its id, type, input, output, error, startTime, endTime, and any tags or metadata added to the run.

    Parameters

    • params: {
          onEnd?: ((run, config?) => void | Promise<void>);
          onError?: ((run, config?) => void | Promise<void>);
          onStart?: ((run, config?) => void | Promise<void>);
      }

      The object containing the callback functions.

      • Optional onEnd?: ((run, config?) => void | Promise<void>)
          • (run, config?): void | Promise<void>
          • Called after the runnable finishes running, with the Run object.

            Parameters

            Returns void | Promise<void>

      • Optional onError?: ((run, config?) => void | Promise<void>)
          • (run, config?): void | Promise<void>
          • Called if the runnable throws an error, with the Run object.

            Parameters

            Returns void | Promise<void>

      • Optional onStart?: ((run, config?) => void | Promise<void>)
          • (run, config?): void | Promise<void>
          • Called before the runnable starts running, with the Run object.

            Parameters

            Returns void | Promise<void>

    Returns Runnable<BaseLanguageModelInput, string, OpenAICallOptions>

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