Example

const model = new Portkey({
mode: "single",
llms: [
{
provider: "openai",
virtual_key: "open-ai-key-1234",
model: "text-davinci-003",
max_tokens: 2000,
},
],
});

// Stream the output of the model and process it
const res = await model.stream(
"Question: Write a story about a king\nAnswer:"
);
for await (const i of res) {
process.stdout.write(i);
}

Hierarchy

Constructors

Properties

CallOptions: BaseLLMCallOptions
ParsedCallOptions: Omit<BaseLLMCallOptions, never>
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.

verbose: boolean

Whether to print out response text.

apiKey?: string = undefined
baseURL?: string = undefined
callbacks?: Callbacks
llms?: null | [LLMOptions] = undefined
metadata?: Record<string, unknown>
mode?: string = undefined
tags?: string[]

Accessors

  • get callKeys(): string[]
  • Keys that the language model accepts as call options.

    Returns string[]

Methods

  • 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)[]>

  • Convenience wrapper for generate that takes in a single string prompt and returns a single string output.

    Parameters

    Returns Promise<string>

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

    Parameters

    Returns Promise<LLMResult>

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

    Parameters

    Returns Promise<LLMResult>

    An LLMResult based on the prompts.

  • Parameters

    Returns Promise<number>

  • Get the parameters used to invoke the model

    Parameters

    Returns any

  • 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.

  • 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.

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

    Parameters

    • text: string

      Input text for the prediction.

    • Optional options: string[] | BaseLLMCallOptions

      Options for the LLM call.

    • Optional callbacks: Callbacks

      Callbacks for the LLM call.

    Returns Promise<string>

    A prediction based on the input text.

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

    Parameters

    • messages: BaseMessage[]

      A list of messages for the prediction.

    • Optional options: string[] | BaseLLMCallOptions

      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.

  • 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, BaseLLMCallOptions>

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