A class that wraps the Google Palm chat model.

Example

const model = new ChatGooglePaLM({
apiKey: "<YOUR API KEY>",
temperature: 0.7,
modelName: "models/chat-bison-001",
topK: 40,
topP: 1,
examples: [
{
input: new HumanMessage("What is your favorite sock color?"),
output: new AIMessage("My favorite sock color be arrrr-ange!"),
},
],
});
const questions = [
new SystemMessage(
"You are a funny assistant that answers in pirate language."
),
new HumanMessage("What is your favorite food?"),
];
const res = await model.call(questions);
console.log({ res });

Hierarchy

Implements

Constructors

Properties

ParsedCallOptions: Omit<BaseLanguageModelCallOptions, 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.

examples: IExample[] = []
modelName: string = "models/chat-bison-001"

Model Name to use

Note: The format must follow the pattern - models/{model}

verbose: boolean

Whether to print out response text.

apiKey?: string

Google Palm API key to use

callbacks?: Callbacks
metadata?: Record<string, unknown>
tags?: string[]
temperature?: number

Controls the randomness of the output.

Values can range from [0.0,1.0], inclusive. A value closer to 1.0 will produce responses that are more varied and creative, while a value closer to 0.0 will typically result in less surprising responses from the model.

Note: The default value varies by model

topK?: number

Top-k changes how the model selects tokens for output.

A top-k of 1 means the selected token is the most probable among all tokens in the model’s vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature).

Note: The default value varies by model

topP?: number

Top-p changes how the model selects tokens for output.

Tokens are selected from most probable to least until the sum of their probabilities equals the top-p value.

For example, if tokens A, B, and C have a probability of .3, .2, and .1 and the top-p value is .5, then the model will select either A or B as the next token (using temperature).

Note: The default value varies by model

Accessors

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

    Returns string[]

Methods

  • Makes a single call to the chat model.

    Parameters

    Returns Promise<BaseMessage>

    A Promise that resolves to a BaseMessage.

  • Makes a single call to the chat model with a prompt value.

    Parameters

    Returns Promise<BaseMessage>

    A Promise that resolves to a BaseMessage.

  • Generates chat based on the input messages.

    Parameters

    Returns Promise<LLMResult>

    A Promise that resolves to an LLMResult.

  • Generates a prompt based on the input prompt values.

    Parameters

    Returns Promise<LLMResult>

    A Promise that resolves to an LLMResult.

  • Parameters

    Returns Promise<number>

  • 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

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

    A new runnable sequence.

  • Predicts the next message based on a text input.

    Parameters

    • text: string

      The text input.

    • Optional options: string[] | BaseLanguageModelCallOptions

      The call options or an array of stop sequences.

    • Optional callbacks: Callbacks

      The callbacks for the language model.

    Returns Promise<string>

    A Promise that resolves to a string.

  • Predicts the next message based on the input messages.

    Parameters

    Returns Promise<BaseMessage>

    A Promise that resolves to a BaseMessage.

  • 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<BaseMessageChunk, any, unknown>

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