Class ChatGooglePaLM

A class that wraps the Google Palm chat model.

Example

const model = new ChatGooglePaLM({
  apiKey: "<YOUR API KEY>",
  temperature: 0.7,
  model: "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.invoke(questions);
console.log({ res });

Hierarchy

  • BaseChatModel
    • ChatGooglePaLM

Implements

Constructors

constructor

Properties

examples model modelName apiKey? temperature? topK? topP?

Constructors

constructor

Properties

examples

examples: IExample[] = []

model

model: string = "models/chat-bison-001"

Model Name to use

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

modelName

modelName: string = "models/chat-bison-001"

Model Name to use

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

Optional apiKey

apiKey?: string

Google Palm API key to use

Optional temperature

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

Optional topK

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

Optional topP

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

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