Interface ChatGoogleBaseInput<AuthOptions>

Input to chat model class.

interface ChatGoogleBaseInput<AuthOptions> {
    apiKey?: string;
    apiVersion?: string;
    authOptions?: AuthOptions;
    convertSystemMessageToHumanContent?: boolean;
    endpoint?: string;
    location?: string;
    maxOutputTokens?: number;
    model?: string;
    modelName?: string;
    platformType?: "gai" | "gcp";
    safetyHandler?: GoogleAISafetyHandler;
    safetySettings?: GoogleAISafetySetting[];
    stopSequences?: string[];
    temperature?: number;
    topK?: number;
    topP?: number;
}

Type Parameters

  • AuthOptions

Hierarchy (view full)

Implemented by

Properties

apiKey?: string

Some APIs allow an API key instead

apiVersion?: string

The version of the API functions. Part of the path.

authOptions?: AuthOptions
convertSystemMessageToHumanContent?: boolean
endpoint?: string

Hostname for the API call (if this is running on GCP)

location?: string

Region where the LLM is stored (if this is running on GCP)

maxOutputTokens?: number

Maximum number of tokens to generate in the completion.

model?: string

Model to use

modelName?: string

Model to use Alias for model

platformType?: "gai" | "gcp"

What platform to run the service on. If not specified, the class should determine this from other means. Either way, the platform actually used will be in the "platform" getter.

safetyHandler?: GoogleAISafetyHandler
safetySettings?: GoogleAISafetySetting[]
stopSequences?: string[]
temperature?: number

Sampling temperature to use

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).

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).

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