Interface GoogleAIBaseLanguageModelCallOptions

The params which can be passed to the API at request time.

interface GoogleAIBaseLanguageModelCallOptions {
    convertSystemMessageToHumanContent?: boolean;
    maxOutputTokens?: number;
    model?: string;
    modelName?: string;
    responseMimeType?: "application/json" | "text/plain";
    safetyHandler?: GoogleAISafetyHandler;
    safetySettings?: GoogleAISafetySetting[];
    stopSequences?: string[];
    temperature?: number;
    tools?: StructuredToolInterface[] | GeminiTool[];
    topK?: number;
    topP?: number;
}

Hierarchy (view full)

Properties

convertSystemMessageToHumanContent? maxOutputTokens? model? modelName? responseMimeType? safetyHandler? safetySettings? stopSequences? temperature? tools? topK? topP?

Properties

Optional convertSystemMessageToHumanContent

convertSystemMessageToHumanContent?: boolean

Optional maxOutputTokens

maxOutputTokens?: number

Maximum number of tokens to generate in the completion.

Optional model

model?: string

Model to use

Optional modelName

modelName?: string

Model to use Alias for model

Optional responseMimeType

responseMimeType?: "application/json" | "text/plain"

Available for gemini-1.5-pro. The output format of the generated candidate text. Supported MIME types:

  • text/plain: Text output.
  • application/json: JSON response in the candidates.

Default

"text/plain"

Optional safetyHandler

safetyHandler?: GoogleAISafetyHandler

Optional safetySettings

safetySettings?: GoogleAISafetySetting[]

Optional stopSequences

stopSequences?: string[]

Optional temperature

temperature?: number

Sampling temperature to use

Optional tools

tools?: StructuredToolInterface[] | GeminiTool[]

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).

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).

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