Interface GoogleVertexAITextInput

Interface representing the input to the Google Vertex AI model.

interface GoogleVertexAITextInput {
    apiVersion?: string;
    authOptions?: GoogleAuthOptions<JSONClient>;
    customModelURL?: string;
    endpoint?: string;
    location?: string;
    maxOutputTokens?: number;
    model?: string;
    temperature?: number;
    topK?: number;
    topP?: number;
}

Hierarchy

  • GoogleVertexAIBaseLLMInput<GoogleAuthOptions>
    • GoogleVertexAITextInput

Properties

apiVersion? authOptions? customModelURL? endpoint? location? maxOutputTokens? model? temperature? topK? topP?

Properties

Optional apiVersion

apiVersion?: string

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

Optional authOptions

authOptions?: GoogleAuthOptions<JSONClient>

Optional customModelURL

customModelURL?: string

If you are planning to connect to a model that lives under a custom endpoint provide the "customModelURL" which will override the automatic URL building

This is necessary in cases when you want to point to a fine-tuned model or a model that has been hidden under VertexAI Endpoints.

In those cases, specifying the GoogleVertexAIModelParams.model param will not be necessary and will be ignored.

See

GoogleVertexAILLMConnection.buildUrl

Optional endpoint

endpoint?: string

Hostname for the API call

Optional location

location?: string

Region where the LLM is stored

Optional maxOutputTokens

maxOutputTokens?: number

Maximum number of tokens to generate in the completion.

Optional model

model?: string

Model to use

Optional temperature

temperature?: number

Sampling temperature to use

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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