Class ChatGoogleVertexAI

Enables calls to the Google Cloud's Vertex AI API to access Large Language Models in a chat-like fashion.

This entrypoint and class are intended to be used in web environments like Edge functions where you do not have access to the file system. It supports passing service account credentials directly as a "GOOGLE_VERTEX_AI_WEB_CREDENTIALS" environment variable or directly as "authOptions.credentials".

Example

const model = new ChatGoogleVertexAI({
  temperature: 0.7,
});
const result = await model.invoke(
  "How do I implement a binary search algorithm in Python?",
);

Hierarchy

  • BaseChatGoogleVertexAI<WebGoogleAuthOptions>
    • ChatGoogleVertexAI

Constructors

constructor

Properties

connection examples maxOutputTokens model streamedConnection temperature topK topP

Methods

createInstance formatParameters convertPrediction convertPredictionChunk

Constructors

constructor

Properties

connection

connection: GoogleVertexAILLMConnection<BaseLanguageModelCallOptions, GoogleVertexAIChatInstance, GoogleVertexAIChatPrediction, WebGoogleAuthOptions>

examples

examples: ChatExample[] = []

Help the model understand what an appropriate response is

maxOutputTokens

maxOutputTokens: number = 1024

Maximum number of tokens to generate in the completion.

model

model: string = "chat-bison"

Model to use

streamedConnection

streamedConnection: GoogleVertexAILLMConnection<BaseLanguageModelCallOptions, GoogleVertexAIChatInstance, GoogleVertexAIChatPrediction, WebGoogleAuthOptions>

temperature

temperature: number = 0.2

Sampling temperature to use

topK

topK: number = 40

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

topP: number = 0.8

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).

Methods

createInstance

formatParameters

StaticconvertPrediction

StaticconvertPredictionChunk

Settings

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Constructors
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