Class GenerativeModel (1.1.0)

The GenerativeModel class is the base class for the generative models on Vertex AI. NOTE: Don't instantiate this class directly. Use vertexai.getGenerativeModel() instead.

Package

@google-cloud/vertexai

Constructors

(constructor)(getGenerativeModelParams)

constructor(getGenerativeModelParams: GetGenerativeModelParams);

Constructs a new instance of the GenerativeModel class

Parameter
Name	Description
`getGenerativeModelParams`	`GetGenerativeModelParams` GetGenerativeModelParams

Methods

countTokens(request)

countTokens(request: CountTokensRequest): Promise<CountTokensResponse>;

Makes an async request to count tokens.

The countTokens function returns the token count and the number of billable characters for a prompt.

Parameter
Name	Description
`request`	`CountTokensRequest` A CountTokensRequest object with the request contents.

Returns
Type	Description
`Promise<CountTokensResponse>`	The CountTokensResponse object with the token count.

Example


const request = {
  contents: [{role: 'user', parts: [{text: 'How are you doing today?'}]}],
};
const resp = await generativeModel.countTokens(request);
console.log('count tokens response: ', resp);

generateContent(request)

generateContent(request: GenerateContentRequest | string): Promise<GenerateContentResult>;

Makes an async call to generate content.

The response will be returned in GenerateContentResult.response.

Parameter
Name	Description
`request`	`GenerateContentRequest \| string` A GenerateContentRequest object with the request contents.

Returns
Type	Description
`Promise<GenerateContentResult>`	The GenerateContentResponse object with the response candidates.

Example


const request = {
  contents: [{role: 'user', parts: [{text: 'How are you doing today?'}]}],
};
const result = await generativeModel.generateContent(request);
console.log('Response: ', JSON.stringify(result.response));

generateContentStream(request)

generateContentStream(request: GenerateContentRequest | string): Promise<StreamGenerateContentResult>;

Makes an async stream request to generate content.

The response is returned chunk by chunk as it's being generated in StreamGenerateContentResult.stream. After all chunks of the response are returned, the aggregated response is available in StreamGenerateContentResult.response.

Parameter
Name	Description
`request`	`GenerateContentRequest \| string` GenerateContentRequest

Returns
Type	Description
`Promise<StreamGenerateContentResult>`	Promise of StreamGenerateContentResult

Example


const request = {
  contents: [{role: 'user', parts: [{text: 'How are you doing today?'}]}],
};
const streamingResult = await generativeModel.generateContentStream(request);
for await (const item of streamingResult.stream) {
  console.log('stream chunk: ', JSON.stringify(item));
}
const aggregatedResponse = await streamingResult.response;
console.log('aggregated response: ', JSON.stringify(aggregatedResponse));

startChat(request)

startChat(request?: StartChatParams): ChatSession;

Instantiates a ChatSession.

The ChatSession class is a stateful class that holds the state of the conversation with the model and provides methods to interact with the model in chat mode. Calling this method doesn't make any calls to a remote endpoint. To make remote call, use ChatSession.sendMessage() or ChatSession.sendMessageStream}.

Parameter
Name	Description
`request`	`StartChatParams` StartChatParams

Returns
Type	Description
`ChatSession`	ChatSession

Example


const chat = generativeModel.startChat();
const result1 = await chat.sendMessage("How can I learn more about Node.js?");
const response1 = await result1.response;
console.log('Response: ', JSON.stringify(response1));

const result2 = await chat.sendMessageStream("What about python?");
const response2 = await result2.response;
console.log('Response: ', JSON.stringify(await response2));