Class v1beta.GenerativeServiceClient (2.4.0)

API for using Large Models that generate multimodal content and have additional capabilities beyond text generation. v1beta

Package

@google-ai/generativelanguage

Constructors

(constructor)(opts, gaxInstance)

constructor(opts?: ClientOptions, gaxInstance?: typeof gax | typeof gax.fallback);

Construct an instance of GenerativeServiceClient.

Parameters
Name	Description
`opts`	`ClientOptions`
`gaxInstance`	`typeof gax \| typeof fallback` : loaded instance of `google-gax`. Useful if you need to avoid loading the default gRPC version and want to use the fallback HTTP implementation. Load only fallback version and pass it to the constructor: ``` const gax = require('google-gax/build/src/fallback'); // avoids loading google-gax with gRPC const client = new GenerativeServiceClient({fallback: true}, gax); ```

Properties

apiEndpoint

get apiEndpoint(): string;

The DNS address for this API service.

apiEndpoint

static get apiEndpoint(): string;

The DNS address for this API service - same as servicePath.

auth

auth: gax.GoogleAuth;

descriptors

descriptors: Descriptors;

generativeServiceStub

generativeServiceStub?: Promise<{
        [name: string]: Function;
    }>;

innerApiCalls

innerApiCalls: {
        [name: string]: Function;
    };

pathTemplates

pathTemplates: {
        [name: string]: gax.PathTemplate;
    };

port

static get port(): number;

The port for this API service.

scopes

static get scopes(): never[];

The scopes needed to make gRPC calls for every method defined in this service.

servicePath

static get servicePath(): string;

The DNS address for this API service.

universeDomain

get universeDomain(): string;

warn

warn: (code: string, message: string, warnType?: string) => void;

Methods

batchEmbedContents(request, options)

batchEmbedContents(request?: protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsRequest, options?: CallOptions): Promise<[
        protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsResponse,
        (protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsRequest | undefined),
        {} | undefined
    ]>;

Generates multiple embeddings from the model given input text in a synchronous call.

Parameters
Name	Description
`request`	`IBatchEmbedContentsRequest` The request object that will be sent.
`options`	`CallOptions` Call options. See CallOptions for more details.

Returns
Type	Description
`Promise<[ protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsResponse, (protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsRequest \| undefined), {} \| undefined ]>`	{Promise} - The promise which resolves to an array. The first element of the array is an object representing BatchEmbedContentsResponse. Please see the documentation for more details and examples.

Example


  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The model's resource name. This serves as an ID for the Model to
   *  use.
   *  This name should match a model name returned by the `ListModels` method.
   *  Format: `models/{model}`
   */
  // const model = 'abc123'
  /**
   *  Required. Embed requests for the batch. The model in each of these requests
   *  must match the model specified `BatchEmbedContentsRequest.model`.
   */
  // const requests = [1,2,3,4]

  // Imports the Generativelanguage library
  const {GenerativeServiceClient} = require('@google-ai/generativelanguage').v1beta;

  // Instantiates a client
  const generativelanguageClient = new GenerativeServiceClient();

  async function callBatchEmbedContents() {
    // Construct request
    const request = {
      model,
      requests,
    };

    // Run request
    const response = await generativelanguageClient.batchEmbedContents(request);
    console.log(response);
  }

  callBatchEmbedContents();

batchEmbedContents(request, options, callback)

batchEmbedContents(request: protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsRequest, options: CallOptions, callback: Callback<protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsResponse, protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`IBatchEmbedContentsRequest`
`options`	`CallOptions`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsResponse, protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsRequest \| null \| undefined, {} \| null \| undefined>`

Returns
Type	Description
`void`

batchEmbedContents(request, callback)

batchEmbedContents(request: protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsRequest, callback: Callback<protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsResponse, protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`IBatchEmbedContentsRequest`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsResponse, protos.google.ai.generativelanguage.v1beta.IBatchEmbedContentsRequest \| null \| undefined, {} \| null \| undefined>`

Returns
Type	Description
`void`

chunkPath(corpus, document, chunk)

chunkPath(corpus: string, document: string, chunk: string): string;

Return a fully-qualified chunk resource name string.

Parameters
Name	Description
`corpus`	`string`
`document`	`string`
`chunk`	`string`

Returns
Type	Description
`string`	{string} Resource name string.

close()

close(): Promise<void>;

Terminate the gRPC channel and close the client.

The client will no longer be usable and all future behavior is undefined.

Returns
Type	Description
`Promise<void>`	{Promise} A promise that resolves when the client is closed.

corpusPath(corpus)

corpusPath(corpus: string): string;

Return a fully-qualified corpus resource name string.

Parameter
Name	Description
`corpus`	`string`

Returns
Type	Description
`string`	{string} Resource name string.

corpusPermissionPath(corpus, permission)

corpusPermissionPath(corpus: string, permission: string): string;

Return a fully-qualified corpusPermission resource name string.

Parameters
Name	Description
`corpus`	`string`
`permission`	`string`

Returns
Type	Description
`string`	{string} Resource name string.

countTokens(request, options)

countTokens(request?: protos.google.ai.generativelanguage.v1beta.ICountTokensRequest, options?: CallOptions): Promise<[
        protos.google.ai.generativelanguage.v1beta.ICountTokensResponse,
        (protos.google.ai.generativelanguage.v1beta.ICountTokensRequest | undefined),
        {} | undefined
    ]>;

Runs a model's tokenizer on input content and returns the token count.

Parameters
Name	Description
`request`	`ICountTokensRequest` The request object that will be sent.
`options`	`CallOptions` Call options. See CallOptions for more details.

Returns
Type	Description
`Promise<[ protos.google.ai.generativelanguage.v1beta.ICountTokensResponse, (protos.google.ai.generativelanguage.v1beta.ICountTokensRequest \| undefined), {} \| undefined ]>`	{Promise} - The promise which resolves to an array. The first element of the array is an object representing CountTokensResponse. Please see the documentation for more details and examples.

Example


  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The model's resource name. This serves as an ID for the Model to
   *  use.
   *  This name should match a model name returned by the `ListModels` method.
   *  Format: `models/{model}`
   */
  // const model = 'abc123'
  /**
   *  Optional. The input given to the model as a prompt.
   */
  // const contents = [1,2,3,4]
  /**
   *  Optional. The overall input given to the model. CountTokens will count
   *  prompt, function calling, etc.
   */
  // const generateContentRequest = {}

  // Imports the Generativelanguage library
  const {GenerativeServiceClient} = require('@google-ai/generativelanguage').v1beta;

  // Instantiates a client
  const generativelanguageClient = new GenerativeServiceClient();

  async function callCountTokens() {
    // Construct request
    const request = {
      model,
    };

    // Run request
    const response = await generativelanguageClient.countTokens(request);
    console.log(response);
  }

  callCountTokens();

countTokens(request, options, callback)

countTokens(request: protos.google.ai.generativelanguage.v1beta.ICountTokensRequest, options: CallOptions, callback: Callback<protos.google.ai.generativelanguage.v1beta.ICountTokensResponse, protos.google.ai.generativelanguage.v1beta.ICountTokensRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`ICountTokensRequest`
`options`	`CallOptions`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.ICountTokensResponse, protos.google.ai.generativelanguage.v1beta.ICountTokensRequest \| null \| undefined, {} \| null \| undefined>`

Returns
Type	Description
`void`

countTokens(request, callback)

countTokens(request: protos.google.ai.generativelanguage.v1beta.ICountTokensRequest, callback: Callback<protos.google.ai.generativelanguage.v1beta.ICountTokensResponse, protos.google.ai.generativelanguage.v1beta.ICountTokensRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`ICountTokensRequest`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.ICountTokensResponse, protos.google.ai.generativelanguage.v1beta.ICountTokensRequest \| null \| undefined, {} \| null \| undefined>`

Returns
Type	Description
`void`

documentPath(corpus, document)

documentPath(corpus: string, document: string): string;

Return a fully-qualified document resource name string.

Parameters
Name	Description
`corpus`	`string`
`document`	`string`

Returns
Type	Description
`string`	{string} Resource name string.

embedContent(request, options)

embedContent(request?: protos.google.ai.generativelanguage.v1beta.IEmbedContentRequest, options?: CallOptions): Promise<[
        protos.google.ai.generativelanguage.v1beta.IEmbedContentResponse,
        (protos.google.ai.generativelanguage.v1beta.IEmbedContentRequest | undefined),
        {} | undefined
    ]>;

Generates an embedding from the model given an input Content.

Parameters
Name	Description
`request`	`IEmbedContentRequest` The request object that will be sent.
`options`	`CallOptions` Call options. See CallOptions for more details.

Returns
Type	Description
`Promise<[ protos.google.ai.generativelanguage.v1beta.IEmbedContentResponse, (protos.google.ai.generativelanguage.v1beta.IEmbedContentRequest \| undefined), {} \| undefined ]>`	{Promise} - The promise which resolves to an array. The first element of the array is an object representing EmbedContentResponse. Please see the documentation for more details and examples.

Example


  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The model's resource name. This serves as an ID for the Model to
   *  use.
   *  This name should match a model name returned by the `ListModels` method.
   *  Format: `models/{model}`
   */
  // const model = 'abc123'
  /**
   *  Required. The content to embed. Only the `parts.text` fields will be
   *  counted.
   */
  // const content = {}
  /**
   *  Optional. Optional task type for which the embeddings will be used. Can
   *  only be set for `models/embedding-001`.
   */
  // const taskType = {}
  /**
   *  Optional. An optional title for the text. Only applicable when TaskType is
   *  `RETRIEVAL_DOCUMENT`.
   *  Note: Specifying a `title` for `RETRIEVAL_DOCUMENT` provides better quality
   *  embeddings for retrieval.
   */
  // const title = 'abc123'
  /**
   *  Optional. Optional reduced dimension for the output embedding. If set,
   *  excessive values in the output embedding are truncated from the end.
   *  Supported by newer models since 2024, and the earlier model
   *  (`models/embedding-001`) cannot specify this value.
   */
  // const outputDimensionality = 1234

  // Imports the Generativelanguage library
  const {GenerativeServiceClient} = require('@google-ai/generativelanguage').v1beta;

  // Instantiates a client
  const generativelanguageClient = new GenerativeServiceClient();

  async function callEmbedContent() {
    // Construct request
    const request = {
      model,
      content,
    };

    // Run request
    const response = await generativelanguageClient.embedContent(request);
    console.log(response);
  }

  callEmbedContent();

embedContent(request, options, callback)

embedContent(request: protos.google.ai.generativelanguage.v1beta.IEmbedContentRequest, options: CallOptions, callback: Callback<protos.google.ai.generativelanguage.v1beta.IEmbedContentResponse, protos.google.ai.generativelanguage.v1beta.IEmbedContentRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`IEmbedContentRequest`
`options`	`CallOptions`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.IEmbedContentResponse, protos.google.ai.generativelanguage.v1beta.IEmbedContentRequest \| null \| undefined, {} \| null \| undefined>`

Returns
Type	Description
`void`

embedContent(request, callback)

embedContent(request: protos.google.ai.generativelanguage.v1beta.IEmbedContentRequest, callback: Callback<protos.google.ai.generativelanguage.v1beta.IEmbedContentResponse, protos.google.ai.generativelanguage.v1beta.IEmbedContentRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`IEmbedContentRequest`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.IEmbedContentResponse, protos.google.ai.generativelanguage.v1beta.IEmbedContentRequest \| null \| undefined, {} \| null \| undefined>`

Returns
Type	Description
`void`

filePath(file)

filePath(file: string): string;

Return a fully-qualified file resource name string.

Parameter
Name	Description
`file`	`string`

Returns
Type	Description
`string`	{string} Resource name string.

generateAnswer(request, options)

generateAnswer(request?: protos.google.ai.generativelanguage.v1beta.IGenerateAnswerRequest, options?: CallOptions): Promise<[
        protos.google.ai.generativelanguage.v1beta.IGenerateAnswerResponse,
        (protos.google.ai.generativelanguage.v1beta.IGenerateAnswerRequest | undefined),
        {} | undefined
    ]>;

Generates a grounded answer from the model given an input GenerateAnswerRequest.

Parameters
Name	Description
`request`	`IGenerateAnswerRequest` The request object that will be sent.
`options`	`CallOptions` Call options. See CallOptions for more details.

Returns
Type	Description
`Promise<[ protos.google.ai.generativelanguage.v1beta.IGenerateAnswerResponse, (protos.google.ai.generativelanguage.v1beta.IGenerateAnswerRequest \| undefined), {} \| undefined ]>`	{Promise} - The promise which resolves to an array. The first element of the array is an object representing . Please see the documentation for more details and examples.

Example


  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Passages provided inline with the request.
   */
  // const inlinePassages = {}
  /**
   *  Content retrieved from resources created via the Semantic Retriever
   *  API.
   */
  // const semanticRetriever = {}
  /**
   *  Required. The name of the `Model` to use for generating the grounded
   *  response.
   *  Format: `model=models/{model}`.
   */
  // const model = 'abc123'
  /**
   *  Required. The content of the current conversation with the model. For
   *  single-turn queries, this is a single question to answer. For multi-turn
   *  queries, this is a repeated field that contains conversation history and
   *  the last `Content` in the list containing the question.
   *  Note: GenerateAnswer currently only supports queries in English.
   */
  // const contents = [1,2,3,4]
  /**
   *  Required. Style in which answers should be returned.
   */
  // const answerStyle = {}
  /**
   *  Optional. A list of unique `SafetySetting` instances for blocking unsafe
   *  content.
   *  This will be enforced on the `GenerateAnswerRequest.contents` and
   *  `GenerateAnswerResponse.candidate`. There should not be more than one
   *  setting for each `SafetyCategory` type. The API will block any contents and
   *  responses that fail to meet the thresholds set by these settings. This list
   *  overrides the default settings for each `SafetyCategory` specified in the
   *  safety_settings. If there is no `SafetySetting` for a given
   *  `SafetyCategory` provided in the list, the API will use the default safety
   *  setting for that category. Harm categories HARM_CATEGORY_HATE_SPEECH,
   *  HARM_CATEGORY_SEXUALLY_EXPLICIT, HARM_CATEGORY_DANGEROUS_CONTENT,
   *  HARM_CATEGORY_HARASSMENT are supported.
   */
  // const safetySettings = [1,2,3,4]
  /**
   *  Optional. Controls the randomness of the output.
   *  Values can range from 0.0,1.0, inclusive. A value closer to 1.0 will
   *  produce responses that are more varied and creative, while a value closer
   *  to 0.0 will typically result in more straightforward responses from the
   *  model. A low temperature (~0.2) is usually recommended for
   *  Attributed-Question-Answering use cases.
   */
  // const temperature = 1234

  // Imports the Generativelanguage library
  const {GenerativeServiceClient} = require('@google-ai/generativelanguage').v1beta;

  // Instantiates a client
  const generativelanguageClient = new GenerativeServiceClient();

  async function callGenerateAnswer() {
    // Construct request
    const request = {
      model,
      contents,
      answerStyle,
    };

    // Run request
    const response = await generativelanguageClient.generateAnswer(request);
    console.log(response);
  }

  callGenerateAnswer();

generateAnswer(request, options, callback)

generateAnswer(request: protos.google.ai.generativelanguage.v1beta.IGenerateAnswerRequest, options: CallOptions, callback: Callback<protos.google.ai.generativelanguage.v1beta.IGenerateAnswerResponse, protos.google.ai.generativelanguage.v1beta.IGenerateAnswerRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`IGenerateAnswerRequest`
`options`	`CallOptions`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.IGenerateAnswerResponse, protos.google.ai.generativelanguage.v1beta.IGenerateAnswerRequest \| null \| undefined, {} \| null \| undefined>`

Returns
Type	Description
`void`

generateAnswer(request, callback)

generateAnswer(request: protos.google.ai.generativelanguage.v1beta.IGenerateAnswerRequest, callback: Callback<protos.google.ai.generativelanguage.v1beta.IGenerateAnswerResponse, protos.google.ai.generativelanguage.v1beta.IGenerateAnswerRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`IGenerateAnswerRequest`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.IGenerateAnswerResponse, protos.google.ai.generativelanguage.v1beta.IGenerateAnswerRequest \| null \| undefined, {} \| null \| undefined>`

Returns
Type	Description
`void`

generateContent(request, options)

generateContent(request?: protos.google.ai.generativelanguage.v1beta.IGenerateContentRequest, options?: CallOptions): Promise<[
        protos.google.ai.generativelanguage.v1beta.IGenerateContentResponse,
        (protos.google.ai.generativelanguage.v1beta.IGenerateContentRequest | undefined),
        {} | undefined
    ]>;

Generates a response from the model given an input GenerateContentRequest.

Input capabilities differ between models, including tuned models. See the [model guide](https://ai.google.dev/models/gemini) and [tuning guide](https://ai.google.dev/docs/model_tuning_guidance) for details.

Parameters
Name	Description
`request`	`IGenerateContentRequest` The request object that will be sent.
`options`	`CallOptions` Call options. See CallOptions for more details.

Returns
Type	Description
`Promise<[ protos.google.ai.generativelanguage.v1beta.IGenerateContentResponse, (protos.google.ai.generativelanguage.v1beta.IGenerateContentRequest \| undefined), {} \| undefined ]>`	{Promise} - The promise which resolves to an array. The first element of the array is an object representing . Please see the documentation for more details and examples.

Example


  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The name of the `Model` to use for generating the completion.
   *  Format: `name=models/{model}`.
   */
  // const model = 'abc123'
  /**
   *  Optional. Developer set system instruction. Currently, text only.
   */
  // const systemInstruction = {}
  /**
   *  Required. The content of the current conversation with the model.
   *  For single-turn queries, this is a single instance. For multi-turn queries,
   *  this is a repeated field that contains conversation history + latest
   *  request.
   */
  // const contents = [1,2,3,4]
  /**
   *  Optional. A list of `Tools` the model may use to generate the next
   *  response.
   *  A `Tool` is a piece of code that enables the system to interact with
   *  external systems to perform an action, or set of actions, outside of
   *  knowledge and scope of the model. The only supported tool is currently
   *  `Function`.
   */
  // const tools = [1,2,3,4]
  /**
   *  Optional. Tool configuration for any `Tool` specified in the request.
   */
  // const toolConfig = {}
  /**
   *  Optional. A list of unique `SafetySetting` instances for blocking unsafe
   *  content.
   *  This will be enforced on the `GenerateContentRequest.contents` and
   *  `GenerateContentResponse.candidates`. There should not be more than one
   *  setting for each `SafetyCategory` type. The API will block any contents and
   *  responses that fail to meet the thresholds set by these settings. This list
   *  overrides the default settings for each `SafetyCategory` specified in the
   *  safety_settings. If there is no `SafetySetting` for a given
   *  `SafetyCategory` provided in the list, the API will use the default safety
   *  setting for that category. Harm categories HARM_CATEGORY_HATE_SPEECH,
   *  HARM_CATEGORY_SEXUALLY_EXPLICIT, HARM_CATEGORY_DANGEROUS_CONTENT,
   *  HARM_CATEGORY_HARASSMENT are supported.
   */
  // const safetySettings = [1,2,3,4]
  /**
   *  Optional. Configuration options for model generation and outputs.
   */
  // const generationConfig = {}

  // Imports the Generativelanguage library
  const {GenerativeServiceClient} = require('@google-ai/generativelanguage').v1beta;

  // Instantiates a client
  const generativelanguageClient = new GenerativeServiceClient();

  async function callGenerateContent() {
    // Construct request
    const request = {
      model,
      contents,
    };

    // Run request
    const response = await generativelanguageClient.generateContent(request);
    console.log(response);
  }

  callGenerateContent();

generateContent(request, options, callback)

generateContent(request: protos.google.ai.generativelanguage.v1beta.IGenerateContentRequest, options: CallOptions, callback: Callback<protos.google.ai.generativelanguage.v1beta.IGenerateContentResponse, protos.google.ai.generativelanguage.v1beta.IGenerateContentRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`IGenerateContentRequest`
`options`	`CallOptions`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.IGenerateContentResponse, protos.google.ai.generativelanguage.v1beta.IGenerateContentRequest \| null \| undefined, {} \| null \| undefined>`

Returns
Type	Description
`void`

generateContent(request, callback)

generateContent(request: protos.google.ai.generativelanguage.v1beta.IGenerateContentRequest, callback: Callback<protos.google.ai.generativelanguage.v1beta.IGenerateContentResponse, protos.google.ai.generativelanguage.v1beta.IGenerateContentRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`IGenerateContentRequest`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.IGenerateContentResponse, protos.google.ai.generativelanguage.v1beta.IGenerateContentRequest \| null \| undefined, {} \| null \| undefined>`

Returns
Type	Description
`void`

getProjectId()

getProjectId(): Promise<string>;

Returns
Type	Description
`Promise<string>`

getProjectId(callback)

getProjectId(callback: Callback<string, undefined, undefined>): void;

Parameter
Name	Description
`callback`	`Callback<string, undefined, undefined>`

Returns
Type	Description
`void`

initialize()

initialize(): Promise<{
        [name: string]: Function;
    }>;

Initialize the client. Performs asynchronous operations (such as authentication) and prepares the client. This function will be called automatically when any class method is called for the first time, but if you need to initialize it before calling an actual method, feel free to call initialize() directly.

You can await on this method if you want to make sure the client is initialized.

Returns
Type	Description
`Promise<{ [name: string]: Function; }>`	{Promise} A promise that resolves to an authenticated service stub.

matchChunkFromChunkName(chunkName)

matchChunkFromChunkName(chunkName: string): string | number;

Parse the chunk from Chunk resource.

Parameter
Name	Description
`chunkName`	`string` A fully-qualified path representing Chunk resource.

Returns
Type	Description
`string \| number`	{string} A string representing the chunk.

matchCorpusFromChunkName(chunkName)

matchCorpusFromChunkName(chunkName: string): string | number;

Parse the corpus from Chunk resource.

Parameter
Name	Description
`chunkName`	`string` A fully-qualified path representing Chunk resource.

Returns
Type	Description
`string \| number`	{string} A string representing the corpus.

matchCorpusFromCorpusName(corpusName)

matchCorpusFromCorpusName(corpusName: string): string | number;

Parse the corpus from Corpus resource.

Parameter
Name	Description
`corpusName`	`string` A fully-qualified path representing Corpus resource.

Returns
Type	Description
`string \| number`	{string} A string representing the corpus.

matchCorpusFromCorpusPermissionName(corpusPermissionName)

matchCorpusFromCorpusPermissionName(corpusPermissionName: string): string | number;

Parse the corpus from CorpusPermission resource.

Parameter
Name	Description
`corpusPermissionName`	`string` A fully-qualified path representing corpus_permission resource.

Returns
Type	Description
`string \| number`	{string} A string representing the corpus.

matchCorpusFromDocumentName(documentName)

matchCorpusFromDocumentName(documentName: string): string | number;

Parse the corpus from Document resource.

Parameter
Name	Description
`documentName`	`string` A fully-qualified path representing Document resource.

Returns
Type	Description
`string \| number`	{string} A string representing the corpus.

matchDocumentFromChunkName(chunkName)

matchDocumentFromChunkName(chunkName: string): string | number;

Parse the document from Chunk resource.

Parameter
Name	Description
`chunkName`	`string` A fully-qualified path representing Chunk resource.

Returns
Type	Description
`string \| number`	{string} A string representing the document.

matchDocumentFromDocumentName(documentName)

matchDocumentFromDocumentName(documentName: string): string | number;

Parse the document from Document resource.

Parameter
Name	Description
`documentName`	`string` A fully-qualified path representing Document resource.

Returns
Type	Description
`string \| number`	{string} A string representing the document.

matchFileFromFileName(fileName)

matchFileFromFileName(fileName: string): string | number;

Parse the file from File resource.

Parameter
Name	Description
`fileName`	`string` A fully-qualified path representing File resource.

Returns
Type	Description
`string \| number`	{string} A string representing the file.

matchModelFromModelName(modelName)

matchModelFromModelName(modelName: string): string | number;

Parse the model from Model resource.

Parameter
Name	Description
`modelName`	`string` A fully-qualified path representing Model resource.

Returns
Type	Description
`string \| number`	{string} A string representing the model.

matchPermissionFromCorpusPermissionName(corpusPermissionName)

matchPermissionFromCorpusPermissionName(corpusPermissionName: string): string | number;

Parse the permission from CorpusPermission resource.

Parameter
Name	Description
`corpusPermissionName`	`string` A fully-qualified path representing corpus_permission resource.

Returns
Type	Description
`string \| number`	{string} A string representing the permission.

matchPermissionFromTunedModelPermissionName(tunedModelPermissionName)

matchPermissionFromTunedModelPermissionName(tunedModelPermissionName: string): string | number;

Parse the permission from TunedModelPermission resource.

Parameter
Name	Description
`tunedModelPermissionName`	`string` A fully-qualified path representing tuned_model_permission resource.

Returns
Type	Description
`string \| number`	{string} A string representing the permission.

matchTunedModelFromTunedModelName(tunedModelName)

matchTunedModelFromTunedModelName(tunedModelName: string): string | number;

Parse the tuned_model from TunedModel resource.

Parameter
Name	Description
`tunedModelName`	`string` A fully-qualified path representing TunedModel resource.

Returns
Type	Description
`string \| number`	{string} A string representing the tuned_model.

matchTunedModelFromTunedModelPermissionName(tunedModelPermissionName)

matchTunedModelFromTunedModelPermissionName(tunedModelPermissionName: string): string | number;

Parse the tuned_model from TunedModelPermission resource.

Parameter
Name	Description
`tunedModelPermissionName`	`string` A fully-qualified path representing tuned_model_permission resource.

Returns
Type	Description
`string \| number`	{string} A string representing the tuned_model.

modelPath(model)

modelPath(model: string): string;

Return a fully-qualified model resource name string.

Parameter
Name	Description
`model`	`string`

Returns
Type	Description
`string`	{string} Resource name string.

streamGenerateContent(request, options)

streamGenerateContent(request?: protos.google.ai.generativelanguage.v1beta.IGenerateContentRequest, options?: CallOptions): gax.CancellableStream;

Generates a streamed response from the model given an input GenerateContentRequest.

Parameters
Name	Description
`request`	`IGenerateContentRequest` The request object that will be sent.
`options`	`CallOptions` Call options. See CallOptions for more details.

Returns
Type	Description
`gax.CancellableStream`	{Stream} An object stream which emits on 'data' event. Please see the documentation for more details and examples.

Example


  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The name of the `Model` to use for generating the completion.
   *  Format: `name=models/{model}`.
   */
  // const model = 'abc123'
  /**
   *  Optional. Developer set system instruction. Currently, text only.
   */
  // const systemInstruction = {}
  /**
   *  Required. The content of the current conversation with the model.
   *  For single-turn queries, this is a single instance. For multi-turn queries,
   *  this is a repeated field that contains conversation history + latest
   *  request.
   */
  // const contents = [1,2,3,4]
  /**
   *  Optional. A list of `Tools` the model may use to generate the next
   *  response.
   *  A `Tool` is a piece of code that enables the system to interact with
   *  external systems to perform an action, or set of actions, outside of
   *  knowledge and scope of the model. The only supported tool is currently
   *  `Function`.
   */
  // const tools = [1,2,3,4]
  /**
   *  Optional. Tool configuration for any `Tool` specified in the request.
   */
  // const toolConfig = {}
  /**
   *  Optional. A list of unique `SafetySetting` instances for blocking unsafe
   *  content.
   *  This will be enforced on the `GenerateContentRequest.contents` and
   *  `GenerateContentResponse.candidates`. There should not be more than one
   *  setting for each `SafetyCategory` type. The API will block any contents and
   *  responses that fail to meet the thresholds set by these settings. This list
   *  overrides the default settings for each `SafetyCategory` specified in the
   *  safety_settings. If there is no `SafetySetting` for a given
   *  `SafetyCategory` provided in the list, the API will use the default safety
   *  setting for that category. Harm categories HARM_CATEGORY_HATE_SPEECH,
   *  HARM_CATEGORY_SEXUALLY_EXPLICIT, HARM_CATEGORY_DANGEROUS_CONTENT,
   *  HARM_CATEGORY_HARASSMENT are supported.
   */
  // const safetySettings = [1,2,3,4]
  /**
   *  Optional. Configuration options for model generation and outputs.
   */
  // const generationConfig = {}

  // Imports the Generativelanguage library
  const {GenerativeServiceClient} = require('@google-ai/generativelanguage').v1beta;

  // Instantiates a client
  const generativelanguageClient = new GenerativeServiceClient();

  async function callStreamGenerateContent() {
    // Construct request
    const request = {
      model,
      contents,
    };

    // Run request
    const stream = await generativelanguageClient.streamGenerateContent(request);
    stream.on('data', (response) => { console.log(response) });
    stream.on('error', (err) => { throw(err) });
    stream.on('end', () => { /* API call completed */ });
  }

  callStreamGenerateContent();

tunedModelPath(tunedModel)

tunedModelPath(tunedModel: string): string;

Return a fully-qualified tunedModel resource name string.

Parameter
Name	Description
`tunedModel`	`string`

Returns
Type	Description
`string`	{string} Resource name string.

tunedModelPermissionPath(tunedModel, permission)

tunedModelPermissionPath(tunedModel: string, permission: string): string;

Return a fully-qualified tunedModelPermission resource name string.

Parameters
Name	Description
`tunedModel`	`string`
`permission`	`string`

Returns
Type	Description
`string`	{string} Resource name string.