Class v1beta.TextServiceClient (2.4.0)

API for using Generative Language Models (GLMs) trained to generate text.

Also known as Large Language Models (LLM)s, these generate text given an input prompt from the user. v1beta

Package

Constructors

(constructor)(opts, gaxInstance)

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

Construct an instance of TextServiceClient.

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 TextServiceClient({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;

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.

textServiceStub

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

universeDomain

get universeDomain(): string;

warn

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

Methods

batchEmbedText(request, options)

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

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

Parameters
Name	Description
`request`	`IBatchEmbedTextRequest` 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.IBatchEmbedTextResponse, (protos.google.ai.generativelanguage.v1beta.IBatchEmbedTextRequest \| undefined), {} \| undefined ]>`	{Promise} - The promise which resolves to an array. The first element of the array is an object representing BatchEmbedTextResponse. 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 embedding.
   *  Examples:
   *   models/embedding-gecko-001
   */
  // const model = 'abc123'
  /**
   *  Optional. The free-form input texts that the model will turn into an
   *  embedding. The current limit is 100 texts, over which an error will be
   *  thrown.
   */
  // const texts = ['abc','def']
  /**
   *  Optional. Embed requests for the batch. Only one of `texts` or `requests`
   *  can be set.
   */
  // const requests = [1,2,3,4]

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

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

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

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

  callBatchEmbedText();

batchEmbedText(request, options, callback)

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

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

Returns
Type	Description
`void`

batchEmbedText(request, callback)

batchEmbedText(request: protos.google.ai.generativelanguage.v1beta.IBatchEmbedTextRequest, callback: Callback<protos.google.ai.generativelanguage.v1beta.IBatchEmbedTextResponse, protos.google.ai.generativelanguage.v1beta.IBatchEmbedTextRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`IBatchEmbedTextRequest`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.IBatchEmbedTextResponse, protos.google.ai.generativelanguage.v1beta.IBatchEmbedTextRequest \| 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.

countTextTokens(request, options)

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

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

Parameters
Name	Description
`request`	`ICountTextTokensRequest` 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.ICountTextTokensResponse, (protos.google.ai.generativelanguage.v1beta.ICountTextTokensRequest \| undefined), {} \| undefined ]>`	{Promise} - The promise which resolves to an array. The first element of the array is an object representing CountTextTokensResponse. 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 free-form input text given to the model as a prompt.
   */
  // const prompt = {}

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

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

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

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

  callCountTextTokens();

countTextTokens(request, options, callback)

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

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

Returns
Type	Description
`void`

countTextTokens(request, callback)

countTextTokens(request: protos.google.ai.generativelanguage.v1beta.ICountTextTokensRequest, callback: Callback<protos.google.ai.generativelanguage.v1beta.ICountTextTokensResponse, protos.google.ai.generativelanguage.v1beta.ICountTextTokensRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`ICountTextTokensRequest`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.ICountTextTokensResponse, protos.google.ai.generativelanguage.v1beta.ICountTextTokensRequest \| 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.

embedText(request, options)

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

Generates an embedding from the model given an input message.

Parameters
Name	Description
`request`	`IEmbedTextRequest` 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.IEmbedTextResponse, protos.google.ai.generativelanguage.v1beta.IEmbedTextRequest \| undefined, {} \| undefined ]>`	{Promise} - The promise which resolves to an array. The first element of the array is an object representing EmbedTextResponse. 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 name to use with the format model=models/{model}.
   */
  // const model = 'abc123'
  /**
   *  Optional. The free-form input text that the model will turn into an
   *  embedding.
   */
  // const text = 'abc123'

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

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

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

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

  callEmbedText();

embedText(request, options, callback)

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

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

Returns
Type	Description
`void`

embedText(request, callback)

embedText(request: protos.google.ai.generativelanguage.v1beta.IEmbedTextRequest, callback: Callback<protos.google.ai.generativelanguage.v1beta.IEmbedTextResponse, protos.google.ai.generativelanguage.v1beta.IEmbedTextRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`IEmbedTextRequest`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.IEmbedTextResponse, protos.google.ai.generativelanguage.v1beta.IEmbedTextRequest \| 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.

generateText(request, options)

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

Generates a response from the model given an input message.

Parameters
Name	Description
`request`	`IGenerateTextRequest` 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.IGenerateTextResponse, (protos.google.ai.generativelanguage.v1beta.IGenerateTextRequest \| undefined), {} \| undefined ]>`	{Promise} - The promise which resolves to an array. The first element of the array is an object representing GenerateTextResponse. 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` or `TunedModel` to use for generating the
   *  completion.
   *  Examples:
   *   models/text-bison-001
   *   tunedModels/sentence-translator-u3b7m
   */
  // const model = 'abc123'
  /**
   *  Required. The free-form input text given to the model as a prompt.
   *  Given a prompt, the model will generate a TextCompletion response it
   *  predicts as the completion of the input text.
   */
  // const prompt = {}
  /**
   *  Optional. Controls the randomness of the output.
   *  Note: The default value varies by model, see the `Model.temperature`
   *  attribute of the `Model` returned the `getModel` function.
   *  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.
   */
  // const temperature = 1234
  /**
   *  Optional. Number of generated responses to return.
   *  This value must be between 1, 8, inclusive. If unset, this will default
   *  to 1.
   */
  // const candidateCount = 1234
  /**
   *  Optional. The maximum number of tokens to include in a candidate.
   *  If unset, this will default to output_token_limit specified in the `Model`
   *  specification.
   */
  // const maxOutputTokens = 1234
  /**
   *  Optional. The maximum cumulative probability of tokens to consider when
   *  sampling.
   *  The model uses combined Top-k and nucleus sampling.
   *  Tokens are sorted based on their assigned probabilities so that only the
   *  most likely tokens are considered. Top-k sampling directly limits the
   *  maximum number of tokens to consider, while Nucleus sampling limits number
   *  of tokens based on the cumulative probability.
   *  Note: The default value varies by model, see the `Model.top_p`
   *  attribute of the `Model` returned the `getModel` function.
   */
  // const topP = 1234
  /**
   *  Optional. The maximum number of tokens to consider when sampling.
   *  The model uses combined Top-k and nucleus sampling.
   *  Top-k sampling considers the set of `top_k` most probable tokens.
   *  Defaults to 40.
   *  Note: The default value varies by model, see the `Model.top_k`
   *  attribute of the `Model` returned the `getModel` function.
   */
  // const topK = 1234
  /**
   *  Optional. A list of unique `SafetySetting` instances for blocking unsafe
   *  content.
   *  that will be enforced on the `GenerateTextRequest.prompt` and
   *  `GenerateTextResponse.candidates`. There should not be more than one
   *  setting for each `SafetyCategory` type. The API will block any prompts 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_DEROGATORY,
   *  HARM_CATEGORY_TOXICITY, HARM_CATEGORY_VIOLENCE, HARM_CATEGORY_SEXUAL,
   *  HARM_CATEGORY_MEDICAL, HARM_CATEGORY_DANGEROUS are supported in text
   *  service.
   */
  // const safetySettings = [1,2,3,4]
  /**
   *  The set of character sequences (up to 5) that will stop output generation.
   *  If specified, the API will stop at the first appearance of a stop
   *  sequence. The stop sequence will not be included as part of the response.
   */
  // const stopSequences = ['abc','def']

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

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

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

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

  callGenerateText();

generateText(request, options, callback)

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

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

Returns
Type	Description
`void`

generateText(request, callback)

generateText(request: protos.google.ai.generativelanguage.v1beta.IGenerateTextRequest, callback: Callback<protos.google.ai.generativelanguage.v1beta.IGenerateTextResponse, protos.google.ai.generativelanguage.v1beta.IGenerateTextRequest | null | undefined, {} | null | undefined>): void;

Parameters
Name	Description
`request`	`IGenerateTextRequest`
`callback`	`Callback<protos.google.ai.generativelanguage.v1beta.IGenerateTextResponse, protos.google.ai.generativelanguage.v1beta.IGenerateTextRequest \| 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.

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.