Class Models

Hierarchy

  • BaseModule
    • Models

Constructors

constructor

Methods

computeTokens countTokens embedContent generateContent generateContentStream generateImages

Constructors

constructor

  • new Models(apiClient: ApiClient): Models

    Parameters

    • apiClient: ApiClient

    Returns Models

Methods

computeTokens

  • computeTokens(params: ComputeTokensParameters): Promise<ComputeTokensResponse>

    Given a list of contents, returns a corresponding TokensInfo containing the list of tokens and list of token ids.

    This method is not supported by the Gemini Developer API.

    Parameters

    Returns Promise<ComputeTokensResponse>

    The response from the API.

    Example

    const response = await ai.models.computeTokens({
     model: 'gemini-2.0-flash',
     contents: 'What is your name?'
    });
    console.log(response);

countTokens

  • countTokens(params: CountTokensParameters): Promise<CountTokensResponse>

    Counts the number of tokens in the given contents. Multimodal input is supported for Gemini models.

    Parameters

    Returns Promise<CountTokensResponse>

    The response from the API.

    Example

    const response = await ai.models.countTokens({
     model: 'gemini-2.0-flash',
     contents: 'The quick brown fox jumps over the lazy dog.'
    });
    console.log(response);

embedContent

  • embedContent(params: EmbedContentParameters): Promise<EmbedContentResponse>

    Calculates embeddings for the given contents. Only text is supported.

    Parameters

    Returns Promise<EmbedContentResponse>

    The response from the API.

    Example

    const response = await ai.models.embedContent({
     model: 'text-embedding-004',
     contents: [
       'What is your name?',
       'What is your favorite color?',
     ],
     config: {
       outputDimensionality: 64,
     },
    });
    console.log(response);

generateContent

  • generateContent(
        params: GenerateContentParameters,
    ): Promise<GenerateContentResponse>

    Makes an API request to generate content with a given model.

    For the model parameter, supported formats for Vertex AI API include:

    • The Gemini model ID, for example: 'gemini-2.0-flash'
    • The full resource name starts with 'projects/', for example: 'projects/my-project-id/locations/us-central1/publishers/google/models/gemini-2.0-flash'
    • The partial resource name with 'publishers/', for example: 'publishers/google/models/gemini-2.0-flash' or 'publishers/meta/models/llama-3.1-405b-instruct-maas'
    • / separated publisher and model name, for example: 'google/gemini-2.0-flash' or 'meta/llama-3.1-405b-instruct-maas'

    For the model parameter, supported formats for Gemini API include:

    • The Gemini model ID, for example: 'gemini-2.0-flash'
    • The model name starts with 'models/', for example: 'models/gemini-2.0-flash'
    • For tuned models, the model name starts with 'tunedModels/', for example: 'tunedModels/1234567890123456789'

    Some models support multimodal input and output.

    Parameters

    Returns Promise<GenerateContentResponse>

    The response from generating content.

    Example

    const response = await ai.models.generateContent({
      model: 'gemini-2.0-flash',
      contents: 'why is the sky blue?',
      config: {
        candidateCount: 2,
      }
    });
    console.log(response);

generateContentStream

  • generateContentStream(
        params: GenerateContentParameters,
    ): Promise<AsyncGenerator<GenerateContentResponse, any, unknown>>

    Makes an API request to generate content with a given model and yields the response in chunks.

    For the model parameter, supported formats for Vertex AI API include:

    • The Gemini model ID, for example: 'gemini-2.0-flash'
    • The full resource name starts with 'projects/', for example: 'projects/my-project-id/locations/us-central1/publishers/google/models/gemini-2.0-flash'
    • The partial resource name with 'publishers/', for example: 'publishers/google/models/gemini-2.0-flash' or 'publishers/meta/models/llama-3.1-405b-instruct-maas'
    • / separated publisher and model name, for example: 'google/gemini-2.0-flash' or 'meta/llama-3.1-405b-instruct-maas'

    For the model parameter, supported formats for Gemini API include:

    • The Gemini model ID, for example: 'gemini-2.0-flash'
    • The model name starts with 'models/', for example: 'models/gemini-2.0-flash'
    • For tuned models, the model name starts with 'tunedModels/', for example: 'tunedModels/1234567890123456789'

    Some models support multimodal input and output.

    Parameters

    Returns Promise<AsyncGenerator<GenerateContentResponse, any, unknown>>

    The response from generating content.

    Example

    const response = await ai.models.generateContentStream({
      model: 'gemini-2.0-flash',
      contents: 'why is the sky blue?',
      config: {
        maxOutputTokens: 200,
      }
    });
    for await (const chunk of response) {
      console.log(chunk);
    }

generateImages

  • generateImages(
        params: GenerateImagesParameters,
    ): Promise<GenerateImagesResponse>

    Generates an image based on a text description and configuration.

    Parameters

    Returns Promise<GenerateImagesResponse>

    The response from the API.

    Example

    const response = await ai.models.generateImages({
     model: 'imagen-3.0-generate-002',
     prompt: 'Robot holding a red skateboard',
     config: {
       numberOfImages: 1,
       includeRaiReason: true,
     },
    });
    console.log(response?.generatedImages?.[0]?.image?.imageBytes);

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