langchain.js
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    Interface GoogleVertexAIMultimodalEmbeddingsParams

    Parameters for the GoogleVertexAIMultimodalEmbeddings class, extending both EmbeddingsParams and GoogleVertexAIConnectionParams.

    interface GoogleVertexAIMultimodalEmbeddingsParams {
        apiVersion?: string;
        authOptions?: GoogleAuthOptions<JSONClient>;
        customModelURL?: string;
        endpoint?: string;
        location?: string;
        maxOutputTokens?: number;
        model?: string;
        temperature?: number;
        topK?: number;
        topP?: number;
    }

    Hierarchy (View Summary)

    • BaseDynamicToolInput
    • GoogleVertexAIBaseLLMInput<GoogleAuthOptions>
      • GoogleVertexAIMultimodalEmbeddingsParams

    Implemented by

    Index

    Properties

    apiVersion? authOptions? customModelURL? endpoint? location? maxOutputTokens? model? temperature? topK? topP?

    Properties

    apiVersion?: string

    The version of the API functions. Part of the path.

    authOptions?: GoogleAuthOptions<JSONClient>
    customModelURL?: string

    If you are planning to connect to a model that lives under a custom endpoint provide the "customModelURL" which will override the automatic URL building

    This is necessary in cases when you want to point to a fine-tuned model or a model that has been hidden under VertexAI Endpoints.

    In those cases, specifying the GoogleVertexAIModelParams.model param will not be necessary and will be ignored.

    GoogleVertexAILLMConnection.buildUrl

    endpoint?: string

    Hostname for the API call

    location?: string

    Region where the LLM is stored

    maxOutputTokens?: number

    Maximum number of tokens to generate in the completion.

    model?: string

    Model to use

    temperature?: number

    Sampling temperature to use

    topK?: number

    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?: number

    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).

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