Class AzureOpenAIEmbeddings

The parameters required to initialize an instance of the Embeddings class.

Hierarchy

Implements

Constructors

constructor

Properties

batchSize caller modelName stripNewLines azureOpenAIApiKey? azureOpenAIEmbeddingsApiDeploymentName? azureOpenAIEndpoint? timeout? user?

Methods

embedDocuments embedQuery

Constructors

constructor

Properties

batchSize

batchSize: number = 512

The maximum number of documents to embed in a single request. This is limited by the OpenAI API to a maximum of 2048.

caller

caller: AsyncCaller

The async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.

modelName

modelName: string = "text-embedding-ada-002"

The model name to provide as part of this embeddings request. Not applicable to Azure OpenAI, where deployment information should be included in the Azure resource URI that's connected to.

stripNewLines

stripNewLines: boolean = true

Whether to strip new lines from the input text. This is recommended by OpenAI for older models, but may not be suitable for all use cases. See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500

Optional azureOpenAIApiKey

azureOpenAIApiKey?: string

API key to use when making requests to Azure OpenAI.

Optional azureOpenAIEmbeddingsApiDeploymentName

azureOpenAIEmbeddingsApiDeploymentName?: string

Optional azureOpenAIEndpoint

azureOpenAIEndpoint?: string

Endpoint to use when making requests to Azure OpenAI

Optional timeout

timeout?: number

Timeout to use when making requests to OpenAI.

Optional user

user?: string

An identifier for the caller or end user of the operation. This may be used for tracking or rate-limiting purposes.

Methods

embedDocuments

  • An abstract method that takes an array of documents as input and returns a promise that resolves to an array of vectors for each document.

    Parameters

    • texts: string[]

      An array of documents to be embedded.

    Returns Promise<number[][]>

    A promise that resolves to an array of vectors for each document.

embedQuery

  • An abstract method that takes a single document as input and returns a promise that resolves to a vector for the query document.

    Parameters

    • document: string

      A single document to be embedded.

    Returns Promise<number[]>

    A promise that resolves to a vector for the query document.

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