Class AzureOpenAIEmbeddings

Class for generating embeddings using the OpenAI API. Extends the Embeddings class and implements OpenAIEmbeddingsParams and AzureOpenAIInput.

Example

// Embed a query using OpenAIEmbeddings to generate embeddings for a given text
const model = new OpenAIEmbeddings();
const res = await model.embedQuery(
  "What would be a good company name for a company that makes colorful socks?",
);
console.log({ res });

Hierarchy (view full)

Constructors

constructor

Properties

batchSize model modelName stripNewLines azureADTokenProvider? azureOpenAIApiDeploymentName? azureOpenAIApiInstanceName? azureOpenAIApiKey? azureOpenAIApiVersion? azureOpenAIBasePath? dimensions? organization? timeout? client clientConfig

Methods

embedDocuments embedQuery embeddingWithRetry

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.

model

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

Model name to use

modelName

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

Model name to use Alias for model

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

OptionalazureADTokenProvider

azureADTokenProvider?: (() => Promise<string>)

A function that returns an access token for Microsoft Entra (formerly known as Azure Active Directory), which will be invoked on every request.

OptionalazureOpenAIApiDeploymentName

azureOpenAIApiDeploymentName?: string

Azure OpenAI API deployment name to use for completions when making requests to Azure OpenAI. This is the name of the deployment you created in the Azure portal. e.g. "my-openai-deployment" this will be used in the endpoint URL: https://{InstanceName}.openai.azure.com/openai/deployments/my-openai-deployment/

OptionalazureOpenAIApiInstanceName

azureOpenAIApiInstanceName?: string

Azure OpenAI API instance name to use when making requests to Azure OpenAI. this is the name of the instance you created in the Azure portal. e.g. "my-openai-instance" this will be used in the endpoint URL: https://my-openai-instance.openai.azure.com/openai/deployments/{DeploymentName}/

OptionalazureOpenAIApiKey

azureOpenAIApiKey?: string

API key to use when making requests to Azure OpenAI.

OptionalazureOpenAIApiVersion

azureOpenAIApiVersion?: string

API version to use when making requests to Azure OpenAI.

OptionalazureOpenAIBasePath

azureOpenAIBasePath?: string

Custom endpoint for Azure OpenAI API. This is useful in case you have a deployment in another region. e.g. setting this value to "https://westeurope.api.cognitive.microsoft.com/openai/deployments" will be result in the endpoint URL: https://westeurope.api.cognitive.microsoft.com/openai/deployments/{DeploymentName}/

Optionaldimensions

dimensions?: number

The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.

Optionalorganization

organization?: string

Optionaltimeout

timeout?: number

Timeout to use when making requests to OpenAI.

Protectedclient

client: OpenAIClient

ProtectedclientConfig

clientConfig: ClientOptions

Methods

embedDocuments

  • embedDocuments(texts): Promise<number[][]>

  • Method to generate embeddings for an array of documents. Splits the documents into batches and makes requests to the OpenAI API to generate embeddings.

    Parameters

    • texts: string[]

      Array of documents to generate embeddings for.

    Returns Promise<number[][]>

    Promise that resolves to a 2D array of embeddings for each document.

embedQuery

  • embedQuery(text): Promise<number[]>

  • Method to generate an embedding for a single document. Calls the embeddingWithRetry method with the document as the input.

    Parameters

    • text: string

      Document to generate an embedding for.

    Returns Promise<number[]>

    Promise that resolves to an embedding for the document.

ProtectedembeddingWithRetry

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