Class AzureOpenAIEmbeddings

Class for generating embeddings using the OpenAI API.

To use with Azure, import the AzureOpenAIEmbeddings class.

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

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

Methods

embedDocuments embedQuery embeddingWithRetry

Constructors

constructor

Properties

OptionalazureADTokenProvider

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

OptionalazureOpenAIApiDeploymentName

azureOpenAIApiDeploymentName?: string

OptionalazureOpenAIApiInstanceName

azureOpenAIApiInstanceName?: string

OptionalazureOpenAIApiKey

azureOpenAIApiKey?: string

OptionalazureOpenAIApiVersion

azureOpenAIApiVersion?: string

OptionalazureOpenAIBasePath

azureOpenAIBasePath?: string

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.

Protectedclient

client: OpenAIClient

ProtectedclientConfig

clientConfig: ClientOptions

Optionaldimensions

dimensions?: number

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

model

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

Model name to use

modelName

modelName: string

Deprecated

Use "model" instead

Optionalorganization

organization?: string

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

Optionaltimeout

timeout?: number

Timeout to use when making requests to OpenAI.

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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