Class HuggingFaceTransformersEmbeddings

Example

const model = new HuggingFaceTransformersEmbeddings({
  modelName: "Xenova/all-MiniLM-L6-v2",
});

// Embed a single query
const res = await model.embedQuery(
  "What would be a good company name for a company that makes colorful socks?"
);
console.log({ res });

// Embed multiple documents
const documentRes = await model.embedDocuments(["Hello world", "Bye bye"]);
console.log({ documentRes });

Hierarchy

Implements

Constructors

constructor

Properties

batchSize caller modelName stripNewLines timeout?

Methods

embedDocuments embedQuery

Constructors

constructor

Properties

batchSize

batchSize: number = 512

The maximum number of documents to embed in a single request.

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 = "Xenova/all-MiniLM-L6-v2"

Model name to use

stripNewLines

stripNewLines: boolean = true

Whether to strip new lines from the input text. This is recommended by OpenAI, but may not be suitable for all use cases.

Optional timeout

timeout?: number

Timeout to use when making requests to OpenAI.

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

    • text: 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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