Class TensorFlowEmbeddings

Class that extends the Embeddings class and provides methods for generating embeddings using the Universal Sentence Encoder model from TensorFlow.js.

Example

const embeddings = new TensorFlowEmbeddings();
const store = new MemoryVectorStore(embeddings);

const documents = [
  "A document",
  "Some other piece of text",
  "One more",
  "And another",
];

await store.addDocuments(
  documents.map((pageContent) => new Document({ pageContent }))
);

Hierarchy

  • Embeddings
    • TensorFlowEmbeddings

Constructors

constructor

Properties

caller

Methods

embedDocuments embedQuery

Constructors

constructor

Properties

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.

Methods

embedDocuments

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

  • Method that takes an array of documents as input and returns a promise that resolves to a 2D array of embeddings for each document. It calls the _embed method with the documents as the input and processes the result to return the embeddings.

    Parameters

    • documents: string[]

      Array of documents to generate embeddings for.

    Returns Promise<number[][]>

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

embedQuery

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

  • Method that takes a document as input and returns a promise that resolves to an embedding for the document. It calls the _embed method with the document as the input and processes the result to return a single embedding.

    Parameters

    • document: string

      Document to generate an embedding for.

    Returns Promise<number[]>

    Promise that resolves to an embedding for the input document.

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Constructors
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Methods