class HuggingFaceTransformersEmbeddingsThe maximum number of documents to embed in a single request.
The async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.
Model name to use
Optional parameters for the pipeline.
Optional parameters for the pretrained model.
Whether to strip new lines from the input text. This is recommended by OpenAI, but may not be suitable for all use cases.
Timeout to use when making requests to OpenAI.
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.
An abstract method that takes a single document as input and returns a promise that resolves to a vector for the query document.
const model = new HuggingFaceTransformersEmbeddings({
model: "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 });