HuggingFace

To use HuggingFace embeddings, you need to import HuggingFaceEmbedding from @llamaindex/huggingface.

Installation

npm i llamaindex @llamaindex/huggingface

import { Document, Settings, VectorStoreIndex } from "llamaindex";
import { HuggingFaceEmbedding } from "@llamaindex/huggingface";

// Update Embed Model
Settings.embedModel = new HuggingFaceEmbedding();

const document = new Document({ text: essay, id_: "essay" });

const index = await VectorStoreIndex.fromDocuments([document]);

const queryEngine = index.asQueryEngine();

const query = "What is the meaning of life?";

const results = await queryEngine.query({
  query,
});

Per default, HuggingFaceEmbedding is using the Xenova/all-MiniLM-L6-v2 model. You can change the model by passing the modelType parameter to the constructor. If you’re not using a quantized model, set the quantized parameter to false.

For example, to use the not quantized BAAI/bge-small-en-v1.5 model, you can use the following code:

import { HuggingFaceEmbedding } from "@llamaindex/huggingface";

Settings.embedModel = new HuggingFaceEmbedding({
  modelType: "BAAI/bge-small-en-v1.5",
  quantized: false,
});

API Reference

• HuggingFaceEmbedding