---
title: HuggingFace | LlamaIndex OSS Documentation
---

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](/typescript/framework-api-reference/classes/huggingfaceembedding/index.md)