> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1782332329-96d87c7.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Hugging Face integrations

> Integrate with Hugging Face using LangChain Python.

This page covers all LangChain integrations with [Hugging Face Hub](https://huggingface.co/) and libraries like [transformers](https://huggingface.co/docs/transformers/index), [sentence transformers](https://sbert.net/), and [datasets](https://huggingface.co/docs/datasets/index).

## Chat models

### ChatHuggingFace

We can use the `Hugging Face` LLM classes or directly use the `ChatHuggingFace` class.

See a [usage example](/oss/python/integrations/chat/huggingface).

```python theme={null}
from langchain_huggingface import ChatHuggingFace
```

## LLMs

### HuggingFaceEndpoint

We can use the `HuggingFaceEndpoint` class to run open source models via serverless [Inference Providers](https://huggingface.co/docs/inference-providers) or via dedicated [Inference Endpoints](https://huggingface.co/inference-endpoints/dedicated).

See a [usage example](/oss/python/integrations/llms/huggingface_endpoint).

```python theme={null}
from langchain_huggingface import HuggingFaceEndpoint
```

### HuggingFacePipeline

We can use the `HuggingFacePipeline` class to run open source models locally.

See a [usage example](/oss/python/integrations/llms/huggingface_pipelines).

```python theme={null}
from langchain_huggingface import HuggingFacePipeline
```

## Embedding models

### HuggingFaceEmbeddings

We can use the `HuggingFaceEmbeddings` class to run open source embedding models locally.

See a [usage example](/oss/python/integrations/embeddings/huggingfacehub).

```python theme={null}
from langchain_huggingface import HuggingFaceEmbeddings
```

### HuggingFaceEndpointEmbeddings

We can use the `HuggingFaceEndpointEmbeddings` class to run open source embedding models via a dedicated [Inference Endpoint](https://huggingface.co/inference-endpoints/dedicated).

See a [usage example](/oss/python/integrations/embeddings/huggingfacehub).

```python theme={null}
from langchain_huggingface import HuggingFaceEndpointEmbeddings
```

### Text Embeddings Inference (TEI)

For self-hosted production serving of Sentence Transformers models, Hugging Face publishes [Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference), a dedicated inference server with batching and GPU support. Point LangChain at a TEI deployment via `HuggingFaceEndpointEmbeddings` or see the dedicated [TEI integration guide](/oss/python/integrations/embeddings/text_embeddings_inference).

### BGE embedding models

> [BGE models on Hugging Face](https://huggingface.co/BAAI) are a strong open-source embedding family from the [Beijing Academy of Artificial Intelligence (BAAI)](https://en.wikipedia.org/wiki/Beijing_Academy_of_Artificial_Intelligence).

BGE models are Sentence Transformers models, so use `HuggingFaceEmbeddings` with `encode_kwargs={"normalize_embeddings": True}`. See a [usage example](/oss/python/integrations/embeddings/bge_huggingface).

***

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