> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1774858546-a100bff.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Bge on Hugging Face integration

> Integrate with the Bge on Hugging Face embedding model using LangChain Python.

> [BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en-v1.5) are one of [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).
> BGE model is created by the [Beijing Academy of Artificial Intelligence (BAAI)](https://en.wikipedia.org/wiki/Beijing_Academy_of_Artificial_Intelligence). `BAAI` is a private non-profit organization engaged in AI research and development.

This notebook shows how to use `BGE Embeddings` through `Hugging Face`

```python theme={null}
pip install -qU  sentence_transformers
```

```python theme={null}
from langchain_community.embeddings import HuggingFaceBgeEmbeddings

model_name = "BAAI/bge-small-en"
model_kwargs = {"device": "cpu"}
encode_kwargs = {"normalize_embeddings": True}
hf = HuggingFaceBgeEmbeddings(
    model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
)
```

Note that you need to pass `query_instruction=""` for `model_name="BAAI/bge-m3"` see [FAQ BGE M3](https://huggingface.co/BAAI/bge-m3#faq).

```python theme={null}
embedding = hf.embed_query("hi this is harrison")
len(embedding)
```

```text theme={null}
384
```

```python theme={null}
```

***

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