langchain/docs/versioned_docs/version-0.2.x/integrations/text_embedding/bge_huggingface.ipynb

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   "source": [
    "# BGE on Hugging Face\n",
    "\n",
    ">[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en) are [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).\n",
    ">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.\n",
    "\n",
    "This notebook shows how to use `BGE Embeddings` through `Hugging Face`"
   ]
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   "source": [
    "%pip install --upgrade --quiet  sentence_transformers"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "9e1d5b6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.embeddings import HuggingFaceBgeEmbeddings\n",
    "\n",
    "model_name = \"BAAI/bge-small-en\"\n",
    "model_kwargs = {\"device\": \"cpu\"}\n",
    "encode_kwargs = {\"normalize_embeddings\": True}\n",
    "hf = HuggingFaceBgeEmbeddings(\n",
    "    model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs\n",
    ")"
   ]
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   "cell_type": "code",
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     "execution_count": 5,
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   ],
   "source": [
    "embedding = hf.embed_query(\"hi this is harrison\")\n",
    "len(embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e596315f",
   "metadata": {},
   "outputs": [],
   "source": []
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