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

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    "# Intel® Extension for Transformers Quantized Text Embeddings\n",
    "\n",
    "Load quantized BGE embedding models generated by [Intel® Extension for Transformers](https://github.com/intel/intel-extension-for-transformers) (ITREX) and use ITREX [Neural Engine](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/llm/runtime/deprecated/docs/Installation.md), a high-performance NLP backend, to accelerate the inference of models without compromising accuracy.\n",
    "\n",
    "Refer to our blog of [Efficient Natural Language Embedding Models with Intel Extension for Transformers](https://medium.com/intel-analytics-software/efficient-natural-language-embedding-models-with-intel-extension-for-transformers-2b6fcd0f8f34) and [BGE optimization example](https://github.com/intel/intel-extension-for-transformers/tree/main/examples/huggingface/pytorch/text-embedding/deployment/mteb/bge) for more details."
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      "/home/yuwenzho/.conda/envs/bge/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "2024-03-04 10:17:17 [INFO] Start to extarct onnx model ops...\n",
      "2024-03-04 10:17:17 [INFO] Extract onnxruntime model done...\n",
      "2024-03-04 10:17:17 [INFO] Start to implement Sub-Graph matching and replacing...\n",
      "2024-03-04 10:17:18 [INFO] Sub-Graph match and replace done...\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.embeddings import QuantizedBgeEmbeddings\n",
    "\n",
    "model_name = \"Intel/bge-small-en-v1.5-sts-int8-static-inc\"\n",
    "encode_kwargs = {\"normalize_embeddings\": True}  # set True to compute cosine similarity\n",
    "\n",
    "model = QuantizedBgeEmbeddings(\n",
    "    model_name=model_name,\n",
    "    encode_kwargs=encode_kwargs,\n",
    "    query_instruction=\"Represent this sentence for searching relevant passages: \",\n",
    ")"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\"\n",
    "query_result = model.embed_query(text)\n",
    "doc_result = model.embed_documents([text])"
   ]
  }
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