langchain/docs/docs/integrations/text_embedding/openvino.ipynb

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   "source": [
    "# OpenVINO\n",
    "[OpenVINO™](https://github.com/openvinotoolkit/openvino) is an open-source toolkit for optimizing and deploying AI inference. The OpenVINO™ Runtime supports various hardware [devices](https://github.com/openvinotoolkit/openvino?tab=readme-ov-file#supported-hardware-matrix) including x86 and ARM CPUs, and Intel GPUs. It can help to boost deep learning performance in Computer Vision, Automatic Speech Recognition, Natural Language Processing and other common tasks.\n",
    "\n",
    "Hugging Face embedding model can be supported by OpenVINO through ``OpenVINOEmbeddings`` class. If you have an Intel GPU, you can specify `model_kwargs={\"device\": \"GPU\"}` to run inference on it."
   ]
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     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install --upgrade-strategy eager \"optimum[openvino,nncf]\" --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "861521a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.embeddings import OpenVINOEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff9be586",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = \"sentence-transformers/all-mpnet-base-v2\"\n",
    "model_kwargs = {\"device\": \"CPU\"}\n",
    "encode_kwargs = {\"mean_pooling\": True, \"normalize_embeddings\": True}\n",
    "\n",
    "ov_embeddings = OpenVINOEmbeddings(\n",
    "    model_name_or_path=model_name,\n",
    "    model_kwargs=model_kwargs,\n",
    "    encode_kwargs=encode_kwargs,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d0a98ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d6c682b",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_result = ov_embeddings.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b57b8ce9-ef7d-4e63-979e-aa8763d1f9a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "[-0.048951778560876846, -0.03986183926463127, -0.02156277745962143]"
      ]
     },
     "execution_count": 5,
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   ],
   "source": [
    "query_result[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb5e74c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_result = ov_embeddings.embed_documents([text])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a6da5ba",
   "metadata": {},
   "source": [
    "## Export IR model\n",
    "It is possible to export your embedding model to the OpenVINO IR format with ``OVModelForFeatureExtraction``, and load the model from local folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a6544a65",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "ov_model_dir = \"all-mpnet-base-v2-ov\"\n",
    "if not Path(ov_model_dir).exists():\n",
    "    ov_embeddings.save_model(ov_model_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "162004c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Compiling the model to CPU ...\n"
     ]
    }
   ],
   "source": [
    "ov_embeddings = OpenVINOEmbeddings(\n",
    "    model_name_or_path=ov_model_dir,\n",
    "    model_kwargs=model_kwargs,\n",
    "    encode_kwargs=encode_kwargs,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92019ef1-5d30-4985-b4e6-c0d98bdfe265",
   "metadata": {},
   "source": [
    "## BGE with OpenVINO\n",
    "We can also access BGE embedding models via the ``OpenVINOBgeEmbeddings`` class with OpenVINO. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66f5c6ba-1446-43e1-b012-800d17cef300",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.embeddings import OpenVINOBgeEmbeddings\n",
    "\n",
    "model_name = \"BAAI/bge-small-en\"\n",
    "model_kwargs = {\"device\": \"CPU\"}\n",
    "encode_kwargs = {\"normalize_embeddings\": True}\n",
    "ov_embeddings = OpenVINOBgeEmbeddings(\n",
    "    model_name_or_path=model_name,\n",
    "    model_kwargs=model_kwargs,\n",
    "    encode_kwargs=encode_kwargs,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72001afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding = ov_embeddings.embed_query(\"hi this is harrison\")\n",
    "len(embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e86c9ae-ec63-48e9-97ba-f23f7a042ed1",
   "metadata": {},
   "source": [
    "For more information refer to:\n",
    "\n",
    "* [OpenVINO LLM guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html).\n",
    "\n",
    "* [OpenVINO Documentation](https://docs.openvino.ai/2024/home.html).\n",
    "\n",
    "* [OpenVINO Get Started Guide](https://www.intel.com/content/www/us/en/content-details/819067/openvino-get-started-guide.html).\n",
    "\n",
    "* [RAG Notebook with LangChain](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-rag-langchain)."
   ]
  }
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