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

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    "# TensorFlow Hub\n",
    "\n",
    ">[TensorFlow Hub](https://www.tensorflow.org/hub) is a repository of trained machine learning models ready for fine-tuning and deployable anywhere. Reuse trained models like `BERT` and `Faster R-CNN` with just a few lines of code.\n",
    ">\n",
    ">\n",
    "Let's load the TensorflowHub Embedding class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f822104b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.embeddings import TensorflowHubEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bac84e46",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "2023-01-30 23:53:01.652176: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-01-30 23:53:34.362802: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "embeddings = TensorflowHubEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4790d770",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f556dcdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_result = embeddings.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "76f1b752",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_results = embeddings.embed_documents([\"foo\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fff99b21",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_results"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "aaad49f8",
   "metadata": {},
   "outputs": [],
   "source": []
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