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   "cell_type": "markdown",
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   "source": [
    "# Grobid\n",
    "\n",
    "GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents.\n",
    "\n",
    "It is designed and expected to be used to parse academic papers, where it works particularly well. Note: if the articles supplied to Grobid are large documents (e.g. dissertations) exceeding a certain number of elements, they might not be processed. \n",
    "\n",
    "This loader uses Grobid to parse PDFs into `Documents` that retain metadata associated with the section of text.\n",
    "\n",
    "---\n",
    "The best approach is to install Grobid via docker, see https://grobid.readthedocs.io/en/latest/Grobid-docker/. \n",
    "\n",
    "(Note: additional instructions can be found [here](https://python.langchain.com/docs/docs/integrations/providers/grobid.mdx).)\n",
    "\n",
    "Once grobid is up-and-running you can interact as described below. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b41bfb1",
   "metadata": {},
   "source": [
    "Now, we can use the data loader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "640e9a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders.generic import GenericLoader\n",
    "from langchain.document_loaders.parsers import GrobidParser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecdc1fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = GenericLoader.from_filesystem(\n",
    "    \"../Papers/\",\n",
    "    glob=\"*\",\n",
    "    suffixes=[\".pdf\"],\n",
    "    parser=GrobidParser(segment_sentences=False),\n",
    ")\n",
    "docs = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "efe9e356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Unlike Chinchilla, PaLM, or GPT-3, we only use publicly available data, making our work compatible with open-sourcing, while most existing models rely on data which is either not publicly available or undocumented (e.g.\"Books -2TB\" or \"Social media conversations\").There exist some exceptions, notably OPT (Zhang et al., 2022), GPT-NeoX (Black et al., 2022), BLOOM (Scao et al., 2022) and GLM (Zeng et al., 2022), but none that are competitive with PaLM-62B or Chinchilla.'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs[3].page_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5be03d17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': 'Unlike Chinchilla, PaLM, or GPT-3, we only use publicly available data, making our work compatible with open-sourcing, while most existing models rely on data which is either not publicly available or undocumented (e.g.\"Books -2TB\" or \"Social media conversations\").There exist some exceptions, notably OPT (Zhang et al., 2022), GPT-NeoX (Black et al., 2022), BLOOM (Scao et al., 2022) and GLM (Zeng et al., 2022), but none that are competitive with PaLM-62B or Chinchilla.',\n",
       " 'para': '2',\n",
       " 'bboxes': \"[[{'page': '1', 'x': '317.05', 'y': '509.17', 'h': '207.73', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '522.72', 'h': '220.08', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '536.27', 'h': '218.27', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '549.82', 'h': '218.65', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '563.37', 'h': '136.98', 'w': '9.46'}], [{'page': '1', 'x': '446.49', 'y': '563.37', 'h': '78.11', 'w': '9.46'}, {'page': '1', 'x': '304.69', 'y': '576.92', 'h': '138.32', 'w': '9.46'}], [{'page': '1', 'x': '447.75', 'y': '576.92', 'h': '76.66', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '590.47', 'h': '219.63', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '604.02', 'h': '218.27', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '617.56', 'h': '218.27', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '631.11', 'h': '220.18', 'w': '9.46'}]]\",\n",
       " 'pages': \"('1', '1')\",\n",
       " 'section_title': 'Introduction',\n",
       " 'section_number': '1',\n",
       " 'paper_title': 'LLaMA: Open and Efficient Foundation Language Models',\n",
       " 'file_path': '/Users/31treehaus/Desktop/Papers/2302.13971.pdf'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs[3].metadata"
   ]
  }
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