langchain/docs/extras/integrations/document_loaders/grobid.ipynb

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    "# 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/extras/integrations/providers/grobid.mdx).)\n",
    "\n",
    "Once grobid is up-and-running you can interact as described below. \n"
   ]
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    "Now, we can use the data loader."
   ]
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  {
   "cell_type": "code",
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   "source": [
    "from langchain.document_loaders.parsers import GrobidParser\n",
    "from langchain.document_loaders.generic import GenericLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecdc1fb9",
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   "source": [
    "loader = GenericLoader.from_filesystem(\n",
    "    \"../Papers/\",\n",
    "    glob=\"*\",\n",
    "    suffixes=[\".pdf\"],\n",
    "    parser=GrobidParser(segment_sentences=False),\n",
    ")\n",
    "docs = loader.load()"
   ]
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       "'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.'"
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       "{'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",
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       " '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'}"
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