example/extract/extract_pdf_with_recursive_splitter.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of loading PDF using recursive splitter\n",
    "\n",
    "Recursive Splitter: Splitting text by recursively look at characters.\n",
    "Recursively tries to split by different characters to find one that works."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Before running the code\n",
    "\n",
    "You will need to `uniflow` conda environment to run this notebook. You can set up the environment following the instruction: https://github.com/CambioML/uniflow/tree/main#installation. Furthermore, make sure you have the following packages installed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip3 install nougat-ocr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys\n",
    "\n",
    "sys.path.append(\".\")\n",
    "sys.path.append(\"..\")\n",
    "sys.path.append(\"../..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/uniflow/lib/python3.10/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"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import pprint\n",
    "from uniflow.flow.client import ExtractClient, TransformClient\n",
    "from uniflow.flow.config import TransformOpenAIConfig, ExtractPDFConfig, SplitterConfig\n",
    "from uniflow.op.model.model_config import OpenAIModelConfig, NougatModelConfig\n",
    "from uniflow.op.prompt import PromptTemplate, Context\n",
    "from uniflow.op.extract.split.splitter_factory import SplitterOpsFactory\n",
    "from uniflow.op.extract.split.constants import RECURSIVE_CHARACTER_SPLITTER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the input data\n",
    "\n",
    "First, let's set current directory and input data directory, and load the raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_cur = os.getcwd()\n",
    "pdf_file = \"1408.5882_page-1.pdf\"\n",
    "input_file = os.path.join(f\"{dir_cur}/data/raw_input/\", pdf_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all the available splitters\n",
    "These are the different splitters we can use to post-process the loaded PDF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ParagraphSplitter', 'MarkdownHeaderSplitter', 'RecursiveCharacterSplitter']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SplitterOpsFactory.list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load the pdf using recursive splitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [\n",
    "    {\"filename\": input_file},\n",
    "]\n",
    "\n",
    "splitter_config = SplitterConfig(\n",
    "            max_chunk_size = 1024,\n",
    "            splitter_func = RECURSIVE_CHARACTER_SPLITTER\n",
    "        )\n",
    "splitter_config.chunk_overlap_size = 5\n",
    "\n",
    "config = ExtractPDFConfig(\n",
    "    model_config=NougatModelConfig(\n",
    "        batch_size = 1 # When batch_size>1, nougat will run on CUDA, otherwise it will run on CPU\n",
    "    ),\n",
    "    splitter_config=splitter_config,\n",
    ")\n",
    "nougat_client = ExtractClient(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:03<00:00,  3.23s/it]\n"
     ]
    }
   ],
   "source": [
    "output = nougat_client.run(data)\n",
    "contexts = output[0]['output'][0]['text']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process the output\n",
    "\n",
    "Let's take a look of the generation output. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('chunk_0: # Convolutional Neural Networks for Sentence Classification Yoon '\n",
      " 'KimNew York Universityyhk255@nyu.edu###### AbstractWe report on a series of '\n",
      " 'experiments with convolutional neural networks (CNN) traine...')\n",
      "('chunk_1: Deep learning models have achieved remarkable results in computer '\n",
      " 'vision [11] and speech recognition [1] in recent years. Within natural '\n",
      " 'language processing, much of the work with deep learning method...')\n",
      "('chunk_2: Convolutional neural networks (CNN) utilize layers with convolving '\n",
      " 'filters that are applied to local features [1]. Originally invented for '\n",
      " 'computer vision, CNN models have subsequently been shown to b...')\n",
      "('chunk_3: In the present work, we train a simple CNN with one layer of '\n",
      " 'convolution on top of word vectors obtained from an unsupervised neural '\n",
      " 'language model. These vectors were trained by Mikolov et al. (2013)...')\n",
      "('chunk_4: Our work is philosophically similar to Razavian et al. (2014) which '\n",
      " 'showed that for image classification, feature extractors obtained from a '\n",
      " 'pre-trained deep learning model perform well on a variety o...')\n"
     ]
    }
   ],
   "source": [
    "for i, _s in enumerate(contexts):\n",
    "    pprint.pprint(f\"chunk_{i}: {_s[:200]}...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End of the notebook\n",
    "\n",
    "Check more Uniflow use cases in the [example folder](https://github.com/CambioML/uniflow/tree/main/example/model#examples)!\n",
    "\n",
    "<a href=\"https://www.cambioml.com/\" title=\"Title\">\n",
    "    <img src=\"../image/cambioml_logo_large.png\" style=\"height: 100px; display: block; margin-left: auto; margin-right: auto;\"/>\n",
    "</a>"
   ]
  }
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