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### Summary Adds a new document loader for processing e-publications. Works with `unstructured>=0.5.4`. You need to have [`pandoc`](https://pandoc.org/installing.html) installed for this loader to work. ### Testing ```python from langchain.document_loaders import UnstructuredEPubLoader loader = UnstructuredEPubLoader("winter-sports.epub", mode="elements") data = loader.load() data[0] ```
320 lines
10 KiB
Plaintext
320 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "20deed05",
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"metadata": {},
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"source": [
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"# Unstructured File Loader\n",
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"This notebook covers how to use Unstructured to load files of many types. Unstructured currently supports loading of text files, powerpoints, html, pdfs, images, and more."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "2886982e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# # Install package\n",
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"!pip install \"unstructured[local-inference]\"\n",
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"!pip install \"detectron2@git+https://github.com/facebookresearch/detectron2.git@v0.6#egg=detectron2\"\n",
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"!pip install layoutparser[layoutmodels,tesseract]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "54d62efd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# # Install other dependencies\n",
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"# # https://github.com/Unstructured-IO/unstructured/blob/main/docs/source/installing.rst\n",
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"# !brew install libmagic\n",
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"# !brew install poppler\n",
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"# !brew install tesseract\n",
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"# # If parsing xml / html documents:\n",
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"# !brew install libxml2\n",
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"# !brew install libxslt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "af6a64f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# import nltk\n",
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"# nltk.download('punkt')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "79d3e549",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import UnstructuredFileLoader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "2593d1dc",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = UnstructuredFileLoader(\"./example_data/state_of_the_union.txt\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "fe34e941",
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"metadata": {},
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"outputs": [],
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"source": [
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"docs = loader.load()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "ee449788",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.\\n\\nLast year COVID-19 kept us apart. This year we are finally together again.\\n\\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans.\\n\\nWith a duty to one another to the American people to the Constit'"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"docs[0].page_content[:400]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7874d01d",
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"metadata": {},
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"source": [
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"## Retain Elements\n",
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"\n",
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"Under the hood, Unstructured creates different \"elements\" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying `mode=\"elements\"`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "ff5b616d",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = UnstructuredFileLoader(\"./example_data/state_of_the_union.txt\", mode=\"elements\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "feca3b6c",
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"metadata": {},
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"outputs": [],
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"source": [
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"docs = loader.load()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "fec5bbac",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),\n",
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" Document(page_content='Last year COVID-19 kept us apart. This year we are finally together again.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),\n",
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" Document(page_content='Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),\n",
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" Document(page_content='With a duty to one another to the American people to the Constitution.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),\n",
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" Document(page_content='And with an unwavering resolve that freedom will always triumph over tyranny.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0)]"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"docs[:5]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "672733fd",
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"metadata": {},
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"source": [
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"## Define a Partitioning Strategy\n",
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"\n",
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"Unstructured document loader allow users to pass in a `strategy` parameter that lets `unstructured` know how to partitioning the document. Currently supported strategies are `\"hi_res\"` (the default) and `\"fast\"`. Hi res partitioning strategies are more accurate, but take longer to process. Fast strategies partition the document more quickly, but trade-off accuracy. Not all document types have separate hi res and fast partitioning strategies. For those document types, the `strategy` kwarg is ignored. In some cases, the high res strategy will fallback to fast if there is a dependency missing (i.e. a model for document partitioning). You can see how to apply a strategy to an `UnstructuredFileLoader` below."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "767238a4",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import UnstructuredFileLoader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "9518b425",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = UnstructuredFileLoader(\"layout-parser-paper-fast.pdf\", strategy=\"fast\", mode=\"elements\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "645f29e9",
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"metadata": {},
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"outputs": [],
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"source": [
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"docs = loader.load()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "60685353",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='1', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'UncategorizedText'}, lookup_index=0),\n",
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" Document(page_content='2', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'UncategorizedText'}, lookup_index=0),\n",
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" Document(page_content='0', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'UncategorizedText'}, lookup_index=0),\n",
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" Document(page_content='2', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'UncategorizedText'}, lookup_index=0),\n",
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" Document(page_content='n', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'Title'}, lookup_index=0)]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"docs[:5]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8de9ef16",
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"metadata": {},
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"source": [
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"## PDF Example\n",
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"\n",
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"Processing PDF documents works exactly the same way. Unstructured detects the file type and extracts the same types of `elements`. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "8ca8a648",
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"metadata": {},
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"outputs": [],
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"source": [
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"!wget https://raw.githubusercontent.com/Unstructured-IO/unstructured/main/example-docs/layout-parser-paper.pdf -P \"../../\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "686e5eb4",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = UnstructuredFileLoader(\"./example_data/layout-parser-paper.pdf\", mode=\"elements\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c90f0e94",
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"metadata": {},
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"outputs": [],
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"source": [
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"docs = loader.load()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "6ec859d8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='LayoutParser : A Unified Toolkit for Deep Learning Based Document Image Analysis', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0),\n",
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" Document(page_content='Zejiang Shen 1 ( (ea)\\n ), Ruochen Zhang 2 , Melissa Dell 3 , Benjamin Charles Germain Lee 4 , Jacob Carlson 3 , and Weining Li 5', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0),\n",
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" Document(page_content='Allen Institute for AI shannons@allenai.org', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0),\n",
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" Document(page_content='Brown University ruochen zhang@brown.edu', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0),\n",
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" Document(page_content='Harvard University { melissadell,jacob carlson } @fas.harvard.edu', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0)]"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"docs[:5]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f52b04cb",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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