langchain/docs/extras/modules/data_connection/document_loaders/integrations/grobid.ipynb
os1ma 2667ddc686
Fix make docs_build and related scripts (#7276)
**Description: a description of the change**

Fixed `make docs_build` and related scripts which caused errors. There
are several changes.

First, I made the build of the documentation and the API Reference into
two separate commands. This is because it takes less time to build. The
commands for documents are `make docs_build`, `make docs_clean`, and
`make docs_linkcheck`. The commands for API Reference are `make
api_docs_build`, `api_docs_clean`, and `api_docs_linkcheck`.

It looked like `docs/.local_build.sh` could be used to build the
documentation, so I used that. Since `.local_build.sh` was also building
API Rerefence internally, I removed that process. `.local_build.sh` also
added some Bash options to stop in error or so. Futher more added `cd
"${SCRIPT_DIR}"` at the beginning so that the script will work no matter
which directory it is executed in.

`docs/api_reference/api_reference.rst` is removed, because which is
generated by `docs/api_reference/create_api_rst.py`, and added it to
.gitignore.

Finally, the description of CONTRIBUTING.md was modified.

**Issue: the issue # it fixes (if applicable)**

https://github.com/hwchase17/langchain/issues/6413

**Dependencies: any dependencies required for this change**

`nbdoc` was missing in group docs so it was added. I installed it with
the `poetry add --group docs nbdoc` command. I am concerned if any
modifications are needed to poetry.lock. I would greatly appreciate it
if you could pay close attention to this file during the review.

**Tag maintainer**
- General / Misc / if you don't know who to tag: @baskaryan

If this PR needs any additional changes, I'll be happy to make them!

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-07-11 22:05:14 -04:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "bdccb278",
"metadata": {},
"source": [
"# Grobid\n",
"\n",
"GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents.\n",
"\n",
"It is particularly good for sturctured PDFs, like academic papers.\n",
"\n",
"This loader uses GROBIB to parse PDFs into `Documents` that retain metadata associated with the section of text.\n",
"\n",
"---\n",
"\n",
"For users on `Mac` - \n",
"\n",
"(Note: additional instructions can be found [here](https://python.langchain.com/docs/ecosystem/integrations/grobid.mdx).)\n",
"\n",
"Install Java (Apple Silicon):\n",
"```\n",
"$ arch -arm64 brew install openjdk@11\n",
"$ brew --prefix openjdk@11\n",
"/opt/homebrew/opt/openjdk@ 11\n",
"```\n",
"\n",
"In `~/.zshrc`:\n",
"```\n",
"export JAVA_HOME=/opt/homebrew/opt/openjdk@11\n",
"export PATH=$JAVA_HOME/bin:$PATH\n",
"```\n",
"\n",
"Then, in Terminal:\n",
"```\n",
"$ source ~/.zshrc\n",
"```\n",
"\n",
"Confirm install:\n",
"```\n",
"$ which java\n",
"/opt/homebrew/opt/openjdk@11/bin/java\n",
"$ java -version \n",
"openjdk version \"11.0.19\" 2023-04-18\n",
"OpenJDK Runtime Environment Homebrew (build 11.0.19+0)\n",
"OpenJDK 64-Bit Server VM Homebrew (build 11.0.19+0, mixed mode)\n",
"```\n",
"\n",
"Then, get [Grobid](https://grobid.readthedocs.io/en/latest/Install-Grobid/#getting-grobid):\n",
"```\n",
"$ curl -LO https://github.com/kermitt2/grobid/archive/0.7.3.zip\n",
"$ unzip 0.7.3.zip\n",
"```\n",
" \n",
"Build\n",
"```\n",
"$ ./gradlew clean install\n",
"```\n",
"\n",
"Then, run the server:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "2d8992fc",
"metadata": {},
"outputs": [],
"source": [
"! get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')"
]
},
{
"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.parsers import GrobidParser\n",
"from langchain.document_loaders.generic import GenericLoader"
]
},
{
"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"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}