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### Scientific Article PDF Parsing via Grobid
`Description:`
This change adds the GrobidParser class, which uses the Grobid library
to parse scientific articles into a universal XML format containing the
article title, references, sections, section text etc. The GrobidParser
uses a local Grobid server to return PDFs document as XML and parses the
XML to optionally produce documents of individual sentences or of whole
paragraphs. Metadata includes the text, paragraph number, pdf relative
bboxes, pages (text may overlap over two pages), section title
(Introduction, Methodology etc), section_number (i.e 1.1, 2.3), the
title of the paper and finally the file path.
Grobid parsing is useful beyond standard pdf parsing as it accurately
outputs sections and paragraphs within them. This allows for
post-fitering of results for specific sections i.e. limiting results to
the methodology section or results. While sections are split via
headings, ideally they could be classified specifically into
introduction, methodology, results, discussion, conclusion. I'm
currently experimenting with chatgpt-3.5 for this function, which could
later be implemented as a textsplitter.
`Dependencies:`
For use, the grobid repo must be cloned and Java must be installed, for
colab this is:
```
!apt-get install -y openjdk-11-jdk -q
!update-alternatives --set java /usr/lib/jvm/java-11-openjdk-amd64/bin/java
!git clone https://github.com/kermitt2/grobid.git
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-11-openjdk-amd64"
os.chdir('grobid')
!./gradlew clean install
```
Once installed the server is ran on localhost:8070 via
```
get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')
```
@rlancemartin, @eyurtsev
Twitter Handle: @Corranmac
Grobid Demo Notebook is
[here](https://colab.research.google.com/drive/1X-St_mQRmmm8YWtct_tcJNtoktbdGBmd?usp=sharing).
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
45 lines
1.4 KiB
Plaintext
45 lines
1.4 KiB
Plaintext
# Grobid
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This page covers how to use the Grobid to parse articles for LangChain.
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It is seperated into two parts: installation and running the server
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## Installation and Setup
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#Ensure You have Java installed
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!apt-get install -y openjdk-11-jdk -q
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!update-alternatives --set java /usr/lib/jvm/java-11-openjdk-amd64/bin/java
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#Clone and install the Grobid Repo
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import os
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!git clone https://github.com/kermitt2/grobid.git
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os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-11-openjdk-amd64"
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os.chdir('grobid')
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!./gradlew clean install
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#Run the server,
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get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')
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You can now use the GrobidParser to produce documents
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```python
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from langchain.document_loaders.parsers import GrobidParser
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from langchain.document_loaders.generic import GenericLoader
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#Produce chunks from article paragraphs
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loader = GenericLoader.from_filesystem(
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"/Users/31treehaus/Desktop/Papers/",
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glob="*",
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suffixes=[".pdf"],
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parser= GrobidParser(segment_sentences=False)
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)
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docs = loader.load()
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#Produce chunks from article sentences
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loader = GenericLoader.from_filesystem(
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"/Users/31treehaus/Desktop/Papers/",
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glob="*",
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suffixes=[".pdf"],
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parser= GrobidParser(segment_sentences=True)
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)
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docs = loader.load()
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```
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Chunk metadata will include bboxes although these are a bit funky to parse, see https://grobid.readthedocs.io/en/latest/Coordinates-in-PDF/
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