text: for extracting, parsing, and re-structuring raw documents.It is particularly good for sturctured PDFs, like academic papers.This loader uses GROBIB to parse PDFs into Documents that retain metadata associated with the section of text.For users on Mac - (Note: additional instructions can be found here.)Install Java (Apple Silicon):$ arch -arm64 brew install openjdk@11$ brew --prefix openjdk@11/opt/homebrew/opt/openjdk@ 11In ~/.zshrc:export JAVA_HOME=/opt/homebrew/opt/openjdk@11export PATH=$JAVA_HOME/bin:$PATHThen, in Terminal:$ source ~/.zshrcConfirm install:$ which java/opt/homebrew/opt/openjdk@11/bin/java$ java -version openjdk version "11.0.19" 2023-04-18OpenJDK Runtime Environment Homebrew (build 11.0.19+0)OpenJDK 64-Bit Server VM Homebrew (build 11.0.19+0, mixed mode)Then, get Grobid:$ curl -LO https://github.com/kermitt2/grobid/archive/0.7.3.zip$ unzip 0.7.3.zipBuild$ ./gradlew clean installThen, run the server:get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')Now, we can use the data loader.from langchain.document_loaders.parsers import GrobidParserfrom langchain.document_loaders.generic import GenericLoaderloader = GenericLoader.from_filesystem(    "../Papers/",    glob="*",    suffixes=[".pdf"],    parser=GrobidParser(segment_sentences=False),)docs = loader.load()docs[3].page_content    'Unlike Chinchilla, PaLM, or GPT-3, we only use publicly available data, making our work compatible with