grobid
nlm-ingestor
| grobid | nlm-ingestor | |
|---|---|---|
| 12 | 3 | |
| 3,157 | 867 | |
| - | 6.6% | |
| 9.2 | 7.0 | |
| 10 days ago | 8 days ago | |
| Java | Python | |
| Apache License 2.0 | Apache License 2.0 |
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grobid
- FLaNK-AIM Weekly 06 May 2024
- Show HN: Open-source Rule-based PDF parser for RAG
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- 🥪 Best Sites For ebooks, articles, research papers etc..🥪
- Grobid – ML software for extracting information from scholarly documents
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How to create a web app that turns academic papers into text documents
Interesting concept. Grobid tries to do the same https://github.com/kermitt2/grobid
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Extract research paper`s references
I would suggest using grobid - a pipeline for extracting scientific PDFs into a common XML format which can be easily parsed. Grobid has quite a nice mature REST API that I've used in some of my own projects. It parses references and matches them to their DOI using the CrossRef API with a reported 95% F1 score. This should make your job pretty simple as far as I can tell - all you'd need to do is run your papers through grobid and then build a citation graph by comparing document DOIs.
- Free/open-source alternatives to Connected Papers...?
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Seeking Advice: How to extract Abstract from scientific journals (.pdfs) 10k+.
Just use science-parse or GROBID. They have been designed for that exact reason.
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Project to rebuild papers with plaintext markup languages
- I ended up using Grobid, which converts the PDF to a very detailed XML format. The format is not a word processing format though, but a format specifically for representing scientific documents. I don't know, if it would, for example, contain tags about bold or italicized text. The tool is working really well, but since you probably cannot use the output XML format directly, it will need some postprocessing, which would be relatively simple with XML parsing libraries.
nlm-ingestor
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Pg_vectorize: The simplest way to do vector search and RAG on Postgres
>tree-based approach to organize and summarize text data, capturing both high-level and low-level details.
https://twitter.com/parthsarthi03/status/1753199233241674040
processes documents, organizing content and improving readability by handling sections, paragraphs, links, tables, lists, page continuations, and removing redundancies, watermarks, and applying OCR, with additional support for HTML and other formats through Apache Tika:
https://github.com/nlmatics/nlm-ingestor
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Show HN: Open-source Rule-based PDF parser for RAG
Here's another notebook from the repo with examples: https://github.com/nlmatics/nlm-ingestor/blob/main/notebooks...
What are some alternatives?
Parsr - Transforms PDF, Documents and Images into Enriched Structured Data
llmsherpa - Developer APIs to Accelerate LLM Projects
CERMINE - Content ExtRactor and MINEr
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
Smile - Statistical Machine Intelligence & Learning Engine
SemanticSlicer - A recursive text chunker that attempts to preserve context.
science-parse - Science Parse parses scientific papers (in PDF form) and returns them in structured form.
datahub - The Metadata Platform for your Data Stack
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
Tribuo - Tribuo - A Java machine learning library
s2orc-doc2json - Parsers for scientific papers (PDF2JSON, TEX2JSON, JATS2JSON)
paperai - 📄 🤖 Semantic search and workflows for medical/scientific papers