paperetl
tika-python
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paperetl | tika-python | |
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12 | 4 | |
315 | 1,406 | |
7.6% | - | |
6.3 | 2.2 | |
5 months ago | 6 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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paperetl
- Show HN: Open-source Rule-based PDF parser for RAG
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Oracle of Zotero: LLM QA of Your Research Library
Nice project!
I've spent quite a lot of time in the medical/scientific literature space. With regards to LLMs, specifically RAG, how the data is chunked is quite important. With that, I have a couple projects that might be beneficial additions.
paperetl (https://github.com/neuml/paperetl) - supports parsing arXiv, PubMed and integrates with GROBID to handle parsing metadata and text from arbitrary papers.
paperai (https://github.com/neuml/paperai) - builds embeddings databases of medical/scientific papers. Supports LLM prompting, semantic workflows and vector search. Built with txtai (https://github.com/neuml/txtai).
While arbitrary chunking/splitting can work, I've found that integrating parsing that has knowledge of medical/scientific paper structure increases the overall accuracy and experience of downstream applications.
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[P] Parse research papers into structured data
paperai | paperetl
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Seeking Advice: How to extract Abstract from scientific journals (.pdfs) 10k+.
paperai and paperetl are a set of projects to consider for this task.
- paperetl: ETL processes for medical and scientific papers
tika-python
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Document Parsing - an unsolved problem?
At my previous job we had the same problem which we solved by using Tika. We called it on the server along with other stuff, but there is also a Python binding.
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Extract text from PDF
Hey, I’ve spent quite a bit of time looking at extracting text as accurately as possibly from PDFs, it’s turns out that it is not as simple as it might seem. It is especially tricky once you get a wide variety of PDFs (including PDFs with image based text or tables). While I unfortunately cannot share the code I used to extract this text, I will tell you that for what I think your doing, the best solution will require a few things. First you should pick a good module. I’ve spent a long time going over open source solutions to this and the best two I’d say are Excalibur and Apache Tika .
Tika is from Apache so yes its original code base is Java but it has bindings in other languages. Checkout Tika-Python!
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Extract text from documents
The Textractor instance is the main entrypoint for extracting text. This method is backed by Apache Tika, a robust text extraction library written in Java. Apache Tika has support for a large number of file formats: PDF, Word, Excel, HTML and others. The Python Tika package automatically installs Tika and starts a local REST API instance used to read extracted data.
What are some alternatives?
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
layout-parser - A Unified Toolkit for Deep Learning Based Document Image Analysis
py-pdf-parser - A Python tool to help extracting information from structured PDFs.
SciencePlots - Matplotlib styles for scientific plotting
contextualized-topic-models - A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021.
paperai - 📄 🤖 Semantic search and workflows for medical/scientific papers
Machine-Learning-Cyrillic-Classifier - This is a web app where you can draw a letter in the russian alphabet and the ML algorithm will predict the letter that you drew.
ciscoconfparse - Parse, Audit, Query, Build, and Modify Cisco IOS-style configurations.
themer - Themer is a colorscheme generator and manager for your desktop.
mexican-government-report - Text Mining on the 2019 Mexican Government Report, covering from extracting text from a PDF file to plotting the results.