pdf-keywords-extractor
docquery
| pdf-keywords-extractor | docquery | |
|---|---|---|
| 5 | 4 | |
| 25 | 1,645 | |
| - | 0.5% | |
| 0.0 | 0.0 | |
| over 1 year ago | about 1 year ago | |
| RobotFramework | Python | |
| MIT License | MIT License |
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pdf-keywords-extractor
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PDF word analyiser
This automation will surface for each page whether the word is present or not in a CSV. You can then load that CSV and count in excel
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Pdfgrep – a commandline utility to search text in PDF files
Tangential:
Some time ago I built an automation [1] that automatically identifies whether the given PDFs contain the specified keywords, outputting the result as a CSV file.
Similar to PDFGrep, probably much slower, but potentially more convenient for people preferring GUIs
[1] https://github.com/bendersej/pdf-keywords-extractor
- I made an open-source automation to extract keywords from any PDFs
- Show HN: I built an open source automation to extract keywords from PDFs
docquery
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Understanding HTML with Large Language Models
There is a visual demo here: https://sites.google.com/view/llm4html/home.
This work is very exciting for a few reasons:
* HTML is an incredibly rich source of visually structured information, with a semi-structured representation. This is as opposed to PDFs, which are usually fed into models with a "flat" representation (words + bounding boxes). Intuitively, this offers the model a more direct way to learn about nested structure, over an almost unlimited source of unsupervised pre-training data.
* Many projects (e.g. Pix2Struct https://arxiv.org/pdf/2210.03347.pdf, also from Google) operate on pixels, which are expensive (both to render and process in the transformer). Operating on HTML directly means smaller, faster, more efficient models.
* (If open sourced) it will be the first (AFAIK) open foundation model for the RPA/automation space (there are several closed projects). They claim they plan to open source the dataset at least, which is very exciting.
I'm particularly excited to extend this and similar (https://arxiv.org/abs/2110.08518) for HTML question answering and web scraping.
Disclaimer: I'm the CEO of Impira, which creates OSS (https://github.com/impira/docquery) and proprietary (http://impira.com/) tools for analyzing business documents. I am not affiliated with this project.
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Pdfgrep – a commandline utility to search text in PDF files
DocQuery (https://github.com/impira/docquery), a project I work on, allows you to do something similar, but search over semantic information in the PDF files (using a large language model that is pre-trained to query business documents).
For example:
$ docquery scan "What is the due date?" /my/invoices/ -
This Week In Python
docquery – An easy way to extract information from documents
- DocQuery: Document Query Engine Powered by Natural Language Processing
What are some alternatives?
pdfgrep - PDFGrep is a GNU/Emacs module providing grep comparable facilities but for PDF files
pdfgrep
rpaframework - Collection of open-source libraries and tools for Robotic Process Automation (RPA), designed to be used with both Robot Framework and Python
natbot - Drive a browser with GPT-3
looqs - FTS desktop file search with previews
django-htmx-patterns - Pattern repository for Django and htmx with full example code
ripgrep-all - rga: ripgrep, but also search in PDFs, E-Books, Office documents, zip, tar.gz, etc.
django-functest - Helpers for creating high-level functional tests in Django, with a unified API for WebTest and Selenium tests.
deptry - Find unused, missing and transitive dependencies in a Python project.