PDF Text Extractor

PDF Text Extractor

PDF Text Extractor allows you to extract text from PDF files. It also supports chunking of the text to prepare the data for usage with large language models.

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PDF Text Extractor

PDF Text Extractor allows you to extract text from PDF files. It also supports chunking of the text to prepare the data for usage with large language models.

Input

  • URLs - URLs of the PDF files you want to extract the text from.
  • Chunk size - the maximum size of a single chunk of text
  • Chunk overlap - how many characters will overlap between neighbouring chunks of text

Output

Each item will contain the URL of the source PDF, index that identifies the position in the extracted text, and lastly, the extracted text.

Sample output

1[{
2  "url": "https://arxiv.org/pdf/2307.12856.pdf",
3  "index": 0,
4  "text": "Preprint
A REAL-WORLD WEBAGENT WITH PLANNING,
LONG CONTEXT UNDERSTANDING, AND
PROGRAM SYNTHESIS
Izzeddin Gur1∗ Hiroki Furuta1,2∗† Austin Huang1 Mustafa Safdari1 Yutaka Matsuo2
Douglas Eck1 Aleksandra Faust1
1Google DeepMind, 2The University of Tokyo
izzeddin@google.com, furuta@weblab.t.u-tokyo.ac.jp
ABSTRACT
Pre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web automation. However, the
performance on real-world websites has still suffered from (1) open domainness,
(2) limited context length, and (3) lack of inductive bias on HTML. We introduce
WebAgent, an LLM-driven agent that learns from self-experience to complete tasks
on real websites following natural language instructions. WebAgent plans ahead by
decomposing instructions into canonical sub-instructions, summarizes long HTML
documents into task-relevant snippets, and acts on websites via Python programs"
5}, 6{ 7 "url": "https://arxiv.org/pdf/2307.12856.pdf", 8 "index": 1, 9 "text": "generated from those. We design WebAgent with Flan-U-PaLM, for grounded code
generation, and HTML-T5, new pre-trained LLMs for long HTML documents
using local and global attention mechanisms and a mixture of long-span denoising
objectives, for planning and summarization. We empirically demonstrate that our
modular recipe improves the success on real websites by over 50%, and that HTMLT5 is the best model to solve various HTML understanding tasks; achieving 18.7%
higher success rate than the prior method on MiniWoB web automation benchmark,
and SoTA performance on Mind2Web, an offline task planning evaluation.
1 INTRODUCTION
Large language models (LLM) (Brown et al., 2020; Chowdhery et al., 2022; OpenAI, 2023) can
solve variety of natural language tasks, such as arithmetic, commonsense, logical reasoning, question
answering, text generation (Brown et al., 2020; Kojima et al., 2022; Wei et al., 2022), and even"
10}, 11{ 12 "url": "https://arxiv.org/pdf/2307.12856.pdf", 13 "index": 2, 14 "text": "interactive decision making tasks (Ahn et al., 2022; Yao et al., 2022b). Recently, LLMs have also
demonstrated success in autonomous web navigation, where the agents control computers or browse
the internet to satisfy the given natural language instructions through the sequence of computer
actions, by leveraging the capability of HTML comprehension and multi-step reasoning (Furuta et al.,
2023; Gur et al., 2022; Kim et al., 2023).
However, web automation on real-world websites has still suffered from (1) the lack of pre-defined
action space, (2) much longer HTML observations than simulators, and (3) the absence of domain
knowledge for HTML in LLMs (Figure 1). Considering the open-ended real-world websites and the
complexity of instructions, defining appropriate action space in advance is challenging. In addition,
although several works have argued that recent LLMs with instruction-finetuning or reinforcement"
15}]

How to use PDF Text Extractor

Follow this tutorial to learn how to use PDF Text Extractor and combine it with LangChain to build an intelligent QA system that can extract answers from PDF documents.

Frequently Asked Questions

Is it legal to scrape job listings or public data?

Yes, if you're scraping publicly available data for personal or internal use. Always review Websute's Terms of Service before large-scale use or redistribution.

Do I need to code to use this scraper?

No. This is a no-code tool — just enter a job title, location, and run the scraper directly from your dashboard or Apify actor page.

What data does it extract?

It extracts job titles, companies, salaries (if available), descriptions, locations, and post dates. You can export all of it to Excel or JSON.

Can I scrape multiple pages or filter by location?

Yes, you can scrape multiple pages and refine by job title, location, keyword, or more depending on the input settings you use.

How do I get started?

You can use the Try Now button on this page to go to the scraper. You’ll be guided to input a search term and get structured results. No setup needed!