Best AI tools for< Structure Documents >

Iflow is an AI assistant application designed to help users efficiently acquire knowledge in various areas, whether it's for daily entertainment, general life knowledge, or professional academic research. It provides real-time answers to questions, summarizes lengthy articles, and assists in structuring documents to enhance creativity and productivity. With Iflow, users can easily enter a state of flow where knowledge flows effortlessly. The application covers a wide range of topics and is equipped with advanced natural language processing capabilities to cater to diverse user needs.

AI Checker is a free online tool that uses advanced artificial intelligence technology to detect AI-generated content. It can accurately identify text written by ChatGPT, Bard, and GPT-4, even when the text is carefully crafted to avoid detection. This AI writing checker works by analyzing the writing style and sentence structure. It looks for patterns that are commonly found in AI-generated content, such as repetitive phrases, vocabulary, keywords, academic language, and grammar errors. Our AI detector analyzes the text and provides a percentage score indicating the likelihood of AI writing similarity. A score of 100% means that the text is almost certainly AI-generated, while a score of 0% means that it is almost certainly human-written.

Cradl AI is an AI-powered tool designed to automate document workflows with no-code AI. It enables users to extract data from any document automatically, integrate with no-code tools, and build custom AI models through an easy-to-use interface. The tool empowers automation teams across industries by extracting data from complex document layouts, regardless of language or structure. Cradl AI offers features such as line item extraction, fine-tuning AI models, human-in-the-loop validation, and seamless integration with automation tools. It is trusted by organizations for business-critical document automation, providing enterprise-level features like encrypted transmission, GDPR compliance, secure data handling, and auto-scaling.

AiText is an AI-powered writing assistant that enhances the quality of texts, corrects spelling and grammar errors, and generates new texts effortlessly. It offers a range of features including vocabulary enhancement, sentence structure optimization, grammar and spell checking, and text generation. The application is versatile and can be used for various purposes such as creating social media posts, emails, articles, reports, and professional documents. It is designed to improve communication clarity, accelerate writing tasks, and enhance writing skills for academic and professional pursuits.

The FileAI website offers an AI-powered file reading assistant that specializes in data extraction from structured documents like financial statements, legal documents, and research papers. It automates tasks related to legal and compliance review, finance and accounting report preparation, and research and academia support. The tool aims to streamline document processing, enhance learning processes, and improve research efficiency. With features like summarizing complex texts, extracting key information, and detecting plagiarism, FileAI caters to users in various industries and educational fields. The platform prioritizes data security and user privacy, ensuring that data is used solely for its intended purpose and deleted after 7 days of non-use.

AI Document Creator is an innovative tool that leverages artificial intelligence to assist users in generating various types of documents efficiently. The application utilizes advanced algorithms to analyze input data and create well-structured documents tailored to the user's needs. With AI Document Creator, users can save time and effort in document creation, ensuring accuracy and consistency in their outputs. The tool is user-friendly and accessible, making it suitable for individuals and businesses seeking to streamline their document creation process.

Everbility is an AI-powered clinical documentation tool designed for Allied Health Professionals. It helps in writing reports, synthesizing client notes, brainstorming ideas, and focusing on client care. The tool saves time by generating progress notes, letters, and assessment reports, while ensuring data privacy and compliance with regulations like HIPAA and Australian Privacy Principles.

MetaGPT is an advanced AI tool that leverages the power of natural language processing to generate human-like text. It uses the latest deep learning models to understand and produce text in various styles and tones. With MetaGPT, users can easily create content for articles, essays, emails, and more with just a few clicks. The tool is designed to assist writers, marketers, students, and anyone looking to enhance their writing productivity and creativity.

Dataku.ai is an advanced data extraction and analysis tool powered by AI technology. It offers seamless extraction of valuable insights from documents and texts, transforming unstructured data into structured, actionable information. The tool provides tailored data extraction solutions for various needs, such as resume extraction for streamlined recruitment processes, review insights for decoding customer sentiments, and leveraging customer data to personalize experiences. With features like market trend analysis and financial document analysis, Dataku.ai empowers users to make strategic decisions based on accurate data. The tool ensures precision, efficiency, and scalability in data processing, offering different pricing plans to cater to different user needs.

Walle is an all-in-one AI assistant and browser extension that provides a range of features to enhance your digital experience. It includes a chatbot for instant problem-solving, an AI reader for summarizing and understanding text, an AI writer for generating human-like content, a chat PDF feature for summarizing and translating PDF documents, and image creation and reading capabilities. Walle is seamlessly integrated into Chrome, Safari, and Edge browsers, making it your indispensable companion for navigating the digital world.

Airparser is an AI-powered email and document parser tool that revolutionizes data extraction by utilizing the GPT parser engine. It allows users to automate the extraction of structured data from various sources such as emails, PDFs, documents, and handwritten texts. With features like automatic extraction, export to multiple platforms, and support for multiple languages, Airparser simplifies data extraction processes for individuals and businesses. The tool ensures data security and offers seamless integration with other applications through APIs and webhooks.

Parsio is an AI-powered document parser that can extract structured data from PDFs, emails, and other documents. It uses natural language processing to understand the context of the document and identify the relevant data points. Parsio can be used to automate a variety of tasks, such as extracting data from invoices, receipts, and emails.

Docugami is an AI-powered document engineering platform that enables business users to extract, analyze, and automate data from various types of documents. It empowers users with immediate impact without the need for extensive machine learning investments or IT development. Docugami's proprietary Business Document Foundation Model leverages Generative AI to transform unstructured text into structured information, allowing users to unlock insights and drive business processes efficiently.

PDFMerse is an AI-powered data extraction tool that revolutionizes how users handle document data. It allows users to effortlessly extract information from PDFs with precision, saving time and enhancing workflow. With cutting-edge AI technology, PDFMerse automates data extraction, ensures data accuracy, and offers versatile output formats like CSV, JSON, and Excel. The tool is designed to dramatically reduce processing time and operational costs, enabling users to focus on higher-value tasks.

ReportGPT is an AI-powered writing assistant that helps users create high-quality documents efficiently. It offers a range of features, including AI-powered writing assistance, an intuitive user interface, customizable document structures, a versatile writing interface, and diverse export options. ReportGPT is suitable for a variety of writing tasks, including research papers, business plans, product reviews and comparisons, technical documentation, grant and proposal writing, and essays.

Aipify is a platform that allows users to build AI-powered APIs in seconds. With Aipify, users can access the latest AI models, including GPT-4, to enhance their applications' capabilities. Aipify's APIs are easy to use and affordable, making them a great choice for businesses of all sizes.

ReportGPT is an AI-powered writing assistant that helps users generate, review, and refine content. It offers a range of features designed to streamline the writing process, including AI-powered writing assistance, an intuitive user interface, customizable document structures, a versatile writing interface, and diverse export options. ReportGPT is suitable for a variety of writing tasks, including research papers, business plans, product reviews and comparisons, technical documentation, grant and proposal writing, and essays.

TextMine is an AI-powered knowledge base designed for businesses to manage and analyze critical documents efficiently. It offers features such as document analysis, smart-search capabilities, automated data extraction, and structured dataset transformation. TextMine helps businesses save time and money by streamlining document management processes and enabling informed decision-making. The application caters to various industries like Technology, Legal Services, and Financial Services, providing solutions for teams in Procurement, Finance, Compliance, CIOs, and CDOs.

WebDB is an open-source and efficient Database IDE that focuses on providing a secure and user-friendly platform for database management. It offers features such as automatic DBMS discovery, credential guessing, time machine for database version control, powerful queries editor with autocomplete and documentation, AI assistant integration, NoSQL structure management, intelligent data generation, and more. With a modern ERD view and support for various databases, WebDB aims to simplify database management tasks and enhance productivity for users.

Talking Tree is an AI-powered document management tool designed for legal professionals to digitize and manage legal documents efficiently. It offers advanced OCR technology and a custom RAG architecture to convert printed and handwritten text into searchable structured data. The platform enables users to find information, draft agreements, and analyze legacy documents with unprecedented speed and accuracy. Talking Tree provides a secure and user-friendly interface with multilingual support, making it a valuable resource for legal research and document management.

Unstract is a no-code platform that enables users to launch APIs and ETL pipelines to structure unstructured documents. With Unstract, users can go beyond co-pilots by enabling machine-to-machine automation. Unstract's Prompt Studio provides a simple, no-code approach to creating prompts for LLMs, vector databases, embedding models, and text extractors. Users can then configure Prompt Studio projects as API deployments or ETL pipelines to automate critical business processes that involve complex documents. Unstract supports a wide range of LLM providers, vector databases, embeddings, text extractors, ETL sources, and ETL destinations, providing users with the flexibility to choose the best tools for their needs.

RAPTOR introduces a novel approach to retrieval-augmented language models by constructing a recursive tree structure from documents. This allows for more efficient and context-aware information retrieval across large texts, addressing common limitations in traditional language models. Users can add documents to the tree, answer questions based on indexed documents, save and load the tree, and extend RAPTOR with custom summarization, question-answering, and embedding models. The tool is designed to be flexible and customizable for various NLP tasks.

Sycamore is a conversational search and analytics platform for complex unstructured data, such as documents, presentations, transcripts, embedded tables, and internal knowledge repositories. It retrieves and synthesizes high-quality answers through bringing AI to data preparation, indexing, and retrieval. Sycamore makes it easy to prepare unstructured data for search and analytics, providing a toolkit for data cleaning, information extraction, enrichment, summarization, and generation of vector embeddings that encapsulate the semantics of data. Sycamore uses your choice of generative AI models to make these operations simple and effective, and it enables quick experimentation and iteration. Additionally, Sycamore uses OpenSearch for indexing, enabling hybrid (vector + keyword) search, retrieval-augmented generation (RAG) pipelining, filtering, analytical functions, conversational memory, and other features to improve information retrieval.

WebDB is a comprehensive and free database Integrated Development Environment (IDE) designed to maximize efficiency in database development and management. It simplifies and enhances database operations with features like DBMS discovery, query editor, time machine, NoSQL structure inferring, modern ERD visualization, and intelligent data generator. Developed with robust web technologies, WebDB is suitable for both novice and experienced database professionals.

RAGHub is a community-driven project focused on cataloging new and emerging frameworks, projects, and resources in the Retrieval-Augmented Generation (RAG) ecosystem. It aims to help users stay ahead of changes in the field by providing a platform for the latest innovations in RAG. The repository includes information on RAG frameworks, evaluation frameworks, optimization frameworks, citation frameworks, engines, search reranker frameworks, projects, resources, and real-world use cases across industries and professions.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

The `sec-parser` project simplifies extracting meaningful information from SEC EDGAR HTML documents by organizing them into semantic elements and a tree structure. It helps in parsing SEC filings for financial and regulatory analysis, analytics and data science, AI and machine learning, causal AI, and large language models. The tool is especially beneficial for AI, ML, and LLM applications by streamlining data pre-processing and feature extraction.

Gemini PDF Chatbot is a Streamlit-based application that allows users to chat with a conversational AI model trained on PDF documents. The chatbot extracts information from uploaded PDF files and answers user questions based on the provided context. It features PDF upload, text extraction, conversational AI using the Gemini model, and a chat interface. Users can deploy the application locally or to the cloud, and the project structure includes main application script, environment variable file, requirements, and documentation. Dependencies include PyPDF2, langchain, Streamlit, google.generativeai, and dotenv.

The receipt-scanner repository is an AI-Powered Receipt and Invoice Scanner for Laravel that allows users to easily extract structured receipt data from images, PDFs, and emails within their Laravel application using OpenAI. It provides a light wrapper around OpenAI Chat and Completion endpoints, supports various input formats, and integrates with Textract for OCR functionality. Users can install the package via composer, publish configuration files, and use it to extract data from plain text, PDFs, images, Word documents, and web content. The scanned receipt data is parsed into a DTO structure with main classes like Receipt, Merchant, and LineItem.

This repository contains data and code for the experiments in the SummHay paper. It includes publicly released Haystacks in conversational and news domains, along with scripts for running the pipeline, visualizing results, and benchmarking automatic evaluation. The data structure includes topics, subtopics, insights, queries, retrievers, summaries, evaluation summaries, and documents. The pipeline involves scripts for retriever scores, summaries, and evaluation scores using GPT-4o. Visualization scripts are provided for compiling and visualizing results. The repository also includes annotated samples for benchmarking and citation information for the SummHay paper.

This project is a tutorial on developing large model applications for novice developers. It aims to provide a comprehensive introduction to large model development, focusing on Alibaba Cloud servers and integrating personal knowledge assistant projects. The tutorial covers the following topics: 1. **Introduction to Large Models**: A simplified introduction for novice developers on what large models are, their characteristics, what LangChain is, and how to develop an LLM application. 2. **How to Call Large Model APIs**: This section introduces various methods for calling APIs of well-known domestic and foreign large model products, including calling native APIs, encapsulating them as LangChain LLMs, and encapsulating them as Fastapi calls. It also provides a unified encapsulation for various large model APIs, such as Baidu Wenxin, Xunfei Xinghuo, and Zh譜AI. 3. **Knowledge Base Construction**: Loading, processing, and vector database construction of different types of knowledge base documents. 4. **Building RAG Applications**: Integrating LLM into LangChain to build a retrieval question and answer chain, and deploying applications using Streamlit. 5. **Verification and Iteration**: How to implement verification and iteration in large model development, and common evaluation methods. The project consists of three main parts: 1. **Introduction to LLM Development**: A simplified version of V1 aims to help beginners get started with LLM development quickly and conveniently, understand the general process of LLM development, and build a simple demo. 2. **LLM Development Techniques**: More advanced LLM development techniques, including but not limited to: Prompt Engineering, processing of multiple types of source data, optimizing retrieval, recall ranking, Agent framework, etc. 3. **LLM Application Examples**: Introduce some successful open source cases, analyze the ideas, core concepts, and implementation frameworks of these application examples from the perspective of this course, and help beginners understand what kind of applications they can develop through LLM. Currently, the first part has been completed, and everyone is welcome to read and learn; the second and third parts are under creation. **Directory Structure Description**: requirements.txt: Installation dependencies in the official environment notebook: Notebook source code file docs: Markdown documentation file figures: Pictures data_base: Knowledge base source file used

Open Parse is a Python library for visually discerning document layouts and chunking them effectively. It is designed to fill the gap in open-source libraries for handling complex documents. Unlike text splitting, which converts a file to raw text and slices it up, Open Parse visually analyzes documents for superior LLM input. It also supports basic markdown for parsing headings, bold, and italics, and has high-precision table support, extracting tables into clean Markdown formats with accuracy that surpasses traditional tools. Open Parse is extensible, allowing users to easily implement their own post-processing steps. It is also intuitive, with great editor support and completion everywhere, making it easy to use and learn.

The LLM RAG Workshop repository provides a workshop on using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to generate and understand text in a human-like manner. It includes instructions on setting up the environment, indexing Zoomcamp FAQ documents, creating a Q&A system, and using OpenAI for generation based on retrieved information. The repository focuses on enhancing language model responses with retrieved information from external sources, such as document databases or search engines, to improve factual accuracy and relevance of generated text.

The Local File Organizer is an AI-powered tool designed to help users organize their digital files efficiently and securely on their local device. By leveraging advanced AI models for text and visual content analysis, the tool automatically scans and categorizes files, generates relevant descriptions and filenames, and organizes them into a new directory structure. All AI processing occurs locally using the Nexa SDK, ensuring privacy and security. With support for multiple file types and customizable prompts, this tool aims to simplify file management and bring order to users' digital lives.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

STaRK is a large-scale semi-structure retrieval benchmark on Textual and Relational Knowledge Bases. It provides natural-sounding and practical queries crafted to incorporate rich relational information and complex textual properties, closely mirroring real-life scenarios. The benchmark aims to assess how effectively large language models can handle the interplay between textual and relational requirements in queries, using three diverse knowledge bases constructed from public sources.

Hello! Could you share some details about the proposal you're working on? I'll then assist further in crafting your proposal.

specialized GPT designed to assist with drafting and understanding legal clauses. It's equipped with a deep understanding of legal terminology and the structure of legal documents.

Overleaf GPT is an interactive assistant for writing detailed Overleaf documents. Overleaf GPT writes complete LaTeX reports, tailored to the user’s requirements. This GPT starts with conceptualizing the structure to iteratively developing the content and providing best-practice formatting in LaTeX.

Expert in answering Business Formation and Structure Legal queries for small businesses in the USA (Powered by LegalNow, ai.legalnow.xyz)

Helps user create professional grade Technical Reviews & Comparisons

BtoB提案書構成のエキスパート

Turns company financial documents into structured income statements.

Irritating Northern advisor on UK building regs for structure. Based on Oliver Rigg and Approved Document A

Provides legal advice in structured format, focusing on Sri Lankan law.

Comprehensive essay support with documentary analysis

I create structured filenames for PDFs from OCR text.

Multimodal summarizer in a structured, academic style.

Guides users in creating GPTs with a structured approach. Experimental! See https://github.com/allisonmorrell/gptbuilder for background, full prompts and files, and to submit ideas and issues.

Veuillez taper aussi vite que possible, ou vous pouvez coller un texte mal rédigé. Je le réviserai ensuite dans un format correctement structuré

The ultimate tool to rewrite any text like a real human. Say goodbye to uniform sentence structures easily found by AI-text detection tools and hello to professional-grade content with a unique human-sounding edge.

Specializes in PlantUML diagrams with structured API and microservice groups

Turns any software update info into structured changelogs in imperative tense.

Expert in architectural and structural design principles.

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Automatically structure and decode academic papers with ease - simply upload a PDF!