Best AI tools for< Extract Patterns >

nventr is an AI platform for predictive automation, offering a suite of products and services powered by predictive analytics. The company focuses on applying new approaches to uncover patterns, extract valuable intelligence, and predict outcomes within vast datasets. nventr solutions support enterprise-grade AI acceleration, intelligent data processing, and digital transformation. The platform, nventr.ai, enables rapid building of AI models and software applications through collaborative tools and cloud-based infrastructure.

Magic Regex Generator is an AI-powered tool that simplifies the process of generating, testing, and editing Regular Expression patterns. Users can describe what they want to match in English, and the AI generates the corresponding regex in the editor for testing and refining. The tool is designed to make working with regex easier and more efficient, allowing users to focus on meaningful tasks without getting bogged down in complex pattern matching.

Recontact is an AI-powered tool designed to help users analyze and gain insights from user calls efficiently. By leveraging AI technology, Recontact can process and extract valuable information from user conversations, enabling users to understand customer needs, identify trends, and generate detailed reports in a matter of minutes. The tool streamlines the process of listening to call transcripts, making affinity diagrams, and understanding customer requirements, saving users valuable time and effort. Recontact is best suited for early-stage founders, user research teams, and customer support teams looking to analyze user interviews, validate startup ideas, and improve customer interactions.

Rgx.tools is an AI-powered text-to-regex generator that helps users create regular expressions quickly and easily. It is a wrapper around OpenAI's gpt-3.5-chat model, which generates clean, readable, and efficient regular expressions based on user input. Rgx.tools is designed to make the process of writing regular expressions less painful and more accessible, even for those with limited experience.

RegexBot is an AI-powered Regex Builder that allows users to test and convert natural language into powerful regular expressions effortlessly. It leverages the power of AI to help users master regular expressions by providing tools to match specific patterns like URLs, email addresses, ZIP codes, and words containing only uppercase letters. With a user-friendly interface, RegexBot simplifies the process of creating and validating regular expressions, making it a valuable tool for developers, data analysts, and anyone working with text data.

Socrates is an AI tool that provides comprehensive analysis and insights into your documents. It utilizes advanced natural language processing algorithms to extract key information, identify patterns, and offer valuable suggestions. With Socrates, users can gain a deeper understanding of their text content, improve accuracy, and enhance decision-making processes. Whether you're a student, researcher, or professional, Socrates can help you unlock the full potential of your documents.

Schwarzthal Tech's Wunderschild is an AI-driven platform for financial crime intelligence. It revolutionizes compliance and investigation techniques by providing intelligence solutions based on network assessment, data linkage, flow aggregation, and machine learning. The platform offers insights on strategic risks related to Politically Exposed Persons, Serious Organised Crime, Terrorism Financing, and more. With features like Compliance, Investigation, Know Your Network, Media Scan, Document Drill, and Transaction Monitoring, Wunderschild helps in detecting suspicious activities, fraud patterns, and risk assessment. The platform is trusted by global companies for KYC, Enhanced Due Diligence, and multi-dimensional risk rating.

BabblerAI is an advanced artificial intelligence tool designed to assist businesses in analyzing and extracting valuable insights from large volumes of text data. The application utilizes natural language processing and machine learning algorithms to provide users with actionable intelligence and automate the process of information extraction. With BabblerAI, users can streamline their data analysis workflows, uncover trends and patterns, and make data-driven decisions with confidence. The tool is user-friendly and offers a range of features to enhance productivity and efficiency in data analysis tasks.

dataset.macgence is an AI-powered data analysis tool that helps users extract valuable insights from their datasets. It offers a user-friendly interface for uploading, cleaning, and analyzing data, making it suitable for both beginners and experienced data analysts. With advanced algorithms and visualization capabilities, dataset.macgence enables users to uncover patterns, trends, and correlations in their data, leading to informed decision-making. Whether you're a business professional, researcher, or student, dataset.macgence can streamline your data analysis process and enhance your data-driven strategies.

Spiral is an AI-powered tool designed to automate 80% of repeat writing, thinking, and creative tasks. It allows users to create Spirals to accelerate any writing task by training it on examples to generate outputs in their desired voice and style. The tool includes a powerful Prompt Builder to help users work faster and smarter, transforming content into tweets, PRDs, proposals, summaries, and more. Spiral extracts patterns from text to deduce voice and style, enabling users to iterate on outputs until satisfied. Users can share Spirals with their team to maximize quality and streamline processes.

Insight7 is a powerful AI-powered tool that helps businesses extract insights from customer and employee interviews. It uses natural language processing and machine learning to analyze large volumes of unstructured data, such as transcripts, audio recordings, and videos. Insight7 can identify key themes, trends, and sentiment, which can then be used to improve products, services, and customer experiences.

FormX.ai is an AI-powered data extraction and conversion tool that automates the process of extracting data from physical documents and converting it into digital formats. It supports a wide range of document types, including invoices, receipts, purchase orders, bank statements, contracts, HR forms, shipping orders, loyalty member applications, annual reports, business certificates, personnel licenses, and more. FormX.ai's pre-configured data extraction models and effortless API integration make it easy for businesses to integrate data extraction into their existing systems and workflows. With FormX.ai, businesses can save time and money on manual data entry and improve the accuracy and efficiency of their data processing.

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.

Kadoa is an AI web scraper tool that extracts unstructured web data at scale automatically, without the need for coding. It offers a fast and easy way to integrate web data into applications, providing high accuracy, scalability, and automation in data extraction and transformation. Kadoa is trusted by various industries for real-time monitoring, lead generation, media monitoring, and more, offering zero setup or maintenance effort and smart navigation capabilities.

RIDO Protocol is a decentralized data protocol that allows users to extract value from their personal data in Web2 and Web3. It provides users with a variety of features, including programmable data generation, programmable access control, and cross-application data sharing. RIDO also has a data marketplace where users can list or offer their data information and ownership. Additionally, RIDO has a DataFi protocol which promotes the flowing of data information and value.

Tablize is a powerful data extraction tool that helps you turn unstructured data into structured, tabular format. With Tablize, you can easily extract data from PDFs, images, and websites, and export it to Excel, CSV, or JSON. Tablize uses artificial intelligence to automate the data extraction process, making it fast and easy to get the data you need.

Podwise is an AI-powered podcast tool designed for podcast lovers to extract structured knowledge from episodes at 10x speed. It offers features such as AI-powered summarization, mind mapping, content outlining, transcription, and seamless integration with knowledge management workflows. Users can subscribe to favorite content, get lightning-speed access to structured knowledge, and discover episodes of interest. Podwise aims to address the challenge of enjoying podcasts, recalling less, and forgetting quickly, by providing a meticulous, accurate, and impactful tool for efficient podcast referencing and note consolidation.

Nex is an AI Knowledge Copilot application designed to help users efficiently extract main points from long YouTube videos and articles. It offers features like summarizing content, providing quick takeaways, highlighting essential parts, and saving inspirations. With Nex, users can improve their information absorption and save time by focusing on the most relevant parts of the content.

Browse AI is an AI-powered data extraction platform that allows users to scrape and monitor data from any website without the need for coding. It offers managed services for stress-free data extraction, turning any website into an API within minutes. Users can monitor websites for changes and access a complete suite of features for data extraction. Browse AI caters to various industries such as E-Commerce, Real Estate, Recruitment, and Investors & VCs, providing valuable insights and data-driven decisions. The platform ensures best-in-class data reliability through automated site layout monitoring, human behavior emulation, and location-based data extraction.

Vocal Remover Oak is an advanced AI tool designed for music producers, video makers, and karaoke enthusiasts to easily separate vocals and accompaniment in audio files. The website offers a free online vocal remover service that utilizes deep learning technology to provide fast processing, high-quality output, and support for various audio and video formats. Users can upload local files or provide YouTube links to extract vocals, accompaniment, and original music. The tool ensures lossless audio output quality and compatibility with multiple formats, making it suitable for professional music production and personal entertainment projects.

BasicLingua is a Python library that provides functionalities for linguistic tasks such as tokenization, stemming, lemmatization, and many others. It is based on the Gemini Language Model, which has demonstrated promising results in dealing with text data. BasicLingua can be used as an API or through a web demo. It is available under the MIT license and can be used in various projects.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

Fabric is an open-source framework for augmenting humans using AI. It provides a structured approach to breaking down problems into individual components and applying AI to them one at a time. Fabric includes a collection of pre-defined Patterns (prompts) that can be used for a variety of tasks, such as extracting the most interesting parts of YouTube videos and podcasts, writing essays, summarizing academic papers, creating AI art prompts, and more. Users can also create their own custom Patterns. Fabric is designed to be easy to use, with a command-line interface and a variety of helper apps. It is also extensible, allowing users to integrate it with their own AI applications and infrastructure.

Lector is a text analysis tool that helps users extract insights from unstructured text data. It provides functionalities such as sentiment analysis, keyword extraction, entity recognition, and text summarization. With Lector, users can easily analyze large volumes of text data to uncover patterns, trends, and valuable information. The tool is designed to be user-friendly and efficient, making it suitable for both beginners and experienced users in the field of natural language processing and text mining.

BAML is a config file format for declaring LLM functions that you can then use in TypeScript or Python. With BAML you can Classify or Extract any structured data using Anthropic, OpenAI or local models (using Ollama) ## Resources  [Discord Community](https://discord.gg/boundaryml)  [Follow us on Twitter](https://twitter.com/boundaryml) * Discord Office Hours - Come ask us anything! We hold office hours most days (9am - 12pm PST). * Documentation - Learn BAML * Documentation - BAML Syntax Reference * Documentation - Prompt engineering tips * Boundary Studio - Observability and more #### Starter projects * BAML + NextJS 14 * BAML + FastAPI + Streaming ## Motivation Calling LLMs in your code is frustrating: * your code uses types everywhere: classes, enums, and arrays * but LLMs speak English, not types BAML makes calling LLMs easy by taking a type-first approach that lives fully in your codebase: 1. Define what your LLM output type is in a .baml file, with rich syntax to describe any field (even enum values) 2. Declare your prompt in the .baml config using those types 3. Add additional LLM config like retries or redundancy 4. Transpile the .baml files to a callable Python or TS function with a type-safe interface. (VSCode extension does this for you automatically). We were inspired by similar patterns for type safety: protobuf and OpenAPI for RPCs, Prisma and SQLAlchemy for databases. BAML guarantees type safety for LLMs and comes with tools to give you a great developer experience:  Jump to BAML code or how Flexible Parsing works without additional LLM calls. | BAML Tooling | Capabilities | | ----------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | BAML Compiler install | Transpiles BAML code to a native Python / Typescript library (you only need it for development, never for releases) Works on Mac, Windows, Linux  | | VSCode Extension install | Syntax highlighting for BAML files Real-time prompt preview Testing UI | | Boundary Studio open (not open source) | Type-safe observability Labeling |

imodelsX is a Scikit-learn friendly library that provides tools for explaining, predicting, and steering text models/data. It also includes a collection of utilities for getting started with text data. **Explainable modeling/steering** | Model | Reference | Output | Description | |---|---|---|---| | Tree-Prompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/tree_prompt) | Explanation + Steering | Generates a tree of prompts to steer an LLM (_Official_) | | iPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/iprompt) | Explanation + Steering | Generates a prompt that explains patterns in data (_Official_) | | AutoPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/autoprompt) | Explanation + Steering | Find a natural-language prompt using input-gradients (⌛ In progress)| | D3 | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/d3) | Explanation | Explain the difference between two distributions | | SASC | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/sasc) | Explanation | Explain a black-box text module using an LLM (_Official_) | | Aug-Linear | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_linear) | Linear model | Fit better linear model using an LLM to extract embeddings (_Official_) | | Aug-Tree | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_tree) | Decision tree | Fit better decision tree using an LLM to expand features (_Official_) | **General utilities** | Model | Reference | |---|---| | LLM wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/llm) | Easily call different LLMs | | | Dataset wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/data) | Download minimially processed huggingface datasets | | | Bag of Ngrams | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/bag_of_ngrams) | Learn a linear model of ngrams | | | Linear Finetune | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/linear_finetune) | Finetune a single linear layer on top of LLM embeddings | | **Related work** * [imodels package](https://github.com/microsoft/interpretml/tree/main/imodels) (JOSS 2021) - interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible). * [Adaptive wavelet distillation](https://arxiv.org/abs/2111.06185) (NeurIPS 2021) - distilling a neural network into a concise wavelet model * [Transformation importance](https://arxiv.org/abs/1912.04938) (ICLR 2020 workshop) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies) * [Hierarchical interpretations](https://arxiv.org/abs/1807.03343) (ICLR 2019) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy * [Interpretation regularization](https://arxiv.org/abs/2006.14340) (ICML 2020) - penalizes CD / ACD scores during training to make models generalize better * [PDR interpretability framework](https://www.pnas.org/doi/10.1073/pnas.1814225116) (PNAS 2019) - an overarching framewwork for guiding and framing interpretable machine learning

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.

Crawl4AI is a powerful and free web crawling service that extracts valuable data from websites and provides LLM-friendly output formats. It supports crawling multiple URLs simultaneously, replaces media tags with ALT, and is completely free to use and open-source. Users can integrate Crawl4AI into Python projects as a library or run it as a standalone local server. The tool allows users to crawl and extract data from specified URLs using different providers and models, with options to include raw HTML content, force fresh crawls, and extract meaningful text blocks. Configuration settings can be adjusted in the `crawler/config.py` file to customize providers, API keys, chunk processing, and word thresholds. Contributions to Crawl4AI are welcome from the open-source community to enhance its value for AI enthusiasts and developers.

CyberScraper 2077 is an advanced web scraping tool powered by AI, designed to extract data from websites with precision and style. It offers a user-friendly interface, supports multiple data export formats, operates in stealth mode to avoid detection, and promises lightning-fast scraping. The tool respects ethical scraping practices, including robots.txt and site policies. With upcoming features like proxy support and page navigation, CyberScraper 2077 is a futuristic solution for data extraction in the digital realm.

Phospho is a text analytics platform for LLM apps. It helps you detect issues and extract insights from text messages of your users or your app. You can gather user feedback, measure success, and iterate on your app to create the best conversational experience for your users.

Screen-pipe is a Rust + WASM tool that allows users to turn their screen into actions using Large Language Models (LLMs). It enables users to record their screen 24/7, extract text from frames, and process text and images for tasks like analyzing sales conversations. The tool is still experimental and aims to simplify the process of recording screens, extracting text, and integrating with various APIs for tasks such as filling CRM data based on screen activities. The project is open-source and welcomes contributions to enhance its functionalities and usability.

The Intelligence Toolkit is a suite of interactive workflows designed to help domain experts make sense of real-world data by identifying patterns, themes, relationships, and risks within complex datasets. It utilizes generative AI (GPT models) to create reports on findings of interest. The toolkit supports analysis of case, entity, and text data, providing various interactive workflows for different intelligence tasks. Users are expected to evaluate the quality of data insights and AI interpretations before taking action. The system is designed for moderate-sized datasets and responsible use of personal case data. It uses the GPT-4 model from OpenAI or Azure OpenAI APIs for generating reports and insights.

Trafilatura is a Python package and command-line tool for gathering text on the Web and simplifying the process of turning raw HTML into structured, meaningful data. It includes components for web crawling, downloads, scraping, and extraction of main texts, metadata, and comments. The tool aims to focus on actual content, avoid noise, and make sense of data and metadata. It is robust, fast, and widely used by companies and institutions. Trafilatura outperforms other libraries in text extraction benchmarks and offers various features like support for sitemaps, parallel processing, configurable extraction of key elements, multiple output formats, and optional add-ons. The tool is actively maintained with regular updates and comprehensive documentation.

This repository contains custom recipes for H2O Driverless AI, which is an Automatic Machine Learning platform for the Enterprise. Custom recipes are Python code snippets that can be uploaded into Driverless AI at runtime to automate feature engineering, model building, visualization, and interpretability. Users can gain control over the optimization choices made by Driverless AI by providing their own custom recipes. The repository includes recipes for various tasks such as data manipulation, data preprocessing, feature selection, data augmentation, model building, scoring, and more. Best practices for creating and using recipes are also provided, including security considerations, performance tips, and safety measures.

This repository is an awesome list dedicated to resources for using Large Language Models (LLMs) to generate JSON or other structured outputs. It includes terminology explanations, hosted and local models, Python libraries, blog articles, videos, Jupyter notebooks, and leaderboards related to LLMs and JSON generation. The repository covers various aspects such as function calling, JSON mode, guided generation, and tool usage with different providers and models.

tinyllm is a lightweight framework designed for developing, debugging, and monitoring LLM and Agent powered applications at scale. It aims to simplify code while enabling users to create complex agents or LLM workflows in production. The core classes, Function and FunctionStream, standardize and control LLM, ToolStore, and relevant calls for scalable production use. It offers structured handling of function execution, including input/output validation, error handling, evaluation, and more, all while maintaining code readability. Users can create chains with prompts, LLM models, and evaluators in a single file without the need for extensive class definitions or spaghetti code. Additionally, tinyllm integrates with various libraries like Langfuse and provides tools for prompt engineering, observability, logging, and finite state machine design.

KDB.AI is a time-based vector database that allows developers to build scalable, reliable, and real-time applications by providing advanced search, recommendation, and personalization for Generative AI applications. It supports multiple index types, distance metrics, top-N and metadata filtered retrieval, as well as Python and REST interfaces. The repository contains samples demonstrating various use-cases such as temporal similarity search, document search, image search, recommendation systems, sentiment analysis, and more. KDB.AI integrates with platforms like ChatGPT, Langchain, and LlamaIndex. The setup steps require Unix terminal, Python 3.8+, and pip installed. Users can install necessary Python packages and run Jupyter notebooks to interact with the samples.

Invariant Analyzer is an open-source scanner designed for LLM-based AI agents to find bugs, vulnerabilities, and security threats. It scans agent execution traces to identify issues like looping behavior, data leaks, prompt injections, and unsafe code execution. The tool offers a library of built-in checkers, an expressive policy language, data flow analysis, real-time monitoring, and extensible architecture for custom checkers. It helps developers debug AI agents, scan for security violations, and prevent security issues and data breaches during runtime. The analyzer leverages deep contextual understanding and a purpose-built rule matching engine for security policy enforcement.

Kollektiv is a Retrieval-Augmented Generation (RAG) system designed to enable users to chat with their favorite documentation easily. It aims to provide LLMs with access to the most up-to-date knowledge, reducing inaccuracies and improving productivity. The system utilizes intelligent web crawling, advanced document processing, vector search, multi-query expansion, smart re-ranking, AI-powered responses, and dynamic system prompts. The technical stack includes Python/FastAPI for backend, Supabase, ChromaDB, and Redis for storage, OpenAI and Anthropic Claude 3.5 Sonnet for AI/ML, and Chainlit for UI. Kollektiv is licensed under a modified version of the Apache License 2.0, allowing free use for non-commercial purposes.

Kernel Memory (KM) is a multi-modal AI Service specialized in the efficient indexing of datasets through custom continuous data hybrid pipelines, with support for Retrieval Augmented Generation (RAG), synthetic memory, prompt engineering, and custom semantic memory processing. KM is available as a Web Service, as a Docker container, a Plugin for ChatGPT/Copilot/Semantic Kernel, and as a .NET library for embedded applications. Utilizing advanced embeddings and LLMs, the system enables Natural Language querying for obtaining answers from the indexed data, complete with citations and links to the original sources. Designed for seamless integration as a Plugin with Semantic Kernel, Microsoft Copilot and ChatGPT, Kernel Memory enhances data-driven features in applications built for most popular AI platforms.

Generate and explain regex patterns from your description, it support English and Chinese.

A specialist in crafting and explaining regex patterns.

Analizo contenido web para una comprensión rápida y profunda.

I'm an image analysis assistant, providing detailed summaries and insights.

I extract data and tables from PDFs to CSV, focusing on data privacy and precision.

Extract the essence of the novel story according to the quantity requirements and generate corresponding images. The images can be used directly to create novel videos.小说推文图片自动批量生成,可自动生成风格一致性图片

Extract insights seamlessly from diverse documents.

Extract from receipts to CSV: Date of Purchase, Item Purchased, Quantity Purchased, Units

PDFChat : Analyse 1000's of PDF's in seconds, extract and chat with PDFs in any language.

Analyze watch images, extract text, and craft sales descriptions. Add 1 or more images for a single watch to get started.

Put your prompt engineering skills to the ultimate test! Embark on a journey to outwit a mythical guardian of ancient secrets. Try to extract the secret passphrase hidden in the system prompt and enter it in chat when you think you have it and claim your glory. Good luck!

An Ivy Leage Scholar GPT equipped to understand your research needs, formulate comprehensive literature review strategies, and extract pertinent information from a plethora of academic databases and journals. I'll then compose a peer review-quality paper with citations.

Open Domain Relation Extractor

Assists with text extraction from PDFs

Takes in any text and extracts the wisdom from it like you spent 3 hours taking handwritten notes.

专注于从剧本文本提取高光片段

Expert in extracting and transcribing text from images.

从网址提取并选择模型计算大小

ce GPT va recevoir des images dans lesquelles il y a des questions QCM codingame ou Problem Solving sur les sujets : Java, Hibernate, Angular, Spring Boot, SQL. Il doit extraire le texte depuis l'image et répondre au question QCM le plus rapidement possible.

我会将复杂的文本信息提炼成流程化的表达，辅助你快速了解一个流程。