Best AI tools for< Populate Documents >

Magical is an AI productivity application that automates repetitive tasks on any website, allowing users to focus on more important work. It offers features like text expansion, autofill, website scraping, AI email writing, and auto form filling. The application is designed to save time and increase efficiency for various tasks across different industries such as recruiting, sales, customer support, and healthcare. Magical is loved by teams and individuals for its ability to personalize messages, overcome writer's block, and automate data entry and research tasks.

Magical is an AI-powered productivity app that automates repetitive tasks on any website. It offers a range of features including text expansion, autofill, data entry and research, and AI-powered writing assistance. Magical is designed to help users save time and focus on more important work.

Fill A Form AI is an innovative tool designed to streamline the form-filling process by leveraging artificial intelligence technology. It offers a one-click solution for filling out forms quickly and efficiently, eliminating the need for tedious data entry tasks. The tool learns from past filled forms, efficiently finds answers from data, and provides powerful features tailored to users' needs. With features like auto-detect form fields, auto-fill from history, and smart collaboration, Fill A Form AI aims to ease users' form-filling experience and save time and effort.

Mailshake is an AI-powered sales engagement and B2B lead platform that offers a comprehensive set of features to streamline outreach campaigns. It allows users to easily populate their CRM with leads, find prospect emails, automate email writing, ensure email deliverability, send personalized emails at scale, and more. With a focus on increasing response rates, generating more sales, and optimizing outreach efforts, Mailshake is trusted by sales teams at over 75,000 companies.

Relume Ipsum is an AI-powered copywriting tool designed to help designers and developers quickly generate realistic website copy. It uses advanced language models to create unique and engaging content based on a brief company description. With Relume Ipsum, users can populate website wireframes with high-quality copy without the need for hiring a copywriter. It offers a range of features to enhance the copywriting process, including the ability to lock certain elements to prevent overwriting, provide feedback to the AI, and generate copy in different styles and tones. Relume Ipsum is suitable for businesses of all sizes and can be used to create website copy for a variety of purposes, including e-commerce, blogs, and corporate websites.

Viddyoze is a powerful video creation software that empowers users to create professional-looking videos for their businesses or brands in minutes. With its extensive library of pre-built video templates, users can choose from a variety of options to achieve their desired goals, such as lead generation, Facebook ads, and brand awareness. Viddyoze also provides exclusive training courses and learning materials to help users build effective video marketing strategies. Additionally, users can access a brand library to add their own branding elements, auto-populate templates, and integrate TrustPilot reviews into their videos.

PodulateAI is an AI-powered platform that enhances YouTube's capabilities by providing tools for interacting with videos using AI technology. Users can chat with YouTube videos, generate quizzes, get summaries, translations, transcriptions, and take notes while watching. The platform is designed to be user-friendly and offers both free and paid plans with unique features like text-to-speech, note-taking, and seamless integration with OpenAI's API.

LARS is an application that enables users to run Large Language Models (LLMs) locally on their devices, upload their own documents, and engage in conversations where the LLM grounds its responses with the uploaded content. The application focuses on Retrieval Augmented Generation (RAG) to increase accuracy and reduce AI-generated inaccuracies. LARS provides advanced citations, supports various file formats, allows follow-up questions, provides full chat history, and offers customization options for LLM settings. Users can force enable or disable RAG, change system prompts, and tweak advanced LLM settings. The application also supports GPU-accelerated inferencing, multiple embedding models, and text extraction methods. LARS is open-source and aims to be the ultimate RAG-centric LLM application.

The text-extract-api is a powerful tool that allows users to convert images, PDFs, or Office documents to Markdown text or JSON structured documents with high accuracy. It is built using FastAPI and utilizes Celery for asynchronous task processing, with Redis for caching OCR results. The tool provides features such as PDF/Office to Markdown and JSON conversion, improving OCR results with LLama, removing Personally Identifiable Information from documents, distributed queue processing, caching using Redis, switchable storage strategies, and a CLI tool for task management. Users can run the tool locally or on cloud services, with support for GPU processing. The tool also offers an online demo for testing purposes.

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.

RAID is the largest and most comprehensive dataset for evaluating AI-generated text detectors. It contains over 10 million documents spanning 11 LLMs, 11 genres, 4 decoding strategies, and 12 adversarial attacks. RAID is designed to be the go-to location for trustworthy third-party evaluation of popular detectors. The dataset covers diverse models, domains, sampling strategies, and attacks, making it a valuable resource for training detectors, evaluating generalization, protecting against adversaries, and comparing to state-of-the-art models from academia and industry.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval. The repo includes sample data so it's ready to try end to end. In this sample application we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions and roles.

JamAI Base is an open-source platform integrating SQLite and LanceDB databases with managed memory and RAG capabilities. It offers built-in LLM, vector embeddings, and reranker orchestration accessible through a spreadsheet-like UI and REST API. Users can transform static tables into dynamic entities, facilitate real-time interactions, manage structured data, and simplify chatbot development. The tool focuses on ease of use, scalability, flexibility, declarative paradigm, and innovative RAG techniques, making complex data operations accessible to users with varying technical expertise.

Pixeltable is a Python library designed for ML Engineers and Data Scientists to focus on exploration, modeling, and app development without the need to handle data plumbing. It provides a declarative interface for working with text, images, embeddings, and video, enabling users to store, transform, index, and iterate on data within a single table interface. Pixeltable is persistent, acting as a database unlike in-memory Python libraries such as Pandas. It offers features like data storage and versioning, combined data and model lineage, indexing, orchestration of multimodal workloads, incremental updates, and automatic production-ready code generation. The tool emphasizes transparency, reproducibility, cost-saving through incremental data changes, and seamless integration with existing Python code and libraries.

Build your dream team with Autogen is a repository that leverages Microsoft Autogen 0.4, Azure OpenAI, and Streamlit to create an end-to-end multi-agent application. It provides an advanced multi-agent framework based on Magentic One, with features such as a friendly UI, single-line deployment, secure code execution, managed identities, and observability & debugging tools. Users can deploy Azure resources and the app with simple commands, work locally with virtual environments, install dependencies, update configurations, and run the application. The repository also offers resources for learning more about building applications with Autogen.

Cache-Augmented Generation (CAG) is an alternative paradigm to Retrieval-Augmented Generation (RAG) that eliminates real-time retrieval delays and errors by preloading all relevant resources into the model's context. CAG leverages extended context windows of large language models (LLMs) to generate responses directly, providing reduced latency, improved reliability, and simplified design. While CAG has limitations in knowledge size and context length, advancements in LLMs are addressing these issues, making CAG a practical and scalable alternative for complex applications.

HybridAGI is the first Programmable LLM-based Autonomous Agent that lets you program its behavior using a **graph-based prompt programming** approach. This state-of-the-art feature allows the AGI to efficiently use any tool while controlling the long-term behavior of the agent. Become the _first Prompt Programmers in history_ ; be a part of the AI revolution one node at a time! **Disclaimer: We are currently in the process of upgrading the codebase to integrate DSPy**

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval.

GPTel is a simple Large Language Model chat client for Emacs, with support for multiple models and backends. It's async and fast, streams responses, and interacts with LLMs from anywhere in Emacs. LLM responses are in Markdown or Org markup. Supports conversations and multiple independent sessions. Chats can be saved as regular Markdown/Org/Text files and resumed later. You can go back and edit your previous prompts or LLM responses when continuing a conversation. These will be fed back to the model. Don't like gptel's workflow? Use it to create your own for any supported model/backend with a simple API.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

The Artificial Intelligence Incident Database (AIID) is a collection of incidents involving the development and use of artificial intelligence (AI). The database is designed to help researchers, policymakers, and the public understand the potential risks and benefits of AI, and to inform the development of policies and practices to mitigate the risks and promote the benefits of AI. The AIID is a collaborative project involving researchers from the University of California, Berkeley, the University of Washington, and the University of Toronto.

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.

Just-Chat is a containerized application that allows users to easily set up and chat with their AI agent. Users can customize their AI assistant using a YAML file, add new capabilities with Python tools, and interact with the agent through a chat web interface. The tool supports various modern models like DeepSeek Reasoner, ChatGPT, LLAMA3.3, etc. Users can also use semantic search capabilities with MeiliSearch to find and reference relevant information based on meaning. Just-Chat requires Docker or Podman for operation and provides detailed installation instructions for both Linux and Windows users.

KG-RAG (Knowledge Graph-based Retrieval Augmented Generation) is a task agnostic framework that combines the explicit knowledge of a Knowledge Graph (KG) with the implicit knowledge of a Large Language Model (LLM). KG-RAG extracts "prompt-aware context" from a KG, which is defined as the minimal context sufficient enough to respond to the user prompt. This framework empowers a general-purpose LLM by incorporating an optimized domain-specific 'prompt-aware context' from a biomedical KG. KG-RAG is specifically designed for running prompts related to Diseases.

Banks is a linguist professor tool that helps generate meaningful LLM prompts using a template language. It provides a user-friendly way to create prompts for various tasks such as blog writing, summarizing documents, lemmatizing text, and generating text using a LLM. The tool supports async operations and comes with predefined filters for data processing. Banks leverages Jinja's macro system to create prompts and interact with OpenAI API for text generation. It also offers a cache mechanism to avoid regenerating text for the same template and context.

This repository contains documentation for the AI on the Edge Device Project. Users can edit Markdown documents in the 'docs' folder, create Pull Requests to merge changes, and Github Actions will regenerate the documentation on the 'gh-pages' branch. The documentation includes parameter documentation, template generation for new parameters, formatting options like boxes using the admonition extension, and local testing instructions using MkDocs.

Expert in populating Word .docx forms with data from other documents, prioritizing accuracy and formal communication.

Open Domain Relation Extractor

The future of business education: Generate and test ideas in a complex global market simulation, populated by autonomous agents. Powered by the MANNS engine for unparalleled entity autonomy and simulated market forces