Accelerated Intelligent Document Processing on AWS is a scalable, serverless solution for automated document processing and information extraction using AWS services. It combines OCR capabilities with generative AI to convert unstructured documents into structured data at scale. The solution features a serverless architecture built on AWS technologies, modular processing patterns, advanced classification support, few-shot example support, custom business logic integration, high throughput processing, built-in resilience, cost optimization, comprehensive monitoring, web user interface, human-in-the-loop integration, AI-powered evaluation, extraction confidence assessment, and document knowledge base query. The architecture uses nested CloudFormation stacks to support multiple document processing patterns while maintaining common infrastructure for queueing, tracking, and monitoring.

Paperless-AI is an automated document analyzer tool designed for Paperless-ngx users. It utilizes the OpenAI API and Ollama (Mistral, llama, phi 3, gemma 2) to automatically scan, analyze, and tag documents. The tool offers features such as automatic document scanning, AI-powered document analysis, automatic title and tag assignment, manual mode for analyzing documents, easy setup through a web interface, document processing dashboard, error handling, and Docker support. Users can configure the tool through a web interface and access a debug interface for monitoring and troubleshooting. Paperless-AI aims to streamline document organization and analysis processes for users with access to Paperless-ngx and AI capabilities.

Instill Core is an open-source orchestrator comprising a collection of source-available projects designed to streamline every aspect of building versatile AI features with unstructured data. It includes Instill VDP (Versatile Data Pipeline) for unstructured data, AI, and pipeline orchestration, Instill Model for scalable MLOps and LLMOps for open-source or custom AI models, and Instill Artifact for unified unstructured data management. Instill Core can be used for tasks such as building, testing, and sharing pipelines, importing, serving, fine-tuning, and monitoring ML models, and transforming documents, images, audio, and video into a unified AI-ready format.

CentralMind Gateway is an AI-first data gateway that securely connects any data source and automatically generates secure, LLM-optimized APIs. It filters out sensitive data, adds traceability, and optimizes for AI workloads. Suitable for companies deploying AI agents for customer support and analytics.

Coding-Aider is a plugin for IntelliJ IDEA that seamlessly integrates Aider's AI-powered coding assistance into the IDE. It boosts productivity by offering rapid access for precision code generation and refactoring, with complete control over the context utilized by the LLM. The plugin provides various features such as AI-powered coding assistance, intuitive access through keyboard shortcuts, persistent file management, dual execution modes, Git integration, real-time progress tracking, multi-file support, web crawling, clipboard image support, and various specialized actions. It also supports structured mode and plans for managing complex features, working directory support, summarized output, and the ability to specify additional arguments for Aider commands. Coding-Aider addresses limitations in existing IntelliJ plugins by offering optimized token usage, a feature-rich terminal interface, a wide range of commands, and robust recovery mechanisms with seamless Git integration.

Cherry Studio is a desktop client that supports multiple LLM providers on Windows, Mac, and Linux. It offers diverse LLM provider support, AI assistants & conversations, document & data processing, practical tools integration, and enhanced user experience. The tool includes features like support for major LLM cloud services, AI web service integration, local model support, pre-configured AI assistants, document processing for text, images, and more, global search functionality, topic management system, AI-powered translation, and cross-platform support with ready-to-use features and themes for a better user experience.

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.

Shandu is an advanced AI research system that automates comprehensive research processes using language models, web scraping, and iterative exploration to generate well-structured reports with citations. It features intelligent state-based workflow, deep exploration, multi-source information synthesis, enhanced web scraping, smart source evaluation, content analysis pipeline, comprehensive report generation, parallel processing, adaptive search strategy, and full citation management.

Replexica is an i18n toolkit for React, to ship multi-language apps fast. It doesn't require extracting text into JSON files, and uses AI-powered API for content processing. It comes in two parts: 1. Replexica Compiler - an open-source compiler plugin for React; 2. Replexica API - an i18n API in the cloud that performs translations using LLMs. (Usage based, has a free tier.) Replexica supports several i18n formats: 1. JSON-free Replexica compiler format; 2. .md files for Markdown content; 3. Legacy JSON and YAML-based formats.

This repository provides sample applications and tutorials to showcase the power of Amazon Bedrock with Python. It helps Python developers understand how to harness Amazon Bedrock in building generative AI-enabled applications. The resources also demonstrate integration with vector databases using RAG (Retrieval-augmented generation) and services like Amazon Aurora, RDS, and OpenSearch. Additionally, it explores using langchain and streamlit to create effective experimental applications.

JLB AI Agent is an innovative solution on the Solana blockchain that leverages artificial intelligence to automate complex tasks and enhance decision-making in the DeFi space. It offers real-time analytics, efficient operations, and seamless integration for both newcomers and experienced crypto enthusiasts. With features like autonomous trading, NFT management, DeFi insights, and comprehensive ecosystem integration, JLB empowers users with cutting-edge technology to navigate the dynamic landscape of blockchain.

Awesome-AI-Coding is a curated list of AI coding topics, projects, datasets, LLM models, embedding models, papers, blogs, products, startups, and peer awesome lists related to artificial intelligence in coding. It includes tools for code completion, code generation, code documentation, and code search, as well as AI models and techniques for improving developer productivity. The repository also features information on various AI-powered developer tools, copilots, and related resources in the AI coding domain.

AIBrix is an open-source initiative providing essential building blocks for scalable GenAI inference infrastructure. It delivers a cloud-native solution optimized for deploying, managing, and scaling large language model (LLM) inference, tailored to enterprise needs. Key features include High-Density LoRA Management, LLM Gateway and Routing, LLM App-Tailored Autoscaler, Unified AI Runtime, Distributed Inference, Distributed KV Cache, Cost-efficient Heterogeneous Serving, and GPU Hardware Failure Detection.

DataHub is an open-source data catalog designed for the modern data stack. It provides a platform for managing metadata, enabling users to discover, understand, and collaborate on data assets within their organization. DataHub offers features such as data lineage tracking, data quality monitoring, and integration with various data sources. It is built with contributions from Acryl Data and LinkedIn, aiming to streamline data management processes and enhance data discoverability across different teams and departments.

Chat Nio is a next-generation AIGC one-stop business solution that combines the advantages of frontend-oriented lightweight deployment projects with powerful API distribution systems. It offers rich model support, beautiful UI design, complete Markdown support, multi-theme support, internationalization support, text-to-image support, powerful conversation sync, model market & preset system, rich file parsing, full model internet search, Progressive Web App (PWA) support, comprehensive backend management, multiple billing methods, innovative model caching, and additional features. The project aims to address limitations in conversation synchronization, billing, file parsing, conversation URL sharing, channel management, and API call support found in existing AIGC commercial sites, while also providing a user-friendly interface design and C-end features.

JLB AI Agent is an innovative solution built on the Solana blockchain that harnesses the power of artificial intelligence to automate complex tasks and optimize decision-making in the DeFi space. It aims to provide real-time analytics, efficient operations, and seamless integration for both newcomers and experienced crypto enthusiasts. The tool offers features like blockchain agent chat terminal, real-time streaming implementation, trading infrastructure, NFT management, AI integration, and more, empowering users with autonomous technology where AI meets the dynamic landscape of blockchain.

**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.

spaCy is an industrial-strength Natural Language Processing (NLP) library in Python and Cython. It incorporates the latest research and is designed for real-world applications. The library offers pretrained pipelines supporting 70+ languages, with advanced neural network models for tasks such as tagging, parsing, named entity recognition, and text classification. It also facilitates multi-task learning with pretrained transformers like BERT, along with a production-ready training system and streamlined model packaging, deployment, and workflow management. spaCy is commercial open-source software released under the MIT license.

The Google Cloud Document AI Samples repository contains code samples and Community Samples demonstrating how to analyze, classify, and search documents using Google Cloud Document AI. It includes various projects showcasing different functionalities such as integrating with Google Drive, processing documents using Python, content moderation with Dialogflow CX, fraud detection, language extraction, paper summarization, tax processing pipeline, and more. The repository also provides access to test document files stored in a publicly-accessible Google Cloud Storage Bucket. Additionally, there are codelabs available for optical character recognition (OCR), form parsing, specialized processors, and managing Document AI processors. Community samples, like the PDF Annotator Sample, are also included. Contributions are welcome, and users can seek help or report issues through the repository's issues page. Please note that this repository is not an officially supported Google product and is intended for demonstrative purposes only.

This repository contains lab files for Azure AI Language modules. It provides hands-on exercises and resources for learning about various AI language technologies on the Azure platform. The labs cover topics such as natural language processing, text analytics, language understanding, and more. By following the exercises in this repository, users can gain practical experience in implementing AI language solutions using Azure services.

The binary-mlc-llm-libs repository contains model libraries stored in a specific format. The file names include metadata such as context window size, sliding window size, and prefill chunk size. Default configurations are provided for some models, with certain metadata values omitted if they are the same as default choices. Users can access various pre-trained language models for different tasks using this repository.

This repository provides a comprehensive guide to Natural Language Processing (NLP), covering topics from Tokenization to Transformer Architecture. It aims to equip users with a solid understanding of NLP concepts, evolution, and core intuition. The repository includes practical examples and hands-on experience to facilitate learning and exploration in the field of NLP.

This repository contains a collection of code samples that demonstrate how to use various Azure AI capabilities to process documents. The samples help engineering teams establish techniques with Azure AI Foundry, Azure OpenAI, Azure AI Document Intelligence, and Azure AI Language services to build solutions for extracting structured data, classifying, and analyzing documents. The techniques simplify custom model training, improve reliability in document processing, and simplify document processing workflows by providing reusable code and patterns that can be easily modified and evaluated for most use cases.

Accelerated Intelligent Document Processing on AWS is a scalable, serverless solution for automated document processing and information extraction using AWS services. It combines OCR capabilities with generative AI to convert unstructured documents into structured data at scale. The solution features a serverless architecture built on AWS technologies, modular processing patterns, advanced classification support, few-shot example support, custom business logic integration, high throughput processing, built-in resilience, cost optimization, comprehensive monitoring, web user interface, human-in-the-loop integration, AI-powered evaluation, extraction confidence assessment, and document knowledge base query. The architecture uses nested CloudFormation stacks to support multiple document processing patterns while maintaining common infrastructure for queueing, tracking, and monitoring.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.