AIProxyBootstrap is a collection of starter apps designed to help users build their own experiences using AIProxy. The sample apps are categorized by services such as OpenAI, Anthropic, etc. Each app provides a template for users to add their AIProxy constants and implements API calls using AIProxySwift. Users can follow the provided instructions to customize the apps for their needs and interact with the AIProxy backend through the iOS simulator.

Local Multimodal AI Chat is a multimodal chat application that integrates various AI models to manage audio, images, and PDFs seamlessly within a single interface. It offers local model processing with Ollama for data privacy, integration with OpenAI API for broader AI capabilities, audio chatting with Whisper AI for accurate voice interpretation, and PDF chatting with Chroma DB for efficient PDF interactions. The application is designed for AI enthusiasts and developers seeking a comprehensive solution for multimodal AI technologies.

mikupad is a lightweight and efficient language model front-end powered by ReactJS, all packed into a single HTML file. Inspired by the likes of NovelAI, it provides a simple yet powerful interface for generating text with the help of various backends.

Ollama Colab Integration V4 is a tool designed to enhance the interaction and management of large language models. It allows users to quantize models within their notebook environment, access a variety of models through a user-friendly interface, and manage public endpoints efficiently. The tool also provides features like LiteLLM proxy control, model insights, and customizable model file templating. Users can troubleshoot model loading issues, CPU fallback strategies, and manage VRAM and RAM effectively. Additionally, the tool offers functionalities for downloading model files from Hugging Face, model conversion with high precision, model quantization using Q and Kquants, and securely uploading converted models to Hugging Face.

The AI Dev Gallery is an app designed to help Windows developers integrate AI capabilities within their own apps and projects. It contains over 25 interactive samples powered by local AI models, allows users to explore, download, and run models from Hugging Face and GitHub, and provides the ability to view the C# source code and export a standalone Visual Studio project for each sample. The app is open-source and welcomes contributions and suggestions from the community.

**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.

CodebaseToPrompt is a simple tool that converts a local directory into a structured prompt for Large Language Models (LLMs). It allows users to select specific files for code review, analysis, or documentation by exploring and filtering through the file tree in a browser-based interface. The tool generates a formatted output that can be directly used with AI tools, provides token count estimates, and supports local storage for saving selections. Users can easily copy the selected files in the desired format for further use.

The AI Workshop repository provides a comprehensive guide to utilizing OpenAI's APIs, including Chat Completion, Embedding, and Assistant APIs. It offers hands-on demonstrations and code examples to help users understand the capabilities of these APIs. The workshop covers topics such as creating interactive chatbots, performing semantic search using text embeddings, and building custom assistants with specific data and context. Users can enhance their understanding of AI applications in education, research, and other domains through practical examples and usage notes.

Yank Note is a highly extensible Markdown editor designed for productivity. It offers features like easy-to-use interface, powerful support for version control and various embedded content, high compatibility with local Markdown files, plug-in extension support, and encryption for saving private files. Users can write their own plug-ins to expand the editor's functionality. However, for more extendability, security protection is sacrificed. The tool supports sync scrolling, outline navigation, version control, encryption, auto-save, editing assistance, image pasting, attachment embedding, code running, to-do list management, quick file opening, integrated terminal, Katex expression, GitHub-style Markdown, multiple data locations, external link conversion, HTML resolving, multiple formats export, TOC generation, table cell editing, title link copying, embedded applets, various graphics embedding, mind map display, custom container support, macro replacement, image hosting service, OpenAI auto completion, and custom plug-ins development.

Writing Tools is an Apple Intelligence-inspired application for Windows, Linux, and macOS that supercharges your writing with an AI LLM. It allows users to instantly proofread, optimize text, and summarize content from webpages, YouTube videos, documents, etc. The tool is privacy-focused, open-source, and supports multiple languages. It offers powerful features like grammar correction, content summarization, and LLM chat mode, making it a versatile writing assistant for various tasks.

Hexabot Community Edition is an open-source chatbot solution designed for flexibility and customization, offering powerful text-to-action capabilities. It allows users to create and manage AI-powered, multi-channel, and multilingual chatbots with ease. The platform features an analytics dashboard, multi-channel support, visual editor, plugin system, NLP/NLU management, multi-lingual support, CMS integration, user roles & permissions, contextual data, subscribers & labels, and inbox & handover functionalities. The directory structure includes frontend, API, widget, NLU, and docker components. Prerequisites for running Hexabot include Docker and Node.js. The installation process involves cloning the repository, setting up the environment, and running the application. Users can access the UI admin panel and live chat widget for interaction. Various commands are available for managing the Docker services. Detailed documentation and contribution guidelines are provided for users interested in contributing to the project.

ROSGPT_Vision is a new robotic framework designed to command robots using only two prompts: a Visual Prompt for visual semantic features and an LLM Prompt to regulate robotic reactions. It is based on the Prompting Robotic Modalities (PRM) design pattern and is used to develop CarMate, a robotic application for monitoring driver distractions and providing real-time vocal notifications. The framework leverages state-of-the-art language models to facilitate advanced reasoning about image data and offers a unified platform for robots to perceive, interpret, and interact with visual data through natural language. LangChain is used for easy customization of prompts, and the implementation includes the CarMate application for driver monitoring and assistance.

Local Multimodal AI Chat is a hands-on project that teaches you how to build a multimodal chat application. It integrates different AI models to handle audio, images, and PDFs in a single chat interface. This project is perfect for anyone interested in AI and software development who wants to gain practical experience with these technologies.

Burpference is an open-source extension designed to capture in-scope HTTP requests and responses from Burp's proxy history and send them to a remote LLM API in JSON format. It automates response capture, integrates with APIs, optimizes resource usage, provides color-coded findings visualization, offers comprehensive logging, supports native Burp reporting, and allows flexible configuration. Users can customize system prompts, API keys, and remote hosts, and host models locally to prevent high inference costs. The tool is ideal for offensive web application engagements to surface findings and vulnerabilities.

AgentForge is a low-code framework tailored for the rapid development, testing, and iteration of AI-powered autonomous agents and Cognitive Architectures. It is compatible with a range of LLM models and offers flexibility to run different models for different agents based on specific needs. The framework is designed for seamless extensibility and database-flexibility, making it an ideal playground for various AI projects. AgentForge is a beta-testing ground and future-proof hub for crafting intelligent, model-agnostic autonomous agents.

The AI Agents Contributions Library is a repository dedicated to managing datasets on voice and cognitive core data for AI agents within the Virtual DAO ecosystem. It provides a structured framework for recording, reviewing, and rewarding contributions from contributors. The repository includes folders for character cards, contribution datasets, fine-tuning resources, text datasets, and voice datasets. Contributors can submit datasets following specific guidelines and formats, and the Virtual DAO team reviews and integrates approved datasets to enhance AI agents' capabilities.

AIProxyBootstrap is a collection of starter apps designed to help users build their own experiences using AIProxy. The sample apps are categorized by services such as OpenAI, Anthropic, etc. Each app provides a template for users to add their AIProxy constants and implements API calls using AIProxySwift. Users can follow the provided instructions to customize the apps for their needs and interact with the AIProxy backend through the iOS simulator.

LocalAI is a free and open-source OpenAI alternative that acts as a drop-in replacement REST API compatible with OpenAI (Elevenlabs, Anthropic, etc.) API specifications for local AI inferencing. It allows users to run LLMs, generate images, audio, and more locally or on-premises with consumer-grade hardware, supporting multiple model families and not requiring a GPU. LocalAI offers features such as text generation with GPTs, text-to-audio, audio-to-text transcription, image generation with stable diffusion, OpenAI functions, embeddings generation for vector databases, constrained grammars, downloading models directly from Huggingface, and a Vision API. It provides a detailed step-by-step introduction in its Getting Started guide and supports community integrations such as custom containers, WebUIs, model galleries, and various bots for Discord, Slack, and Telegram. LocalAI also offers resources like an LLM fine-tuning guide, instructions for local building and Kubernetes installation, projects integrating LocalAI, and a how-tos section curated by the community. It encourages users to cite the repository when utilizing it in downstream projects and acknowledges the contributions of various software from the community.

Local Multimodal AI Chat is a hands-on project that teaches you how to build a multimodal chat application. It integrates different AI models to handle audio, images, and PDFs in a single chat interface. This project is perfect for anyone interested in AI and software development who wants to gain practical experience with these technologies.

OpenAI for Workers AI is a simple, quick, and dirty implementation of OpenAI's API on Cloudflare's new Workers AI platform. It allows developers to use the OpenAI SDKs with the new LLMs without having to rewrite all of their code. The API currently supports completions, chat completions, audio transcription, embeddings, audio translation, and image generation. It is not production ready but will be semi-regularly updated with new features as they roll out to Workers AI.

Use the OpenAI API with Ruby! 🤖🩵 Stream text with GPT-4, transcribe and translate audio with Whisper, or create images with DALL·E... Hire me | 🎮 Ruby AI Builders Discord | 🐦 Twitter | 🧠 Anthropic Gem | 🚂 Midjourney Gem ## Table of Contents * Ruby OpenAI * Table of Contents * Installation * Bundler * Gem install * Usage * Quickstart * With Config * Custom timeout or base URI * Extra Headers per Client * Logging * Errors * Faraday middleware * Azure * Ollama * Counting Tokens * Models * Examples * Chat * Streaming Chat * Vision * JSON Mode * Functions * Edits * Embeddings * Batches * Files * Finetunes * Assistants * Threads and Messages * Runs * Runs involving function tools * Image Generation * DALL·E 2 * DALL·E 3 * Image Edit * Image Variations * Moderations * Whisper * Translate * Transcribe * Speech * Errors * Development * Release * Contributing * License * Code of Conduct

Deepgram JavaScript SDK. Power your apps with world-class speech and Language AI models.

Whisper-WebUI is a Gradio-based browser interface for Whisper, serving as an Easy Subtitle Generator. It supports generating subtitles from various sources such as files, YouTube, and microphone. The tool also offers speech-to-text and text-to-text translation features, utilizing Facebook NLLB models and DeepL API. Users can translate subtitle files from other languages to English and vice versa. The project integrates faster-whisper for improved VRAM usage and transcription speed, providing efficiency metrics for optimized whisper models. Additionally, users can choose from different Whisper models based on size and language requirements.

Edgen is a local GenAI API server that serves as a drop-in replacement for OpenAI's API. It provides multi-endpoint support for chat completions and speech-to-text, is model agnostic, offers optimized inference, and features model caching. Built in Rust, Edgen is natively compiled for Windows, MacOS, and Linux, eliminating the need for Docker. It allows users to utilize GenAI locally on their devices for free and with data privacy. With features like session caching, GPU support, and support for various endpoints, Edgen offers a scalable, reliable, and cost-effective solution for running GenAI applications locally.

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.