This repository contains the source code for the Apache Airflow website, including directories for archived documentation versions, landing pages, license templates, and the Sphinx theme. To work on the site locally, users need to install coreutils, Node.js, NPM, and HUGO, and run specific scripts provided in the repository. Contributors can refer to the contributor's guide for detailed instructions on how to contribute to the website.

Lumigator is an open-source platform developed by Mozilla.ai to help users select the most suitable language model for their specific needs. It supports the evaluation of summarization tasks using sequence-to-sequence models such as BART and BERT, as well as causal models like GPT and Mistral. The platform aims to make model selection transparent, efficient, and empowering by providing a framework for comparing LLMs using task-specific metrics to evaluate how well a model fits a project's needs. Lumigator is in the early stages of development and plans to expand support to additional machine learning tasks and use cases in the future.

Browser Copilot is a browser extension that enables users to utilize AI assistants for various web application tasks. It provides a versatile UI and framework to implement copilots that can automate tasks, extract information, interact with web applications, and utilize service APIs. Users can easily install copilots, start chats, save prompts, and toggle the copilot on or off. The project also includes a sample copilot implementation for testing purposes and encourages community contributions to expand the catalog of copilots.

Atomic Agents is a modular and extensible framework designed for creating powerful applications. It follows the principles of Atomic Design, emphasizing small and single-purpose components. Leveraging Pydantic for data validation and serialization, the framework offers a set of tools and agents that can be combined to build AI applications. It depends on the Instructor package and supports various APIs like OpenAI, Cohere, Anthropic, and Gemini. Atomic Agents is suitable for developers looking to create AI agents with a focus on modularity and flexibility.

NeoHaskell is a newcomer-friendly and productive dialect of Haskell. It aims to be easy to learn and use, while also powerful enough for app development with minimal effort and maximum confidence. The project prioritizes design and documentation before implementation, with ongoing work on design documents for community sharing.

Cohere's Build-Your-Own-Connector framework allows integration of Cohere's Command LLM via the Chat API endpoint to any datastore/software holding text information with a search endpoint. Enables user queries grounded in proprietary information. Use-cases include question/answering, knowledge working, comms summary, and research. Repository provides code for popular datastores and a template connector. Requires Python 3.11+ and Poetry. Connectors can be built and deployed using Docker. Environment variables set authorization values. Pre-commits for linting. Connectors tailored to integrate with Cohere's Chat API for creating chatbots. Connectors return documents as JSON objects for Cohere's API to generate answers with citations.

MD-Agent is a LLM-agent based toolset for Molecular Dynamics. It uses Langchain and a collection of tools to set up and execute molecular dynamics simulations, particularly in OpenMM. The tool assists in environment setup, installation, and usage by providing detailed steps. It also requires API keys for certain functionalities, such as OpenAI and paper-qa for literature searches. Contributions to the project are welcome, with a detailed Contributor's Guide available for interested individuals.

PythonDataScienceFullThrottle is a comprehensive repository containing various Python scripts, libraries, and tools for data science enthusiasts. It includes a wide range of functionalities such as data preprocessing, visualization, machine learning algorithms, and statistical analysis. The repository aims to provide a one-stop solution for individuals looking to dive deep into the world of data science using Python.

Godot RL Agents is an open-source package that facilitates the integration of Machine Learning algorithms with games created in the Godot Engine. It provides interfaces for popular RL frameworks, support for memory-based agents, 2D and 3D games, AI sensors, and is licensed under MIT. Users can train agents in the Godot editor, create custom environments, export trained agents in ONNX format, and utilize advanced features like different RL training frameworks.

Serena is a powerful coding agent that integrates with existing LLMs to provide essential semantic code retrieval and editing tools. It is free to use and does not require API keys or subscriptions. Serena can be used for coding tasks such as analyzing, planning, and editing code directly on your codebase. It supports various programming languages and offers semantic code analysis capabilities through language servers. Serena can be integrated with different LLMs using the model context protocol (MCP) or Agno framework. The tool provides a range of functionalities for code retrieval, editing, and execution, making it a versatile coding assistant for developers.

Dialog is an API-focused tool designed to simplify the deployment of Large Language Models (LLMs) for programmers interested in AI. It allows users to deploy any LLM based on the structure provided by dialog-lib, enabling them to spend less time coding and more time training their models. The tool aims to humanize Retrieval-Augmented Generative Models (RAGs) and offers features for better RAG deployment and maintenance. Dialog requires a knowledge base in CSV format and a prompt configuration in TOML format to function effectively. It provides functionalities for loading data into the database, processing conversations, and connecting to the LLM, with options to customize prompts and parameters. The tool also requires specific environment variables for setup and configuration.

This repository contains community-driven practical examples of building AI applications and solving various tasks with AI using open-source tools and models. Everyone is welcome to contribute, and we value everybody's contribution! There are several ways you can contribute to the Open-Source AI Cookbook: Submit an idea for a desired example/guide via GitHub Issues. Contribute a new notebook with a practical example. Improve existing examples by fixing issues/typos. Before contributing, check currently open issues and pull requests to avoid working on something that someone else is already working on.

Dot is a standalone, open-source application designed for seamless interaction with documents and files using local LLMs and Retrieval Augmented Generation (RAG). It is inspired by solutions like Nvidia's Chat with RTX, providing a user-friendly interface for those without a programming background. Pre-packaged with Mistral 7B, Dot ensures accessibility and simplicity right out of the box. Dot allows you to load multiple documents into an LLM and interact with them in a fully local environment. Supported document types include PDF, DOCX, PPTX, XLSX, and Markdown. Users can also engage with Big Dot for inquiries not directly related to their documents, similar to interacting with ChatGPT. Built with Electron JS, Dot encapsulates a comprehensive Python environment that includes all necessary libraries. The application leverages libraries such as FAISS for creating local vector stores, Langchain, llama.cpp & Huggingface for setting up conversation chains, and additional tools for document management and interaction.

Airbroke is an open-source error catcher tool designed for modern web applications. It provides a PostgreSQL-based backend with an Airbrake-compatible HTTP collector endpoint and a React-based frontend for error management. The tool focuses on simplicity, maintaining a small database footprint even under heavy data ingestion. Users can ask AI about issues, replay HTTP exceptions, and save/manage bookmarks for important occurrences. Airbroke supports multiple OAuth providers for secure user authentication and offers occurrence charts for better insights into error occurrences. The tool can be deployed in various ways, including building from source, using Docker images, deploying on Vercel, Render.com, Kubernetes with Helm, or Docker Compose. It requires Node.js, PostgreSQL, and specific system resources for deployment.

The ChatGPT Telegram Bot is a powerful Telegram bot that utilizes various GPT models, including GPT3.5, GPT4, GPT4 Turbo, GPT4 Vision, DALL·E 3, Groq Mixtral-8x7b/LLaMA2-70b, and Claude2.1/Claude3 opus/sonnet API. It enables users to engage in efficient conversations and information searches on Telegram. The bot supports multiple AI models, online search with DuckDuckGo and Google, user-friendly interface, efficient message processing, document interaction, Markdown rendering, and convenient deployment options like Zeabur, Replit, and Docker. Users can set environment variables for configuration and deployment. The bot also provides Q&A functionality, supports model switching, and can be deployed in group chats with whitelisting. The project is open source under GPLv3 license.

xef.ai is a one-stop library designed to bring the power of modern AI to applications and services. It offers integration with Large Language Models (LLM), image generation, and other AI services. The library is packaged in two layers: core libraries for basic AI services integration and integrations with other libraries. xef.ai aims to simplify the transition to modern AI for developers by providing an idiomatic interface, currently supporting Kotlin. Inspired by LangChain and Hugging Face, xef.ai may transmit source code and user input data to third-party services, so users should review privacy policies and take precautions. Libraries are available in Maven Central under the `com.xebia` group, with `xef-core` as the core library. Developers can add these libraries to their projects and explore examples to understand usage.

This repository contains the source code for the Apache Airflow website, including directories for archived documentation versions, landing pages, license templates, and the Sphinx theme. To work on the site locally, users need to install coreutils, Node.js, NPM, and HUGO, and run specific scripts provided in the repository. Contributors can refer to the contributor's guide for detailed instructions on how to contribute to the website.

Open AI Cellular is the core software for Open AI Cellular. It provides documentation on installation, quick start guide, and usage. The repository contains submodules and requires sphinx with the read-the-docs theme for building core documentation. The resulting documentation is stored in the 'docs/build/html' directory.

DocsGPT is an open-source documentation assistant powered by GPT models. It simplifies the process of searching for information in project documentation by allowing developers to ask questions and receive accurate answers. With DocsGPT, users can say goodbye to manual searches and quickly find the information they need. The tool aims to revolutionize project documentation experiences and offers features like live previews, Discord community, guides, and contribution opportunities. It consists of a Flask app, Chrome extension, similarity search index creation script, and a frontend built with Vite and React. Users can quickly get started with DocsGPT by following the provided setup instructions and can contribute to its development by following the guidelines in the CONTRIBUTING.md file. The project follows a Code of Conduct to ensure a harassment-free community environment for all participants. DocsGPT is licensed under MIT and is built with LangChain.

This repository contains the source code for the Apache Airflow website, including directories for archived documentation versions, landing pages, license templates, and the Sphinx theme. To work on the site locally, users need to install coreutils, Node.js, NPM, and HUGO, and run specific scripts provided in the repository. Contributors can refer to the contributor's guide for detailed instructions on how to contribute to the website.

Lumentis is a tool that allows users to generate beautiful and comprehensive documentation from meeting transcripts and large documents with a single command. It reads transcripts, asks questions to understand themes and audience, generates an outline, and creates detailed pages with visual variety and styles. Users can switch models for different tasks, control the process, and deploy the generated docs to Vercel. The tool is designed to be open, clean, fast, and easy to use, with upcoming features including folders, PDFs, auto-transcription, website scraping, scientific papers handling, summarization, and continuous updates.

Dify Docs is a repository that houses the documentation website code and Markdown source files for docs.dify.ai. It contains assets, content, and data folders that are licensed under a CC-BY license.

Code2Prompt is a powerful command-line tool that generates comprehensive prompts from codebases, designed to streamline interactions between developers and Large Language Models (LLMs) for code analysis, documentation, and improvement tasks. It bridges the gap between codebases and LLMs by converting projects into AI-friendly prompts, enabling users to leverage AI for various software development tasks. The tool offers features like holistic codebase representation, intelligent source tree generation, customizable prompt templates, smart token management, Gitignore integration, flexible file handling, clipboard-ready output, multiple output options, and enhanced code readability.

The Microsoft Semantic Kernel Documentation GitHub repository contains technical product documentation for Semantic Kernel. It serves as the home of technical content for Microsoft products and services. Contributors can learn how to make contributions by following the Docs contributor guide. The project follows the Microsoft Open Source Code of Conduct.

anythingllm-docs is a documentation repository for the AnythingLLM project. It contains detailed guides, setup instructions, and information on features and legal aspects of the project. The repository structure is organized into public, pages, components, and configuration files. Users can contribute by creating issues and pull requests following specific guidelines. The project is licensed under the MIT License and has been migrated to NextJS with the help of @ShadowArcanist.

RepoAgent is an LLM-powered framework designed for repository-level code documentation generation. It automates the process of detecting changes in Git repositories, analyzing code structure through AST, identifying inter-object relationships, replacing Markdown content, and executing multi-threaded operations. The tool aims to assist developers in understanding and maintaining codebases by providing comprehensive documentation, ultimately improving efficiency and saving time.