This repository, llama-index, contains a collection of apps powered by LlamaIndex. LlamaIndex is an open-source project that provides a simple interface between LLMs and external data sources like APIs, PDFs, SQL etc. It provides indices over structured and unstructured data, helping to abstract away the differences across data sources. The repository includes apps like chat-with-pdf and summarize-url, showcasing the capabilities of LlamaIndex in interacting with PDFs and summarizing URLs.

The `xaitk-saliency` package is an open source Explainable AI (XAI) framework for visual saliency algorithm interfaces and implementations, designed for analytics and autonomy applications. It provides saliency algorithms for various image understanding tasks such as image classification, image similarity, object detection, and reinforcement learning. The toolkit targets data scientists and developers who aim to incorporate visual saliency explanations into their workflow or product, offering both direct accessibility for experimentation and modular integration into systems and applications through Strategy and Adapter patterns. The package includes documentation, examples, and a demonstration tool for visual saliency generation in a user-interface.

This repository contains a collection of apps powered by LangChain, an open-source framework designed to aid the development of applications leveraging large language models (LLMs). It can be used for various tasks such as chatbots, text summarisation, data generation, code understanding, question answering, and evaluation. The repository showcases different applications built using LangChain and other tools like OpenAI, Chroma, Gemini, Helicone, Serper API, Pinecone, and Tavily Search API.

TiDB.AI is a conversational search RAG (Retrieval-Augmented Generation) app based on TiDB Serverless Vector Storage. It provides an out-of-the-box and embeddable QA robot experience based on knowledge from official and documentation sites. The platform features a Perplexity-style Conversational Search page with an advanced built-in website crawler for comprehensive coverage. Users can integrate an embeddable JavaScript snippet into their website for instant responses to product-related queries. The tech stack includes Next.js, TypeScript, Tailwind CSS, shadcn/ui for design, TiDB for database storage, Kysely for SQL query building, NextAuth.js for authentication, Vercel for deployments, and LlamaIndex for the RAG framework. TiDB.AI is open-source under the Apache License, Version 2.0.

OpenModelDB is a community-driven database of AI upscaling models, providing a centralized platform for users to access and compare various models. The repository contains a collection of models and model metadata, facilitating easy exploration and evaluation of different AI upscaling solutions. With a focus on enhancing the accessibility and usability of AI models, OpenModelDB aims to streamline the process of finding and selecting the most suitable models for specific tasks or projects.

CRAB is a framework for building LLM agent benchmark environments in a Python-centric way. It is cross-platform and multi-environment, allowing the creation of agent environments supporting various deployment options. The framework offers easy-to-use configuration with the ability to add new actions and define environments seamlessly. CRAB also provides a novel benchmarking suite with tasks and evaluators defined in Python, along with a unique graph evaluator method for detailed metrics.

formsflow.ai is a Free, Open-Source, Low Code Development Platform for rapidly building powerful business applications. It combines leading Open-Source applications including form.io forms, Camunda’s workflow engine, Keycloak’s security, and Redash’s data analytics into a seamless, integrated platform. Check out the installation documentation for installation instructions and features documentation to explore features and capabilities in detail.

LightLLM is a Python-based LLM (Large Language Model) inference and serving framework known for its lightweight design, scalability, and high-speed performance. It offers features like tri-process asynchronous collaboration, Nopad for efficient attention operations, dynamic batch scheduling, FlashAttention integration, tensor parallelism, Token Attention for zero memory waste, and Int8KV Cache. The tool supports various models like BLOOM, LLaMA, StarCoder, Qwen-7b, ChatGLM2-6b, Baichuan-7b, Baichuan2-7b, Baichuan2-13b, InternLM-7b, Yi-34b, Qwen-VL, Llava-7b, Mixtral, Stablelm, and MiniCPM. Users can deploy and query models using the provided server launch commands and interact with multimodal models like QWen-VL and Llava using specific queries and images.

PyScripter is a free and open-source Python Integrated Development Environment (IDE) aiming to compete with commercial Windows-based IDEs for other languages. It offers features like LLM-assisted coding and provides support for Python development projects. The tool is designed to enhance the coding experience for Python developers by providing a user-friendly interface and a range of functionalities to streamline the development process.

HAMi is a Heterogeneous AI Computing Virtualization Middleware designed to manage Heterogeneous AI Computing Devices in a Kubernetes cluster. It allows for device sharing, device memory control, device type specification, and device UUID specification. The tool is easy to use and does not require modifying task YAML files. It includes features like hard limits on device memory, partial device allocation, streaming multiprocessor limits, and core usage specification. HAMi consists of components like a mutating webhook, scheduler extender, device plugins, and in-container virtualization techniques. It is suitable for scenarios requiring device sharing, specific device memory allocation, GPU balancing, low utilization optimization, and scenarios needing multiple small GPUs. The tool requires prerequisites like NVIDIA drivers, CUDA version, nvidia-docker, Kubernetes version, glibc version, and helm. Users can install, upgrade, and uninstall HAMi, submit tasks, and monitor cluster information. The tool's roadmap includes supporting additional AI computing devices, video codec processing, and Multi-Instance GPUs (MIG).

The Fire-Flyer File System (3FS) is a high-performance distributed file system designed for AI training and inference workloads. It leverages modern SSDs and RDMA networks to provide a shared storage layer that simplifies development of distributed applications. Key features include performance, disaggregated architecture, strong consistency, file interfaces, data preparation, dataloaders, checkpointing, and KVCache for inference. The system is well-documented with design notes, setup guide, USRBIO API reference, and P specifications. Performance metrics include peak throughput, GraySort benchmark results, and KVCache optimization. The source code is available on GitHub for cloning and installation of dependencies. Users can build 3FS and run test clusters following the provided instructions. Issues can be reported on the GitHub repository.

Protofy is a full-stack, batteries-included low-code enabled web/app and IoT system with an API system and real-time messaging. It is based on Protofy (protoflow + visualui + protolib + protodevices) + Expo + Next.js + Tamagui + Solito + Express + Aedes + Redbird + Many other amazing packages. Protofy can be used to fast prototype Apps, webs, IoT systems, automations, or APIs. It is a ultra-extensible CMS with supercharged capabilities, mobile support, and IoT support (esp32 thanks to esphome).

AIQC is an open source Python package that provides a declarative API for end-to-end MLOps in order to make deep learning more accessible to researchers. It utilizes a SQLite object-relational model for machine learning objects and stacks standardized workflows for various analyses, data types, and libraries. The benefits include a 90% reduction in data wrangling, reproducibility, and no need to install and maintain application and database servers for experiment tracking. AIQC is pip-installable and provides a Dash-Plotly UI for real-time experiment tracking.

DotRecast is a C# port of Recast & Detour, a navigation library used in many AAA and indie games and engines. It provides automatic navmesh generation, fast turnaround times, detailed customization options, and is dependency-free. Recast Navigation is divided into multiple modules, each contained in its own folder: - DotRecast.Core: Core utils - DotRecast.Recast: Navmesh generation - DotRecast.Detour: Runtime loading of navmesh data, pathfinding, navmesh queries - DotRecast.Detour.TileCache: Navmesh streaming. Useful for large levels and open-world games - DotRecast.Detour.Crowd: Agent movement, collision avoidance, and crowd simulation - DotRecast.Detour.Dynamic: Robust support for dynamic nav meshes combining pre-built voxels with dynamic objects which can be freely added and removed - DotRecast.Detour.Extras: Simple tool to import navmeshes created with A* Pathfinding Project - DotRecast.Recast.Toolset: All modules - DotRecast.Recast.Demo: Standalone, comprehensive demo app showcasing all aspects of Recast & Detour's functionality - Tests: Unit tests Recast constructs a navmesh through a multi-step mesh rasterization process: 1. First Recast rasterizes the input triangle meshes into voxels. 2. Voxels in areas where agents would not be able to move are filtered and removed. 3. The walkable areas described by the voxel grid are then divided into sets of polygonal regions. 4. The navigation polygons are generated by re-triangulating the generated polygonal regions into a navmesh. You can use Recast to build a single navmesh, or a tiled navmesh. Single meshes are suitable for many simple, static cases and are easy to work with. Tiled navmeshes are more complex to work with but better support larger, more dynamic environments. Tiled meshes enable advanced Detour features like re-baking, hierarchical path-planning, and navmesh data-streaming.

Ollamac is a macOS app designed for interacting with Ollama models. It is optimized for macOS, allowing users to easily use any model from the Ollama library. The app features a user-friendly interface, chat archive for saving interactions, and real-time communication using HTTP streaming technology. Ollamac is open-source, enabling users to contribute to its development and enhance its capabilities. It requires macOS 14 or later and the Ollama system to be installed on the user's Mac with at least one Ollama model downloaded.

Onyx is an open-source Gen-AI and Enterprise Search tool that serves as an AI Assistant connected to company documents, apps, and people. It provides a chat interface, can be deployed anywhere, and offers features like user authentication, role management, chat persistence, and UI for configuring AI Assistants. Onyx acts as an Enterprise Search tool across various workplace platforms, enabling users to access team-specific knowledge and perform tasks like document search, AI answers for natural language queries, and integration with common workplace tools like Slack, Google Drive, Confluence, etc.

rag-chatbot is a tool that allows users to chat with multiple PDFs using Ollama and LlamaIndex. It provides an easy setup for running on local machines or Kaggle notebooks. Users can leverage models from Huggingface and Ollama, process multiple PDF inputs, and chat in multiple languages. The tool offers a simple UI with Gradio, supporting chat with history and QA modes. Setup instructions are provided for both Kaggle and local environments, including installation steps for Docker, Ollama, Ngrok, and the rag_chatbot package. Users can run the tool locally and access it via a web interface. Future enhancements include adding evaluation, better embedding models, knowledge graph support, improved document processing, MLX model integration, and Corrective RAG.

PDFToChat is a tool that allows users to chat with their PDF documents in seconds. It is powered by Together AI and Pinecone, utilizing a tech stack including Next.js, Mixtral, M2 Bert, LangChain.js, MongoDB Atlas, Bytescale, Vercel, Clerk, and Tailwind CSS. Users can deploy the tool to Vercel or any other host by setting up Together.ai, MongoDB Atlas database, Bytescale, Clerk, and Vercel. The tool enables users to interact with PDFs through chat, with future tasks including adding features like trash icon for deleting PDFs, exploring different embedding models, implementing auto scrolling, improving replies, benchmarking accuracy, researching chunking and retrieval best practices, adding demo video, upgrading to Next.js 14, adding analytics, customizing tailwind prose, saving chats in postgres DB, compressing large PDFs, implementing custom uploader, session tracking, error handling, and support for images in PDFs.

The 'END TO END GENERATIVE AI PROJECTS' repository is a collection of awesome industry projects utilizing Large Language Models (LLM) for various tasks such as chat applications with PDFs, image to speech generation, video transcribing and summarizing, resume tracking, text to SQL conversion, invoice extraction, medical chatbot, financial stock analysis, and more. The projects showcase the deployment of LLM models like Google Gemini Pro, HuggingFace Models, OpenAI GPT, and technologies such as Langchain, Streamlit, LLaMA2, LLaMAindex, and more. The repository aims to provide end-to-end solutions for different AI applications.

This repository, llama-index, contains a collection of apps powered by LlamaIndex. LlamaIndex is an open-source project that provides a simple interface between LLMs and external data sources like APIs, PDFs, SQL etc. It provides indices over structured and unstructured data, helping to abstract away the differences across data sources. The repository includes apps like chat-with-pdf and summarize-url, showcasing the capabilities of LlamaIndex in interacting with PDFs and summarizing URLs.

PapersGPT For Zotero is an AI plugin that enhances papers reading and research efficiency by integrating cutting-edge LLMs and offering seamless Zotero integration. Users can ask questions, extract insights, and converse with PDFs directly, making it a powerful research assistant for scholars, researchers, and anyone dealing with large amounts of text in PDF format. The plugin ensures privacy and data safety by using locally stored models and modules, with the ability to switch between different models easily. It provides a user-friendly interface for managing and chatting documents within Zotero, making research tasks more streamlined and productive.

LoLLMs WebUI (Lord of Large Language Multimodal Systems: One tool to rule them all) is a user-friendly interface to access and utilize various LLM (Large Language Models) and other AI models for a wide range of tasks. With over 500 AI expert conditionings across diverse domains and more than 2500 fine tuned models over multiple domains, LoLLMs WebUI provides an immediate resource for any problem, from car repair to coding assistance, legal matters, medical diagnosis, entertainment, and more. The easy-to-use UI with light and dark mode options, integration with GitHub repository, support for different personalities, and features like thumb up/down rating, copy, edit, and remove messages, local database storage, search, export, and delete multiple discussions, make LoLLMs WebUI a powerful and versatile tool.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's no-code Connector Builder or low-code CDK. Airbyte is used by data engineers and analysts at companies of all sizes to build and manage their data pipelines.

Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.