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.

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.

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.

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.

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.

IBM Generative AI Python SDK is a tool designed for the Tech Preview program for IBM Foundation Models Studio. It brings IBM Generative AI (GenAI) into Python programs, offering various operations and types. Users can start a trial version or request a demo via the provided link. The SDK was recently rewritten and released under V2 in 2024, with a migration guide available. Contributors are welcome to participate in the open-source project by contributing documentation, tests, bug fixes, and new functionality.

Tero Subtitler is an open source, cross-platform, and free subtitle editing software with a user-friendly interface. It offers fully fledged editing with SMPTE and MEDIA modes, support for various subtitle formats, multi-level undo/redo, search and replace, auto-backup, source and transcription modes, translation memory, audiovisual preview, timeline with waveform visualizer, manipulation tools, formatting options, quality control features, translation and transcription capabilities, validation tools, automation for correcting errors, and more. It also includes features like exporting subtitles to MP3, importing/exporting Blu-ray SUP format, generating blank video, generating video with hardcoded subtitles, video dubbing, and more. The tool utilizes powerful multimedia playback engines like mpv, advanced audio/video manipulation tools like FFmpeg, tools for automatic transcription like whisper.cpp/Faster-Whisper, auto-translation API like Google Translate, and ElevenLabs TTS for video dubbing.

Geoweaver is an in-browser software that enables users to easily compose and execute full-stack data processing workflows using online spatial data facilities, high-performance computation platforms, and open-source deep learning libraries. It provides server management, code repository, workflow orchestration software, and history recording capabilities. Users can run it from both local and remote machines. Geoweaver aims to make data processing workflows manageable for non-coder scientists and preserve model run history. It offers features like progress storage, organization, SSH connection to external servers, and a web UI with Python support.

TruLens provides a set of tools for developing and monitoring neural nets, including large language models. This includes both tools for evaluation of LLMs and LLM-based applications with _TruLens-Eval_ and deep learning explainability with _TruLens-Explain_. _TruLens-Eval_ and _TruLens-Explain_ are housed in separate packages and can be used independently.

Mirascope is an LLM toolkit for lightning-fast, high-quality development. Building with Mirascope feels like writing the Python code you’re already used to writing.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment interfaces and libraries for understanding AI systems. It empowers developers and stakeholders to develop and monitor AI responsibly, enabling better data-driven actions. The toolbox includes visualization widgets for model assessment, error analysis, interpretability, fairness assessment, and mitigations library. It also offers a JupyterLab extension for managing machine learning experiments and a library for measuring gender bias in NLP datasets.

Spider is a high-performance web crawler and indexer designed to handle data curation workloads efficiently. It offers features such as concurrency, streaming, decentralization, headless Chrome rendering, HTTP proxies, cron jobs, subscriptions, smart mode, blacklisting, whitelisting, budgeting depth, dynamic AI prompt scripting, CSS scraping, and more. Users can easily get started with the Spider Cloud hosted service or set up local installations with spider-cli. The tool supports integration with Node.js and Python for additional flexibility. With a focus on speed and scalability, Spider is ideal for extracting and organizing data from the web.

QGate-Model is a machine learning meta-model with synthetic data, designed for MLOps and feature store. It is independent of machine learning solutions, with definitions in JSON and data in CSV/parquet formats. This meta-model is useful for comparing capabilities and functions of machine learning solutions, independently testing new versions of machine learning solutions, and conducting various types of tests (unit, sanity, smoke, system, regression, function, acceptance, performance, shadow, etc.). It can also be used for external test coverage when internal test coverage is not available or weak.

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.