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.

Docq is a private and secure GenAI tool designed to extract knowledge from business documents, enabling users to find answers independently. It allows data to stay within organizational boundaries, supports self-hosting with various cloud vendors, and offers multi-model and multi-modal capabilities. Docq is extensible, open-source (AGPLv3), and provides commercial licensing options. The tool aims to be a turnkey solution for organizations to adopt AI innovation safely, with plans for future features like more data ingestion options and model fine-tuning.

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.

AI-Writer is an AI content generation toolkit called Alwrity that automates and enhances the process of blog creation, optimization, and management. It integrates advanced AI models for text generation, image creation, and data analysis, offering features such as online research integration, long-form content generation, AI content planning, multilingual support, prevention of AI hallucinations, multimodal content generation, SEO optimization, and integration with platforms like Wordpress and Jekyll. The toolkit is designed for automated blog management and requires appropriate API keys and access credentials for full functionality.

Slips is a powerful endpoint behavioral intrusion prevention and detection system that uses machine learning to detect malicious behaviors in network traffic. It can work with network traffic in real-time, PCAP files, and network flows from tools like Suricata, Zeek/Bro, and Argus. Slips threat detection is based on machine learning models, threat intelligence feeds, and expert heuristics. It gathers evidence of malicious behavior and triggers alerts when enough evidence is accumulated. The tool is Python-based and supported on Linux and MacOS, with blocking features only on Linux. Slips relies on Zeek network analysis framework and Redis for interprocess communication. It offers a graphical user interface for easy monitoring and analysis.

ClearML is a suite of tools designed to streamline the machine learning workflow. It includes an experiment manager, MLOps/LLMOps, data management, and model serving capabilities. ClearML is open-source and offers a free tier hosting option. It supports various ML/DL frameworks and integrates with Jupyter Notebook and PyCharm. ClearML provides extensive logging capabilities, including source control info, execution environment, hyper-parameters, and experiment outputs. It also offers automation features, such as remote job execution and pipeline creation. ClearML is designed to be easy to integrate, requiring only two lines of code to add to existing scripts. It aims to improve collaboration, visibility, and data transparency within ML teams.

Kaizen is an open-source project that helps teams ensure quality in their software delivery by providing a suite of tools for code review, test generation, and end-to-end testing. It integrates with your existing code repositories and workflows, allowing you to streamline your software development process. Kaizen generates comprehensive end-to-end tests, provides UI testing and review, and automates code review with insightful feedback. The file structure includes components for API server, logic, actors, generators, LLM integrations, documentation, and sample code. Getting started involves installing the Kaizen package, generating tests for websites, and executing tests. The tool also runs an API server for GitHub App actions. Contributions are welcome under the AGPL License.

ClaudeSync is a powerful tool designed to seamlessly synchronize local files with Claude.ai projects. It bridges the gap between local development environment and Claude.ai's knowledge base, offering real-time synchronization, CLI for easy management, support for multiple organizations and projects, intelligent file filtering, configurable sync interval, two-way synchronization, and more. It ensures data privacy, open source transparency, and comes with disclaimers for use at own risk. Users can quickly start syncing by installing, logging in, selecting organization and project, and running sync. Advanced features include API, organization, project, file, chat management, configuration, synchronization modes, scheduled sync, providers, custom ignore file, and troubleshooting. Contributions are welcome, and communication channels include GitHub Issues and Discord. Licensed under MIT License.

Embedded inference of Deep Learning models is quite challenging due to high compute requirements. TI’s Edge AI software product helps optimize and accelerate inference on TI’s embedded devices. It supports heterogeneous execution of DNNs across cortex-A based MPUs, TI’s latest generation C7x DSP, and DNN accelerator (MMA). The solution simplifies the product life cycle of DNN development and deployment by providing a rich set of tools and optimized libraries.

LLM Guard is a comprehensive tool designed to fortify the security of Large Language Models (LLMs). It offers sanitization, detection of harmful language, prevention of data leakage, and resistance against prompt injection attacks, ensuring that your interactions with LLMs remain safe and secure.

MindNLP is an open-source NLP library based on MindSpore. It provides a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly. Key features of MindNLP include: * Comprehensive data processing: Several classical NLP datasets are packaged into a friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc. * Friendly NLP model toolset: MindNLP provides various configurable components. It is friendly to customize models using MindNLP. * Easy-to-use engine: MindNLP simplified complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily. MindNLP supports a wide range of NLP tasks, including: * Language modeling * Machine translation * Question answering * Sentiment analysis * Sequence labeling * Summarization MindNLP also supports industry-leading Large Language Models (LLMs), including Llama, GLM, RWKV, etc. For support related to large language models, including pre-training, fine-tuning, and inference demo examples, you can find them in the "llm" directory. To install MindNLP, you can either install it from Pypi, download the daily build wheel, or install it from source. The installation instructions are provided in the documentation. MindNLP is released under the Apache 2.0 license. If you find this project useful in your research, please consider citing the following paper: @misc{mindnlp2022, title={{MindNLP}: a MindSpore NLP library}, author={MindNLP Contributors}, howpublished = {\url{https://github.com/mindlab-ai/mindnlp}}, year={2022} }

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.

Midscene.js is an AI-powered automation SDK that allows users to control web pages, perform assertions, and extract data in JSON format using natural language. It offers features such as natural language interaction, understanding UI and providing responses in JSON, intuitive assertion based on AI understanding, compatibility with public multimodal LLMs like GPT-4o, visualization tool for easy debugging, and a brand new experience in automation development.

AmbuFlow is a mobile application developed using HTML, CSS, JavaScript, and Google API to notify patients of nearby hospitals and provide estimated ambulance arrival times. It offers critical details like patient's location and enhances GPS route management with real-time traffic data for efficient navigation. The app helps users find nearby hospitals, track ambulances in real-time, and manage ambulance routes based on traffic and distance. It ensures quick emergency response, real-time tracking, enhanced communication, resource management, and a user-friendly interface for seamless navigation in high-stress situations.

R2R (RAG to Riches) is a fast and efficient framework for serving high-quality Retrieval-Augmented Generation (RAG) to end users. The framework is designed with customizable pipelines and a feature-rich FastAPI implementation, enabling developers to quickly deploy and scale RAG-based applications. R2R was conceived to bridge the gap between local LLM experimentation and scalable production solutions. **R2R is to LangChain/LlamaIndex what NextJS is to React**. A JavaScript client for R2R deployments can be found here. ### Key Features * **🚀 Deploy** : Instantly launch production-ready RAG pipelines with streaming capabilities. * **🧩 Customize** : Tailor your pipeline with intuitive configuration files. * **🔌 Extend** : Enhance your pipeline with custom code integrations. * **⚖️ Autoscale** : Scale your pipeline effortlessly in the cloud using SciPhi. * **🤖 OSS** : Benefit from a framework developed by the open-source community, designed to simplify RAG deployment.

TaskingAI brings Firebase's simplicity to **AI-native app development**. The platform enables the creation of GPTs-like multi-tenant applications using a wide range of LLMs from various providers. It features distinct, modular functions such as Inference, Retrieval, Assistant, and Tool, seamlessly integrated to enhance the development process. TaskingAI’s cohesive design ensures an efficient, intelligent, and user-friendly experience in AI application development.

Oxen is a data version control library, written in Rust. It's designed to be fast, reliable, and easy to use. Oxen can be used in a variety of ways, from a simple command line tool to a remote server to sync to, to integrations into other ecosystems such as python.

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.

PandaETL is an open-source, no-code ETL tool designed to extract and parse data from various document types including PDFs, emails, websites, audio files, and more. With an intuitive interface and powerful backend, PandaETL simplifies the process of data extraction and transformation, making it accessible to users without programming skills.

The Radicalbit AI Monitoring Platform provides a comprehensive solution for monitoring Machine Learning and Large Language models in production. It helps proactively identify and address potential performance issues by analyzing data quality, model quality, and model drift. The repository contains files and projects for running the platform, including UI, API, SDK, and Spark components. Installation using Docker compose is provided, allowing deployment with a K3s cluster and interaction with a k9s container. The platform documentation includes a step-by-step guide for installation and creating dashboards. Community engagement is encouraged through a Discord server. The roadmap includes adding functionalities for batch and real-time workloads, covering various model types and tasks.

The 'airflow-provider-great-expectations' repository contains a set of Airflow operators for Great Expectations, a Python library used for testing and validating data. The operators enable users to run Great Expectations validations and checks within Apache Airflow workflows. The package requires Airflow 2.1.0+ and Great Expectations >=v0.13.9. It provides functionalities to work with Great Expectations V3 Batch Request API, Checkpoints, and allows passing kwargs to Checkpoints at runtime. The repository includes modules for a base operator and examples of DAGs with sample tasks demonstrating the operator's functionality.

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.