This public repository contains API, tools, and packages for Datagrok, a web-based data analytics platform. It offers support for scientific domains, applications, connectors to web services, visualizations, file importing, scientific methods in R, Python, or Julia, file metadata extractors, custom predictive models, platform enhancements, and more. The open-source packages are free to use, with restrictions on server computational capacities for the public environment. Academic institutions can use Datagrok for research and education, benefiting from reproducible and scalable computations and data augmentation capabilities. Developers can contribute by creating visualizations, scientific methods, file editors, connectors to web services, and more.

Psychic is a tool that provides a platform for users to access psychic readings and services. It offers a range of features such as tarot card readings, astrology consultations, and spiritual guidance. Users can connect with experienced psychics and receive personalized insights and advice on various aspects of their lives. The platform is designed to be user-friendly and intuitive, making it easy for users to navigate and explore the different services available. Whether you're looking for guidance on love, career, or personal growth, Psychic has you covered.

The Oracle Cloud Infrastructure Data Science and AI services Examples repository provides demos, tutorials, and code examples showcasing various features of the OCI Data Science service and AI services. It offers tools for data scientists to develop and deploy machine learning models efficiently, with features like Accelerated Data Science SDK, distributed training, batch processing, and machine learning pipelines. Whether you're a beginner or an experienced practitioner, OCI Data Science Services provide the resources needed to build, train, and deploy models easily.

RecAI is a project that explores the integration of Large Language Models (LLMs) into recommender systems, addressing the challenges of interactivity, explainability, and controllability. It aims to bridge the gap between general-purpose LLMs and domain-specific recommender systems, providing a holistic perspective on the practical requirements of LLM4Rec. The project investigates various techniques, including Recommender AI agents, selective knowledge injection, fine-tuning language models, evaluation, and LLMs as model explainers, to create more sophisticated, interactive, and user-centric recommender systems.

Kaapana is an open-source toolkit for state-of-the-art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. Obtaining large amounts of medical data necessary for developing and training modern machine learning methods is an extremely challenging effort that often fails in a multi-center setting, e.g. due to technical, organizational and legal hurdles. A federated approach where the data remains under the authority of the individual institutions and is only processed on-site is, in contrast, a promising approach ideally suited to overcome these difficulties. Following this federated concept, the goal of Kaapana is to provide a framework and a set of tools for sharing data processing algorithms, for standardized workflow design and execution as well as for performing distributed method development. This will facilitate data analysis in a compliant way enabling researchers and clinicians to perform large-scale multi-center studies. By adhering to established standards and by adopting widely used open technologies for private cloud development and containerized data processing, Kaapana integrates seamlessly with the existing clinical IT infrastructure, such as the Picture Archiving and Communication System (PACS), and ensures modularity and easy extensibility.

This project is a tutorial on developing large model applications for novice developers. It aims to provide a comprehensive introduction to large model development, focusing on Alibaba Cloud servers and integrating personal knowledge assistant projects. The tutorial covers the following topics: 1. **Introduction to Large Models**: A simplified introduction for novice developers on what large models are, their characteristics, what LangChain is, and how to develop an LLM application. 2. **How to Call Large Model APIs**: This section introduces various methods for calling APIs of well-known domestic and foreign large model products, including calling native APIs, encapsulating them as LangChain LLMs, and encapsulating them as Fastapi calls. It also provides a unified encapsulation for various large model APIs, such as Baidu Wenxin, Xunfei Xinghuo, and Zh譜AI. 3. **Knowledge Base Construction**: Loading, processing, and vector database construction of different types of knowledge base documents. 4. **Building RAG Applications**: Integrating LLM into LangChain to build a retrieval question and answer chain, and deploying applications using Streamlit. 5. **Verification and Iteration**: How to implement verification and iteration in large model development, and common evaluation methods. The project consists of three main parts: 1. **Introduction to LLM Development**: A simplified version of V1 aims to help beginners get started with LLM development quickly and conveniently, understand the general process of LLM development, and build a simple demo. 2. **LLM Development Techniques**: More advanced LLM development techniques, including but not limited to: Prompt Engineering, processing of multiple types of source data, optimizing retrieval, recall ranking, Agent framework, etc. 3. **LLM Application Examples**: Introduce some successful open source cases, analyze the ideas, core concepts, and implementation frameworks of these application examples from the perspective of this course, and help beginners understand what kind of applications they can develop through LLM. Currently, the first part has been completed, and everyone is welcome to read and learn; the second and third parts are under creation. **Directory Structure Description**: requirements.txt: Installation dependencies in the official environment notebook: Notebook source code file docs: Markdown documentation file figures: Pictures data_base: Knowledge base source file used

Ianvs is a distributed synergy AI benchmarking project incubated in KubeEdge SIG AI. It aims to test the performance of distributed synergy AI solutions following recognized standards, providing end-to-end benchmark toolkits, test environment management tools, test case control tools, and benchmark presentation tools. It also collaborates with other organizations to establish comprehensive benchmarks and related applications. The architecture includes critical components like Test Environment Manager, Test Case Controller, Generation Assistant, Simulation Controller, and Story Manager. Ianvs documentation covers quick start, guides, dataset descriptions, algorithms, user interfaces, stories, and roadmap.

Akeru.ai is an open-source AI platform leveraging the power of decentralization. It offers transparent, safe, and highly available AI capabilities. The platform aims to give developers access to open-source and transparent AI resources through its decentralized nature hosted on an edge network. Akeru API introduces features like retrieval, function calling, conversation management, custom instructions, data input optimization, user privacy, testing and iteration, and comprehensive documentation. It is ideal for creating AI agents and enhancing web and mobile applications with advanced AI capabilities. The platform runs on a Bittensor Subnet design that aims to democratize AI technology and promote an equitable AI future. Akeru.ai embraces decentralization challenges to ensure a decentralized and equitable AI ecosystem with security features like watermarking and network pings. The API architecture integrates with technologies like Bun, Redis, and Elysia for a robust, scalable solution.

EmbedJs is a NodeJS framework that simplifies RAG application development by efficiently processing unstructured data. It segments data, creates relevant embeddings, and stores them in a vector database for quick retrieval.

DevOpsGPT is an AI-driven software development automation solution that combines Large Language Models (LLM) with DevOps tools to convert natural language requirements into working software. It improves development efficiency by eliminating the need for tedious requirement documentation, shortens development cycles, reduces communication costs, and ensures high-quality deliverables. The Enterprise Edition offers features like existing project analysis, professional model selection, and support for more DevOps platforms. The tool automates requirement development, generates interface documentation, provides pseudocode based on existing projects, facilitates code refinement, enables continuous integration, and supports software version release. Users can run DevOpsGPT with source code or Docker, and the tool comes with limitations in precise documentation generation and understanding existing project code. The product roadmap includes accurate requirement decomposition, rapid import of development requirements, and integration of more software engineering and professional tools for efficient software development tasks under AI planning and execution.

This repository is part of the Certified Agentic & Robotic AI Engineer program, covering the first quarter of the course work. It focuses on Modern AI Python Programming, emphasizing static typing for robust and scalable AI development. The course includes modules on Python fundamentals, object-oriented programming, advanced Python concepts, AI-assisted Python programming, web application basics with Python, and the future of Python in AI. Upon completion, students will be able to write proficient Modern Python code, apply OOP principles, implement asynchronous programming, utilize AI-powered tools, develop basic web applications, and understand the future directions of Python in AI.

LemonAI is a versatile machine learning library designed to simplify the process of building and deploying AI models. It provides a wide range of tools and algorithms for data preprocessing, model training, and evaluation. With LemonAI, users can easily experiment with different machine learning techniques and optimize their models for various tasks. The library is well-documented and beginner-friendly, making it suitable for both novice and experienced data scientists. LemonAI aims to streamline the development of AI applications and empower users to create innovative solutions using state-of-the-art machine learning methods.

Moon is a monitoring and alerting platform suitable for multiple domains, supporting various application scenarios such as cloud-native, Internet of Things (IoT), and Artificial Intelligence (AI). It simplifies operational work of cloud-native monitoring, boasts strong IoT and AI support capabilities, and meets diverse monitoring needs across industries. Capable of real-time data monitoring, intelligent alerts, and fault response for various fields.

Finic is an open source python-based integration platform designed to simplify integration workflows for both business users and developers. It offers a drag-and-drop UI, a dedicated Python environment for each workflow, and generative AI features to streamline transformation tasks. With a focus on decoupling integration from product code, Finic aims to provide faster and more flexible integrations by supporting custom connectors. The tool is open source and allows deployment to users' own cloud environments with minimal legal friction.

AgentSociety is an advanced framework designed for building agents in urban simulation environments. It integrates LLMs' planning, memory, and reasoning capabilities to generate realistic behaviors. The framework supports dataset-based, text-based, and rule-based environments with interactive visualization. It includes tools for interviews, surveys, interventions, and metric recording tailored for social experimentation.

OpenSSA is an open-source framework for creating efficient, domain-specific AI agents. It enables the development of Small Specialist Agents (SSAs) that solve complex problems in specific domains. SSAs tackle multi-step problems that require planning and reasoning beyond traditional language models. They apply OODA for deliberative reasoning (OODAR) and iterative, hierarchical task planning (HTP). This "System-2 Intelligence" breaks down complex tasks into manageable steps. SSAs make informed decisions based on domain-specific knowledge. With OpenSSA, users can create agents that process, generate, and reason about information, making them more effective and efficient in solving real-world challenges.

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.

Sorrentum is an open-source project that aims to combine open-source development, startups, and brilliant students to build machine learning, AI, and Web3 / DeFi protocols geared towards finance and economics. The project provides opportunities for internships, research assistantships, and development grants, as well as the chance to work on cutting-edge problems, learn about startups, write academic papers, and get internships and full-time positions at companies working on Sorrentum applications.

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.

Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.

This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)

Mojo is a new programming language that bridges the gap between research and production by combining Python syntax and ecosystem with systems programming and metaprogramming features. Mojo is still young, but it is designed to become a superset of Python over time.

PandasAI is a Python library that makes it easy to ask questions to your data in natural language. It helps you to explore, clean, and analyze your data using generative AI.

Databend is an open-source cloud data warehouse that serves as a cost-effective alternative to Snowflake. With its focus on fast query execution and data ingestion, it's designed for complex analysis of the world's largest datasets.

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.