The Conversational Azure OpenAI Accelerator is a tool designed to provide rapid, no-cost custom demos tailored to customer use cases, from internal HR/IT to external contact centers. It focuses on top use cases of GenAI conversation and summarization, plus live backend data integration. The tool automates conversations across voice and text channels, providing a valuable way to save money and improve customer and employee experience. By combining Azure OpenAI + Cognitive Search, users can efficiently deploy a ChatGPT experience using web pages, knowledge base articles, and data sources. The tool enables simultaneous deployment of conversational content to chatbots, IVR, voice assistants, and more in one click, eliminating the need for in-depth IT involvement. It leverages Microsoft's advanced AI technologies, resulting in a conversational experience that can converse in human-like dialogue, respond intelligently, and capture content for omni-channel unified analytics.

This repository serves as a hub for Microsoft Official Build & Modernize AI Applications reference solutions and content. It provides access to projects demonstrating how to build Generative AI applications using Azure services like Azure OpenAI, Azure Container Apps, Azure Kubernetes, and Azure Cosmos DB. The solutions include Vector Search & AI Assistant, Real-Time Payment and Transaction Processing, and Medical Claims Processing. Additionally, there are workshops like the Intelligent App Workshop for Microsoft Copilot Stack, focusing on infusing intelligence into traditional software systems using foundation models and design thinking.

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.

Azure HPC On-Demand Platform (az-hop) provides an end-to-end deployment mechanism for a base HPC infrastructure on Azure. It delivers a complete HPC cluster solution ready for users to run applications, which is easy to deploy and manage for HPC administrators. az-hop leverages various Azure building blocks and can be used as-is or easily customized and extended to meet any uncovered requirements. Industry-standard tools like Terraform, Ansible, and Packer are used to provision and configure this environment, which contains: - An HPC OnDemand Portal for all user access, remote shell access, remote visualization access, job submission, file access, and more - An Active Directory for user authentication and domain control - Open PBS or SLURM as a Job Scheduler - Dynamic resources provisioning and autoscaling is done by Azure CycleCloud pre-configured job queues and integrated health-checks to quickly avoid non-optimal nodes - A Jumpbox to provide admin access - A common shared file system for home directory and applications is delivered by Azure Netapp Files - Grafana dashboards to monitor your cluster - Remote Visualization with noVNC and GPU acceleration with VirtualGL

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.

AI-Powered Search provides code examples for the book 'AI-Powered Search' by Trey Grainger, Doug Turnbull, and Max Irwin. The book teaches modern machine learning techniques for building search engines that continuously learn from users and content to deliver more intelligent and domain-aware search experiences. It covers semantic search, retrieval augmented generation, question answering, summarization, fine-tuning transformer-based models, personalized search, machine-learned ranking, click models, and more. The code examples are in Python, leveraging PySpark for data processing and Apache Solr as the default search engine. The repository is open source under the Apache License, Version 2.0.

Trace is a new AutoDiff-like tool for training AI systems end-to-end with general feedback. It generalizes the back-propagation algorithm by capturing and propagating an AI system's execution trace. Implemented as a PyTorch-like Python library, users can write Python code directly and use Trace primitives to optimize certain parts, similar to training neural networks.

APIPark is an open-source AI Gateway and Developer Portal that enables users to easily manage, integrate, and deploy AI and API services. It provides robust API management features, including creation, monitoring, and access control, to help developers efficiently and securely develop and manage their APIs. The platform aims to solve challenges such as connecting to powerful AI models, managing complex AI & API call relationships, overseeing API creation and security, simplifying fault detection and troubleshooting, and enhancing the visibility and valuation of data assets.

This repository provides a sample solution for building generative AI agents using Amazon Bedrock, Amazon DynamoDB, Amazon Kendra, Amazon Lex, and LangChain. The solution creates a generative AI financial services agent capable of assisting users with account information, loan applications, and answering natural language questions. It serves as a launchpad for developers to create personalized conversational agents for applications like chatbots and virtual assistants.

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.

Dioptra is a software test platform for assessing the trustworthy characteristics of artificial intelligence (AI). It supports the NIST AI Risk Management Framework by providing functionality to assess, analyze, and track identified AI risks. Dioptra provides a REST API and can be controlled via a web interface or Python client for designing, managing, executing, and tracking experiments. It aims to be reproducible, traceable, extensible, interoperable, modular, secure, interactive, shareable, and reusable.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

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.

The Enterprise Azure OpenAI Hub is a comprehensive repository designed to guide users through the world of Generative AI on the Azure platform. It offers a structured learning experience to accelerate the transition from concept to production in an Enterprise context. The hub empowers users to explore various use cases with Azure services, ensuring security and compliance. It provides real-world examples and playbooks for practical insights into solving complex problems and developing cutting-edge AI solutions. The repository also serves as a library of proven patterns, aligning with industry standards and promoting best practices for secure and compliant AI development.

Seismometer is a suite of tools designed to evaluate AI model performance in healthcare settings. It helps healthcare organizations assess the accuracy of AI models and ensure equitable care for diverse patient populations. The tool allows users to validate model performance using standardized evaluation criteria based on local data and workflows. It includes templates for analyzing statistical performance, fairness across different cohorts, and the impact of interventions on outcomes. Seismometer is continuously evolving to incorporate new validation and analysis techniques.

VectorHub is a free and open-sourced learning hub for people interested in adding vector retrieval to their ML stack. On VectorHub you will find practical resources to help you create MVPs with easy-to-follow learning materials, solve use case specific challenges in vector retrieval, get confident in taking their MVPs to production and making them actually useful, and learn about vendors in the space and select the ones that fit their use-case.

The Conversational Azure OpenAI Accelerator is a tool designed to provide rapid, no-cost custom demos tailored to customer use cases, from internal HR/IT to external contact centers. It focuses on top use cases of GenAI conversation and summarization, plus live backend data integration. The tool automates conversations across voice and text channels, providing a valuable way to save money and improve customer and employee experience. By combining Azure OpenAI + Cognitive Search, users can efficiently deploy a ChatGPT experience using web pages, knowledge base articles, and data sources. The tool enables simultaneous deployment of conversational content to chatbots, IVR, voice assistants, and more in one click, eliminating the need for in-depth IT involvement. It leverages Microsoft's advanced AI technologies, resulting in a conversational experience that can converse in human-like dialogue, respond intelligently, and capture content for omni-channel unified analytics.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

Botpress is a platform for building next-generation chatbots and assistants powered by OpenAI. It provides a range of tools and integrations to help developers quickly and easily create and deploy chatbots for various use cases.

LongCite is a tool that enables Large Language Models (LLMs) to generate fine-grained citations in long-context Question Answering (QA) scenarios. It provides models trained on GLM-4-9B and Meta-Llama-3.1-8B, supporting up to 128K context. Users can deploy LongCite chatbots, generate accurate responses, and obtain precise sentence-level citations. The tool includes components for model deployment, Coarse to Fine (CoF) pipeline for data construction, model training using LongCite-45k dataset, evaluation with LongBench-Cite benchmark, and citation generation.

BlinkID iOS is a mobile SDK that enables developers to easily integrate ID scanning and data extraction capabilities into their iOS applications. The SDK supports scanning and processing various types of identity documents, such as passports, driver's licenses, and ID cards. It provides accurate and fast data extraction, including personal information and document details. With BlinkID iOS, developers can enhance their apps with secure and reliable ID verification functionality, improving user experience and streamlining identity verification processes.

ai-apps is a collection of browser extensions that enhance various AI-powered services like Amazon shopping, Brave Search, ChatGPT, DuckDuckGo, and Google Search. These extensions provide functionalities such as adding AI answers to search engines, auto-clearing ChatGPT query history, auto-playing ChatGPT responses, keeping ChatGPT sessions fresh, and more. The repository offers tools to improve user experience and interaction with AI technologies across different platforms and services.

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

This repository provides code samples in Python, C#, REST, and JavaScript for vector support in Azure AI Search. It includes demos for various languages showcasing vectorization of data, creating indexes, and querying vector data. Additionally, it offers tools like Azure AI Search Lab for experimenting with AI-enabled search scenarios in Azure and templates for deploying custom chat-with-your-data solutions. The repository also features documentation on vector search, hybrid search, creating and querying vector indexes, and REST API references for Azure AI Search and Azure OpenAI Service.

The Intel® Geti™ SDK is a python package that enables teams to rapidly develop AI models by easing the complexities of model development and enhancing collaboration between teams. It provides tools to interact with an Intel® Geti™ server via the REST API, allowing for project creation, downloading, uploading, deploying for local inference with OpenVINO, setting project and model configuration, launching and monitoring training jobs, and media upload and prediction. The SDK also includes tutorial-style Jupyter notebooks demonstrating its usage.

Booster is a powerful inference accelerator designed for scaling large language models within production environments or for experimental purposes. It is built with performance and scaling in mind, supporting various CPUs and GPUs, including Nvidia CUDA, Apple Metal, and OpenCL cards. The tool can split large models across multiple GPUs, offering fast inference on machines with beefy GPUs. It supports both regular FP16/FP32 models and quantised versions, along with popular LLM architectures. Additionally, Booster features proprietary Janus Sampling for code generation and non-English languages.

xFasterTransformer is an optimized solution for Large Language Models (LLMs) on the X86 platform, providing high performance and scalability for inference on mainstream LLM models. It offers C++ and Python APIs for easy integration, along with example codes and benchmark scripts. Users can prepare models in a different format, convert them, and use the APIs for tasks like encoding input prompts, generating token ids, and serving inference requests. The tool supports various data types and models, and can run in single or multi-rank modes using MPI. A web demo based on Gradio is available for popular LLM models like ChatGLM and Llama2. Benchmark scripts help evaluate model inference performance quickly, and MLServer enables serving with REST and gRPC interfaces.

The Amazon Transcribe Live Call Analytics (LCA) with Agent Assist Sample Solution is designed to help contact centers assess and optimize caller experiences in real time. It leverages Amazon machine learning services like Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker to transcribe and extract insights from contact center audio. The solution provides real-time supervisor and agent assist features, integrates with existing contact centers, and offers a scalable, cost-effective approach to improve customer interactions. The end-to-end architecture includes features like live call transcription, call summarization, AI-powered agent assistance, and real-time analytics. The solution is event-driven, ensuring low latency and seamless processing flow from ingested speech to live webpage updates.

This repository contains recipes for building and running containerized AI and LLM applications with Podman. It provides model servers that serve machine-learning models via an API, allowing developers to quickly prototype new AI applications locally. The recipes include components like model servers and AI applications for tasks such as chat, summarization, object detection, etc. Images for sample applications and models are available in `quay.io`, and bootable containers for AI training on Linux OS are enabled.

XLearning is a scheduling platform for big data and artificial intelligence, supporting various machine learning and deep learning frameworks. It runs on Hadoop Yarn and integrates frameworks like TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning offers scalability, compatibility, multiple deep learning framework support, unified data management based on HDFS, visualization display, and compatibility with code at native frameworks. It provides functions for data input/output strategies, container management, TensorBoard service, and resource usage metrics display. XLearning requires JDK >= 1.7 and Maven >= 3.3 for compilation, and deployment on CentOS 7.2 with Java >= 1.7 and Hadoop 2.6, 2.7, 2.8.