This repository provides Terraform configurations to automatically set up Google Cloud resources and deploy Dify in a highly available configuration. It includes features such as serverless hosting, auto-scaling, and data persistence. Users need a Google Cloud account, Terraform, and gcloud CLI installed to use this tool. The configuration involves setting environment-specific values and creating a GCS bucket for managing Terraform state. The tool allows users to initialize Terraform, create Artifact Registry repository, build and push container images, plan and apply Terraform changes, and cleanup resources when needed.

LangStream is a tool for natural language processing tasks, providing a CLI for easy installation and usage. Users can try sample applications like Chat Completions and create their own applications using the developer documentation. It supports running on Kubernetes for production-ready deployment, with support for various Kubernetes distributions and external components like Apache Kafka or Apache Pulsar cluster. Users can deploy LangStream locally using minikube and manage the cluster with mini-langstream. Development requirements include Docker, Java 17, Git, Python 3.11+, and PIP, with the option to test local code changes using mini-langstream.

ChatOpsLLM is a project designed to empower chatbots with effortless DevOps capabilities. It provides an intuitive interface and streamlined workflows for managing and scaling language models. The project incorporates robust MLOps practices, including CI/CD pipelines with Jenkins and Ansible, monitoring with Prometheus and Grafana, and centralized logging with the ELK stack. Developers can find detailed documentation and instructions on the project's website.

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.

Trieve is an advanced relevance API for hybrid search, recommendations, and RAG. It offers a range of features including self-hosting, semantic dense vector search, typo tolerant full-text/neural search, sub-sentence highlighting, recommendations, convenient RAG API routes, the ability to bring your own models, hybrid search with cross-encoder re-ranking, recency biasing, tunable popularity-based ranking, filtering, duplicate detection, and grouping. Trieve is designed to be flexible and customizable, allowing users to tailor it to their specific needs. It is also easy to use, with a simple API and well-documented features.

Dstack is an open-source orchestration engine for running AI workloads in any cloud. It supports a wide range of cloud providers (such as AWS, GCP, Azure, Lambda, TensorDock, Vast.ai, CUDO, RunPod, etc.) as well as on-premises infrastructure. With Dstack, you can easily set up and manage dev environments, tasks, services, and pools for your AI workloads.

Middleware is an open-source engineering management tool that helps engineering leaders measure and analyze team effectiveness using DORA metrics. It integrates with CI/CD tools, automates DORA metric collection and analysis, visualizes key performance indicators, provides customizable reports and dashboards, and integrates with project management platforms. Users can set up Middleware using Docker or manually, generate encryption keys, set up backend and web servers, and access the application to view DORA metrics. The tool calculates DORA metrics using GitHub data, including Deployment Frequency, Lead Time for Changes, Mean Time to Restore, and Change Failure Rate. Middleware aims to provide DORA metrics to users based on their Git data, simplifying the process of tracking software delivery performance and operational efficiency.

Langflow is an open-source Python-powered visual framework designed for building multi-agent and RAG applications. It is fully customizable, language model agnostic, and vector store agnostic. Users can easily create flows by dragging components onto the canvas, connect them, and export the flow as a JSON file. Langflow also provides a command-line interface (CLI) for easy management and configuration, allowing users to customize the behavior of Langflow for development or specialized deployment scenarios. The tool can be deployed on various platforms such as Google Cloud Platform, Railway, and Render. Contributors are welcome to enhance the project on GitHub by following the contributing guidelines.

OpenMeter is a real-time and scalable usage metering tool for AI, usage-based billing, infrastructure, and IoT use cases. It provides a REST API for integrations and offers client SDKs in Node.js, Python, Go, and Web. OpenMeter is licensed under the Apache 2.0 License.

CodeBox is a cloud infrastructure tool designed for running Python code in an isolated environment. It also offers simple file input/output capabilities and will soon support vector database operations. Users can install CodeBox using pip and utilize it by setting up an API key. The tool allows users to execute Python code snippets and interact with the isolated environment. CodeBox is currently in early development stages and requires manual handling for certain operations like refunds and cancellations. The tool is open for contributions through issue reporting and pull requests. It is licensed under MIT and can be contacted via email at [email protected].

A discord bot built to interface with the InvokeAI fork of stable-diffusion. It is a work in progress for a major rewrite of the arty project, compatible with `invokeai 5.1.1`. The bot supports various functionalities like building node graphs from job requests, refreshing renders using png metadata, removing backgrounds, job progress tracking, and LLM integration. Users can install custom invokeai nodes for advanced functionality and launch the bot natively or with docker. Patches and pull requests are welcomed.

OpenCharacter is an open-source tool that allows users to create and run characters locally with local models or use the hosted version. The stack includes Next.js for frontend, TailwindCSS for styling, Drizzle ORM for database access, NextAuth for authentication, Cloudflare D1 for serverless databases, Cloudflare Pages for hosting, and ShadcnUI as the component library. Users can integrate OpenCharacter with OpenRouter by configuring the OpenRouter API key. The tool is fully scalable, composable, and cost-effective, with powerful tools like Wrangler for database management and migrations. No environment variables are needed, making it easy to use and deploy.

Nuclia frontend apps and libraries repository contains various frontend applications and libraries for the Nuclia platform. It includes components such as Dashboard, Widget, SDK, Sistema (design system), NucliaDB admin, CI/CD Deployment, and Maintenance page. The repository provides detailed instructions on installation, dependencies, and usage of these components for both Nuclia employees and external developers. It also covers deployment processes for different components and tools like ArgoCD for monitoring deployments and logs. The repository aims to facilitate the development, testing, and deployment of frontend applications within the Nuclia ecosystem.

Verifywise is a tool designed to help developers easily verify the correctness of their code. It provides a simple and intuitive interface for running various types of tests and checks on codebases, ensuring that the code meets quality standards and requirements. With Verifywise, developers can automate the verification process, saving time and effort in identifying and fixing potential issues in their code. The tool supports multiple programming languages and frameworks, making it versatile and adaptable to different project requirements. Whether you are working on a small personal project or a large-scale software development initiative, Verifywise can help you ensure the reliability and robustness of your codebase.

Modelence is an all-in-one TypeScript framework for startups shipping production apps, aiming to eliminate boilerplate for standard web app features. It provides authentication, database setup, cron jobs, AI observability, and email functionalities. Modelence requires Node.js 20.20 or higher. Developers can create projects, install dependencies, and start the development server quickly. For local development, contributors can clone the repository, install dependencies, build the package, and test changes in a real application. Modelence offers examples for further guidance.

CodeNomad is a fast, multi-instance workspace designed for users who spend extended hours in OpenCode. It provides a premium, low-latency environment with features like managing multiple OpenCode sessions side-by-side, global command palette for keyboard-first control, rich media previews, and browser support via CodeNomad Server. Users can choose between a Desktop App (Electron-based) with global shortcuts and deeper system integration, a Tauri App for lightweight high-performance experience, or run CodeNomad as a local server accessed via web browser. The tool supports multi-instance workspace, long-session native scrolling, command palette for easy navigation, and deep task awareness to monitor background tasks and child sessions without interruptions.

This repository contains scripts for installing HPC and AI libraries and tools to build Azure HPC/AI images. It streamlines the process of provisioning compute-intensive workloads and crafting advanced AI models in the cloud, ensuring efficiency and reliability in deployments.

This repository provides Terraform configurations to automatically set up Google Cloud resources and deploy Dify in a highly available configuration. It includes features such as serverless hosting, auto-scaling, and data persistence. Users need a Google Cloud account, Terraform, and gcloud CLI installed to use this tool. The configuration involves setting environment-specific values and creating a GCS bucket for managing Terraform state. The tool allows users to initialize Terraform, create Artifact Registry repository, build and push container images, plan and apply Terraform changes, and cleanup resources when needed.

Podman AI Lab is an open source extension for Podman Desktop designed to work with Large Language Models (LLMs) on a local environment. It features a recipe catalog with common AI use cases, a curated set of open source models, and a playground for learning, prototyping, and experimentation. Users can quickly and easily get started bringing AI into their applications without depending on external infrastructure, ensuring data privacy and security.

Azure OpenAI Service is a central capability with Azure API Management, providing guidance and tools for organizations to implement Azure OpenAI in a production environment with an emphasis on cost control, secure access, and usage monitoring. It includes infrastructure-as-code templates, CI/CD pipelines, secure access management, usage monitoring, load balancing, streaming requests, and end-to-end samples like ChatApp and Azure Dashboards.

The Pinecone AWS Reference Architecture with Pulumi is a distributed system designed for vector-database-enabled semantic search over Postgres records. It serves as a starting point for specific use cases or as a learning resource. The architecture is permissively licensed and supported by Pinecone's open-source team, facilitating the setup of high-scale use cases for Pinecone's scalable vector database.

Build your dream team with Autogen is a repository that leverages Microsoft Autogen 0.4, Azure OpenAI, and Streamlit to create an end-to-end multi-agent application. It provides an advanced multi-agent framework based on Magentic One, with features such as a friendly UI, single-line deployment, secure code execution, managed identities, and observability & debugging tools. Users can deploy Azure resources and the app with simple commands, work locally with virtual environments, install dependencies, update configurations, and run the application. The repository also offers resources for learning more about building applications with Autogen.

Action MCP is a powerful tool for managing and automating your cloud infrastructure. It provides a user-friendly interface to easily create, update, and delete resources on popular cloud platforms. With Action MCP, you can streamline your deployment process, reduce manual errors, and improve overall efficiency. The tool supports various cloud providers and offers a wide range of features to meet your infrastructure management needs. Whether you are a developer, system administrator, or DevOps engineer, Action MCP can help you simplify and optimize your cloud operations.

Burr is a Python library and UI that makes it easy to develop applications that make decisions based on state (chatbots, agents, simulations, etc...). Burr includes a UI that can track/monitor those decisions in real time.

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.

This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

Kong, or Kong API Gateway, is a cloud-native, platform-agnostic, scalable API Gateway distinguished for its high performance and extensibility via plugins. It also provides advanced AI capabilities with multi-LLM support. By providing functionality for proxying, routing, load balancing, health checking, authentication (and more), Kong serves as the central layer for orchestrating microservices or conventional API traffic with ease. Kong runs natively on Kubernetes thanks to its official Kubernetes Ingress Controller.

AI-in-a-Box is a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction, while maintaining the highest standards of quality and efficiency. It provides essential guidance on the responsible use of AI and LLM technologies, specific security guidance for Generative AI (GenAI) applications, and best practices for scaling OpenAI applications within Azure. The available accelerators include: Azure ML Operationalization in-a-box, Edge AI in-a-box, Doc Intelligence in-a-box, Image and Video Analysis in-a-box, Cognitive Services Landing Zone in-a-box, Semantic Kernel Bot in-a-box, NLP to SQL in-a-box, Assistants API in-a-box, and Assistants API Bot in-a-box.

This repository contains reference architectures and test cases for distributed model training with Amazon SageMaker Hyperpod, AWS ParallelCluster, AWS Batch, and Amazon EKS. The test cases cover different types and sizes of models as well as different frameworks and parallel optimizations (Pytorch DDP/FSDP, MegatronLM, NemoMegatron...).

The AWS Generative AI Constructs Library is an open-source extension of the AWS Cloud Development Kit (AWS CDK) that provides multi-service, well-architected patterns for quickly defining solutions in code to create predictable and repeatable infrastructure, called constructs. The goal of AWS Generative AI CDK Constructs is to help developers build generative AI solutions using pattern-based definitions for their architecture. The patterns defined in AWS Generative AI CDK Constructs are high level, multi-service abstractions of AWS CDK constructs that have default configurations based on well-architected best practices. The library is organized into logical modules using object-oriented techniques to create each architectural pattern model.

OpenVINO™ Model Server (OVMS) is a high-performance system for serving models. Implemented in C++ for scalability and optimized for deployment on Intel architectures, the model server uses the same architecture and API as TensorFlow Serving and KServe while applying OpenVINO for inference execution. Inference service is provided via gRPC or REST API, making deploying new algorithms and AI experiments easy.

Deploy langgenius/dify, an LLM based chat bot app on kubernetes with helm chart.