Comet Opik is a repository containing two main services: a frontend and a backend. It provides a Python SDK for easy installation. Users can run the full application locally with minikube, following specific installation prerequisites. The repository structure includes directories for applications like Opik backend, with detailed instructions available in the README files. Users can manage the installation using simple k8s commands and interact with the application via URLs for checking the running application and API documentation. The repository aims to facilitate local development and testing of Opik using Kubernetes technology.

OpenLIT is an OpenTelemetry-native GenAI and LLM Application Observability tool. It's designed to make the integration process of observability into GenAI projects as easy as pie – literally, with just **a single line of code**. Whether you're working with popular LLM Libraries such as OpenAI and HuggingFace or leveraging vector databases like ChromaDB, OpenLIT ensures your applications are monitored seamlessly, providing critical insights to improve performance and reliability.

Generative AI models have shown tremendous usefulness in increasing accessibility and automation of a wide range of tasks. This repository contains the `biochatter` Python package, a generic backend library for the connection of biomedical applications to conversational AI. It aims to provide a common framework for deploying, testing, and evaluating diverse models and auxiliary technologies in the biomedical domain. BioChatter is part of the BioCypher ecosystem, connecting natively to BioCypher knowledge graphs.

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.

OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agent, offline RL, self-play, and natural language. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. It supports a universal interface for all tasks/environments, single-agent and multi-agent tasks, offline RL training with expert dataset, self-play training, reinforcement learning training for natural language tasks, DeepSpeed, Arena for evaluation, importing models and datasets from Hugging Face, user-defined environments, models, and datasets, gymnasium environments, callbacks, visualization tools, unit testing, and code coverage testing. It also supports various algorithms like PPO, DQN, SAC, and environments like Gymnasium, MuJoCo, Atari, and more.

Deep Chat is a fully customizable AI chat component that can be injected into your website with minimal to no effort. Whether you want to create a chatbot that leverages popular APIs such as ChatGPT or connect to your own custom service, this component can do it all! Explore deepchat.dev to view all of the available features, how to use them, examples and more!

Open Interface is a self-driving software that automates computer tasks by sending user requests to a language model backend (e.g., GPT-4V) and simulating keyboard and mouse inputs to execute the steps. It course-corrects by sending current screenshots to the language models. The tool supports MacOS, Linux, and Windows, and requires setting up the OpenAI API key for access to GPT-4V. It can automate tasks like creating meal plans, setting up custom language model backends, and more. Open Interface is currently not efficient in accurate spatial reasoning, tracking itself in tabular contexts, and navigating complex GUI-rich applications. Future improvements aim to enhance the tool's capabilities with better models trained on video walkthroughs. The tool is cost-effective, with user requests priced between $0.05 - $0.20, and offers features like interrupting the app and primary display visibility in multi-monitor setups.

Audio Webui is a tool designed to provide a user-friendly interface for audio processing tasks. It supports automatic installers, Docker deployment, local manual installation, Google Colab integration, and common command line flags. Users can easily download, install, update, and run the tool for various audio-related tasks. The tool requires Python 3.10, Git, and ffmpeg for certain features. It also offers extensions for additional functionalities.

Outspeed is a PyTorch-inspired SDK for building real-time AI applications on voice and video input. It offers low-latency processing of streaming audio and video, an intuitive API familiar to PyTorch users, flexible integration of custom AI models, and tools for data preprocessing and model deployment. Ideal for developing voice assistants, video analytics, and other real-time AI applications processing audio-visual data.

SuperDuperDB is a Python framework for integrating AI models, APIs, and vector search engines directly with your existing databases, including hosting of your own models, streaming inference and scalable model training/fine-tuning. Build, deploy and manage any AI application without the need for complex pipelines, infrastructure as well as specialized vector databases, and moving our data there, by integrating AI at your data's source: - Generative AI, LLMs, RAG, vector search - Standard machine learning use-cases (classification, segmentation, regression, forecasting recommendation etc.) - Custom AI use-cases involving specialized models - Even the most complex applications/workflows in which different models work together SuperDuperDB is **not** a database. Think `db = superduper(db)`: SuperDuperDB transforms your databases into an intelligent platform that allows you to leverage the full AI and Python ecosystem. A single development and deployment environment for all your AI applications in one place, fully scalable and easy to manage.

last_layer is a security library designed to protect LLM applications from prompt injection attacks, jailbreaks, and exploits. It acts as a robust filtering layer to scrutinize prompts before they are processed by LLMs, ensuring that only safe and appropriate content is allowed through. The tool offers ultra-fast scanning with low latency, privacy-focused operation without tracking or network calls, compatibility with serverless platforms, advanced threat detection mechanisms, and regular updates to adapt to evolving security challenges. It significantly reduces the risk of prompt-based attacks and exploits but cannot guarantee complete protection against all possible threats.

Findto is a decentralized search tool for the Web and AI that puts people in control of algorithms. It aims to provide a better search experience by offering diverse sources, privacy and carbon level information, trends exploration, autosuggest, voice search, and more. Findto encourages a free search experience and promotes a healthier internet by empowering users with democratic choices.

AIOS, a Large Language Model (LLM) Agent operating system, embeds large language model into Operating Systems (OS) as the brain of the OS, enabling an operating system "with soul" -- an important step towards AGI. AIOS is designed to optimize resource allocation, facilitate context switch across agents, enable concurrent execution of agents, provide tool service for agents, maintain access control for agents, and provide a rich set of toolkits for LLM Agent developers.

CAMEL is an open-source library designed for the study of autonomous and communicative agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.

Eko is a lightweight and flexible command-line tool for managing environment variables in your projects. It allows you to easily set, get, and delete environment variables for different environments, making it simple to manage configurations across development, staging, and production environments. With Eko, you can streamline your workflow and ensure consistency in your application settings without the need for complex setup or configuration files.

Floneum is a graph editor that makes it easy to develop your own AI workflows. It uses large language models (LLMs) to run AI models locally, without any external dependencies or even a GPU. This makes it easy to use LLMs with your own data, without worrying about privacy. Floneum also has a plugin system that allows you to improve the performance of LLMs and make them work better for your specific use case. Plugins can be used in any language that supports web assembly, and they can control the output of LLMs with a process similar to JSONformer or guidance.

Comet Opik is a repository containing two main services: a frontend and a backend. It provides a Python SDK for easy installation. Users can run the full application locally with minikube, following specific installation prerequisites. The repository structure includes directories for applications like Opik backend, with detailed instructions available in the README files. Users can manage the installation using simple k8s commands and interact with the application via URLs for checking the running application and API documentation. The repository aims to facilitate local development and testing of Opik using Kubernetes technology.

AirGo is a front and rear end separation, multi user, multi protocol proxy service management system, simple and easy to use. It supports vless, vmess, shadowsocks, and hysteria2.

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

The 'llm-code-interpreter' repository is a deprecated plugin that provides a code interpreter on steroids for ChatGPT by E2B. It gives ChatGPT access to a sandboxed cloud environment with capabilities like running any code, accessing Linux OS, installing programs, using filesystem, running processes, and accessing the internet. The plugin exposes commands to run shell commands, read files, and write files, enabling various possibilities such as running different languages, installing programs, starting servers, deploying websites, and more. It is powered by the E2B API and is designed for agents to freely experiment within a sandboxed environment.

Pezzo is a fully cloud-native and open-source LLMOps platform that allows users to observe and monitor AI operations, troubleshoot issues, save costs and latency, collaborate, manage prompts, and deliver AI changes instantly. It supports various clients for prompt management, observability, and caching. Users can run the full Pezzo stack locally using Docker Compose, with prerequisites including Node.js 18+, Docker, and a GraphQL Language Feature Support VSCode Extension. Contributions are welcome, and the source code is available under the Apache 2.0 License.

Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.

This repository contains shared codebase for two projects: gcloud-aio and gcloud-rest. gcloud-aio is built for Python 3's asyncio, while gcloud-rest is a threadsafe requests-based implementation. It provides clients for Google Cloud services like Auth, BigQuery, Datastore, KMS, PubSub, Storage, and Task Queue. Users can install the library using pip and refer to the documentation for usage details. Developers can contribute to the project by following the contribution guide.

Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intensive applications, such as big data and AI applications. It implements dataset abstraction, scalable cache runtime, automated data operations, elasticity and scheduling, and is runtime platform agnostic. Key concepts include Dataset and Runtime. Prerequisites include Kubernetes version > 1.16, Golang 1.18+, and Helm 3. The tool offers features like accelerating remote file accessing, machine learning, accelerating PVC, preloading dataset, and on-the-fly dataset cache scaling. Contributions are welcomed, and the project is under the Apache 2.0 license with a vendor-neutral approach.

AIGES is a core component of the Athena Serving Framework, designed as a universal encapsulation tool for AI developers to deploy AI algorithm models and engines quickly. By integrating AIGES, you can deploy AI algorithm models and engines rapidly and host them on the Athena Serving Framework, utilizing supporting auxiliary systems for networking, distribution strategies, data processing, etc. The Athena Serving Framework aims to accelerate the cloud service of AI algorithm models and engines, providing multiple guarantees for cloud service stability through cloud-native architecture. You can efficiently and securely deploy, upgrade, scale, operate, and monitor models and engines without focusing on underlying infrastructure and service-related development, governance, and operations.