YAMS (Yet Another Memory System) is a persistent memory system designed for Large Language Models (LLMs) and applications. It provides content-addressed storage with features such as deduplication, compression, full-text search, and vector search. The system is built with SHA-256 content-addressed store, block-level deduplication, full-text search using SQLite FTS5, semantic search with embeddings, WAL-backed durability, high-throughput I/O, and thread-safe operations. YAMS supports Linux x86_64/ARM64 and macOS x86_64/ARM64 platforms. It is recommended to build using Conan for managing dependencies and ensuring proper installation. Users can interact with YAMS through a command-line interface for tasks like initialization, adding content, searching, and retrieving data. Additionally, YAMS provides LLM-friendly patterns for caching web content, storing code diffs, and integrating with other systems through an API in C++. Troubleshooting tips include creating a default Conan profile and handling PDF support issues during the build process. The project is licensed under Apache-2.0.

Candle-vllm is an efficient and easy-to-use platform designed for inference and serving local LLMs, featuring an OpenAI compatible API server. It offers a highly extensible trait-based system for rapid implementation of new module pipelines, streaming support in generation, efficient management of key-value cache with PagedAttention, and continuous batching. The tool supports chat serving for various models and provides a seamless experience for users to interact with LLMs through different interfaces.

Jina-Serve is a framework for building and deploying AI services that communicate via gRPC, HTTP and WebSockets. It provides native support for major ML frameworks and data types, high-performance service design with scaling and dynamic batching, LLM serving with streaming output, built-in Docker integration and Executor Hub, one-click deployment to Jina AI Cloud, and enterprise-ready features with Kubernetes and Docker Compose support. Users can create gRPC-based AI services, build pipelines, scale services locally with replicas, shards, and dynamic batching, deploy to the cloud using Kubernetes, Docker Compose, or JCloud, and enable token-by-token streaming for responsive LLM applications.

The GenAI repository contains pipelines that implement image and text generation tasks. The implementation uses OpenVINO capabilities to optimize the pipelines. Each sample covers a family of models and suggests certain modifications to adapt the code to specific needs. It includes the following pipelines: 1. Benchmarking script for large language models 2. Text generation C++ samples that support most popular models like LLaMA 2 3. Stable Diffuison (with LoRA) C++ image generation pipeline 4. Latent Consistency Model (with LoRA) C++ image generation pipeline

The Mistral Python Client is a tool inspired by cohere-python that allows users to interact with the Mistral AI API. It provides functionalities to access and utilize the AI capabilities offered by Mistral. Users can easily install the client using pip and manage dependencies using poetry. The client includes examples demonstrating how to use the API for various tasks, such as chat interactions. To get started, users need to obtain a Mistral API Key and set it as an environment variable. Overall, the Mistral Python Client simplifies the integration of Mistral AI services into Python applications.

Echo Editor is a modern AI-powered WYSIWYG rich-text editor for Vue, featuring a beautiful UI with shadcn-vue components. It provides AI-powered writing assistance, Markdown support with real-time preview, rich text formatting, tables, code blocks, custom font sizes and styles, Word document import, I18n support, extensible architecture for creating extensions, TypeScript and Tailwind CSS support. The tool aims to enhance the writing experience by combining advanced features with user-friendly design.

Celery AsyncIO Pool is a free software tool licensed under GNU Affero General Public License v3+. It provides an AsyncIO worker pool for Celery, enabling users to leverage the power of AsyncIO in their Celery applications. The tool allows for easy installation using Poetry, pip, or directly from GitHub. Users can configure Celery to use the AsyncIO pool provided by celery-aio-pool, or they can wait for the upcoming support for out-of-tree worker pools in Celery 5.3. The tool is actively maintained and welcomes contributions from the community.

AnglE is a library for training state-of-the-art BERT/LLM-based sentence embeddings with just a few lines of code. It also serves as a general sentence embedding inference framework, allowing for inferring a variety of transformer-based sentence embeddings. The library supports various loss functions such as AnglE loss, Contrastive loss, CoSENT loss, and Espresso loss. It provides backbones like BERT-based models, LLM-based models, and Bi-directional LLM-based models for training on single or multi-GPU setups. AnglE has achieved significant performance on various benchmarks and offers official pretrained models for both BERT-based and LLM-based models.

FastRTC is a real-time communication library for Python that allows users to turn any Python function into a real-time audio and video stream over WebRTC or WebSockets. It provides features like automatic voice detection, UI launching, WebRTC support, WebSocket support, telephone support, and customizable backend for production applications. The library offers various examples and usage scenarios for audio and video streaming, object detection, voice APIs, chat applications, and more.

starknet-agent-kit is a NestJS-based toolkit for creating AI agents that can interact with the Starknet blockchain. It allows users to perform various actions such as retrieving account information, creating accounts, transferring assets, playing with DeFi, interacting with dApps, and executing RPC read methods. The toolkit provides a secure environment for developing AI agents while emphasizing caution when handling sensitive information. Users can make requests to the Starknet agent via API endpoints and utilize tools from Langchain directly.

Obsei is an open-source, low-code, AI powered automation tool that consists of an Observer to collect unstructured data from various sources, an Analyzer to analyze the collected data with various AI tasks, and an Informer to send analyzed data to various destinations. The tool is suitable for scheduled jobs or serverless applications as all Observers can store their state in databases. Obsei is still in alpha stage, so caution is advised when using it in production. The tool can be used for social listening, alerting/notification, automatic customer issue creation, extraction of deeper insights from feedbacks, market research, dataset creation for various AI tasks, and more based on creativity.

MCPHub.nvim is a powerful Neovim plugin that integrates MCP (Model Context Protocol) servers into your workflow. It offers a centralized config file for managing servers and tools, with an intuitive UI for testing resources. Ideal for LLM integration, it provides programmatic API access and interactive testing through the `:MCPHub` command.

Educhain is a powerful Python package that leverages Generative AI to create engaging and personalized educational content. It enables users to generate multiple-choice questions, create lesson plans, and support various LLM models. Users can export questions to JSON, PDF, and CSV formats, customize prompt templates, and generate questions from text, PDF, URL files, youtube videos, and images. Educhain outperforms traditional methods in content generation speed and quality. It offers advanced configuration options and has a roadmap for future enhancements, including integration with popular Learning Management Systems and a mobile app for content generation on-the-go.

E2M is a Python library that can parse and convert various file types into Markdown format. It supports the conversion of multiple file formats, including doc, docx, epub, html, htm, url, pdf, ppt, pptx, mp3, and m4a. The ultimate goal of the E2M project is to provide high-quality data for Retrieval-Augmented Generation (RAG) and model training or fine-tuning. The core architecture consists of a Parser responsible for parsing various file types into text or image data, and a Converter responsible for converting text or image data into Markdown format.

Code-graph is a tool composed of FalkorDB Graph DB, Code-Graph-Backend, and Code-Graph-Frontend. It allows users to store and query graphs, manage backend logic, and interact with the website. Users can run the components locally by setting up environment variables and installing dependencies. The tool supports analyzing C & Python source files with plans to add support for more languages in the future. It provides a local repository analysis feature and a live demo accessible through a web browser.

LangDB AI Gateway is an open-source enterprise AI gateway built in Rust. It provides a unified interface to all LLMs using the OpenAI API format, focusing on high performance, enterprise readiness, and data control. The gateway offers features like comprehensive usage analytics, cost tracking, rate limiting, data ownership, and detailed logging. It supports various LLM providers and provides OpenAI-compatible endpoints for chat completions, model listing, embeddings generation, and image generation. Users can configure advanced settings, such as rate limiting, cost control, dynamic model routing, and observability with OpenTelemetry tracing. The gateway can be run with Docker Compose and integrated with MCP tools for server communication.

Metaflow is a user-friendly library designed to assist scientists and engineers in developing and managing real-world data science projects. Initially created at Netflix, Metaflow aimed to enhance the productivity of data scientists working on diverse projects ranging from traditional statistics to cutting-edge deep learning. For further information, refer to Metaflow's website and documentation.

Collective Mind (CM) is a collection of portable, extensible, technology-agnostic and ready-to-use automation recipes with a human-friendly interface (aka CM scripts) to unify and automate all the manual steps required to compose, run, benchmark and optimize complex ML/AI applications on any platform with any software and hardware: see online catalog and source code. CM scripts require Python 3.7+ with minimal dependencies and are continuously extended by the community and MLCommons members to run natively on Ubuntu, MacOS, Windows, RHEL, Debian, Amazon Linux and any other operating system, in a cloud or inside automatically generated containers while keeping backward compatibility - please don't hesitate to report encountered issues here and contact us via public Discord Server to help this collaborative engineering effort! CM scripts were originally developed based on the following requirements from the MLCommons members to help them automatically compose and optimize complex MLPerf benchmarks, applications and systems across diverse and continuously changing models, data sets, software and hardware from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors: * must work out of the box with the default options and without the need to edit some paths, environment variables and configuration files; * must be non-intrusive, easy to debug and must reuse existing user scripts and automation tools (such as cmake, make, ML workflows, python poetry and containers) rather than substituting them; * must have a very simple and human-friendly command line with a Python API and minimal dependencies; * must require minimal or zero learning curve by using plain Python, native scripts, environment variables and simple JSON/YAML descriptions instead of inventing new workflow languages; * must have the same interface to run all automations natively, in a cloud or inside containers. CM scripts were successfully validated by MLCommons to modularize MLPerf inference benchmarks and help the community automate more than 95% of all performance and power submissions in the v3.1 round across more than 120 system configurations (models, frameworks, hardware) while reducing development and maintenance costs.

The Mistral Python Client is a tool inspired by cohere-python that allows users to interact with the Mistral AI API. It provides functionalities to access and utilize the AI capabilities offered by Mistral. Users can easily install the client using pip and manage dependencies using poetry. The client includes examples demonstrating how to use the API for various tasks, such as chat interactions. To get started, users need to obtain a Mistral API Key and set it as an environment variable. Overall, the Mistral Python Client simplifies the integration of Mistral AI services into Python applications.

ComposeAI is an Android & iOS application similar to ChatGPT, built using Compose Multiplatform. It utilizes various technologies such as Compose Multiplatform, Material 3, OpenAI Kotlin, Voyager, Koin, SQLDelight, Multiplatform Settings, Coil3, Napier, BuildKonfig, Firebase Analytics & Crashlytics, and AdMob. The app architecture follows Google's latest guidelines. Users need to set up their own OpenAI API key before using the app.

aiohttp-devtools provides dev tools for developing applications with aiohttp and associated libraries. It includes CLI commands for running a local server with live reloading and serving static files. The tools aim to simplify the development process by automating tasks such as setting up a new application and managing dependencies. Developers can easily create and run aiohttp applications, manage static files, and utilize live reloading for efficient development.

BitBom Minefield is a tool that uses roaring bit maps to graph Software Bill of Materials (SBOMs) with a focus on speed, air-gapped operation, scalability, and customizability. It is optimized for rapid data processing, operates securely in isolated environments, supports millions of nodes effortlessly, and allows users to extend the project without relying on upstream changes. The tool enables users to manage and explore software dependencies within isolated environments by offline processing and analyzing SBOMs.

Gez is a high-performance micro frontend framework based on ESM. It uses Rspack compilation and maps modules to URLs with strong caching and content-based hashing. Gez embraces modern micro frontend architecture by leveraging ESM and importmap for dependency management, providing reliable isolation with module scope, seamless integration with any modern frontend framework, intuitive development experience, and optimal performance with zero runtime overhead and reliable caching strategies.

Forge is a powerful open-source tool for building modern web applications. It provides a simple and intuitive interface for developers to quickly scaffold and deploy projects. With Forge, you can easily create custom components, manage dependencies, and streamline your development workflow. Whether you are a beginner or an experienced developer, Forge offers a flexible and efficient solution for your web development needs.

DB2Rest is a modern low-code REST DATA API platform that simplifies the development of intelligent applications. It seamlessly integrates existing and new databases with language models (LMs/LLMs) and vector stores, enabling the rapid delivery of context-aware, reasoning applications without vendor lock-in.

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.

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.

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)

Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

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 low-code Connector Development Kit (CDK). Airbyte is used by data engineers and analysts at companies of all sizes to move data for a variety of purposes, including data warehousing, data analysis, and machine learning.

Chronon is a platform that simplifies and improves ML workflows by providing a central place to define features, ensuring point-in-time correctness for backfills, simplifying orchestration for batch and streaming pipelines, offering easy endpoints for feature fetching, and guaranteeing and measuring consistency. It offers benefits over other approaches by enabling the use of a broad set of data for training, handling large aggregations and other computationally intensive transformations, and abstracting away the infrastructure complexity of data plumbing.