The 'airflow-provider-great-expectations' repository contains a set of Airflow operators for Great Expectations, a Python library used for testing and validating data. The operators enable users to run Great Expectations validations and checks within Apache Airflow workflows. The package requires Airflow 2.1.0+ and Great Expectations >=v0.13.9. It provides functionalities to work with Great Expectations V3 Batch Request API, Checkpoints, and allows passing kwargs to Checkpoints at runtime. The repository includes modules for a base operator and examples of DAGs with sample tasks demonstrating the operator's functionality.

Repopack is a powerful tool that packs your entire repository into a single, AI-friendly file. It optimizes your codebase for AI comprehension, is simple to use with customizable options, and respects Gitignore files for security. The tool generates a packed file with clear separators and AI-oriented explanations, making it ideal for use with Generative AI tools like Claude or ChatGPT. Repopack offers command line options, configuration settings, and multiple methods for setting ignore patterns to exclude specific files or directories during the packing process. It includes features like comment removal for supported file types and a security check using Secretlint to detect sensitive information in files.

Repomix is a powerful tool that packs your entire repository into a single, AI-friendly file. It is designed to format your codebase for easy understanding by AI tools like Large Language Models (LLMs), Claude, ChatGPT, and Gemini. Repomix offers features such as AI optimization, token counting, simplicity in usage, customization options, Git awareness, and security-focused checks using Secretlint. It allows users to pack their entire repository or specific directories/files using glob patterns, and even supports processing remote Git repositories. The tool generates output in plain text, XML, or Markdown formats, with options for including/excluding files, removing comments, and performing security checks. Repomix also provides a global configuration option, custom instructions for AI context, and a security check feature to detect sensitive information in files.

aiobotocore is an async client for Amazon services using botocore and aiohttp/asyncio. It provides a mostly full-featured asynchronous version of botocore, allowing users to interact with various AWS services asynchronously. The library supports operations such as uploading objects to S3, getting object properties, listing objects, and deleting objects. It also offers context manager examples for managing resources efficiently. aiobotocore supports multiple AWS services like S3, DynamoDB, SNS, SQS, CloudFormation, and Kinesis, with basic methods tested for each service. Users can run tests using moto for mocked tests or against personal Amazon keys. Additionally, the tool enables type checking and code completion for better development experience.

HuixiangDou is a **group chat** assistant based on LLM (Large Language Model). Advantages: 1. Design a two-stage pipeline of rejection and response to cope with group chat scenario, answer user questions without message flooding, see arxiv2401.08772 2. Low cost, requiring only 1.5GB memory and no need for training 3. Offers a complete suite of Web, Android, and pipeline source code, which is industrial-grade and commercially viable Check out the scenes in which HuixiangDou are running and join WeChat Group to try AI assistant inside. If this helps you, please give it a star ⭐

MLP Mixer - Pytorch is an all-MLP solution for vision tasks, developed by Google AI, implemented in Pytorch. It provides an architecture that does not require convolutions or attention mechanisms, offering an alternative approach for image and video processing. The tool is designed to handle tasks related to image classification and video recognition, utilizing multi-layer perceptrons (MLPs) for feature extraction and classification. Users can easily install the tool using pip and integrate it into their Pytorch projects to experiment with MLP-based vision models.

Suno AI API is an open-source project that allows developers to integrate the music generation capabilities of Suno.ai into their own applications. The API provides a simple and convenient way to generate music, lyrics, and other audio content using Suno.ai's powerful AI models. With Suno AI API, developers can easily add music generation functionality to their apps, websites, and other projects.

Ivy is an open-source machine learning framework that enables you to: * 🔄 **Convert code into any framework** : Use and build on top of any model, library, or device by converting any code from one framework to another using `ivy.transpile`. * ⚒️ **Write framework-agnostic code** : Write your code once in `ivy` and then choose the most appropriate ML framework as the backend to leverage all the benefits and tools. Join our growing community 🌍 to connect with people using Ivy. **Let's** unify.ai **together 🦾**

LitData is a tool designed for blazingly fast, distributed streaming of training data from any cloud storage. It allows users to transform and optimize data in cloud storage environments efficiently and intuitively, supporting various data types like images, text, video, audio, geo-spatial, and multimodal data. LitData integrates smoothly with frameworks such as LitGPT and PyTorch, enabling seamless streaming of data to multiple machines. Key features include multi-GPU/multi-node support, easy data mixing, pause & resume functionality, support for profiling, memory footprint reduction, cache size configuration, and on-prem optimizations. The tool also provides benchmarks for measuring streaming speed and conversion efficiency, along with runnable templates for different data types. LitData enables infinite cloud data processing by utilizing the Lightning.ai platform to scale data processing with optimized machines.

Ivy is an open-source machine learning framework that enables users to convert code between different ML frameworks and write framework-agnostic code. It allows users to transpile code from one framework to another, making it easy to use building blocks from different frameworks in a single project. Ivy also serves as a flexible framework that breaks free from framework limitations, allowing users to publish code that is interoperable with various frameworks and future frameworks. Users can define trainable modules and layers using Ivy's stateful API, making it easy to build and train models across different backends.

Eval-Scope is a framework designed to support the evaluation of large language models (LLMs) by providing pre-configured benchmark datasets, common evaluation metrics, model integration, automatic evaluation for objective questions, complex task evaluation using expert models, reports generation, visualization tools, and model inference performance evaluation. It is lightweight, easy to customize, supports new dataset integration, model hosting on ModelScope, deployment of locally hosted models, and rich evaluation metrics. Eval-Scope also supports various evaluation modes like single mode, pairwise-baseline mode, and pairwise (all) mode, making it suitable for assessing and improving LLMs.

Primitive cuda kernel library for ppl.nn.llm, part of PPL.LLM system, tested on Ampere and Hopper, requires Linux on x86_64 or arm64 CPUs, GCC >= 9.4.0, CMake >= 3.18, Git >= 2.7.0, CUDA Toolkit >= 11.4. 11.6 recommended. Provides cuda kernel functionalities for deep learning tasks.

Composio is a production-ready toolset for AI agents that enables users to integrate AI agents with various agentic tools effortlessly. It provides support for over 100 tools across different categories, including popular softwares like GitHub, Notion, Linear, Gmail, Slack, and more. Composio ensures managed authorization with support for six different authentication protocols, offering better agentic accuracy and ease of use. Users can easily extend Composio with additional tools, frameworks, and authorization protocols. The toolset is designed to be embeddable and pluggable, allowing for seamless integration and consistent user experience.

LangGraph for Java is a library designed for building stateful, multi-agent applications with LLMs. It is a porting of the original LangGraph from the LangChain AI project to Java. The library allows users to define agent states, nodes, and edges in a graph structure to create complex workflows. It integrates with LangChain4j and provides tools for executing actions based on agent decisions. LangGraph for Java enables users to create asynchronous node actions, conditional edges, and normal edges to model decision-making processes in applications.

Ant Design X is a tool for crafting AI-driven interfaces effortlessly. It is built on the best practices of enterprise-level AI products, offering flexible and diverse atomic components for various AI dialogue scenarios. The tool provides out-of-the-box model integration with inference services compatible with OpenAI standards. It also enables efficient management of conversation data flows, supports rich template options, complete TypeScript support, and advanced theme customization. Ant Design X is designed to enhance development efficiency and deliver exceptional AI interaction experiences.

MarkLLM is an open-source toolkit designed for watermarking technologies within large language models (LLMs). It simplifies access, understanding, and assessment of watermarking technologies, supporting various algorithms, visualization tools, and evaluation modules. The toolkit aids researchers and the community in ensuring the authenticity and origin of machine-generated text.

A Python package that helps Data and Analytics engineers render dbt projects in Apache Airflow DAGs. It enables teams to automatically render their dbt projects in a granular level, creating individual Airflow tasks for every model, seed, snapshot, and test within the dbt project. This allows for full control at the task-level, improving visibility and management of data models within the team.

The 'airflow-provider-great-expectations' repository contains a set of Airflow operators for Great Expectations, a Python library used for testing and validating data. The operators enable users to run Great Expectations validations and checks within Apache Airflow workflows. The package requires Airflow 2.1.0+ and Great Expectations >=v0.13.9. It provides functionalities to work with Great Expectations V3 Batch Request API, Checkpoints, and allows passing kwargs to Checkpoints at runtime. The repository includes modules for a base operator and examples of DAGs with sample tasks demonstrating the operator's functionality.

The Radicalbit AI Monitoring Platform provides a comprehensive solution for monitoring Machine Learning and Large Language models in production. It helps proactively identify and address potential performance issues by analyzing data quality, model quality, and model drift. The repository contains files and projects for running the platform, including UI, API, SDK, and Spark components. Installation using Docker compose is provided, allowing deployment with a K3s cluster and interaction with a k9s container. The platform documentation includes a step-by-step guide for installation and creating dashboards. Community engagement is encouraged through a Discord server. The roadmap includes adding functionalities for batch and real-time workloads, covering various model types and tasks.

DataHub is an open-source data catalog designed for the modern data stack. It provides a platform for managing metadata, enabling users to discover, understand, and collaborate on data assets within their organization. DataHub offers features such as data lineage tracking, data quality monitoring, and integration with various data sources. It is built with contributions from Acryl Data and LinkedIn, aiming to streamline data management processes and enhance data discoverability across different teams and departments.

The Open Data Editor (ODE) is a no-code application to explore, validate and publish data in a simple way. It is an open source project powered by the Frictionless Framework. The ODE is currently available for download and testing in beta.

Instructor is a Typescript library for structured extraction in Typescript, powered by llms, designed for simplicity, transparency, and control. It stands out for its simplicity, transparency, and user-centric design. Whether you're a seasoned developer or just starting out, you'll find Instructor's approach intuitive and steerable.

Aiohttp pydantic is an aiohttp view to easily parse and validate requests. You define using function annotations what your methods for handling HTTP verbs expect, and Aiohttp pydantic parses the HTTP request for you, validates the data, and injects the parameters you want. It provides features like query string, request body, URL path, and HTTP headers validation, as well as Open API Specification generation.

island-ai is a TypeScript toolkit tailored for developers engaging with structured outputs from Large Language Models. It offers streamlined processes for handling, parsing, streaming, and leveraging AI-generated data across various applications. The toolkit includes packages like zod-stream for interfacing with LLM streams, stream-hooks for integrating streaming JSON data into React applications, and schema-stream for JSON streaming parsing based on Zod schemas. Additionally, related packages like @instructor-ai/instructor-js focus on data validation and retry mechanisms, enhancing the reliability of data processing workflows.

The vscode-dbt-power-user is an open-source extension that enhances the functionality of Visual Studio Code to seamlessly work with dbt™. It provides features such as auto-complete for dbt™ code, previewing query results, column lineage visualization, generating dbt™ models, documentation generation, deferring model builds, running parent/child models and tests with a click, compiled query preview and explanation, project health check, SQL validation, BigQuery cost estimation, and other features like dbt™ logs viewer. The extension is fully compatible with dev containers, code spaces, and remote extensions, supporting dbt™ versions above 1.0.

SheetCopilot is an assistant agent that manipulates spreadsheets by following user commands. It leverages Large Language Models (LLMs) to interact with spreadsheets like a human expert, enabling non-expert users to complete tasks on complex software such as Google Sheets and Excel via a language interface. The tool observes spreadsheet states, polishes generated solutions based on external action documents and error feedback, and aims to improve success rate and efficiency. SheetCopilot offers a dataset with diverse task categories and operations, supporting operations like entry & manipulation, management, formatting, charts, and pivot tables. Users can interact with SheetCopilot in Excel or Google Sheets, executing tasks like calculating revenue, creating pivot tables, and plotting charts. The tool's evaluation includes performance comparisons with leading LLMs and VBA-based methods on specific datasets, showcasing its capabilities in controlling various aspects of a spreadsheet.

Wren Engine is a semantic engine designed to serve as the backbone of the semantic layer for LLMs. It simplifies the user experience by translating complex data structures into a business-friendly format, enabling end-users to interact with data using familiar terminology. The engine powers the semantic layer with advanced capabilities to define and manage modeling definitions, metadata, schema, data relationships, and logic behind calculations and aggregations through an analytics-as-code design approach. By leveraging Wren Engine, organizations can ensure a developer-friendly semantic layer that reflects nuanced data relationships and dynamics, facilitating more informed decision-making and strategic insights.

The mslearn-knowledge-mining repository contains lab files for Azure AI Knowledge Mining modules. It provides resources for learning and implementing knowledge mining techniques using Azure AI services. The repository is designed to help users explore and understand how to leverage AI for knowledge mining purposes within the Azure ecosystem.

The Looker GenAI Extension provides code examples and resources for building a Looker Extension that integrates with Vertex AI Large Language Models (LLMs). Users can leverage the power of LLMs to enhance data exploration and analysis within Looker. The extension offers generative explore functionality to ask natural language questions about data and generative insights on dashboards to analyze data by asking questions. It leverages components like BQML Remote Models, BQML Remote UDF with Vertex AI, and Custom Fine Tune Model for different integration options. Deployment involves setting up infrastructure with Terraform and deploying the Looker Extension by creating a Looker project, copying extension files, configuring BigQuery connection, connecting to Git, and testing the extension. Users can save example prompts and configure user settings for the extension. Development of the Looker Extension environment includes installing dependencies, starting the development server, and building for production.

Postgres.new is an in-browser Postgres sandbox with AI assistance that allows users to spin up unlimited Postgres databases directly in the browser. Each database comes with a large language model (LLM) enabling features like drag-and-drop CSV import, report generation, chart creation, and database diagram building. The tool utilizes PGlite, a WASM version of Postgres, to run databases in the browser and store data in IndexedDB for persistence. The monorepo includes a frontend built with Next.js and a backend serving S3-backed PGlite databases over the PG wire protocol using pg-gateway.

This repository focuses on utilizing generative AI to bridge the gap between natural language questions and SQL queries, aiming to improve data consumption in enterprise data warehouses. It addresses challenges in SQL query generation, such as foreign key relationships and table joins, and highlights the importance of accuracy metrics like Execution Accuracy (EX) and Exact Set Match Accuracy (EM). The workshop content covers advanced prompt engineering, Retrieval Augmented Generation (RAG), fine-tuning models, and security measures against prompt and SQL injections.

The 'airflow-provider-great-expectations' repository contains a set of Airflow operators for Great Expectations, a Python library used for testing and validating data. The operators enable users to run Great Expectations validations and checks within Apache Airflow workflows. The package requires Airflow 2.1.0+ and Great Expectations >=v0.13.9. It provides functionalities to work with Great Expectations V3 Batch Request API, Checkpoints, and allows passing kwargs to Checkpoints at runtime. The repository includes modules for a base operator and examples of DAGs with sample tasks demonstrating the operator's functionality.