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.

Vespa is an open-source search and AI engine that provides a unified platform for building and deploying search and AI applications. Vespa sample applications showcase various use cases and features of Vespa, including basic search, recommendation, semantic search, image search, text ranking, e-commerce search, question answering, search-as-you-type, and ML inference serving.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

Freeciv-web is an open-source turn-based strategy game that can be played in any HTML5 capable web-browser. It features in-depth gameplay, a wide variety of game modes and options. Players aim to build cities, collect resources, organize their government, and build an army to create the best civilization. The game offers both multiplayer and single-player modes, with a 2D version with isometric graphics and a 3D WebGL version available. The project consists of components like Freeciv-web, Freeciv C server, Freeciv-proxy, Publite2, and pbem for play-by-email support. Developers interested in contributing can check the GitHub issues and TODO file for tasks to work on.

Aphrodite is an inference engine optimized for serving HuggingFace-compatible models at scale. It leverages vLLM's Paged Attention technology to deliver high-performance model inference for multiple concurrent users. The engine supports continuous batching, efficient key/value management, optimized CUDA kernels, quantization support, distributed inference, and modern samplers. It can be easily installed and launched, with Docker support for deployment. Aphrodite requires Linux or Windows OS, Python 3.8 to 3.12, and CUDA >= 11. It is designed to utilize 90% of GPU VRAM but offers options to limit memory usage. Contributors are welcome to enhance the engine.

Pathway's LLM (Large Language Model) Apps provide a platform to quickly deploy AI applications using the latest knowledge from data sources. The Python application examples in this repository are Docker-ready, exposing an HTTP API to the frontend. These apps utilize the Pathway framework for data synchronization, API serving, and low-latency data processing without the need for additional infrastructure dependencies. They connect to document data sources like S3, Google Drive, and Sharepoint, offering features like real-time data syncing, easy alert setup, scalability, monitoring, security, and unification of application logic.

Athina is an open-source library designed to help engineers improve the reliability and performance of Large Language Models (LLMs) through eval-driven development. It offers plug-and-play preset evals for catching and preventing bad outputs, measuring model performance, running experiments, A/B testing models, detecting regressions, and monitoring production data. Athina provides a solution to the flaws in current LLM developer workflows by offering rapid experimentation, customizable evaluators, integrated dashboard, consistent metrics, historical record tracking, and easy setup. It includes preset evaluators for RAG applications and summarization accuracy, as well as the ability to write custom evals. Athina's evals can run on both development and production environments, providing consistent metrics and removing the need for manual infrastructure setup.

Aphrodite is the official backend engine for PygmalionAI, serving as the inference endpoint for the website. It allows serving Hugging Face-compatible models with fast speeds. Features include continuous batching, efficient K/V management, optimized CUDA kernels, quantization support, distributed inference, and 8-bit KV Cache. The engine requires Linux OS and Python 3.8 to 3.12, with CUDA >= 11 for build requirements. It supports various GPUs, CPUs, TPUs, and Inferentia. Users can limit GPU memory utilization and access full commands via CLI.

AIS-catcher: A multi-platform AIS Receiver

This repository provides a hands-on workshop for developers and solution builders to build a real-life serverless LLM application using foundation models (FMs) through Amazon Bedrock and advanced design patterns such as Reason and Act (ReAct) Agent, text-to-SQL, and Retrieval Augmented Generation (RAG). It guides users through labs to explore common and advanced LLM application design patterns, helping them build a complex Agentic LLM assistant capable of answering retrieval and analytical questions on internal knowledge bases. The repository includes labs on IaC with AWS CDK, building serverless LLM assistants with AWS Lambda and Amazon Bedrock, refactoring LLM assistants into custom agents, extending agents with semantic retrieval, and querying SQL databases. Users need to set up AWS Cloud9, configure model access on Amazon Bedrock, and use Amazon SageMaker Studio environment to run data-pipelines notebooks.

ManipVQA is a framework that enhances Multimodal Large Language Models (MLLMs) with manipulation-centric knowledge through a Visual Question-Answering (VQA) format. It addresses the deficiency of conventional MLLMs in understanding affordances and physical concepts crucial for manipulation tasks. By infusing robotics-specific knowledge, including tool detection, affordance recognition, and physical concept comprehension, ManipVQA improves the performance of robots in manipulation tasks. The framework involves fine-tuning MLLMs with a curated dataset of interactive objects, enabling robots to understand and execute natural language instructions more effectively.

GenAI Factory is a collection of end-to-end blueprints to deploy generative AI infrastructures in Google Cloud Platform (GCP), following security best practices. It embraces Infrastructure as Code (IaC) best practices, implements infrastructure in Terraform, and follows the least-privilege principle. The tool is compatible with Cloud Foundation Fabric FAST project-factory and application templates, allowing users to deploy various AI applications and systems on GCP.

The IBM Analog Hardware Acceleration Kit is an open-source Python toolkit for exploring and using the capabilities of in-memory computing devices in the context of artificial intelligence. It consists of two main components: Pytorch integration and Analog devices simulator. The Pytorch integration provides a series of primitives and features that allow using the toolkit within PyTorch, including analog neural network modules, analog training using torch training workflow, and analog inference using torch inference workflow. The Analog devices simulator is a high-performant (CUDA-capable) C++ simulator that allows for simulating a wide range of analog devices and crossbar configurations by using abstract functional models of material characteristics with adjustable parameters. Along with the two main components, the toolkit includes other functionalities such as a library of device presets, a module for executing high-level use cases, a utility to automatically convert a downloaded model to its equivalent Analog model, and integration with the AIHW Composer platform. The toolkit is currently in beta and under active development, and users are advised to be mindful of potential issues and keep an eye for improvements, new features, and bug fixes in upcoming versions.

Vespa is a scalable open-source serving engine that enables users to store, compute, and rank big data at user serving time. Pyvespa provides a Python API to Vespa, allowing users to create, modify, deploy, and interact with running Vespa instances. The library's primary purpose is to facilitate faster prototyping and familiarization with Vespa features.

Spring AI Alibaba is an AI application framework for Java developers that seamlessly integrates with Alibaba Cloud QWen LLM services and cloud-native infrastructures. It provides features like support for various AI models, high-level AI agent abstraction, function calling, and RAG support. The framework aims to simplify the development, evaluation, deployment, and observability of AI native Java applications. It offers open-source framework and ecosystem integrations to support features like prompt template management, event-driven AI applications, and more.

LLM-D is a machine learning model for sentiment analysis. It is designed to classify text data into positive, negative, or neutral sentiment categories. The model is trained on a large dataset of labeled text samples and uses natural language processing techniques to analyze and predict sentiment in new text inputs. LLM-D is a powerful tool for businesses and researchers looking to understand customer feedback, social media sentiment, and other text data sources. It can be easily integrated into existing applications or used as a standalone tool for sentiment analysis tasks.

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.

TableLLM is a large language model designed for efficient tabular data manipulation tasks in real office scenarios. It can generate code solutions or direct text answers for tasks like insert, delete, update, query, merge, and chart operations on tables embedded in spreadsheets or documents. The model has been fine-tuned based on CodeLlama-7B and 13B, offering two scales: TableLLM-7B and TableLLM-13B. Evaluation results show its performance on benchmarks like WikiSQL, Spider, and self-created table operation benchmark. Users can use TableLLM for code and text generation tasks on tabular data.

AI Analyst by E2B is an AI-powered code and data analysis tool built with Next.js and the E2B SDK. It allows users to analyze data with Meta's Llama 3.1, upload CSV files, and create interactive charts. The tool is powered by E2B Sandbox, Vercel's AI SDK, Next.js, and echarts library for interactive charts. Supported LLM providers include TogetherAI and Fireworks, with various chart types available for visualization.

Xpert is a powerful tool for data analysis and visualization. It provides a user-friendly interface to explore and manipulate datasets, perform statistical analysis, and create insightful visualizations. With Xpert, users can easily import data from various sources, clean and preprocess data, analyze trends and patterns, and generate interactive charts and graphs. Whether you are a data scientist, analyst, researcher, or student, Xpert simplifies the process of data analysis and visualization, making it accessible to users with varying levels of expertise.

Doris is a lightweight and user-friendly data visualization tool designed for quick and easy exploration of datasets. It provides a simple interface for users to upload their data and generate interactive visualizations without the need for coding. With Doris, users can easily create charts, graphs, and dashboards to analyze and present their data in a visually appealing way. The tool supports various data formats and offers customization options to tailor visualizations to specific needs. Whether you are a data analyst, researcher, or student, Doris simplifies the process of data exploration and presentation.

Clewdr is a collaborative platform for data analysis and visualization. It allows users to upload datasets, perform various data analysis tasks, and create interactive visualizations. The platform supports multiple users working on the same project simultaneously, enabling real-time collaboration and sharing of insights. Clewdr is designed to streamline the data analysis process and facilitate communication among team members. With its user-friendly interface and powerful features, Clewdr is suitable for data scientists, analysts, researchers, and anyone working with data to gain valuable insights and make informed decisions.

CISO Assistant is a tool that helps organizations manage their cybersecurity posture and compliance. It provides a centralized platform for managing security controls, threats, and risks. CISO Assistant also includes a library of pre-built frameworks and tools to help organizations quickly and easily implement best practices.

SuperSonic is a next-generation BI platform that integrates Chat BI (powered by LLM) and Headless BI (powered by semantic layer) paradigms. This integration ensures that Chat BI has access to the same curated and governed semantic data models as traditional BI. Furthermore, the implementation of both paradigms benefits from the integration: * Chat BI's Text2SQL gets augmented with context-retrieval from semantic models. * Headless BI's query interface gets extended with natural language API. SuperSonic provides a Chat BI interface that empowers users to query data using natural language and visualize the results with suitable charts. To enable such experience, the only thing necessary is to build logical semantic models (definition of metric/dimension/tag, along with their meaning and relationships) through a Headless BI interface. Meanwhile, SuperSonic is designed to be extensible and composable, allowing custom implementations to be added and configured with Java SPI. The integration of Chat BI and Headless BI has the potential to enhance the Text2SQL generation in two dimensions: 1. Incorporate data semantics (such as business terms, column values, etc.) into the prompt, enabling LLM to better understand the semantics and reduce hallucination. 2. Offload the generation of advanced SQL syntax (such as join, formula, etc.) from LLM to the semantic layer to reduce complexity. With these ideas in mind, we develop SuperSonic as a practical reference implementation and use it to power our real-world products. Additionally, to facilitate further development we decide to open source SuperSonic as an extensible framework.

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.