This repository contains notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage generative AI workflows using Generative AI on Google Cloud, powered by Vertex AI. For more Vertex AI samples, please visit the Vertex AI samples Github repository.

Phi Cookbook is a repository containing hands-on examples with Microsoft's Phi models, which are a series of open source AI models developed by Microsoft. Phi is currently the most powerful and cost-effective small language model with benchmarks in various scenarios like multi-language, reasoning, text/chat generation, coding, images, audio, and more. Users can deploy Phi to the cloud or edge devices to build generative AI applications with limited computing power.

Giselle is an open source AI tool designed for agentic workflows, facilitating seamless collaboration between humans and AI. It offers cloud hosting with free agent time, self-hosting options, and a Vibe Cording Guide for using AI coding assistants. Giselle is suitable for developers and non-engineers alike, empowering users to leverage AI capabilities without extensive coding knowledge. The tool is actively developed, with a roadmap in progress, and welcomes contributions from the community under the Apache License Version 2.0.

hugegraph-ai aims to explore the integration of HugeGraph with artificial intelligence (AI) and provide comprehensive support for developers to leverage HugeGraph's AI capabilities in their projects. It includes modules for large language models, graph machine learning, and a Python client for HugeGraph. The project aims to address challenges like timeliness, hallucination, and cost-related issues by integrating graph systems with AI technologies.

Refly.AI is an open-source AI-native creation engine that empowers users to transform ideas into production-ready content. It features a free-form canvas interface with multi-threaded conversations, knowledge base integration, contextual memory, intelligent search, WYSIWYG AI editor, and more. Users can leverage AI-powered capabilities, context memory, knowledge base integration, quotes, and AI document editing to enhance their content creation process. Refly offers both cloud and self-hosting options, making it suitable for individuals, enterprises, and organizations. The tool is designed to facilitate human-AI collaboration and streamline content creation workflows.

CosmosAIGraph is an AI-powered graph and RAG implementation of OmniRAG pattern, utilizing Azure Cosmos DB and other sources. It includes presentations, reference application documentation, FAQs, and a reference dataset of Python libraries pre-vectorized. The project focuses on Azure Cosmos DB for NoSQL and Apache Jena implementation for the in-memory RDF graph. It provides DockerHub images, with plans to add RBAC and Microsoft Entra ID/AAD authentication support, update AI model to gpt-4.5, and offer generic graph examples with a graph generation solution.

Dify is an open-source LLM app development platform that combines AI workflow, RAG pipeline, agent capabilities, model management, observability features, and more. It allows users to quickly go from prototype to production. Key features include: 1. Workflow: Build and test powerful AI workflows on a visual canvas. 2. Comprehensive model support: Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions. 3. Prompt IDE: Intuitive interface for crafting prompts, comparing model performance, and adding additional features. 4. RAG Pipeline: Extensive RAG capabilities that cover everything from document ingestion to retrieval. 5. Agent capabilities: Define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools. 6. LLMOps: Monitor and analyze application logs and performance over time. 7. Backend-as-a-Service: All of Dify's offerings come with corresponding APIs for easy integration into your own business logic.

MindWork AI Studio is a desktop application that provides a unified chat interface for Large Language Models (LLMs). It is free to use for personal and commercial purposes, offers independence in choosing LLM providers, provides unrestricted usage through the providers API, and is cost-effective with pay-as-you-go pricing. The app prioritizes privacy, flexibility, minimal storage and memory usage, and low impact on system resources. Users can support the project through monthly contributions or one-time donations, with opportunities for companies to sponsor the project for public relations and marketing benefits. Planned features include support for more LLM providers, system prompts integration, text replacement for privacy, and advanced interactions tailored for various use cases.

Kalavai is an open-source platform that transforms everyday devices into an AI supercomputer by aggregating resources from multiple machines. It facilitates matchmaking of resources for large AI projects, making AI hardware accessible and affordable. Users can create local and public pools, connect with the community's resources, and share computing power. The platform aims to be a management layer for research groups and organizations, enabling users to unlock the power of existing hardware without needing a devops team. Kalavai CLI tool helps manage both versions of the platform.

The agent-starter-pack is a collection of production-ready Generative AI Agent templates built for Google Cloud. It accelerates development by providing a holistic, production-ready solution, addressing common challenges in building and deploying GenAI agents. The tool offers pre-built agent templates, evaluation tools, production-ready infrastructure, and customization options. It also provides CI/CD automation and data pipeline integration for RAG agents. The starter pack covers all aspects of agent development, from prototyping and evaluation to deployment and monitoring. It is designed to simplify project creation, template selection, and deployment for agent development on Google Cloud.

The AI Platform Engineering repository provides a collection of tools and resources for building and deploying AI models. It includes libraries for data preprocessing, model training, and model serving. The repository also contains example code and tutorials to help users get started with AI development. Whether you are a beginner or an experienced AI engineer, this repository offers valuable insights and best practices to streamline your AI projects.

DevChat is an open-source workflow engine that enables developers to create intelligent, automated workflows for engaging with users through a chat panel within their IDEs. It combines script writing flexibility, latest AI models, and an intuitive chat GUI to enhance user experience and productivity. DevChat simplifies the integration of AI in software development, unlocking new possibilities for developers.

WrenAI is a data assistant tool that helps users get results and insights faster by asking questions in natural language, without writing SQL. It leverages Large Language Models (LLM) with Retrieval-Augmented Generation (RAG) technology to enhance comprehension of internal data. Key benefits include fast onboarding, secure design, and open-source availability. WrenAI consists of three core services: Wren UI (intuitive user interface), Wren AI Service (processes queries using a vector database), and Wren Engine (platform backbone). It is currently in alpha version, with new releases planned biweekly.

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.

AI-Writer is an AI content generation toolkit called Alwrity that automates and enhances the process of blog creation, optimization, and management. It integrates advanced AI models for text generation, image creation, and data analysis, offering features such as online research integration, long-form content generation, AI content planning, multilingual support, prevention of AI hallucinations, multimodal content generation, SEO optimization, and integration with platforms like Wordpress and Jekyll. The toolkit is designed for automated blog management and requires appropriate API keys and access credentials for full functionality.

Buildel is an AI automation platform that empowers users to create versatile workflows without writing code. It supports multiple providers and interfaces, offers pre-built use cases, and allows users to bring their own API keys. Ideal for AI-powered document retrieval, conversational interfaces, and data integration. Users can get started at app.buildel.ai or run Buildel locally with Node.js, Elixir/Erlang, Docker, Git, and JQ installed. Join the community on Discord for support and discussions.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

ONNX Runtime Generative AI is a library that provides the generative AI loop for ONNX models, including inference with ONNX Runtime, logits processing, search and sampling, and KV cache management. Users can call a high level `generate()` method, or run each iteration of the model in a loop. It supports greedy/beam search and TopP, TopK sampling to generate token sequences, has built in logits processing like repetition penalties, and allows for easy custom scoring.

Jupyter AI connects generative AI with Jupyter notebooks. It provides a user-friendly and powerful way to explore generative AI models in notebooks and improve your productivity in JupyterLab and the Jupyter Notebook. Specifically, Jupyter AI offers: * An `%%ai` magic that turns the Jupyter notebook into a reproducible generative AI playground. This works anywhere the IPython kernel runs (JupyterLab, Jupyter Notebook, Google Colab, Kaggle, VSCode, etc.). * A native chat UI in JupyterLab that enables you to work with generative AI as a conversational assistant. * Support for a wide range of generative model providers, including AI21, Anthropic, AWS, Cohere, Gemini, Hugging Face, NVIDIA, and OpenAI. * Local model support through GPT4All, enabling use of generative AI models on consumer grade machines with ease and privacy.

Khoj is an open-source, personal AI assistant that extends your capabilities by creating always-available AI agents. You can share your notes and documents to extend your digital brain, and your AI agents have access to the internet, allowing you to incorporate real-time information. Khoj is accessible on Desktop, Emacs, Obsidian, Web, and Whatsapp, and you can share PDF, markdown, org-mode, notion files, and GitHub repositories. You'll get fast, accurate semantic search on top of your docs, and your agents can create deeply personal images and understand your speech. Khoj is self-hostable and always will be.

LangChain.dart is a Dart port of the popular LangChain Python framework created by Harrison Chase. LangChain provides a set of ready-to-use components for working with language models and a standard interface for chaining them together to formulate more advanced use cases (e.g. chatbots, Q&A with RAG, agents, summarization, extraction, etc.). The components can be grouped into a few core modules: * **Model I/O:** LangChain offers a unified API for interacting with various LLM providers (e.g. OpenAI, Google, Mistral, Ollama, etc.), allowing developers to switch between them with ease. Additionally, it provides tools for managing model inputs (prompt templates and example selectors) and parsing the resulting model outputs (output parsers). * **Retrieval:** assists in loading user data (via document loaders), transforming it (with text splitters), extracting its meaning (using embedding models), storing (in vector stores) and retrieving it (through retrievers) so that it can be used to ground the model's responses (i.e. Retrieval-Augmented Generation or RAG). * **Agents:** "bots" that leverage LLMs to make informed decisions about which available tools (such as web search, calculators, database lookup, etc.) to use to accomplish the designated task. The different components can be composed together using the LangChain Expression Language (LCEL).

Danswer is an open-source Gen-AI Chat and Unified Search tool that connects to your company's docs, apps, and people. It provides a Chat interface and plugs into any LLM of your choice. Danswer can be deployed anywhere and for any scale - on a laptop, on-premise, or to cloud. Since you own the deployment, your user data and chats are fully in your own control. Danswer is MIT licensed and designed to be modular and easily extensible. The system also comes fully ready for production usage with user authentication, role management (admin/basic users), chat persistence, and a UI for configuring Personas (AI Assistants) and their Prompts. Danswer also serves as a Unified Search across all common workplace tools such as Slack, Google Drive, Confluence, etc. By combining LLMs and team specific knowledge, Danswer becomes a subject matter expert for the team. Imagine ChatGPT if it had access to your team's unique knowledge! It enables questions such as "A customer wants feature X, is this already supported?" or "Where's the pull request for feature Y?"

Infinity is an AI-native database designed for LLM applications, providing incredibly fast full-text and vector search capabilities. It supports a wide range of data types, including vectors, full-text, and structured data, and offers a fused search feature that combines multiple embeddings and full text. Infinity is easy to use, with an intuitive Python API and a single-binary architecture that simplifies deployment. It achieves high performance, with 0.1 milliseconds query latency on million-scale vector datasets and up to 15K QPS.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.