aip-community-registry

AIP Community Registry is a collection of community-built applications and projects leveraging Palantir's AIP Platform. It showcases real-world implementations from developers using AIP in production. The registry features various solutions demonstrating practical implementations and integration patterns across different use cases.

A massive list including a huge amount of products and services that are completely free! ⭐ Star on GitHub • 🤝 Contribute # Table of Contents * APIs, Data & ML * Artificial Intelligence * BaaS * Code Editors * Code Generation * DNS * Databases * Design & UI * Domains * Email * Font * For Students * Forms * Linux Distributions * Messaging & Streaming * PaaS * Payments & Billing * SSL

This project provides a collective list of Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) examples such as chatbot with question and answering (ChatQnA), code generation (CodeGen), document summary (DocSum), etc.

AgentMark is a tool designed to make it easy for developers to develop, test, and evaluate AI Agents. It combines Markdown syntax with JSX components to create reliable Agents. The tool seamlessly integrates with SDKs, offering comprehensive tooling such as full type safety, unified prompt configuration, syntax highlighting, loops and conditionals, custom SDK adapters, and support for text, object, image, and speech generation across multiple model providers.

Enhance coding efficiency with expert-led GitHub Copilot course for C#/.NET developers. Learn to integrate AI-powered coding assistance, automate testing, and boost collaboration using Visual Studio Code and Copilot Chat. From autocompletion to unit testing, cover essential techniques for cleaner, faster, smarter code.

Awesome Generative AI & LLM APIs is a curated list of useful APIs that allow developers to integrate generative models into their applications without building the models from scratch. These APIs provide an interface for generating text, images, or other content, and include pre-trained language models for various tasks. The goal of this project is to create a hub for developers to create innovative applications, enhance user experiences, and drive progress in the AI field.

CrewAI quickstart is a small project providing starter templates for an easy start with CrewAI. It includes notebooks, Python scripts, GUI with Streamlit, and Local LLMs for various tasks like web search, CSV lookup, web scraping, PDF search, and more. Contributions are welcome to enhance the project.

AIOps Modules is a collection of reusable Infrastructure as Code (IAC) modules that work with SeedFarmer CLI. The modules are decoupled and can be aggregated using GitOps principles to achieve desired use cases, removing heavy lifting for end users. They must be generic for reuse in Machine Learning and Foundation Model Operations domain, adhering to SeedFarmer Guide structure. The repository includes deployment steps, project manifests, and various modules for SageMaker, Mlflow, FMOps/LLMOps, MWAA, Step Functions, EKS, and example use cases. It also supports Industry Data Framework (IDF) and Autonomous Driving Data Framework (ADDF) Modules.

Awesome CrewAI is a curated collection of open-source projects built by the CrewAI community, aimed at unlocking the full potential of AI agents for supercharging business processes and decision-making. It includes integrations, tutorials, and tools that showcase the capabilities of CrewAI in various domains.

AI-in-a-Box is a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction, while maintaining the highest standards of quality and efficiency. It provides essential guidance on the responsible use of AI and LLM technologies, specific security guidance for Generative AI (GenAI) applications, and best practices for scaling OpenAI applications within Azure. The available accelerators include: Azure ML Operationalization in-a-box, Edge AI in-a-box, Doc Intelligence in-a-box, Image and Video Analysis in-a-box, Cognitive Services Landing Zone in-a-box, Semantic Kernel Bot in-a-box, NLP to SQL in-a-box, Assistants API in-a-box, and Assistants API Bot in-a-box.

This repository is for the book 'Building AI Applications with ChatGPT APIs' published by Packt. It provides code examples and instructions for mastering ChatGPT, Whisper, and DALL-E APIs through building innovative AI projects. Readers will learn to develop AI applications using ChatGPT APIs, integrate them with frameworks like Flask and Django, create AI-generated art with DALL-E APIs, and optimize ChatGPT models through fine-tuning.

This repository provides code samples in Python, C#, REST, and JavaScript for vector support in Azure AI Search. It includes demos for various languages showcasing vectorization of data, creating indexes, and querying vector data. Additionally, it offers tools like Azure AI Search Lab for experimenting with AI-enabled search scenarios in Azure and templates for deploying custom chat-with-your-data solutions. The repository also features documentation on vector search, hybrid search, creating and querying vector indexes, and REST API references for Azure AI Search and Azure OpenAI Service.

The .NET Conf Focus on AI 2024 repository contains content from the event focusing on incorporating AI into .NET applications and services. It includes slides and demos showcasing various AI-powered web apps, AI models, generative AI apps, and more. The repository serves as a resource for developers looking to explore AI integration with .NET technologies.

Bedrock Engineer is an AI assistant for software development tasks powered by Amazon Bedrock. It combines large language models with file system operations and web search functionality to support development processes. The autonomous AI agent provides interactive chat, file system operations, web search, project structure management, code analysis, code generation, data analysis, agent and tool customization, chat history management, and multi-language support. Users can select agents, customize them, select tools, and customize tools. The tool also includes a website generator for React.js, Vue.js, Svelte.js, and Vanilla.js, with support for inline styling, Tailwind.css, and Material UI. Users can connect to design system data sources and generate AWS Step Functions ASL definitions.

The Machine Learning Road Map is a comprehensive guide designed to take individuals from various levels of machine learning knowledge to a basic understanding of machine learning principles using high-quality, free resources. It aims to simplify the complex and rapidly growing field of machine learning by providing a structured roadmap for learning. The guide emphasizes the importance of understanding AI for everyone, the need for patience in learning machine learning due to its complexity, and the value of learning from experts in the field. It covers five different paths to learning about machine learning, catering to consumers, aspiring AI researchers, ML engineers, developers interested in building ML applications, and companies looking to implement AI solutions.

AIP Community Registry is a collection of community-built applications and projects leveraging Palantir's AIP Platform. It showcases real-world implementations from developers using AIP in production. The registry features various solutions demonstrating practical implementations and integration patterns across different use cases.

GraphLLM is a graph-based framework designed to process data using LLMs. It offers a set of tools including a web scraper, PDF parser, YouTube subtitles downloader, Python sandbox, and TTS engine. The framework provides a GUI for building and debugging graphs with advanced features like loops, conditionals, parallel execution, streaming of results, hierarchical graphs, external tool integration, and dynamic scheduling. GraphLLM is a low-level framework that gives users full control over the raw prompt and output of models, with a steeper learning curve. It is tested with llama70b and qwen 32b, under heavy development with breaking changes expected.

Tribe AI is a low code tool designed to rapidly build and coordinate multi-agent teams. It leverages the langgraph framework to customize and coordinate teams of agents, allowing tasks to be split among agents with different strengths for faster and better problem-solving. The tool supports persistent conversations, observability, tool calling, human-in-the-loop functionality, easy deployment with Docker, and multi-tenancy for managing multiple users and teams.

CrewAI is a cutting-edge framework designed to orchestrate role-playing autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. It enables AI agents to assume roles, share goals, and operate in a cohesive unit, much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions. With features like role-based agent design, autonomous inter-agent delegation, flexible task management, and support for various LLMs, CrewAI offers a dynamic and adaptable solution for both development and production workflows.

Voyant Travel Assistant is a meta-agent pipeline designed for fast, trustworthy answers with clear provenance and resilient I/O. It focuses on AI-first planning, strict JSON parsing, non-blocking async I/O, and verification pipelines. The tool analyzes user messages, plans tool calls, executes actions, and blends responses from various tools. It supports tools like weather, country information, attractions, travel search, flight information, and policy extraction. Users can interact with the tool through a CLI interface and benefit from its architecture that ensures reliable responses and observability through stored receipts and verification processes.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.