This repository is part of Intelligent Document Processing with AWS AI Services workshop. It aims to automate the extraction of information from complex content in various document formats such as insurance claims, mortgages, healthcare claims, contracts, and legal contracts using AWS Machine Learning services like Amazon Textract and Amazon Comprehend. The repository provides hands-on labs to familiarize users with these AI services and build solutions to automate business processes that rely on manual inputs and intervention across different file types and formats.

AI2Apps is a visual IDE for building LLM-based AI agent applications, enabling developers to efficiently create AI agents through drag-and-drop, with features like design-to-development for rapid prototyping, direct packaging of agents into apps, powerful debugging capabilities, enhanced user interaction, efficient team collaboration, flexible deployment, multilingual support, simplified product maintenance, and extensibility through plugins.

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

This repository is a rich resource offering examples of synthetic image generation, manipulation, and reasoning using Azure Machine Learning, Computer Vision, OpenAI, and open-source frameworks like Stable Diffusion. It provides practical insights into image processing applications, including content generation, video analysis, avatar creation, and image manipulation with various tools and APIs.

The Artificial Intelligence Controller Interface (AICI) lets you build Controllers that constrain and direct output of a Large Language Model (LLM) in real time. Controllers are flexible programs capable of implementing constrained decoding, dynamic editing of prompts and generated text, and coordinating execution across multiple, parallel generations. Controllers incorporate custom logic during the token-by-token decoding and maintain state during an LLM request. This allows diverse Controller strategies, from programmatic or query-based decoding to multi-agent conversations to execute efficiently in tight integration with the LLM itself.

TrinityX is an open-source HPC, AI, and cloud platform designed to provide all services required in a modern system, with full customization options. It includes default services like Luna node provisioner, OpenLDAP, SLURM or OpenPBS, Prometheus, Grafana, OpenOndemand, and more. TrinityX also sets up NFS-shared directories, OpenHPC applications, environment modules, HA, and more. Users can install TrinityX on Enterprise Linux, configure network interfaces, set up passwordless authentication, and customize the installation using Ansible playbooks. The platform supports HA, OpenHPC integration, and provides detailed documentation for users to contribute to the project.

PromptAgent is a repository for a novel automatic prompt optimization method that crafts expert-level prompts using language models. It provides a principled framework for prompt optimization by unifying prompt sampling and rewarding using MCTS algorithm. The tool supports different models like openai, palm, and huggingface models. Users can run PromptAgent to optimize prompts for specific tasks by strategically sampling model errors, generating error feedbacks, simulating future rewards, and searching for high-reward paths leading to expert prompts.

Sublayer is a model-agnostic Ruby AI Agent framework that provides base classes for building Generators, Actions, Tasks, and Agents to create AI-powered applications in Ruby. It supports various AI models and providers, such as OpenAI, Gemini, and Claude. Generators generate specific outputs, Actions perform operations, Agents are autonomous entities for tasks or monitoring, and Triggers decide when Agents are activated. The framework offers sample Generators and usage examples for building AI applications.

The Multi-Agent -Custom Automation Engine Solution Accelerator is an AI-driven orchestration system that manages a group of AI agents to accomplish tasks based on user input. It uses a FastAPI backend to handle HTTP requests, processes them through various specialized agents, and stores stateful information using Azure Cosmos DB. The system allows users to focus on what matters by coordinating activities across an organization, enabling GenAI to scale, and is applicable to most industries. It is intended for developing and deploying custom AI solutions for specific customers, providing a foundation to accelerate building out multi-agent systems.

Stagehand is an AI web browsing framework that simplifies and extends web automation using three simple APIs: act, extract, and observe. It aims to provide a lightweight, configurable framework without complex abstractions, allowing users to automate web tasks reliably. The tool generates Playwright code based on atomic instructions provided by the user, enabling natural language-driven web automation. Stagehand is open source, maintained by the Browserbase team, and supports different models and model providers for flexibility in automation tasks.

Cellm is an Excel extension that allows users to leverage Large Language Models (LLMs) like ChatGPT within cell formulas. It enables users to extract AI responses to text ranges, making it useful for automating repetitive tasks that involve data processing and analysis. Cellm supports various models from Anthropic, Mistral, OpenAI, and Google, as well as locally hosted models via Llamafiles, Ollama, or vLLM. The tool is designed to simplify the integration of AI capabilities into Excel for tasks such as text classification, data cleaning, content summarization, entity extraction, and more.

The NaLLM project repository explores the synergies between Neo4j and Large Language Models (LLMs) through three primary use cases: Natural Language Interface to a Knowledge Graph, Creating a Knowledge Graph from Unstructured Data, and Generating a Report using static and LLM data. The repository contains backend and frontend code organized for easy navigation. It includes blog posts, a demo database, instructions for running demos, and guidelines for contributing. The project aims to showcase the potential of Neo4j and LLMs in various applications.

A web application to explore the capabilities of Google Maps Platform Route Optimization (GMPRO). It helps users understand the data model and functions of the API by presenting interactive forms, tables, and maps. The tool is intended for exploratory use only and should not be deployed in production. Users can construct scenarios, tune constraint parameters, and visualize routes before implementing their own solutions for integrating Route Optimization into their business processes. The application incurs charges related to cloud resources and API usage, and users should be cautious about generating high usage volumes, especially for large scenarios.

db-ally is a library for creating natural language interfaces to data sources. It allows developers to outline specific use cases for a large language model (LLM) to handle, detailing the desired data format and the possible operations to fetch this data. db-ally effectively shields the complexity of the underlying data source from the model, presenting only the essential information needed for solving the specific use cases. Instead of generating arbitrary SQL, the model is asked to generate responses in a simplified query language.

PythonDataScienceFullThrottle is a comprehensive repository containing various Python scripts, libraries, and tools for data science enthusiasts. It includes a wide range of functionalities such as data preprocessing, visualization, machine learning algorithms, and statistical analysis. The repository aims to provide a one-stop solution for individuals looking to dive deep into the world of data science using Python.

Arch is an intelligent Layer 7 gateway designed to protect, observe, and personalize LLM applications with APIs. It handles tasks like detecting and rejecting jailbreak attempts, calling backend APIs, disaster recovery, and observability. Built on Envoy Proxy, it offers features like function calling, prompt guardrails, traffic management, and standards-based observability. Arch aims to improve the speed, security, and personalization of generative AI applications.

Open-DocLLM is an open-source project that addresses data extraction and processing challenges using OCR and LLM technologies. It consists of two main layers: OCR for reading document content and LLM for extracting specific content in a structured manner. The project offers a larger context window size compared to JP Morgan's DocLLM and integrates tools like Tesseract OCR and Mistral for efficient data analysis. Users can run the models on-premises using LLM studio or Ollama, and the project includes a FastAPI app for testing purposes.

Awesome AI is a repository that collects and shares resources in the fields of large language models (LLM), AI-assisted programming, AI drawing, and more. It explores the application and development of generative artificial intelligence. The repository provides information on various AI tools, models, and platforms, along with tutorials and web products related to AI technologies.

QMedia is an open-source multimedia AI content search engine designed specifically for content creators. It provides rich information extraction methods for text, image, and short video content. The tool integrates unstructured text, image, and short video information to build a multimodal RAG content Q&A system. Users can efficiently search for image/text and short video materials, analyze content, provide content sources, and generate customized search results based on user interests and needs. QMedia supports local deployment for offline content search and Q&A for private data. The tool offers features like content cards display, multimodal content RAG search, and pure local multimodal models deployment. Users can deploy different types of models locally, manage language models, feature embedding models, image models, and video models. QMedia aims to spark new ideas for content creation and share AI content creation concepts in an open-source manner.

This repository is part of Intelligent Document Processing with AWS AI Services workshop. It aims to automate the extraction of information from complex content in various document formats such as insurance claims, mortgages, healthcare claims, contracts, and legal contracts using AWS Machine Learning services like Amazon Textract and Amazon Comprehend. The repository provides hands-on labs to familiarize users with these AI services and build solutions to automate business processes that rely on manual inputs and intervention across different file types and formats.

ScrapeGraphAI is a web scraping Python library that utilizes LangChain, LLM, and direct graph logic to create scraping pipelines. Users can specify the information they want to extract, and the library will handle the extraction process. The tool is designed to simplify web scraping tasks by providing a streamlined and efficient approach to data extraction.

Parsera is a lightweight Python library designed for scraping websites using LLMs. It offers simplicity and efficiency by minimizing token usage, enhancing speed, and reducing costs. Users can easily set up and run the tool to extract specific elements from web pages, generating JSON output with relevant data. Additionally, Parsera supports integration with various chat models, such as Azure, expanding its functionality and customization options for web scraping tasks.

ScrapeGraphAI is a web scraping Python library that utilizes LangChain, LLM, and direct graph logic to create scraping pipelines. Users can specify the information they want to extract, and the library will handle the extraction process. This repository contains an official demo/trial for the ScrapeGraphAI library, showcasing its capabilities in web scraping tasks. The tool is designed to simplify the process of extracting data from websites by providing a user-friendly interface and powerful scraping functionalities.

You2Txt is a tool developed for the Vercel + Nvidia 2-hour hackathon that converts any YouTube video into a transcribed .txt file. The project won first place in the hackathon and is hosted at you2txt.com. Due to rate limiting issues with YouTube requests, it is recommended to run the tool locally. The project was created using Next.js, Tailwind, v0, and Claude, and can be built and accessed locally for development purposes.

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

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 Intel® Geti™ SDK is a python package that enables teams to rapidly develop AI models by easing the complexities of model development and enhancing collaboration between teams. It provides tools to interact with an Intel® Geti™ server via the REST API, allowing for project creation, downloading, uploading, deploying for local inference with OpenVINO, setting project and model configuration, launching and monitoring training jobs, and media upload and prediction. The SDK also includes tutorial-style Jupyter notebooks demonstrating its usage.

Booster is a powerful inference accelerator designed for scaling large language models within production environments or for experimental purposes. It is built with performance and scaling in mind, supporting various CPUs and GPUs, including Nvidia CUDA, Apple Metal, and OpenCL cards. The tool can split large models across multiple GPUs, offering fast inference on machines with beefy GPUs. It supports both regular FP16/FP32 models and quantised versions, along with popular LLM architectures. Additionally, Booster features proprietary Janus Sampling for code generation and non-English languages.

xFasterTransformer is an optimized solution for Large Language Models (LLMs) on the X86 platform, providing high performance and scalability for inference on mainstream LLM models. It offers C++ and Python APIs for easy integration, along with example codes and benchmark scripts. Users can prepare models in a different format, convert them, and use the APIs for tasks like encoding input prompts, generating token ids, and serving inference requests. The tool supports various data types and models, and can run in single or multi-rank modes using MPI. A web demo based on Gradio is available for popular LLM models like ChatGLM and Llama2. Benchmark scripts help evaluate model inference performance quickly, and MLServer enables serving with REST and gRPC interfaces.

The Amazon Transcribe Live Call Analytics (LCA) with Agent Assist Sample Solution is designed to help contact centers assess and optimize caller experiences in real time. It leverages Amazon machine learning services like Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker to transcribe and extract insights from contact center audio. The solution provides real-time supervisor and agent assist features, integrates with existing contact centers, and offers a scalable, cost-effective approach to improve customer interactions. The end-to-end architecture includes features like live call transcription, call summarization, AI-powered agent assistance, and real-time analytics. The solution is event-driven, ensuring low latency and seamless processing flow from ingested speech to live webpage updates.

This repository contains recipes for building and running containerized AI and LLM applications with Podman. It provides model servers that serve machine-learning models via an API, allowing developers to quickly prototype new AI applications locally. The recipes include components like model servers and AI applications for tasks such as chat, summarization, object detection, etc. Images for sample applications and models are available in `quay.io`, and bootable containers for AI training on Linux OS are enabled.

XLearning is a scheduling platform for big data and artificial intelligence, supporting various machine learning and deep learning frameworks. It runs on Hadoop Yarn and integrates frameworks like TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning offers scalability, compatibility, multiple deep learning framework support, unified data management based on HDFS, visualization display, and compatibility with code at native frameworks. It provides functions for data input/output strategies, container management, TensorBoard service, and resource usage metrics display. XLearning requires JDK >= 1.7 and Maven >= 3.3 for compilation, and deployment on CentOS 7.2 with Java >= 1.7 and Hadoop 2.6, 2.7, 2.8.