project-lakechain

Project Lakechain is a cloud-native, AI-powered framework for building document processing pipelines on AWS. It provides a composable API with built-in middlewares for common tasks, scalable architecture, cost efficiency, GPU and CPU support, and the ability to create custom transform middlewares. With ready-made examples and emphasis on modularity, Lakechain simplifies the deployment of scalable document pipelines for tasks like metadata extraction, NLP analysis, text summarization, translations, audio transcriptions, computer vision, and more.

Nextpy is a cutting-edge software development framework optimized for AI-based code generation. It provides guardrails for defining AI system boundaries, structured outputs for prompt engineering, a powerful prompt engine for efficient processing, better AI generations with precise output control, modularity for multiplatform and extensible usage, developer-first approach for transferable knowledge, and containerized & scalable deployment options. It offers 4-10x faster performance compared to Streamlit apps, with a focus on cooperation within the open-source community and integration of key components from various projects.

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.

Hopsworks is a data platform for ML with a Python-centric Feature Store and MLOps capabilities. It provides collaboration for ML teams, offering a secure, governed platform for developing, managing, and sharing ML assets. Hopsworks supports project-based multi-tenancy, team collaboration, development tools for Data Science, and is available on any platform including managed cloud services and on-premise installations. The platform enables end-to-end responsibility from raw data to managed features and models, supports versioning, lineage, and provenance, and facilitates the complete MLOps life cycle.

Kgateway is a feature-rich, fast, and flexible Kubernetes-native API gateway built on top of Envoy proxy and the Kubernetes Gateway API. It excels in function-level routing, supports legacy apps, microservices, and serverless, offers robust discovery capabilities, integrates seamlessly with open-source projects, and is designed to support hybrid applications with various technologies, architectures, protocols, and clouds.

ReductStore is a high-performance time series database designed for storing and managing large amounts of unstructured blob data. It offers features such as real-time querying, batching data, and HTTP(S) API for edge computing, computer vision, and IoT applications. The database ensures data integrity, implements retention policies, and provides efficient data access, making it a cost-effective solution for applications requiring unstructured data storage and access at specific time intervals.

OpenDAN is an open source Personal AI OS that consolidates various AI modules for personal use. It empowers users to create powerful AI agents like assistants, tutors, and companions. The OS allows agents to collaborate, integrate with services, and control smart devices. OpenDAN offers features like rapid installation, AI agent customization, connectivity via Telegram/Email, building a local knowledge base, distributed AI computing, and more. It aims to simplify life by putting AI in users' hands. The project is in early stages with ongoing development and future plans for user and kernel mode separation, home IoT device control, and an official OpenDAN SDK release.

GLake is an acceleration library and utilities designed to optimize GPU memory management and IO transmission for AI large model training and inference. It addresses challenges such as GPU memory bottleneck and IO transmission bottleneck by providing efficient memory pooling, sharing, and tiering, as well as multi-path acceleration for CPU-GPU transmission. GLake is easy to use, open for extension, and focuses on improving training throughput, saving inference memory, and accelerating IO transmission. It offers features like memory fragmentation reduction, memory deduplication, and built-in security mechanisms for troubleshooting GPU memory issues.

The Persian License Plate Recognition (PLPR) system is a state-of-the-art solution designed for detecting and recognizing Persian license plates in images and video streams. Leveraging advanced deep learning models and a user-friendly interface, it ensures reliable performance across different scenarios. The system offers advanced detection using YOLOv5 models, precise recognition of Persian characters, real-time processing capabilities, and a user-friendly GUI. It is well-suited for applications in traffic monitoring, automated vehicle identification, and similar fields. The system's architecture includes modules for resident management, entrance management, and a detailed flowchart explaining the process from system initialization to displaying results in the GUI. Hardware requirements include an Intel Core i5 processor, 8 GB RAM, a dedicated GPU with at least 4 GB VRAM, and an SSD with 20 GB of free space. The system can be installed by cloning the repository and installing required Python packages. Users can customize the video source for processing and run the application to upload and process images or video streams. The system's GUI allows for parameter adjustments to optimize performance, and the Wiki provides in-depth information on the system's architecture and model training.

AgentUp is an active development tool that provides a developer-first agent framework for creating AI agents with enterprise-grade infrastructure. It allows developers to define agents with configuration, ensuring consistent behavior across environments. The tool offers secure design, configuration-driven architecture, extensible ecosystem for customizations, agent-to-agent discovery, asynchronous task architecture, deterministic routing, and MCP support. It supports multiple agent types like reactive agents and iterative agents, making it suitable for chatbots, interactive applications, research tasks, and more. AgentUp is built by experienced engineers from top tech companies and is designed to make AI agents production-ready, secure, and reliable.

ExecuTorch is an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of PyTorch models to edge devices. Key value propositions of ExecuTorch are: * **Portability:** Compatibility with a wide variety of computing platforms, from high-end mobile phones to highly constrained embedded systems and microcontrollers. * **Productivity:** Enabling developers to use the same toolchains and SDK from PyTorch model authoring and conversion, to debugging and deployment to a wide variety of platforms. * **Performance:** Providing end users with a seamless and high-performance experience due to a lightweight runtime and utilizing full hardware capabilities such as CPUs, NPUs, and DSPs.

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.

This repository focuses on Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs). It provides code and resources to augment LLMs with real-time data for dynamic, context-aware applications. The content covers topics such as semantic search, fine-tuning embeddings, building RAG chatbots, evaluating LLMs, and using knowledge graphs in RAG. Prerequisites include Python skills, knowledge of machine learning and LLMs, and introductory experience with NLP and AI models.

Mini LLM Flow is a 100-line minimalist LLM framework designed for agents, task decomposition, RAG, etc. It aims to be the framework used by LLMs, focusing on high-level programming paradigms while stripping away low-level implementation details. It serves as a learning resource and allows LLMs to design, build, and maintain projects themselves.

BMF (Babit Multimedia Framework) is a cross-platform, multi-language, customizable multimedia processing framework developed by ByteDance. It offers native compatibility with Linux, Windows, and macOS, Python, Go, and C++ APIs, and high performance with strong GPU acceleration. BMF allows developers to enhance its features independently and provides efficient data conversion across popular frameworks and hardware devices. BMFLite is a client-side lightweight framework used in apps like Douyin/Xigua, serving over one billion users daily. BMF is widely used in video streaming, live transcoding, cloud editing, and mobile pre/post processing scenarios.

Project Lakechain is a cloud-native, AI-powered framework for building document processing pipelines on AWS. It provides a composable API with built-in middlewares for common tasks, scalable architecture, cost efficiency, GPU and CPU support, and the ability to create custom transform middlewares. With ready-made examples and emphasis on modularity, Lakechain simplifies the deployment of scalable document pipelines for tasks like metadata extraction, NLP analysis, text summarization, translations, audio transcriptions, computer vision, and more.

Docling is a tool that bundles PDF document conversion to JSON and Markdown in an easy, self-contained package. It can convert any PDF document to JSON or Markdown format, understand detailed page layout, reading order, recover table structures, extract metadata such as title, authors, references, and language, and optionally apply OCR for scanned PDFs. The tool is designed to be stable, lightning fast, and suitable for macOS and Linux environments.

Ebook-MCP is a powerful Model Context Protocol (MCP) server designed for processing electronic books. It provides standardized APIs for seamless integration between LLM applications and e-book processing capabilities. The tool supports EPUB and PDF formats, enabling users to manage their digital library, have interactive reading experiences, support active learning, and easily navigate content through natural language queries. By bridging traditional e-books with AI capabilities, Ebook-MCP enhances the value users can extract from their digital reading materials.

The Simple Chat Application is a web-based platform that facilitates secure interactions with generative AI models, leveraging Azure OpenAI. It features Retrieval-Augmented Generation (RAG) for grounding conversations in user data. Users can upload personal or group documents processed using Azure AI Document Intelligence and Azure OpenAI Embeddings. The application offers optional features like Content Safety, Image Generation, Video and Audio processing, Document Classification, User Feedback, Conversation Archiving, Metadata Extraction, and Enhanced Citations. It uses Azure Cosmos DB for storage, Azure Active Directory for authentication, and runs on Azure App Service. Suitable for enterprise use, it supports knowledge discovery, content generation, and collaborative AI tasks in a secure, Azure-native framework.

This project provides tools to enhance the capabilities of Large Language Models (LLMs) in generating SPARQL queries for specific endpoints. It includes reusable components, a chat web service, and an experimental MCP server. The system integrates Retrieval-Augmented Generation (RAG) and SPARQL query validation through endpoint schemas to ensure accurate query generation on large-scale knowledge graphs. Components can work independently or as part of a chat-based system requiring endpoint metadata. Features include metadata extraction, SPARQL query validation, deployable chat system, and live example chat system at chat.expasy.org.

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.

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.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

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.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.