Awesome RAG is a curated list of retrieval-augmented generation (RAG) in large language models. It includes papers, surveys, general resources, lectures, talks, tutorials, workshops, tools, and other collections related to retrieval-augmented generation. The repository aims to provide a comprehensive overview of the latest advancements, techniques, and applications in the field of RAG.

Wealth Tracker is a personal finance management tool designed to help users track their income, expenses, and investments in one place. With intuitive features and customizable categories, users can easily monitor their financial health and make informed decisions. The tool provides detailed reports and visualizations to analyze spending patterns and set financial goals. Whether you are budgeting, saving for a big purchase, or planning for retirement, Wealth Tracker offers a comprehensive solution to manage your money effectively.

Ruler is a tool designed to centralize AI coding assistant instructions, providing a single source of truth for managing instructions across multiple AI coding tools. It helps in avoiding inconsistent guidance, duplicated effort, context drift, onboarding friction, and complex project structures by automatically distributing instructions to the right configuration files. With support for nested rule loading, Ruler can handle complex project structures with context-specific instructions for different components. It offers features like centralised rule management, nested rule loading, automatic distribution, targeted agent configuration, MCP server propagation, .gitignore automation, and a simple CLI for easy configuration management.

The Google Workspace MCP Server is a production-ready server that integrates major Google Workspace services with AI assistants. It supports single-user and multi-user authentication via OAuth 2.1, making it a powerful backend for custom applications. Built with FastMCP for optimal performance, it features advanced authentication handling, service caching, and streamlined development patterns. The server provides full natural language control over Google Calendar, Drive, Gmail, Docs, Sheets, Slides, Forms, Tasks, and Chat through all MCP clients, AI assistants, and developer tools. It supports free Google accounts and Google Workspace plans with expanded app options like Chat & Spaces. The server also offers private cloud instance options.

Docutranslate is a versatile tool for translating documents efficiently. It supports multiple file formats and languages, making it ideal for businesses and individuals needing quick and accurate translations. The tool uses advanced algorithms to ensure high-quality translations while maintaining the original document's formatting. With its user-friendly interface, Docutranslate simplifies the translation process and saves time for users. Whether you need to translate legal documents, technical manuals, or personal letters, Docutranslate is the go-to solution for all your document translation needs.

LinguaGacha is a next-generation text translator using AI technology. It supports one-click translation of novels, games, subtitles, and other text content in multiple languages such as Chinese, English, Japanese, Korean, and Russian. The tool offers fast translation speed, automatic terminology generation, high translation quality, and accurate text style and code reproduction. It is recommended for creating embedded Chinese translations and is compatible with various AI models and interfaces.

Gzm Design is a free and open-source poster designer developed using the latest mainstream technologies such as Vue3, Vite4, TypeScript, etc. It provides features like PSD import, JSON import, multiple pages support, shortcut key support, template import, layer management, ruler tool, pen tool, element editing, preview, file download, canvas zooming and dragging, border stroke, filling, blending modes, text formatting, group handling, canvas size modification, rich text support, masking, shadow effects, undo/redo functionality, QR code tool, barcode tool, and ruler line npm package encapsulation.

FIT Framework is a Java enterprise AI development framework that provides a multi-language function engine (FIT), a flow orchestration engine (WaterFlow), and a Java ecosystem alternative solution (FEL). It runs in native/Spring dual mode, supports plug-and-play and intelligent deployment, seamlessly unifying large models and business systems. FIT Core offers language-agnostic computation base with plugin hot-swapping and intelligent deployment. WaterFlow Engine breaks the dimensional barrier of BPM and reactive programming, enabling graphical orchestration and declarative API-driven logic composition. FEL revolutionizes LangChain for the Java ecosystem, encapsulating large models, knowledge bases, and toolchains to integrate AI capabilities into Java technology stack seamlessly. The framework emphasizes engineering practices with intelligent conventions to reduce boilerplate code and offers flexibility for deep customization in complex scenarios.

video2blog is an open-source project aimed at converting videos into textual notes. The tool follows a process of extracting video information using yt-dlp, downloading the video, downloading subtitles if available, translating subtitles if not in Chinese, generating Chinese subtitles using whisper if no subtitles exist, converting subtitles to articles using gemini, and manually inserting images from the video into the article. The tool provides a solution for creating blog content from video resources, enhancing accessibility and content creation efficiency.

genai is a multi-AI providers library for Rust that aims to provide a common and ergonomic single API to various generative AI providers such as OpenAI, Anthropic, Cohere, Ollama, and Gemini. It focuses on standardizing chat completion APIs across major AI services, prioritizing ergonomics and commonality. The library initially focuses on text chat APIs and plans to expand to support images, function calling, and more in the future versions. Version 0.1.x will have breaking changes in patches, while version 0.2.x will follow semver more strictly. genai does not provide a full representation of a given AI provider but aims to simplify the differences at a lower layer for ease of use.

VimLM is an AI-powered coding assistant for Vim that integrates AI for code generation, refactoring, and documentation directly into your Vim workflow. It offers native Vim integration with split-window responses and intuitive keybindings, offline first execution with MLX-compatible models, contextual awareness with seamless integration with codebase and external resources, conversational workflow for iterating on responses, project scaffolding for generating and deploying code blocks, and extensibility for creating custom LLM workflows with command chains.

YomiToku is a Japanese-focused AI document image analysis engine that provides full-text OCR and layout analysis capabilities for images. It recognizes, extracts, and converts text information and figures in images. It includes 4 AI models trained on Japanese datasets for tasks such as detecting text positions, recognizing text strings, analyzing layouts, and recognizing table structures. The models are specialized for Japanese document images, supporting recognition of over 7000 Japanese characters and analyzing layout structures specific to Japanese documents. It offers features like layout analysis, table structure analysis, and reading order estimation to extract information from document images without disrupting their semantic structure. YomiToku supports various output formats such as HTML, markdown, JSON, and CSV, and can also extract figures, tables, and images from documents. It operates efficiently in GPU environments, enabling fast and effective analysis of document transcriptions without requiring high-end GPUs.

AMchat is a large language model that integrates advanced math concepts, exercises, and solutions. The model is based on the InternLM2-Math-7B model and is specifically designed to answer advanced math problems. It provides a comprehensive dataset that combines Math and advanced math exercises and solutions. Users can download the model from ModelScope or OpenXLab, deploy it locally or using Docker, and even retrain it using XTuner for fine-tuning. The tool also supports LMDeploy for quantization, OpenCompass for evaluation, and various other features for model deployment and evaluation. The project contributors have provided detailed documentation and guides for users to utilize the tool effectively.

ChatGPT WebUI is a user-friendly web graphical interface for various LLMs like ChatGPT, providing simplified features such as core ChatGPT conversation and document retrieval dialogues. It has been optimized for better RAG retrieval accuracy and supports various search engines. Users can deploy local language models easily and interact with different LLMs like GPT-4, Azure OpenAI, and more. The tool offers powerful functionalities like GPT4 API configuration, system prompt setup for role-playing, and basic conversation features. It also provides a history of conversations, customization options, and a seamless user experience with themes, dark mode, and PWA installation support.

This is an open-source project for playing the game King of Glory with an artificial intelligence model. The first phase of the project has been completed, and future upgrades will be built upon this foundation. The second phase of the project has started, and progress is expected to proceed according to plan. For any questions, feel free to join the QQ exchange group: 687853827. The project aims to learn artificial intelligence and strictly prohibits cheating. Detailed installation instructions are available in the doc/README.md file. Environment installation video: (bilibili) Welcome to follow, like, tip, comment, and provide your suggestions.

HiveChat is an AI chat application designed for small and medium teams. It supports various models such as DeepSeek, Open AI, Claude, and Gemini. The tool allows easy configuration by one administrator for the entire team to use different AI models. It supports features like email or Feishu login, LaTeX and Markdown rendering, DeepSeek mind map display, image understanding, AI agents, cloud data storage, and integration with multiple large model service providers. Users can engage in conversations by logging in, while administrators can configure AI service providers, manage users, and control account registration. The technology stack includes Next.js, Tailwindcss, Auth.js, PostgreSQL, Drizzle ORM, and Ant Design.

Basdonax AI RAG v1.0 is a repository that contains all the necessary resources to create your own AI-powered secretary using the RAG from Basdonax AI. It leverages open-source models from Meta and Microsoft, namely 'Llama3-7b' and 'Phi3-4b', allowing users to upload documents and make queries. This tool aims to simplify life for individuals by harnessing the power of AI. The installation process involves choosing between different data models based on GPU capabilities, setting up Docker, pulling the desired model, and customizing the assistant prompt file. Once installed, users can access the RAG through a local link and enjoy its functionalities.

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.

Awesome Agents is a curated list of open source AI agents designed for various tasks such as private interactions with documents, chat implementations, autonomous research, human-behavior simulation, code generation, HR queries, domain-specific research, and more. The agents leverage Large Language Models (LLMs) and other generative AI technologies to provide solutions for complex tasks and projects. The repository includes a diverse range of agents for different use cases, from conversational chatbots to AI coding engines, and from autonomous HR assistants to vision task solvers.

Lumi AI is a friendly AI sidekick with a human-like personality that offers features like file upload and analysis, web search, local chat storage, custom instructions, changeable conversational style, enhanced context retention, voice query input, and various tools. The project has been developed with contributions from a team of developers, designers, and testers, and is licensed under Apache 2.0 and MIT licenses.

Awesome RAG is a curated list of retrieval-augmented generation (RAG) in large language models. It includes papers, surveys, general resources, lectures, talks, tutorials, workshops, tools, and other collections related to retrieval-augmented generation. The repository aims to provide a comprehensive overview of the latest advancements, techniques, and applications in the field of RAG.

Ai2 Scholar QA is a system for answering scientific queries and literature review by gathering evidence from multiple documents across a corpus and synthesizing an organized report with evidence for each claim. It consists of a retrieval component and a three-step generator pipeline. The retrieval component fetches relevant evidence passages using the Semantic Scholar public API and reranks them. The generator pipeline includes quote extraction, planning and clustering, and summary generation. The system is powered by the ScholarQA class, which includes components like PaperFinder and MultiStepQAPipeline. It requires environment variables for Semantic Scholar API and LLMs, and can be run as local docker containers or embedded into another application as a Python package.

Baibot is a versatile chatbot framework designed to simplify the process of creating and deploying chatbots. It provides a user-friendly interface for building custom chatbots with various functionalities such as natural language processing, conversation flow management, and integration with external APIs. Baibot is highly customizable and can be easily extended to suit different use cases and industries. With Baibot, developers can quickly create intelligent chatbots that can interact with users in a seamless and engaging manner, enhancing user experience and automating customer support processes.

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).

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment interfaces and libraries for understanding AI systems. It empowers developers and stakeholders to develop and monitor AI responsibly, enabling better data-driven actions. The toolbox includes visualization widgets for model assessment, error analysis, interpretability, fairness assessment, and mitigations library. It also offers a JupyterLab extension for managing machine learning experiments and a library for measuring gender bias in NLP datasets.

LLMLingua is a tool that utilizes a compact, well-trained language model to identify and remove non-essential tokens in prompts. This approach enables efficient inference with large language models, achieving up to 20x compression with minimal performance loss. The tool includes LLMLingua, LongLLMLingua, and LLMLingua-2, each offering different levels of prompt compression and performance improvements for tasks involving large language models.

Starter examples for building LLM apps with Streamlit. This repository showcases a growing collection of LLM minimum working examples, including a Chatbot, File Q&A, Chat with Internet search, LangChain Quickstart, LangChain PromptTemplate, and Chat with user feedback. Users can easily get their own OpenAI API key and set it as an environment variable in Streamlit apps to run the examples locally.

LMOps is a research initiative focusing on fundamental research and technology for building AI products with foundation models, particularly enabling AI capabilities with Large Language Models (LLMs) and Generative AI models. The project explores various aspects such as prompt optimization, longer context handling, LLM alignment, acceleration of LLMs, LLM customization, and understanding in-context learning. It also includes tools like Promptist for automatic prompt optimization, Structured Prompting for efficient long-sequence prompts consumption, and X-Prompt for extensible prompts beyond natural language. Additionally, LLMA accelerators are developed to speed up LLM inference by referencing and copying text spans from documents. The project aims to advance technologies that facilitate prompting language models and enhance the performance of LLMs in various scenarios.

This repository focuses on exploring tools that enhance the performance of language models for various tasks. It provides a structured list of literature relevant to tool-augmented language models, covering topics such as tool basics, tool use paradigm, scenarios, advanced methods, and evaluation. The repository includes papers, preprints, and books that discuss the use of tools in conjunction with language models for tasks like reasoning, question answering, mathematical calculations, accessing knowledge, interacting with the world, and handling non-textual modalities.

GaiaNet-node is a tool that allows users to run their own GaiaNet node, enabling them to interact with an AI agent. The tool provides functionalities to install the default node software stack, initialize the node with model files and vector database files, start the node, stop the node, and update configurations. Users can use pre-set configurations or pass a custom URL for initialization. The tool is designed to facilitate communication with the AI agent and access node information via a browser. GaiaNet-node requires sudo privilege for installation but can also be installed without sudo privileges with specific commands.

LLMOps Duke AIPI is a course focused on operationalizing Large Language Models, teaching methodologies for developing applications using software development best practices with large language models. The course covers various topics such as generative AI concepts, setting up development environments, interacting with large language models, using local large language models, applied solutions with LLMs, extensibility using plugins and functions, retrieval augmented generation, introduction to Python web frameworks for APIs, DevOps principles, deploying machine learning APIs, LLM platforms, and final presentations. Students will learn to build, share, and present portfolios using Github, YouTube, and Linkedin, as well as develop non-linear life-long learning skills. Prerequisites include basic Linux and programming skills, with coursework available in Python or Rust. Additional resources and references are provided for further learning and exploration.

Awesome-AISourceHub is a repository that collects high-quality information sources in the field of AI technology. It serves as a synchronized source of information to avoid information gaps and information silos. The repository aims to provide valuable resources for individuals such as AI book authors, enterprise decision-makers, and tool developers who frequently use Twitter to share insights and updates related to AI advancements. The platform emphasizes the importance of accessing information closer to the source for better quality content. Users can contribute their own high-quality information sources to the repository by following specific steps outlined in the contribution guidelines. The repository covers various platforms such as Twitter, public accounts, knowledge planets, podcasts, blogs, websites, YouTube channels, and more, offering a comprehensive collection of AI-related resources for individuals interested in staying updated with the latest trends and developments in the AI field.