This repository contains a WhatsApp bot that utilizes OpenAI's GPT and DALL-E 2 to respond to user inputs. Users can interact with the bot through voice messages, which are transcribed and responded to. The bot requires Node.js, npm, an OpenAI API key, and a WhatsApp account. It uses Puppeteer to run a real instance of Whatsapp Web to avoid being blocked. However, there is a risk of being blocked by WhatsApp as it does not allow bots or unofficial clients on its platform. The bot is not free to use, and users will be charged by OpenAI for each request made.

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?"

TempMail V2 is a simple web application that allows users to generate temporary email addresses and view the emails received by these addresses. It uses guerrillamail API for creating emails. The project is for educational purposes only and should be used legally. Users can generate new temporary email addresses, load emails, view email content, and download attachments. Dependencies include jQuery for API requests and DOM manipulation, and Font Awesome for icons.

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.

SunoAPI is an unofficial client for Suno AI, built on Python and Streamlit. It supports functions like generating music and obtaining music information. Users can set up multiple account information to be saved for use. The tool also features built-in maintenance and activation functions for tokens, eliminating concerns about token expiration. It supports multiple languages and allows users to upload pictures for generating songs based on image content analysis.

Cody is a free, open-source AI coding assistant that can write and fix code, provide AI-generated autocomplete, and answer your coding questions. Cody fetches relevant code context from across your entire codebase to write better code that uses more of your codebase's APIs, impls, and idioms, with less hallucination.

ChatGPT is a desktop application available on Mac, Windows, and Linux that provides a powerful AI wrapper experience. It allows users to interact with AI models for various tasks such as generating text, answering questions, and engaging in conversations. The application is designed to be user-friendly and accessible to both beginners and advanced users. ChatGPT aims to enhance the user experience by offering a seamless interface for leveraging AI capabilities in everyday scenarios.

Manim Voiceover is a plugin for the Manim animation library that allows users to easily add voiceovers to their videos directly in Python without the need for a separate video editor. It also provides the ability to record voiceovers using a command line interface and supports auto-generated AI voices from various services. Users can trigger animations at specific words in the voiceover, thanks to OpenAI Whisper. The plugin supports TTS services such as Azure Text to Speech, Coqui TTS, gTTS, and pyttsx3. It also offers features for translating voiceovers into other languages using machine translation services like DeepL.

The Microsoft AI Chat Protocol SDK is a library for easily building AI Chat interfaces from services that follow the AI Chat Protocol API Specification. By agreeing on a standard API contract, AI backend consumption and evaluation can be performed easily and consistently across different services. It allows developers to develop AI chat interfaces, consume and evaluate AI inference backends, and incorporate HTTP middleware for logging and authentication.

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.

The edge2ai-workshop repository provides a hands-on workshop for building an IoT Predictive Maintenance workflow. It includes lab exercises for setting up components like NiFi, Streams Processing, Data Visualization, and more on a single host. The repository also covers use cases such as credit card fraud detection. Users can follow detailed instructions, prerequisites, and connectivity guidelines to connect to their cluster and explore various services. Additionally, troubleshooting tips are provided for common issues like MiNiFi not sending messages or CEM not picking up new NARs.

Enterprise Commerce is a Next.js commerce starter that helps you launch your high-performance Shopify storefront in minutes, not weeks. It leverages the power of Vector Search and AI to deliver a superior online shopping experience without the development headaches.

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.

Moonshot is a simple and modular tool developed by the AI Verify Foundation to evaluate Language Model Models (LLMs) and LLM applications. It brings Benchmarking and Red-Teaming together to assist AI developers, compliance teams, and AI system owners in assessing LLM performance. Moonshot can be accessed through various interfaces including User-friendly Web UI, Interactive Command Line Interface, and seamless integration into MLOps workflows via Library APIs or Web APIs. It offers features like benchmarking LLMs from popular model providers, running relevant tests, creating custom cookbooks and recipes, and automating Red Teaming to identify vulnerabilities in AI systems.

GPT Researcher is an autonomous agent designed for comprehensive online research on a variety of tasks. It can produce detailed, factual, and unbiased research reports with customization options. The tool addresses issues of speed, determinism, and reliability by leveraging parallelized agent work. The main idea involves running 'planner' and 'execution' agents to generate research questions, seek related information, and create research reports. GPT Researcher optimizes costs and completes tasks in around 3 minutes. Features include generating long research reports, aggregating web sources, an easy-to-use web interface, scraping web sources, and exporting reports to various formats.

NucliaDB is a robust database that allows storing and searching on unstructured data. It is an out of the box hybrid search database, utilizing vector, full text and graph indexes. NucliaDB is written in Rust and Python. We designed it to index large datasets and provide multi-teanant support. When utilizing NucliaDB with Nuclia cloud, you are able to the power of an NLP database without the hassle of data extraction, enrichment and inference. We do all the hard work for you.

This repository contains a WhatsApp bot that utilizes OpenAI's GPT and DALL-E 2 to respond to user inputs. Users can interact with the bot through voice messages, which are transcribed and responded to. The bot requires Node.js, npm, an OpenAI API key, and a WhatsApp account. It uses Puppeteer to run a real instance of Whatsapp Web to avoid being blocked. However, there is a risk of being blocked by WhatsApp as it does not allow bots or unofficial clients on its platform. The bot is not free to use, and users will be charged by OpenAI for each request made.

MaxKB is a knowledge base Q&A system based on the LLM large language model. MaxKB = Max Knowledge Base, which aims to become the most powerful brain of the enterprise.

Large Language Models (LLM) are used to browse the Wolfram directory and associated URLs to create the category structure and good word embeddings. The goal is to generate enriched prompts for GPT, Wikipedia, Arxiv, Google Scholar, Stack Exchange, or Google search. The focus is on one subdirectory: Probability & Statistics. Documentation is in the project textbook `Projects4.pdf`, which is available in the folder. It is recommended to download the document and browse your local copy with Chrome, Edge, or other viewers. Unlike on GitHub, you will be able to click on all the links and follow the internal navigation features. Look for projects related to NLP and LLM / xLLM. The best starting point is project 7.2.2, which is the core project on this topic, with references to all satellite projects. The project textbook (with solutions to all projects) is the core document needed to participate in the free course (deep tech dive) called **GenAI Fellowship**. For details about the fellowship, follow the link provided. An uncompressed version of `crawl_final_stats.txt.gz` is available on Google drive, which contains all the crawled data needed as input to the Python scripts in the XLLM5 and XLLM6 folders.

BlossomLM is a series of open-source conversational large language models. This project aims to provide a high-quality general-purpose SFT dataset in both Chinese and English, making fine-tuning accessible while also providing pre-trained model weights. **Hint**: BlossomLM is a personal non-commercial project.

InternLM is a powerful language model series with features such as 200K context window for long-context tasks, outstanding comprehensive performance in reasoning, math, code, chat experience, instruction following, and creative writing, code interpreter & data analysis capabilities, and stronger tool utilization capabilities. It offers models in sizes of 7B and 20B, suitable for research and complex scenarios. The models are recommended for various applications and exhibit better performance than previous generations. InternLM models may match or surpass other open-source models like ChatGPT. The tool has been evaluated on various datasets and has shown superior performance in multiple tasks. It requires Python >= 3.8, PyTorch >= 1.12.0, and Transformers >= 4.34 for usage. InternLM can be used for tasks like chat, agent applications, fine-tuning, deployment, and long-context inference.

Discord AI Bot is a chatbot designed to interact with Ollama and AUTOMATIC1111 Stable Diffusion on Discord. The project is now archived due to lack of maintenance. Users can set up the bot by installing Node.js, Ollama, and a model, creating a Discord bot, and starting the bot with the necessary configurations. Additionally, Docker setup instructions are provided for easy deployment. The bot can be interacted with by mentioning it in Discord messages.

J.A.R.V.I.S. is an offline large language model fine-tuned on custom and open datasets to mimic Jarvis's dialog with Stark. It prioritizes privacy by running locally and excels in responding like Jarvis with a similar tone. Current features include time/date queries, web searches, playing YouTube videos, and webcam image descriptions. Users can interact with Jarvis via command line after installing the model locally using Ollama. Future plans involve voice cloning, voice-to-text input, and deploying the voice model as an API.

AirDC++ Web Client is a locally installed application designed for flexible file sharing within groups over a local network or the internet. It utilizes the Advanced Direct Connect protocol to create file sharing communities with thousands of users. The application offers a responsive web user interface, allows sharing local directories, searching for shared files, saving files, chatting capabilities, browsing shared directories, extension support, and a web API for HTTP REST and WebSockets.

Zep is a long-term memory service for AI Assistant apps. With Zep, you can provide AI assistants with the ability to recall past conversations, no matter how distant, while also reducing hallucinations, latency, and cost. Zep persists and recalls chat histories, and automatically generates summaries and other artifacts from these chat histories. It also embeds messages and summaries, enabling you to search Zep for relevant context from past conversations. Zep does all of this asyncronously, ensuring these operations don't impact your user's chat experience. Data is persisted to database, allowing you to scale out when growth demands. Zep also provides a simple, easy to use abstraction for document vector search called Document Collections. This is designed to complement Zep's core memory features, but is not designed to be a general purpose vector database. Zep allows you to be more intentional about constructing your prompt: 1. automatically adding a few recent messages, with the number customized for your app; 2. a summary of recent conversations prior to the messages above; 3. and/or contextually relevant summaries or messages surfaced from the entire chat session. 4. and/or relevant Business data from Zep Document Collections.

doc2plan is a browser-based application that helps users create personalized learning plans by extracting content from documents. It features a Creator for manual or AI-assisted plan construction and a Viewer for interactive plan navigation. Users can extract chapters, key topics, generate quizzes, and track progress. The application includes AI-driven content extraction, quiz generation, progress tracking, plan import/export, assistant management, customizable settings, viewer chat with text-to-speech and speech-to-text support, and integration with various Retrieval-Augmented Generation (RAG) models. It aims to simplify the creation of comprehensive learning modules tailored to individual needs.

This repository contains a WhatsApp bot that utilizes OpenAI's GPT and DALL-E 2 to respond to user inputs. Users can interact with the bot through voice messages, which are transcribed and responded to. The bot requires Node.js, npm, an OpenAI API key, and a WhatsApp account. It uses Puppeteer to run a real instance of Whatsapp Web to avoid being blocked. However, there is a risk of being blocked by WhatsApp as it does not allow bots or unofficial clients on its platform. The bot is not free to use, and users will be charged by OpenAI for each request made.

OmniSteward is an AI-powered steward system based on large language models that can interact with users through voice or text to help control smart home devices and computer programs. It supports multi-turn dialogue, tool calling for complex tasks, multiple LLM models, voice recognition, smart home control, computer program management, online information retrieval, command line operations, and file management. The system is highly extensible, allowing users to customize and share their own tools.

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.