TurboSeek is an open source AI search engine powered by Together.ai. It utilizes Next.js with Tailwind for the app router, Together AI for LLM inference, Mixtral 8x7B & Llama-3 for the LLMs, Bing for the search API, Helicone for observability, and Plausible for website analytics. The tool takes a user's question, queries the Bing search API for top results, scrapes text from the links, sends the question and context to Mixtral-8x7B, and generates follow-up questions using Llama-3-8B. Future tasks include optimizing source parsing, ignoring video links, adding regeneration option, ensuring proper citations, enabling sharing, implementing scrolling during answers, fixing hard refresh, adding caching with upstash redis, incorporating advanced RAG techniques, and adding authentication with Clerk and postgres/prisma.

Llama Tutor is an open source AI personal tutor powered by Llama 3 70B & Together.ai. It utilizes a tech stack including Llama 3.1 70B, Together AI, Next.js app router with Tailwind, Serper for the search API, Helicone for observability, and Plausible for website analytics. Users can clone the repo, set up accounts for necessary APIs, configure environment variables, and run the app locally. Future tasks include adding share & copy buttons, follow-up questions, splitting pages, organizing icons, enhancing the landing page, implementing a mobile menu, exploring generative UI, and improving dropdowns.

nanoPerplexityAI is an open-source implementation of a large language model service that fetches information from Google. It involves a simple architecture where the user query is checked by the language model, reformulated for Google search, and an answer is generated and saved in a markdown file. The tool requires minimal setup and is designed for easy visualization of answers.

Orion is a web-based chat interface that simplifies interactions with multiple AI model providers. It provides a unified platform for chatting and exploring various large language models (LLMs) such as Ollama, OpenAI (GPT model), Cohere (Command-r models), Google (Gemini models), Anthropic (Claude models), Groq Inc., Cerebras, and SambaNova. Users can easily navigate and assess different AI models through an intuitive, user-friendly interface. Orion offers features like browser-based access, code execution with Google Gemini, text-to-speech (TTS), speech-to-text (STT), seamless integration with multiple AI models, customizable system prompts, language translation tasks, document uploads for analysis, and more. API keys are stored locally, and requests are sent directly to official providers' APIs without external proxies.

BlinkShot is an open source real-time AI image generator powered by Flux through Together.ai. It utilizes Flux Schnell from BFL for the image model, Together AI for inference, Next.js app router with Tailwind for the frontend, Helicone for observability, and Plausible for website analytics. Users can clone the repository, add their Together AI API key, and run the app locally to generate AI images. Future tasks include adding a call-to-action to fork the code on GitHub, implementing a download button on hover, allowing users to adjust resolutions and steps, adding an app description to the footer, and introducing themes.

Devika is an advanced AI software engineer that can understand high-level human instructions, break them down into steps, research relevant information, and write code to achieve the given objective. Devika utilizes large language models, planning and reasoning algorithms, and web browsing abilities to intelligently develop software. Devika aims to revolutionize the way we build software by providing an AI pair programmer who can take on complex coding tasks with minimal human guidance. Whether you need to create a new feature, fix a bug, or develop an entire project from scratch, Devika is here to assist you.

DataLine is an AI-driven data analysis and visualization tool designed for technical and non-technical users to explore data quickly. It offers privacy-focused data storage on the user's device, supports various data sources, generates charts, executes queries, and facilitates report building. The tool aims to speed up data analysis tasks for businesses and individuals by providing a user-friendly interface and natural language querying capabilities.

This repository contains a Rest-Backend for a Conversational Agent that allows embedding documents, semantic search, QA based on documents, and document processing with Large Language Models. It uses Aleph Alpha and OpenAI Large Language Models to generate responses to user queries, includes a vector database, and provides a REST API built with FastAPI. The project also features semantic search, secret management for API keys, installation instructions, and development guidelines for both backend and frontend components.

DistiLlama is a Chrome extension that leverages a locally running Large Language Model (LLM) to perform various tasks, including text summarization, chat, and document analysis. It utilizes Ollama as the locally running LLM instance and LangChain for text summarization. DistiLlama provides a user-friendly interface for interacting with the LLM, allowing users to summarize web pages, chat with documents (including PDFs), and engage in text-based conversations. The extension is easy to install and use, requiring only the installation of Ollama and a few simple steps to set up the environment. DistiLlama offers a range of customization options, including the choice of LLM model and the ability to configure the summarization chain. It also supports multimodal capabilities, allowing users to interact with the LLM through text, voice, and images. DistiLlama is a valuable tool for researchers, students, and professionals who seek to leverage the power of LLMs for various tasks without compromising data privacy.

SolidGPT is an AI searching assistant for developers that helps with code and workspace semantic search. It provides features such as talking to your codebase, asking questions about your codebase, semantic search and summary in Notion, and getting questions answered from your codebase and Notion without context switching. The tool ensures data safety by not collecting users' data and uses the OpenAI series model API.

Obsidian Ollama Chat is a plugin that enables users to interact with their local LLM (Large Language Model) to ask questions about their own notes. The plugin facilitates running a lightweight python server for indexing notes, allowing users to set a model URL, index files on startup and modification, and open a modal to ask questions. Future plans include text streaming for querying, a chat window for communication, and commands for quick queries like summarizing notes or topics.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval. The repo includes sample data so it's ready to try end to end. In this sample application we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions and roles.

This repository provides a deployment guide for utilizing Google Cloud's Generative AI tools in marketing scenarios. It includes step-by-step instructions, examples of crafting marketing materials, and supplementary Jupyter notebooks. The demos cover marketing insights, audience analysis, trendspotting, content search, content generation, and workspace integration. Users can access and visualize marketing data, analyze trends, improve search experience, and generate compelling content. The repository structure includes backend APIs, frontend code, sample notebooks, templates, and installation scripts.

Pathway's LLM (Large Language Model) Apps provide a platform to quickly deploy AI applications using the latest knowledge from data sources. The Python application examples in this repository are Docker-ready, exposing an HTTP API to the frontend. These apps utilize the Pathway framework for data synchronization, API serving, and low-latency data processing without the need for additional infrastructure dependencies. They connect to document data sources like S3, Google Drive, and Sharepoint, offering features like real-time data syncing, easy alert setup, scalability, monitoring, security, and unification of application logic.

This sample shows how to build a serverless chat experience with Retrieval-Augmented Generation using LangChain.js and Azure. The application is hosted on Azure Static Web Apps and Azure Functions, with Azure Cosmos DB for MongoDB vCore as the vector database. You can use it as a starting point for building more complex AI applications.

RAGGENIE is a low-code RAG builder tool designed to simplify the creation of conversational AI applications. It offers out-of-the-box plugins for connecting to various data sources and building conversational AI on top of them, including integration with pre-built agents for actions. The tool is open-source under the MIT license, with a current focus on making it easy to build RAG applications and future plans for maintenance, monitoring, and transitioning applications from pilots to production.

TurboSeek is an open source AI search engine powered by Together.ai. It utilizes Next.js with Tailwind for the app router, Together AI for LLM inference, Mixtral 8x7B & Llama-3 for the LLMs, Bing for the search API, Helicone for observability, and Plausible for website analytics. The tool takes a user's question, queries the Bing search API for top results, scrapes text from the links, sends the question and context to Mixtral-8x7B, and generates follow-up questions using Llama-3-8B. Future tasks include optimizing source parsing, ignoring video links, adding regeneration option, ensuring proper citations, enabling sharing, implementing scrolling during answers, fixing hard refresh, adding caching with upstash redis, incorporating advanced RAG techniques, and adding authentication with Clerk and postgres/prisma.

Aegis Stack is a system for creating and evolving modular Python applications quickly, without the need for extensive testing or clean architecture. It allows users to go from idea to working prototype rapidly, using familiar tools. The stack includes a CLI, a built-in system dashboard called Overseer, and an optional conversational interface named Illiana. Users can start with basic components and add or remove features as needed, without being locked into initial choices. Aegis Stack aims to provide a flexible and efficient development environment for Python applications.

AI Study Assistant is an app designed to enhance learning experience and boost academic performance. It serves as a personal tutor, lecture summarizer, writer, and question generator powered by Google PaLM 2. Features include interacting with an AI chatbot, summarizing lectures, generating essays, and creating practice questions. The app is built using 100% Kotlin, Jetpack Compose, Clean Architecture, and MVVM design pattern, with technologies like Ktor, Room DB, Hilt, and Kotlin coroutines. AI Study Assistant aims to provide comprehensive AI-powered assistance for students in various academic tasks.

This project is an end-to-end pipeline for constructing knowledge graphs from news articles using Neo4j and Diffbot. It also utilizes OpenAI LLMs to generate questions based on the knowledge graph. The application offers news monitoring capabilities, data extraction from text, and organization/personal information enrichment. Users can interact with the chatbot interface to ask questions and receive answers based on the knowledge graph.

Easy Dataset is a specialized application designed to streamline the creation of fine-tuning datasets for Large Language Models (LLMs). It offers an intuitive interface for uploading domain-specific files, intelligently splitting content, generating questions, and producing high-quality training data for model fine-tuning. With Easy Dataset, users can transform domain knowledge into structured datasets compatible with all OpenAI-format compatible LLM APIs, making the fine-tuning process accessible and efficient.

This repository provides a hands-on approach to Retrieval-Augmented Generation (RAG) techniques, simplifying advanced concepts into understandable implementations using Python libraries like openai, numpy, and matplotlib. It offers a collection of Jupyter Notebooks with concise explanations, step-by-step implementations, code examples, evaluations, and visualizations for various RAG techniques. The goal is to make RAG more accessible and demystify its workings for educational purposes.

DeepTutor is an AI-powered personalized learning assistant that offers a suite of modules for massive document knowledge Q&A, interactive learning visualization, knowledge reinforcement with practice exercise generation, deep research, and idea generation. The tool supports multi-agent collaboration, dynamic topic queues, and structured outputs for various tasks. It provides a unified system entry for activity tracking, knowledge base management, and system status monitoring. DeepTutor is designed to streamline learning and research processes by leveraging AI technologies and interactive features.

Chat Chat is a unified chat and search to AI platform with a simple and easy-to-use interface. It supports major AI providers such as Anthropic, OpenAI, Cohere, and Google Gemini, and is easy to self-host. Chat Chat can be used for a variety of tasks, including searching for information, getting help with writing, and translating languages.

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.