This repository showcases a project that indexes millions of Wikipedia articles using Upstash Vector. It includes a semantic search engine and a RAG chatbot SDK. The project involves preparing and embedding Wikipedia articles, indexing vectors, building a semantic search engine, and implementing a RAG chatbot. Key features include indexing over 144 million vectors, multilingual support, cross-lingual semantic search, and a RAG chatbot. Technologies used include Upstash Vector, Upstash Redis, Upstash RAG Chat SDK, SentenceTransformers, and Meta-Llama-3-8B-Instruct for LLM provider.

OpenGlass is an open-source project that allows users to transform any regular glasses into smart glasses using affordable off-the-shelf components. With a cost of less than $25, users can enhance their glasses to record their daily activities, recognize people, identify objects, translate text, and more. The project provides detailed instructions on hardware setup and software installation, making it accessible for DIY enthusiasts and tech enthusiasts alike. By following the steps outlined in the repository, users can create their own smart glasses and explore various functionalities offered by the project.

OP Vault is a tool that leverages the OP Stack (OpenAI + Pinecone Vector Database) to allow users to upload custom knowledgebase files and ask questions about their contents. It provides a user-friendly Golang server and React frontend for querying human-readable content like books and documents, making it valuable for knowledge extraction and question-answering. Users can upload entire libraries, receive specific answers with file and section references, and explore the power of the OP Stack in a practical interface.

ShipStation is an AI-based website and agents generation platform that optimizes landing page websites and generic connect-anything-to-anything services. It enables seamless communication between service providers and integration partners, offering features like user authentication, project management, code editing, payment integration, and real-time progress tracking. The project architecture includes server-side (Node.js) and client-side (React with Vite) components. Prerequisites include Node.js, npm or yarn, Anthropic API key, Supabase account, Tavily API key, and Razorpay account. Setup instructions involve cloning the repository, setting up Supabase, configuring environment variables, and starting the backend and frontend servers. Users can access the application through the browser, sign up or log in, create landing pages or portfolios, and get websites stored in an S3 bucket. Deployment to Heroku involves building the client project, committing changes, and pushing to the main branch. Contributions to the project are encouraged, and the license encourages doing good.

The Ersilia Model Hub is a unified platform of pre-trained AI/ML models dedicated to infectious and neglected disease research. It offers an open-source, low-code solution that provides seamless access to AI/ML models for drug discovery. Models housed in the hub come from two sources: published models from literature (with due third-party acknowledgment) and custom models developed by the Ersilia team or contributors.

Yet Another Chatbot is a sophisticated multimodal chat interface powered by advanced AI models and equipped with a variety of tools. This chatbot can search and browse the web in real-time, query Wikipedia for information, perform news and map searches, execute Python code, compose long-form articles mixing text and images, generate, search, and compare images, analyze documents and images, search and download arXiv papers, save conversations as text and audio files, manage checklists, and track personal improvements. It offers tools for web interaction, Wikipedia search, Python scripting, content management, image handling, arXiv integration, conversation generation, file management, personal improvement, and checklist management.

The Codebase Context Specification (CCS) project aims to standardize embedding contextual information within codebases to enhance understanding for both AI and human developers. It introduces a convention similar to `.env` and `.editorconfig` files but focused on documenting code for both AI and humans. By providing structured contextual metadata, collaborative documentation guidelines, and standardized context files, developers can improve code comprehension, collaboration, and development efficiency. The project includes a linter for validating context files and provides guidelines for using the specification with AI assistants. Tooling recommendations suggest creating memory systems, IDE plugins, AI model integrations, and agents for context creation and utilization. Future directions include integration with existing documentation systems, dynamic context generation, and support for explicit context overriding.

CoLLM is a novel method that integrates collaborative information into Large Language Models (LLMs) for recommendation. It converts recommendation data into language prompts, encodes them with both textual and collaborative information, and uses a two-step tuning method to train the model. The method incorporates user/item ID fields in prompts and employs a conventional collaborative model to generate user/item representations. CoLLM is built upon MiniGPT-4 and utilizes pretrained Vicuna weights for training.

BaoGPT is an AI project designed to facilitate asking questions about YouTube videos. It features a web UI based on Gradio and Discord integration. The tool utilizes a pipeline that routes input questions to either a greeting-like branch or a query & answer branch. The query analysis is performed by the LLM, which extracts attributes as filters and optimizes and rewrites questions for better vector retrieval in the vector DB. The tool then retrieves top-k candidates for grading and outputs final relative documents after grading. Lastly, the LLM performs summarization based on the reranking output, providing answers and attaching sources to the user.

Wandbot is a question-answering bot designed for Weights & Biases documentation. It employs Retrieval Augmented Generation with a ChromaDB backend for efficient responses. The bot features periodic data ingestion, integration with Discord and Slack, and performance monitoring through logging. It has a fallback mechanism for model selection and is evaluated based on retrieval accuracy and model-generated responses. The implementation includes creating document embeddings, constructing the Q&A RAGPipeline, model selection, deployment on FastAPI, Discord, and Slack, logging and analysis with Weights & Biases Tables, and performance evaluation.

COMFYUI LLM PARTY is a node library designed for LLM workflow development in ComfyUI, an extremely minimalist UI interface primarily used for AI drawing and SD model-based workflows. The project aims to provide a complete set of nodes for constructing LLM workflows, enabling users to easily integrate them into existing SD workflows. It features various functionalities such as API integration, local large model integration, RAG support, code interpreters, online queries, conditional statements, looping links for large models, persona mask attachment, and tool invocations for weather lookup, time lookup, knowledge base, code execution, web search, and single-page search. Users can rapidly develop web applications using API + Streamlit and utilize LLM as a tool node. Additionally, the project includes an omnipotent interpreter node that allows the large model to perform any task, with recommendations to use the 'show_text' node for display output.

Gemma is a family of open-weights Large Language Model (LLM) by Google DeepMind, based on Gemini research and technology. This repository contains an inference implementation and examples, based on the Flax and JAX frameworks. Gemma can run on CPU, GPU, and TPU, with model checkpoints available for download. It provides tutorials, reference implementations, and Colab notebooks for tasks like sampling and fine-tuning. Users can contribute to Gemma through bug reports and pull requests. The code is licensed under the Apache License, Version 2.0.

SlideDeck AI is a tool that leverages Generative Artificial Intelligence to co-create slide decks on any topic. Users can describe their topic and let SlideDeck AI generate a PowerPoint slide deck, streamlining the presentation creation process. The tool offers an iterative workflow with a conversational interface for creating and improving presentations. It uses Mistral Nemo Instruct to generate initial slide content, searches and downloads images based on keywords, and allows users to refine content through additional instructions. SlideDeck AI provides pre-defined presentation templates and a history of instructions for users to enhance their presentations.

Discover the journey of building a generative AI application using LangChain.js and Azure. This demo explores the development process from idea to production, using a RAG-based approach for a Q&A system based on YouTube video transcripts. The application allows to ask text-based questions about a YouTube video and uses the transcript of the video to generate responses. The code comes in two versions: local prototype using FAISS and Ollama with LLaMa3 model for completion and all-minilm-l6-v2 for embeddings, and Azure cloud version using Azure AI Search and GPT-4 Turbo model for completion and text-embedding-3-large for embeddings. Either version can be run as an API using the Azure Functions runtime.

WebLLM Chat is a private AI chat interface that combines WebLLM with a user-friendly design, leveraging WebGPU to run large language models natively in your browser. It offers browser-native AI experience with WebGPU acceleration, guaranteed privacy as all data processing happens locally, offline accessibility, user-friendly interface with markdown support, and open-source customization. The project aims to democratize AI technology by making powerful tools accessible directly to end-users, enhancing the chatting experience and broadening the scope for deployment of self-hosted and customizable language models.

The Intel Gaudi Tutorials repository contains source files for tutorials on using PyTorch and PyTorch Lightning on the Intel Gaudi AI Processor. The tutorials cater to users from beginner to advanced levels and cover various tasks such as fine-tuning models, running inference, and setting up DeepSpeed for training large language models. Users need access to an Intel Gaudi 2 Accelerator card or node, run the Intel Gaudi PyTorch Docker image, clone the tutorial repository, install Jupyterlab, and run the Jupyterlab server to follow along with the tutorials.

Kumo search is an end-to-end search engine framework that supports full-text search, inverted index, forward index, sorting, caching, hierarchical indexing, intervention system, feature collection, offline computation, storage system, and more. It runs on the EA (Elastic automic infrastructure architecture) platform, enabling engineering automation, service governance, real-time data, service degradation, and disaster recovery across multiple data centers and clusters. The framework aims to provide a ready-to-use search engine framework to help users quickly build their own search engines. Users can write business logic in Python using the AOT compiler in the project, which generates C++ code and binary dynamic libraries for rapid iteration of the search engine.

Build your own conversational search engine using less than 500 lines of code. Features built-in support for LLM, search engine, customizable UI interface, and shareable cached search results. Setup includes Bing and Google search engines. Utilize LLM and KV functions with Lepton for seamless integration. Easily deploy to Lepton AI or your own environment with one-click deployment options.

This repository showcases a project that indexes millions of Wikipedia articles using Upstash Vector. It includes a semantic search engine and a RAG chatbot SDK. The project involves preparing and embedding Wikipedia articles, indexing vectors, building a semantic search engine, and implementing a RAG chatbot. Key features include indexing over 144 million vectors, multilingual support, cross-lingual semantic search, and a RAG chatbot. Technologies used include Upstash Vector, Upstash Redis, Upstash RAG Chat SDK, SentenceTransformers, and Meta-Llama-3-8B-Instruct for LLM provider.

Blockoli is a high-performance tool for code indexing, embedding generation, and semantic search tool for use with LLMs. It is built in Rust and uses the ASTerisk crate for semantic code parsing. Blockoli allows you to efficiently index, store, and search code blocks and their embeddings using vector similarity. Key features include indexing code blocks from a codebase, generating vector embeddings for code blocks using a pre-trained model, storing code blocks and their embeddings in a SQLite database, performing efficient similarity search on code blocks using vector embeddings, providing a REST API for easy integration with other tools and platforms, and being fast and memory-efficient due to its implementation in Rust.

The Mistral JavaScript client is a library that allows you to interact with the Mistral AI API. With this client, you can perform various tasks such as listing models, chatting with streaming, chatting without streaming, and generating embeddings. To use the client, you can install it in your project using npm and then set up the client with your API key. Once the client is set up, you can use it to perform the desired tasks. For example, you can use the client to chat with a model by providing a list of messages. The client will then return the response from the model. You can also use the client to generate embeddings for a given input. The embeddings can then be used for various downstream tasks such as clustering or classification.

A collection of LLM services you can self host via docker or modal labs to support your applications development. The goal is to provide docker containers or modal labs deployments of common patterns when using LLMs and endpoints to integrate easily with existing codebases using the openai api. It supports GPT4all's embedding api, JSONFormer api for chat completion, Cross Encoders based on sentence transformers, and provides documentation using MkDocs.

OpenAI Kotlin API client is a Kotlin client for OpenAI's API with multiplatform and coroutines capabilities. It allows users to interact with OpenAI's API using Kotlin programming language. The client supports various features such as models, chat, images, embeddings, files, fine-tuning, moderations, audio, assistants, threads, messages, and runs. It also provides guides on getting started, chat & function call, file source guide, and assistants. Sample apps are available for reference, and troubleshooting guides are provided for common issues. The project is open-source and licensed under the MIT license, allowing contributions from the community.

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.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.