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Knowbo is a custom chatbot tool that allows users to create a chatbot for their website in just 2 minutes. The chatbot learns directly from the website or documentation, providing up-to-date information to users. With features like easy deployment, chat history tracking, and customization options, Knowbo aims to revolutionize customer experience by reducing the load on support teams and offering a seamless way for users to get their questions answered quickly.

Milo is an AI-powered co-pilot for parents, designed to help them manage the chaos of family life. It uses GPT-4, the latest in large-language models, to sort and organize information, send reminders, and provide updates. Milo is designed to be accurate and solve complex problems, and it learns and gets better based on user feedback. It can be used to manage tasks such as adding items to a grocery list, getting updates on the week's schedule, and sending screenshots of birthday invitations.

Vendorful is an AI-powered tool designed to assist users in responding to Requests for Proposals (RFPs), Requests for Information (RFIs), and Security Questionnaires. The tool leverages artificial intelligence to generate draft responses with better-than-human accuracy based on the user's content. Vendorful aims to streamline the RFP response process, save time, and increase the chances of winning deals by providing contextual smarts and flexible answers.

WebPilot is an AI tool designed to enhance your GPTs by enabling them to perform various tasks such as opening URL/file links, using multiple search engines, accessing all types of websites, loading dynamic web content, and providing enhanced answers. It offers a super easy way to interact with webpages, assisting in tasks like responding to emails, writing in forms, and solving quizzes. WebPilot is free, open-source, and has been featured by Google Extension Store as an established publisher.

VoiceGPT is an Android app that provides a voice-based interface to interact with AI language models like ChatGPT, Bing AI, and Bard. It offers features such as unlimited free messages, voice input and output in 67+ languages, a floating bubble for easy switching between apps, OCR text recognition, code execution, image generation with DALL-E 2, and support for ChatGPT Plus accounts. VoiceGPT is designed to be accessible for users with visual impairments, dyslexia, or other conditions, and it can be set as the default assistant to be activated hands-free with a custom hotword.

Parade is a capacity management platform designed for freight brokerages and 3PLs to streamline operations, automate bookings, and improve margins. The platform leverages advanced AI to optimize pricing, bidding, and carrier management, helping users book more loads efficiently. Parade integrates seamlessly with existing tech stacks, offering precise pricing, optimized bidding, and enhanced shipper connectivity. The platform boasts a range of features and benefits aimed at increasing efficiency, reducing costs, and boosting margins for freight businesses.

The website offers an AI-powered tool called SwapFans that allows users to load balance and receive discounts. Users can easily FaceSwap any social media videos and swap entire Instagram and TikTok accounts with high-speed FaceSwap AI. The tool is designed to help users manage their social media presence effectively and efficiently.

PixieBrix is an AI engagement platform that allows users to build, deploy, and manage internal AI tools to drive team productivity. It unifies AI landscapes with oversight and governance for enterprise scale. The platform is enterprise-ready and fully customizable to meet unique needs, and can be deployed on any site, making it easy to integrate into existing systems. PixieBrix leverages the power of AI and automation to harness the latest technology to streamline workflows and take productivity to new heights.

TLDRai.com is an AI tool designed to help users summarize any text into concise and easy-to-digest content, enabling them to free themselves from information overload. The tool utilizes AI technology to provide efficient text summarization services, making it a valuable resource for individuals seeking quick and accurate summaries of lengthy texts.

The website promptsplitter.com is experiencing an Argo Tunnel error on the Cloudflare network. Users encountering this error are advised to wait a few minutes if they are visitors, or ensure that cloudflared is running and can reach the network if they are the website owners. The error message provides guidance on troubleshooting steps to resolve the issue.

Merlin AI is a YouTube transcript tool that allows users to create summaries of YouTube videos. It is easy to use and can be added to Chrome as an extension. Merlin AI is powered by an undocumented API and features the latest build.

Daxtra is an AI-powered recruitment technology tool designed to help staffing and recruiting professionals find, parse, match, and engage the best candidates quickly and efficiently. The tool offers a suite of products that seamlessly integrate with existing ATS or CRM systems, automating various recruitment processes such as candidate data loading, CV/resume formatting, information extraction, and job matching. Daxtra's solutions cater to corporates, vendors, job boards, and social media partners, providing a comprehensive set of developer components to enhance recruitment workflows.

Lex Fridman is an AI tool developed by Lex Fridman, a Research Scientist at MIT, focusing on human-robot interaction and machine learning. The tool offers various resources such as podcasts, research publications, and studies related to AI-assisted driving data collection, autonomous vehicle systems, gaze estimation, and cognitive load estimation. It aims to provide insights into the safe and enjoyable interaction between humans and AI in driving scenarios.

Kolank is an AI tool that provides a unified API for accessing a wide range of Language Model Models (LLMs) and providers. It offers features such as model comparison based on price, latency, output, context, and throughput, OpenAI compatible API integration, transparency in tracking API calls and token expenditure, cost reduction by paying for performance, load balancing with fallbacks, and easy integration with preferred LLMs using Python, Javascript, and Curl.

LiteLLM is an AI tool that offers a Unified API for Azure OpenAI Vertex AI Bedrock. It provides a proxy server for managing authentication, load balancing, and spend tracking across a wide range of LLMs. LiteLLM is designed to simplify the integration and management of various AI services in the OpenAI format. With features like cloud deployment, open-source availability, and extensive provider integrations, LiteLLM aims to streamline AI development workflows and enhance operational efficiency.

Avaturn is a realistic 3D avatar creator that uses generative AI to turn a 2D photo into a recognizable and realistic 3D avatar. With endless options for avatar customization, you can create a unique look for each and everyone. Export your avatar as a 3D model and load it in Blender, Unity, Unreal Engine, Maya, Cinema4D, or any other 3D environment. The avatars come with a standard humanoid body rig, ARKit blendshapes, and visemes. They are compatible with Mixamo animations and VTubing software.

Epicflow is an AI-based multi-project and resource management software designed to help organizations deliver more projects on time with available resources, increase profitability, and make informed project decisions using real-time data and predictive analytics. The software bridges demand and supply by matching talent based on competencies, experience, and availability. It offers features like AI assistant, What-If Analysis, Future Load Graph, Historical Load Graph, Task List, and Competence Management Pipeline. Epicflow is trusted by leading companies in various industries for high performance and flawless project delivery.

Ermine.ai is an AI tool that provides local audio recording and transcription services. Users can easily transcribe audio files into text using this tool. The application currently supports Chrome browser and is working on adding support for Firefox. It requires the browser to load and initialize the transcription model, which may take a few minutes during the first use. The tool is designed to offer fast transcription services with support for English language only.

RAG Me Up is a generic framework that enables users to perform Retrieve and Generate (RAG) on their own dataset easily. It consists of a small server and UIs for communication. Best run on GPU with 16GB vRAM. Users can combine RAG with fine-tuning using LLaMa2Lang repository. The tool allows configuration for LLM, data, LLM parameters, prompt, and document splitting. Funding is sought to democratize AI and advance its applications.

Open WebUI is an extensible, feature-rich, and user-friendly self-hosted WebUI designed to operate entirely offline. It supports various LLM runners, including Ollama and OpenAI-compatible APIs. For more information, be sure to check out our Open WebUI Documentation.

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.

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The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

This repository serves as a companion to the Build AI-Powered Apps with OpenAI and Node.js course on Frontend Masters. It includes course notes and provides alternative approaches for deprecated Langchain methods by installing the Langchain community module and importing loaders for document processing from PDFs and YouTube videos.

Dot is a standalone, open-source application designed for seamless interaction with documents and files using local LLMs and Retrieval Augmented Generation (RAG). It is inspired by solutions like Nvidia's Chat with RTX, providing a user-friendly interface for those without a programming background. Pre-packaged with Mistral 7B, Dot ensures accessibility and simplicity right out of the box. Dot allows you to load multiple documents into an LLM and interact with them in a fully local environment. Supported document types include PDF, DOCX, PPTX, XLSX, and Markdown. Users can also engage with Big Dot for inquiries not directly related to their documents, similar to interacting with ChatGPT. Built with Electron JS, Dot encapsulates a comprehensive Python environment that includes all necessary libraries. The application leverages libraries such as FAISS for creating local vector stores, Langchain, llama.cpp & Huggingface for setting up conversation chains, and additional tools for document management and interaction.

The LLM RAG Workshop repository provides a workshop on using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to generate and understand text in a human-like manner. It includes instructions on setting up the environment, indexing Zoomcamp FAQ documents, creating a Q&A system, and using OpenAI for generation based on retrieved information. The repository focuses on enhancing language model responses with retrieved information from external sources, such as document databases or search engines, to improve factual accuracy and relevance of generated text.

This repository contains a sophisticated deterministic graph-based solution for answering complex questions using a controllable autonomous agent. The solution is designed to ensure that answers are solely based on the provided data, avoiding hallucinations. It involves various steps such as PDF loading, text preprocessing, summarization, database creation, encoding, and utilizing large language models. The algorithm follows a detailed workflow involving planning, retrieval, answering, replanning, content distillation, and performance evaluation. Heuristics and techniques implemented focus on content encoding, anonymizing questions, task breakdown, content distillation, chain of thought answering, verification, and model performance evaluation.

This repository provides a set of ROS 2 packages to integrate llama.cpp into ROS 2. By using the llama_ros packages, you can easily incorporate the powerful optimization capabilities of llama.cpp into your ROS 2 projects by running GGUF-based LLMs and VLMs.

Giskard is an open-source Python library that automatically detects performance, bias & security issues in AI applications. The library covers LLM-based applications such as RAG agents, all the way to traditional ML models for tabular data.

Julep is an advanced platform for creating stateful and functional AI apps powered by large language models. It offers features like statefulness by design, automatic function calling, production-ready deployment, cron-like asynchronous functions, 90+ built-in tools, and the ability to switch between different LLMs easily. Users can build AI applications without the need to write code for embedding, saving, and retrieving conversation history, and can connect to third-party applications using Composio. Julep simplifies the process of getting started with AI apps, whether they are conversational, functional, or agentic.

RAPTOR introduces a novel approach to retrieval-augmented language models by constructing a recursive tree structure from documents. This allows for more efficient and context-aware information retrieval across large texts, addressing common limitations in traditional language models. Users can add documents to the tree, answer questions based on indexed documents, save and load the tree, and extend RAPTOR with custom summarization, question-answering, and embedding models. The tool is designed to be flexible and customizable for various NLP tasks.

Catalyst is a C# Natural Language Processing library designed for speed, inspired by spaCy's design. It provides pre-trained models, support for training word and document embeddings, and flexible entity recognition models. The library is fast, modern, and pure-C#, supporting .NET standard 2.0. It is cross-platform, running on Windows, Linux, macOS, and ARM. Catalyst offers non-destructive tokenization, named entity recognition, part-of-speech tagging, language detection, and efficient binary serialization. It includes pre-built models for language packages and lemmatization. Users can store and load models using streams. Getting started with Catalyst involves installing its NuGet Package and setting the storage to use the online repository. The library supports lazy loading of models from disk or online. Users can take advantage of C# lazy evaluation and native multi-threading support to process documents in parallel. Training a new FastText word2vec embedding model is straightforward, and Catalyst also provides algorithms for fast embedding search and dimensionality reduction.

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are: 1. **Chunking and Embedding Job** : The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be trigerred via an event to keep the data updated. 2. **Query Service** : The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic. 3. **LLM / Embedding Model Deployment** : Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API. 4. **Vector DB deployment** : Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way. Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app. ### Advantages of using Cognita are: 1. A central reusable repository of parsers, loaders, embedders and retrievers. 2. Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team. 3. Fully API driven - which allows integration with other systems. > If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries. ### Features: 1. Support for multiple document retrievers that use `Similarity Search`, `Query Decompostion`, `Document Reranking`, etc 2. Support for SOTA OpenSource embeddings and reranking from `mixedbread-ai` 3. Support for using LLMs using `Ollama` 4. Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

Pebblo enables developers to safely load data and promote their Gen AI app to deployment without worrying about the organization’s compliance and security requirements. The project identifies semantic topics and entities found in the loaded data and summarizes them on the UI or a PDF report.

VectorFlow is an open source, high throughput, fault tolerant vector embedding pipeline. It provides a simple API endpoint for ingesting large volumes of raw data, processing, and storing or returning the vectors quickly and reliably. The tool supports text-based files like TXT, PDF, HTML, and DOCX, and can be run locally with Kubernetes in production. VectorFlow offers functionalities like embedding documents, running chunking schemas, custom chunking, and integrating with vector databases like Pinecone, Qdrant, and Weaviate. It enforces a standardized schema for uploading data to a vector store and supports features like raw embeddings webhook, chunk validation webhook, S3 endpoint, and telemetry. The tool can be used with the Python client and provides detailed instructions for running and testing the functionalities.

WDoc is a powerful Retrieval-Augmented Generation (RAG) system designed to summarize, search, and query documents across various file types. It supports querying tens of thousands of documents simultaneously, offers tailored summaries to efficiently manage large amounts of information, and includes features like supporting multiple file types, various LLMs, local and private LLMs, advanced RAG capabilities, advanced summaries, trust verification, markdown formatted answers, sophisticated embeddings, extensive documentation, scriptability, type checking, lazy imports, caching, fast processing, shell autocompletion, notification callbacks, and more. WDoc is ideal for researchers, students, and professionals dealing with extensive information sources.

wdoc is a powerful Retrieval-Augmented Generation (RAG) system designed to summarize, search, and query documents across various file types. It aims to handle large volumes of diverse document types, making it ideal for researchers, students, and professionals dealing with extensive information sources. wdoc uses LangChain to process and analyze documents, supporting tens of thousands of documents simultaneously. The system includes features like high recall and specificity, support for various Language Model Models (LLMs), advanced RAG capabilities, advanced document summaries, and support for multiple tasks. It offers markdown-formatted answers and summaries, customizable embeddings, extensive documentation, scriptability, and runtime type checking. wdoc is suitable for power users seeking document querying capabilities and AI-powered document summaries.

This repository documents how to optimize common algorithms based on CUDA. It includes subdirectories with code implementations for specific optimizations. The optimizations cover topics such as compiling PyTorch from source, NVIDIA's reduce optimization, OneFlow's elementwise template, fast atomic add for half data types, upsample nearest2d optimization in OneFlow, optimized indexing in PyTorch, OneFlow's softmax kernel, linear attention optimization, and more. The repository also includes learning resources related to deep learning frameworks, compilers, and optimization techniques.

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