# RAG

Ducky is a fully managed AI retrieval service designed for developers who need fast and accurate results. It supports semantic search, including Retrieval-Augmented Generation (RAG), and provides a straightforward Python SDK for quickly building excellent search functionalities.

Contextual AI Reranker is a revolutionary AI model designed to solve information conflicts and inaccurate ranking issues in enterprise-level Retrieval Augmented Generation (RAG) systems. It can accurately rank retrieval results based on user-provided natural language instructions, ensuring that the most relevant information is displayed first. This product is based on advanced AI technology and has demonstrated excellent performance through industry-standard BEIR benchmark tests and internal dataset validation. Its main advantages include high accuracy, strong instruction-following capabilities, and flexible customization options, suitable for various fields such as finance, technology, and professional services. The product currently offers a free trial and is accessed via API, facilitating quick deployment and use by enterprises.

wdoc is a RAG system developed by Olicorne (a medical student) to address document querying and summarization using retrieval-augmented generation technology. It supports multiple file types (such as PDFs, web pages, YouTube videos, etc.) and combines various language models to provide high-recall and high-precision query results. wdoc's main advantages include robust support for multiple file types, efficient retrieval capabilities, and flexible extensibility. It is suitable for researchers, students, and professionals, helping them process large amounts of information quickly. wdoc is currently under development, and the developer welcomes user feedback and feature requests to continuously improve the product.

Site RAG is a Chrome extension designed to assist users in quickly obtaining answers to questions while browsing the web, using natural language processing technology. It supports querying with the current page content as context and allows the indexing of entire website content into a vector database for subsequent retrieval-enhanced generation (RAG). The product runs entirely in the local browser, ensuring user data security, and supports connection to a locally running Ollama instance for inference. It primarily targets users who need to rapidly extract information from web content, such as developers, researchers, and students. This product is currently offered for free, making it suitable for those seeking immediate assistance while browsing the web.

This product is a React component specifically designed for RAG (Retrieval-Augmented Generation) AI assistants. It combines Upstash Vector for similarity searching, Together AI as the LLM (Large Language Model), and Vercel AI SDK for streaming responses. This modular design enables developers to rapidly incorporate RAG capabilities into Next.js applications, greatly simplifying the development process while providing high customizability. Key advantages include responsive design, support for streaming responses, persistent chat history, and dark/light mode support. The component primarily targets developers looking to integrate intelligent chat functionality into Web applications, particularly those working with the Next.js framework, streamlining the integration process and reducing development costs while delivering powerful capabilities.

RAG-logger is an open-source logging tool designed for Retrieval-Augmented Generation (RAG) applications. It is a lightweight open-source alternative tailored to the specific logging needs of RAG, focusing on providing comprehensive logging features such as query tracking, retrieval result logging, LLM interaction logging, and step-by-step performance monitoring. It utilizes a JSON-based log format, supports daily log organization, automatic file management, and metadata enrichment. With its open-source, lightweight, and RAG-focused features, RAG-logger offers developers an effective tool for monitoring and analyzing the performance of RAG applications.

Command R7B is a high-performance, scalable large language model (LLM) introduced by Cohere, specifically designed for enterprise applications. It delivers top-tier speed, efficiency, and quality while maintaining a compact model size, significantly lowering the production deployment costs of AI applications on standard GPUs, edge devices, or even CPUs. Command R7B excels in multilingual support, retrieval-augmented generation (RAG), reasoning, tool usage, and agent behavior, making it ideal for enterprises focusing on optimizing speed, cost efficiency, and computational resources.

E2M is a Python library capable of parsing and converting multiple file types into Markdown format. It employs a parser-converter architecture, supporting the conversion of a variety of file formats including doc, docx, epub, html, htm, url, pdf, ppt, pptx, mp3, and m4a. The ultimate aim of the E2M project is to provide high-quality data for Retrieval-Augmented Generation (RAG) and model training or fine-tuning.

vision-is-all-you-need is a demonstration project showcasing the Vision RAG (V-RAG) architecture. The V-RAG architecture directly embeds PDF file pages (or other documents) into vectors using Vision Language Models (VLM), eliminating the need for cumbersome chunk processing. This technology enhances the efficiency and accuracy of document retrieval, especially when dealing with large datasets. Background information indicates that this is an innovative tool leveraging the latest AI technologies to improve document processing capabilities. The project is currently open-source and free to use.

Minima is an open-source, fully localized Retrieval-Augmented Generation (RAG) model, capable of integrating with ChatGPT and the Model Context Protocol (MCP). It supports three modes: full local installation, querying local documents through ChatGPT, and querying local files using Anthropic Claude. The primary advantages of Minima include localized data processing, privacy protection, and the ability to leverage powerful language models to enhance retrieval and generation tasks. Background information indicates that Minima supports multiple file formats and allows users to customize configurations to fit different usage scenarios. Minima is free and open-source, targeting developers and enterprises seeking localized AI solutions.

Qwen-Agent is an Agent framework built on Qwen>=2.0, equipped with capabilities for instruction following, tool usage, planning, and memory. The framework showcases example applications such as browser assistants, code interpreters, and custom assistants. The primary advantages of Qwen-Agent include its high scalability and modular design, allowing developers to integrate various tools and functionalities as needed. Background information indicates that Qwen-Agent aims to provide developers with a robust toolkit for building and deploying applications based on large language models. Qwen-Agent is open-source on GitHub, enabling community contributions and collaboration.

Inquir is a powerful tool designed to create personalized search engines tailored to your data. It unlocks features such as custom search solutions, data aggregation, AI-driven Retrieval-Augmented Generation (RAG) systems, and context-aware search capabilities. Take the first step towards enhancing user experience by launching your engine or scheduling a demonstration.

Chonkie is a text chunking library designed for Retrieval-Augmented Generation (RAG) applications. It is lightweight, fast, and user-friendly. The library provides various text chunking methods, supports multiple tokenizers, and boasts high performance. Key advantages of Chonkie include rich functionality, ease of use, rapid processing speeds, extensive support, and a lightweight design. It is suitable for developers and researchers who require efficient text data processing, especially in natural language processing and machine learning. Chonkie is open-source and complies with the MIT license, making it freely available.

Trieve is an AI-first infrastructure API that combines language models and tools for fine-tuning ranking and relevance, providing a one-stop solution for search, recommendations, RAG, and analytics. It can continuously improve automatically based on dozens of feedback signals, ensuring relevance. Trieve supports semantic vector search, BM25, and SPLADE full-text search, as well as hybrid search combining full-text and semantic vector searches. Additionally, it offers product promotion and relevance adjustment features to help users fine-tune search results via API or no-code dashboards to achieve KPIs. Built on the best foundations, it utilizes open-source embedding models and LLMs, running on its own servers to ensure data security.

Dabarqus is a Retrieval-Augmented Generation (RAG) framework that allows users to provide private data to large language models (LLMs) in real time. This tool facilitates the storage of various data sources (such as PDFs, emails, and raw data) into semantic indices, referred to as 'memory repositories,' through REST APIs, SDKs, and CLI tools. Dabarqus supports LLM-style prompts, enabling users to interact with memory repositories in a straightforward manner without needing to construct special queries or learn a new query language. Additionally, it allows for the creation and utilization of multiple semantic indices (memory repositories), organizing data by topics, categories, or other grouping methods. The product background of Dabarqus emphasizes its aim to simplify the integration of private data with AI language models, enhancing the efficiency and accuracy of data retrieval.

Vectorize is a platform focused on transforming unstructured data into optimized vector search indices, specifically designed for retrieval-augmented generation (RAG). By connecting various data sources such as content management systems, file systems, CRM, and collaboration tools, it helps users create productivity-enhancing assistant systems and innovative customer experiences. Key advantages of Vectorize include ease of use, rapid deployment, and high-accuracy search results, making it suitable for enterprises that need to handle large amounts of data and wish to quickly realize AI applications.

Epsilla is a no-code Retrieval-Augmented Generation as a Service (RAG-as-a-Service) platform that allows users to build production-ready Large Language Model (LLM) applications based on private or public data. The platform offers a one-stop solution, including data management, RAG tools, CI/CD-style evaluations, and enterprise-level security measures, designed to lower the total cost of ownership (TCO), enhance query speed and throughput, while ensuring timeliness and security of information.

Kotaemon is an open-source tool based on the Retrieval-Augmented Generation (RAG) model designed to interact with user documents through a chat interface. It supports various language model API providers and local language models, offering a clean and customizable user interface suitable for end users conducting document Q&A and developers building their own RAG Q&A workflows.

Ragie is a Retrieval-Augmented Generation (RAG) as a service product targeted at developers, providing easy-to-use APIs and SDKs to help them quickly launch and implement generative AI applications. Ragie features advanced capabilities such as LLM re-ranking, summary indexing, and entity extraction to ensure accurate and reliable information. It supports direct connections to popular data sources like Google Drive and Notion, along with automatic syncing to keep data current. Led by Craft Ventures, Ragie offers a straightforward pricing strategy with no setup fees or hidden costs.

RAG_Techniques is a collection focused on Retrieval-Augmented Generation (RAG) systems, aimed at enhancing the accuracy, efficiency, and contextual richness of these systems. It serves as a cutting-edge technology hub that drives the development and innovation of RAG technology through community contributions and a collaborative environment.

Easy-RAG is a Retrieval-Augmented Generation (RAG) system that is ideal for learners to understand and master RAG technology, while also being convenient for developers to use and expand independently. This system enhances retrieval efficiency and generation quality by integrating knowledge graph extraction tools, reranking mechanisms, and the FAISS vector database.

RAGFoundry is a library designed to enhance the ability of large language models (LLMs) to utilize external information by fine-tuning models on specially created RAG-augmented datasets. The library facilitates efficient model training using Parameter-Efficient Fine-Tuning (PEFT), allowing users to easily measure performance improvements with RAG-specific metrics. It features a modular design, enabling workflow customization through configuration files.

Korvus is a search SDK built on Postgres, unifying the entire RAG (Retrieval Augmented Generation) process into a single database query. It offers high-performance, customizable search capabilities while minimizing infrastructure concerns. Korvus leverages PostgresML's pgml and pgvector extensions, compressing the RAG workflow within Postgres itself. It supports multi-language SDKs, including Python, JavaScript, Rust, and C, allowing developers to seamlessly integrate into existing technology stacks.

Retrieval-Augmented Generation (RAG) is a cutting-edge technique that enhances the capabilities of generative models by integrating external knowledge sources, leading to higher quality and reliability in generated content. LangChain is a powerful framework designed specifically for building and deploying robust language model applications. This tutorial series offers a comprehensive, step-by-step guide to help you implement RAG using LangChain. It starts with an introduction to the fundamental RAG process and gradually delves into areas like query transformation, document embedding, routing mechanisms, query construction, indexing strategies, retrieval techniques, and the generation stage. Ultimately, it integrates all these concepts into a practical scenario, showcasing the power and flexibility of RAG.

RAGElo is a toolkit that leverages the Elo rating system to help select the best-performing Large Language Model (LLM) agents enhanced with Retrieval Augmented Generation (RAG). While prototyping and integrating generative LLMs in production has become easier, evaluation remains the most challenging aspect of these solutions. RAGElo addresses this by comparing the answers of different RAG pipelines and prompts to multiple questions, calculating rankings for various setups. This provides a clear overview of which configurations are effective and which are not.

DB-GPT is an open-source AI-native data application development framework that, utilizing AWEL (Agentic Workflow Expression Language) and agent technologies, simplifies the integration of large models with data. Through its capabilities in multi-model management, Text2SQL optimization, RAG framework optimization, and multi-agent framework collaboration, DB-GPT empowers enterprises and developers to build customized applications with less code. In the era of Data 3.0, DB-GPT, based on models and databases, provides foundational data intelligence technologies for building enterprise-level reporting, analysis, and business insights.

GoMate is a model based on the Retrieval-Augmented Generation (RAG) framework, focused on delivering reliable input and trustworthy output. By combining retrieval and generation technologies, it enhances the accuracy and reliability of information retrieval and text generation. GoMate is suitable for fields requiring efficient and accurate information processing, such as natural language processing and question answering.

Omakase RAG Orchestrator is a project aimed at addressing the challenges encountered when building RAG applications. It provides a comprehensive web application and API to encapsulate large language models (LLMs) and their wrappers. The project integrates Django, Llamaindex, and Google Drive to enhance the application's usability, scalability, and data and user access management.

Verba is an open-source application designed to provide an end-to-end, seamless, and user-friendly retrieval-augmented generation (RAG) interface. It combines cutting-edge RAG techniques with Weaviate's context-aware database, supporting both local and cloud deployments, making it easy to explore datasets and extract insights.

Command R+ is an advanced RAG optimization model designed for enterprise-level workloads. It is the first to be launched on Microsoft Azure. This model boasts a 128k token context window, delivering best-in-class retrieval-augmented generation (RAG) performance. It supports 10 key languages for multilingual coverage, and features tool usage capabilities to automate complex business processes. Pricing for Command R+: $3.00/M input tokens, $15.00/M output tokens. It is suitable for a wide range of enterprise scenarios, such as finance, human resources, sales, marketing, and customer support.