# Retrieval-Augmented Generation

ViDoRAG is a novel multimodal retrieval-augmented generation framework developed by Alibaba's Natural Language Processing team, designed for complex reasoning tasks involving visually rich documents. This framework significantly improves the robustness and accuracy of generative models through dynamic iterative reasoning agents and a Gaussian Mixture Model (GMM)-driven multimodal retrieval strategy. Key advantages of ViDoRAG include efficient handling of visual and textual information, support for multi-hop reasoning, and high scalability. The framework is suitable for scenarios requiring information retrieval and generation from large-scale documents, such as intelligent question answering, document analysis, and content creation. Its open-source nature and flexible, modular design make it a valuable tool for researchers and developers in the multimodal generation field.

M2RAG is a benchmark codebase for retrieval-augmented generation in multimodal contexts. It answers questions by retrieving multimodal documents, evaluating the ability of multimodal large language models (MLLMs) to leverage knowledge from multimodal contexts. The model is evaluated on tasks such as image captioning, multimodal question answering, fact verification, and image re-ranking, aiming to improve the effectiveness of models in multimodal contextual learning. M2RAG provides researchers with a standardized testing platform to help advance the development of multimodal language models.

bRAG-langchain is an open-source project focused on the research and application of Retrieval-Augmented Generation (RAG) technology. RAG is an AI technology that combines retrieval and generation. By retrieving relevant documents and generating answers, it provides users with more accurate and comprehensive information. This project provides a guide to RAG implementation, from basic to advanced, helping developers quickly get started and build their own RAG applications. Its key advantages are its open-source nature, flexibility, and ease of extension, making it suitable for various applications requiring natural language processing and information retrieval.

KET-RAG (Knowledge-Enhanced Text Retrieval Augmented Generation) is a powerful retrieval-augmented generation framework enhanced with knowledge graph technology. It achieves efficient knowledge retrieval and generation through a multi-granularity indexing framework, such as a knowledge graph skeleton and a text-keyword bipartite graph. This framework significantly improves retrieval and generation quality while reducing indexing costs, making it well-suited for large-scale RAG applications. Developed in Python, KET-RAG supports flexible configuration and extension, catering to the needs of developers and researchers seeking efficient knowledge retrieval and generation.

MiniRAG is a retrieval-augmented generation system designed for small language models, aimed at simplifying RAG processes and enhancing efficiency. It addresses the performance limitations of small models within traditional RAG frameworks through a semantically aware heterogeneous graph indexing mechanism and lightweight topological enhanced retrieval methods. This model shows significant advantages in resource-constrained scenarios, such as on mobile devices or edge computing environments. Its open-source nature allows for easy adoption and improvement within the developer community.

CohereForAI/c4ai-command-r7b-12-2024 is a multilingual model with 7 billion parameters, focusing on advanced tasks such as reasoning, summarization, question answering, and code generation. The model supports retrieval-augmented generation (RAG) and can utilize and combine multiple tools to accomplish more complex tasks. It excels in enterprise-related code use cases and supports 23 languages.

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.

VisRAG is an innovative retrieval-augmented generation (RAG) process based on visual language models (VLMs). Unlike traditional text-based RAG, VisRAG embeds documents directly as images through a VLM, which enhances the generative capabilities of the VLM. This method maximizes the retention of data information from the original documents, eliminating the information loss introduced during parsing. The application of the VisRAG model on multimodal documents demonstrates its strong potential in information retrieval and enhanced text generation.

LightRAG is a retrieval-augmented generation model designed to enhance performance in text generation tasks by combining the strengths of retrieval and generation. The model delivers more accurate and relevant information while maintaining generation speed, which is especially crucial for applications requiring quick and precise information retrieval. The development of LightRAG stems from the need for improvements over existing text generation models, particularly in scenarios involving large datasets and complex queries. Currently, it is open-source and freely available, providing researchers and developers with a powerful tool to explore and implement retrieval-based text generation tasks.

LlamaIndex.TS is a framework designed for building applications based on large language models (LLMs). It focuses on helping users ingest, structure, and access private or domain-specific data. This framework provides a natural language interface to connect humans with inferred data, enabling developers to enhance their software capabilities through LLMs without needing to become experts in machine learning or natural language processing. LlamaIndex.TS supports popular runtime environments such as Node.js, Vercel Edge Functions, and Deno.

C4AI Command R 08-2024 is a large language model with 3.5 billion parameters developed by Cohere and Cohere For AI, optimized for diverse applications such as reasoning, summarization, and question-answering. The model supports training in 23 languages and has been evaluated in 10 languages, exhibiting high-performance retrieval-augmented generation (RAG) capabilities. It aligns with human preferences for usefulness and safety through supervised fine-tuning and preference training. Additionally, the model features dialogue tool usage, capable of generating tool-based responses through specific prompt templates.

C4AI Command R+ 08-2024 is a large-scale research model with 104 billion parameters, demonstrating highly advanced capabilities, including retrieval-augmented generation (RAG) and tool usage for automating complex tasks. The model supports training in 23 languages and has been evaluated in 10 of those languages. It optimizes various use cases, including reasoning, summarization, and question-answering.

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.

Rerank 3 is a new foundational model optimized for enterprise search and retrieval-augmented generation (RAG) systems. It supports multilingual and multi-structured data search, provides high-precision semantic ranking, significantly improves response accuracy and latency, and greatly reduces the overall cost of ownership. Rerank 3 can be seamlessly integrated into any database or search engine and supports seamless integration with existing applications' native search functionality.