AI Professional Tools

LightRAG: A Lightweight Framework for Building Retrieval Augmented Generation (RAG) Applications

LightRAG is an open-source Python framework developed by a team from the School of Data Science at the University of Hong Kong to simplify and accelerate the process of building Retrieval Augmented Generation (RAG) applications. It does this by combining knowledge graphs with traditional vector retrieval techniques to provide more accurate and contextualized Large Language Models (LLMs)...

RAG-Anything: an all-in-one RAG system that can handle graphic forms

RAG-Anything is a fully integrated multimodal document processing RAG system built on LightRAG. Most of the traditional question and answer systems (RAG) can only process plain text content, but the documents we come into contact with on a daily basis, such as PDFs, Word documents or presentations, often contain text, images, tables...

Medical-RAG: A Retrieval-Augmented Generation Framework for Constructing Chinese Medical Q&As

Medical-RAG 是一个专为中文医疗领域设计的问答智能体项目. 它基于检索增强生成（RAG）技术，通过结合外部知识库来提升大型语言模型（LLM）在医疗建议上的准确性和安全性. 该项目的核心是利用高性能向量数据库 Mi...

ComoRAG: a cognitive memory retrieval tool for long narrative reasoning

ComoRAG is a Retrieval Augmented Generation (RAG) system designed to address long documents and multi-document narrative comprehension. Traditional RAG methods often encounter difficulties when dealing with long stories or novels due to the complexity of the plot and the evolving character relationships. This is due to the fact that most of them use stateless, single-check ...

DeepSieve: a RAG Intelligent Information Screening Tool for Processing Complex Query Sources

DeepSieve is an open source Retrieval Augmented Generation (RAG) framework hosted on GitHub that focuses on processing complex queries and multi-source data. It provides efficient information filtering capabilities by decomposing queries, routing sub-questions, reflecting on failed retrievals and fusing answers.DeepSieve is developed by MinghoKwok ....

RAGLight: Lightweight Retrieval Augmentation Generation Python Library

RAGLight is a lightweight, modular Python library designed to implement Retrieval Augmented Generation (RAG). It improves the contextual understanding of Large Language Models (LLMs) by combining document retrieval and natural language generation.RAGLight supports multiple language models, embedded models, and vector stores,...

llmware: an open source framework for rapidly building enterprise-class RAG applications

llmware is an open source framework focused on helping developers rapidly build enterprise-class Retrieval Augmentation Generation (RAG) applications. It provides over 50 small, specialized Large Language Models (LLMs) that support running in local or private cloud environments, and is particularly suited to data security-sensitive industries such as financial, legal, and compliance...

Vespa.ai: an open source platform for building efficient AI search and recommendation systems

Vespa.ai is an open source AI search and recommendation platform that focuses on processing large-scale data to provide efficient search, recommendation and personalized services. It supports vector search, text search and structured data processing, combined with machine learning models to achieve real-time inference.Vespa can handle hundreds of millions of data, response speed...

NodeRAG: A Heterogeneous Graph-Based Tool for Accurate Information Retrieval and Generation

NodeRAG is an open source Retrieval Augmented Generation (RAG) system hosted on GitHub and developed by Terry-Xu-666. It optimizes information retrieval and generation through heterogeneous graph structures, significantly improving retrieval accuracy and contextual relevance.NodeRAG supports local deployment, provides a user-friendly interface and can be .....

Morphik Core: an open source RAG platform for processing multimodal data

Morphik Core is an open source project developed by the morphik-org team and hosted on GitHub. It used to be called DataBridge Core, but is now renamed Morphik Core.This tool is a database designed for AI applications that can process text, images...

Rankify: a Python toolkit supporting information retrieval and reordering

Rankify is an open source Python toolkit developed by the Data Science Group at the University of Innsbruck, Austria. It focuses on information retrieval, reordering and retrieval augmentation generation (RAG), providing a unified framework. The toolkit comes with 40 built-in pre-retrieved benchmark datasets, support for 7 retrieval techniques and 24 ...

HippoRAG: A multi-hop knowledge retrieval framework based on long term memory

HippoRAG is an open source framework developed by the OSU-NLP group at The Ohio State University, inspired by human long term memory mechanisms. It combines Retrieval Augmented Generation (RAG), Knowledge Graph, and Personalized PageRank techniques to help Large Language Models (LLMs) continuously integrate knowledge from external documents.Hippo.....

LettuceDetect: an efficient tool for detecting hallucinations in the RAG system

LettuceDetect is a lightweight open-source tool developed by KRLabsOrg that specializes in detecting hallucinatory content generated in Retrieval Augmented Generation (RAG) systems. It helps developers improve the accuracy of RAG systems by comparing context, questions and answers, and identifying parts of the answer that are not supported by the context...

dsRAG: A Retrieval Engine for Unstructured Data and Complex Queries

dsRAG is a high-performance retrieval engine designed to handle complex queries on unstructured data. It performs particularly well in handling challenging queries in dense text such as financial reports, legal documents, and academic papers. dsRAG employs three key approaches to improve performance: semantic segmentation, contextual self...

VideoRAG: A RAG framework for understanding ultra-long videos with support for multimodal retrieval and knowledge graph construction

VideoRAG is a retrieval-enhanced generative framework designed for processing and understanding very long contextual videos. The tool combines a graph-driven textual knowledge base with hierarchical multimodal context encoding to efficiently process hundreds of hours of video content on a single NVIDIA RTX 3090 GPU.VideoRAG works by moving...

PRAG: Parameterized Retrieval Augmentation Generation Tool for Improving the Performance of Q&A Systems

PRAG (Parametric Retrieval-Augmented Generation) is an innovative retrieval-augmented generation tool that aims to enhance generation by embedding external knowledge directly into the parameter space of a Large Language Model (LLM). The tool overcomes the limitations of traditional contextual retrieval-augmented generation methods...

ColiVara: Visual Embedding Based Document Storage and Retrieval Service

ColiVara is a document storage and retrieval service based on visual embedding technology. It eliminates the need for Optical Character Recognition (OCR) or text extraction and avoids the problem of broken forms or lost images.ColiVara supports over 100 file formats including PDF, DOCX, PPTX, etc. and is able to automatically intercept web pages...

Deeptrain: converting video content into large model retrievable information

Deeptrain is a platform focusing on AI video processing, which can effectively integrate video content into various AI applications through its advanced technology that supports over 200 language models. Users can train models directly by providing video URLs without having to download the videos.Deeptrain offers the ability to create video from...

UltraRAG: A One-Stop RAG System Solution to Simplify Data Construction and Model Fine-Tuning

UltraRAG is a RAG (Retrieval Augmented Generation) system solution jointly proposed by the THUNLP group at Tsinghua University, the NEUIR group at Northeastern University, Modelbest.Inc and the 9#AISoft team. The framework is based on agile deployment and modular construction, providing automated data construction, model fine-tuning, and inference evaluation techniques body...