From 451257aed5611710dab83f5957d54c11b3135ce4 Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 5 Nov 2025 16:58:20 +0800 Subject: [PATCH] Doc: Update news with recent features --- README-zh.md | 12 ++++++++++-- README.md | 12 ++++++++++-- 2 files changed, 20 insertions(+), 4 deletions(-) diff --git a/README-zh.md b/README-zh.md index 552d566a..c90889dd 100644 --- a/README-zh.md +++ b/README-zh.md @@ -53,13 +53,17 @@ ## 🎉 新闻 -- [X] [2025.06.16]🎯📢我们的团队发布了[RAG-Anything](https://github.com/HKUDS/RAG-Anything)，一个用于无缝处理文本、图像、表格和方程式的全功能多模态 RAG 系统。 +- [x] [2025.11.05]🎯📢添加**基于RAGAS的**LightRAG评估框架。 +- [x] [2025.10.22]🎯📢消除处理**大规模数据集**的瓶颈。 +- [x] [2025.09.15]🎯📢显著提升**小型LLM**（如Qwen3-30B-A3B）的知识图谱提取准确性。 +- [x] [2025.08.29]🎯📢现已支持**Reranker**，显著提升混合查询性能。 +- [x] [2025.08.04]🎯📢支持**文档删除**并重新生成知识图谱以确保查询性能。 +- [x] [2025.06.16]🎯📢我们的团队发布了[RAG-Anything](https://github.com/HKUDS/RAG-Anything)，一个用于无缝处理文本、图像、表格和方程式的全功能多模态 RAG 系统。 - [X] [2025.06.05]🎯📢LightRAG现已集成[RAG-Anything](https://github.com/HKUDS/RAG-Anything)，支持全面的多模态文档解析与RAG能力（PDF、图片、Office文档、表格、公式等）。详见下方[多模态处理模块](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#多模态文档处理rag-anything集成)。 - [X] [2025.03.18]🎯📢LightRAG现已支持引文功能。 - [X] [2025.02.05]🎯📢我们团队发布了[VideoRAG](https://github.com/HKUDS/VideoRAG)，用于理解超长上下文视频。 - [X] [2025.01.13]🎯📢我们团队发布了[MiniRAG](https://github.com/HKUDS/MiniRAG)，使用小型模型简化RAG。 - [X] [2025.01.06]🎯📢现在您可以[使用PostgreSQL进行存储](#using-postgresql-for-storage)。 -- [X] [2024.12.31]🎯📢LightRAG现在支持[通过文档ID删除](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete)。 - [X] [2024.11.25]🎯📢LightRAG现在支持无缝集成[自定义知识图谱](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg)，使用户能够用自己的领域专业知识增强系统。 - [X] [2024.11.19]🎯📢LightRAG的综合指南现已在[LearnOpenCV](https://learnopencv.com/lightrag)上发布。非常感谢博客作者。 - [X] [2024.11.11]🎯📢LightRAG现在支持[通过实体名称删除实体](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete)。 @@ -1459,6 +1463,10 @@ LightRAG服务器提供全面的知识图谱可视化功能。它支持各种重 ![iShot_2025-03-23_12.40.08](./README.assets/iShot_2025-03-23_12.40.08.png) +## RAGAS评估 + +**RAGAS**（Retrieval Augmented Generation Assessment，检索增强生成评估）是一个使用LLM对RAG系统进行无参考评估的框架。我们提供了基于RAGAS的评估脚本。详细信息请参阅[基于RAGAS的评估框架](lightrag/evaluation/README.md)。 + ## 评估 ### 数据集 diff --git a/README.md b/README.md index b844a18d..f2d514ec 100644 --- a/README.md +++ b/README.md @@ -51,13 +51,17 @@ --- ## 🎉 News -- [X] [2025.06.16]🎯📢Our team has released [RAG-Anything](https://github.com/HKUDS/RAG-Anything) an All-in-One Multimodal RAG System for seamless text, image, table, and equation processing. +- [x] [2025.11.05]🎯📢Add **RAGAS-based** Evaluation Framework for LightRAG. +- [x] [2025.10.22]🎯📢MualEliminate bottlenecks in processing **large-scale datasets**. +- [x] [2025.09.15]🎯📢Significantly enhances KG extraction accuracy for **small LLMs** like Qwen3-30B-A3B. +- [x] [2025.08.29]🎯📢**Reranker** is supported now , significantly boosting performance for mixed queries. +- [x] [2025.08.04]🎯📢**Document deletion** with KG regeneration to ensure query performance. +- [x] [2025.06.16]🎯📢Our team has released [RAG-Anything](https://github.com/HKUDS/RAG-Anything) an All-in-One Multimodal RAG System for seamless text, image, table, and equation processing. - [X] [2025.06.05]🎯📢LightRAG now supports comprehensive multimodal data handling through [RAG-Anything](https://github.com/HKUDS/RAG-Anything) integration, enabling seamless document parsing and RAG capabilities across diverse formats including PDFs, images, Office documents, tables, and formulas. Please refer to the new [multimodal section](https://github.com/HKUDS/LightRAG/?tab=readme-ov-file#multimodal-document-processing-rag-anything-integration) for details. - [X] [2025.03.18]🎯📢LightRAG now supports citation functionality, enabling proper source attribution. - [X] [2025.02.05]🎯📢Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos. - [X] [2025.01.13]🎯📢Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models. - [X] [2025.01.06]🎯📢You can now [use PostgreSQL for Storage](#using-postgresql-for-storage). -- [X] [2024.12.31]🎯📢LightRAG now supports [deletion by document ID](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete). - [X] [2024.11.25]🎯📢LightRAG now supports seamless integration of [custom knowledge graphs](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg), empowering users to enhance the system with their own domain expertise. - [X] [2024.11.19]🎯📢A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author. - [X] [2024.11.11]🎯📢LightRAG now supports [deleting entities by their names](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete). @@ -1539,6 +1543,10 @@ The LightRAG Server offers a comprehensive knowledge graph visualization feature ![iShot_2025-03-23_12.40.08](./README.assets/iShot_2025-03-23_12.40.08.png) +## RAGAS-based Evaluation + +**RAGAS** (Retrieval Augmented Generation Assessment) is a framework for reference-free evaluation of RAG systems using LLMs. There is an evaluation script based on RAGAS. For detailed information, please refer to [RAGAS-based Evaluation Framework](lightrag/evaluation/README.md). + ## Evaluation ### Dataset