From 73e7e48d81311eb7d0fbcf79971b79d323239cca Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BA=84=E8=88=9F?= Date: Mon, 30 Jun 2025 14:55:10 +0800 Subject: [PATCH 1/2] v0.8.0 Release Notes #andy --- README.md | 11 +++++++---- README_cn.md | 11 +++++++---- 2 files changed, 14 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 3b1eea8d8..ac19c9a80 100644 --- a/README.md +++ b/README.md @@ -73,7 +73,12 @@ In this process, each step can use different operators, such as exact match retr # 3. Release Notes ## 3.1 Latest Updates - +* 2025.06.27 : Released KAG 0.8.0 Version + * Support for two modes: private domain knowledge bases (including structured and unstructured data) and public domain knowledge bases. This includes the ability to integrate public web data sources such as LBS and WebSearch via the MCP protocol. + * Improved the management of private domain knowledge base indexing, incorporating multiple foundational index types such as Outline, Summary, KnowledgeUnit, AtomicQuery, Chunk, and Table. This supports developers in customizing indexes and synchronizing them with product interfaces. + * Decoupled the knowledge base from applications. The knowledge base now manages private domain data (both structured and unstructured) and public domain data, while applications can link to multiple knowledge bases. Based on the index types used during knowledge base construction, the system automatically adapts the corresponding retrieval engine to recall data. + * Fully embraced the MCP protocol, enabling seamless integration of KAG reasoning and Q&A into agent workflows (based on the MCP protocol). + * Adapted to the KAG-Thinker model. Through optimizations such as broad decomposition and deep solving of complex problems, knowledge boundary determination, and noise-resistant retrieval results, the stability of the KAG framework's reasoning paradigm and the rigor of its reasoning logic have been significantly improved under the guidance of iterative thinking paradigms. * 2025.04.17 : Released KAG 0.7 Version * First, we refactored the KAG-Solver framework. Added support for two task planning modes, static and iterative, while implementing a more rigorous knowledge layering mechanism for the reasoning phase. * Second, we optimized the product experience: introduced dual modes—"Simple Mode" and "Deep Reasoning"—during the reasoning phase, along with support for streaming inference output, automatic rendering of graph indexes, and linking generated content to original references. @@ -85,9 +90,7 @@ In this process, each step can use different operators, such as exact match retr ## 3.2 Future Plans -* Logical reasoning optimization, conversational tasks support -* kag-model release, kag solution for event reasoning knowledge graph and medical knowledge graph -* kag front-end open source, distributed build support, mathematical reasoning optimization +* We will continue to focus on enhancing large models' ability to leverage external knowledge bases. Our goal is to achieve bidirectional enhancement and seamless integration between large models and symbolic knowledge, improving the factuality, rigor, and consistency of reasoning and Q&A in professional scenarios. We will also keep releasing updates to push the boundaries of capability and drive adoption in vertical domains. # 4. Quick Start diff --git a/README_cn.md b/README_cn.md index a29291413..6448e8ab8 100644 --- a/README_cn.md +++ b/README_cn.md @@ -63,7 +63,12 @@ KAG 提出了一种逻辑符号引导的混合求解和推理引擎。该引擎 # 3. 版本发布 ## 3.1 最近更新 - +* 2025.06.27 : 发布KAG 0.8.0 版本 + * 扩展了私域知识库（含结构化、非结构化数据）、公网知识库 两种模式，支持通过MCP 协议引入LBS、WebSearch 等公网数据源 + * 升级了私域知识库索引管理的能力，内置Outline、Summary、KnowledgeUnit、AtomicQuery、Chunk、Table 等多种基础索引类型 + * 将知识库和应用解耦，知识库管理私域数据（结构化 & 非结构化）、公网数据；应用可关联多知识库，基于知识库构建阶段的索引类型，自动适配对应的检索器完成数据召回 + * 全面拥抱MCP，提供在agent 流程中接入KAG 推理问答（基于MCP 协议）的能力 + * 完成了对KAG-Thinker 模型的适配。通过复杂问题的广度拆分和深度求解、知识边界判定、检索结果抗噪等优化，在多轮迭代式思考范式的牵引下，提升了KAG框架推理范式的稳定性，推理逻辑的严谨性 * 2025.04.17 : 发布KAG 0.7 版本 * 我们对框架进行了全面重构。新增了对static和iterative两种任务规划模式的支持，同时实现了更严谨的推理阶段知识分层机制 * 我们对产品体验进行了全面优化：在推理阶段新增"简易模式"和"深度推理"双模式，并支持流式推理输出、图索引自动渲染、生成内容关联原始文献等 @@ -75,9 +80,7 @@ KAG 提出了一种逻辑符号引导的混合求解和推理引擎。该引擎 ## 3.2 后续计划 -* 逻辑推理 优化、对话式任务支持 -* kag-model 发布、事理图谱 和 医疗图谱的 kag 解决方案发布 -* kag 前端开源、分布式构建支持、数学推理 优化 +* 我们持续致力于持续提升大模型利用外部知识库的能力，实现大模型与符号知识的双向增强和有机融合，不断提升专业场景推理问答的事实性、严谨性和一致性等，我们也将持续发布，不断提升能力的上限，不断推进垂直领域的落地 # 4. 快速开始 From 25e17f6cc6c0063e00fffe2273ba878b71b6b0c8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BA=84=E8=88=9F?= Date: Mon, 30 Jun 2025 15:06:10 +0800 Subject: [PATCH 2/2] v0.8.0 Release Notes #andy --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index ac19c9a80..189179325 100644 --- a/README.md +++ b/README.md @@ -74,11 +74,11 @@ In this process, each step can use different operators, such as exact match retr ## 3.1 Latest Updates * 2025.06.27 : Released KAG 0.8.0 Version - * Support for two modes: private domain knowledge bases (including structured and unstructured data) and public domain knowledge bases. This includes the ability to integrate public web data sources such as LBS and WebSearch via the MCP protocol. - * Improved the management of private domain knowledge base indexing, incorporating multiple foundational index types such as Outline, Summary, KnowledgeUnit, AtomicQuery, Chunk, and Table. This supports developers in customizing indexes and synchronizing them with product interfaces. - * Decoupled the knowledge base from applications. The knowledge base now manages private domain data (both structured and unstructured) and public domain data, while applications can link to multiple knowledge bases. Based on the index types used during knowledge base construction, the system automatically adapts the corresponding retrieval engine to recall data. - * Fully embraced the MCP protocol, enabling seamless integration of KAG reasoning and Q&A into agent workflows (based on the MCP protocol). - * Adapted to the KAG-Thinker model. Through optimizations such as broad decomposition and deep solving of complex problems, knowledge boundary determination, and noise-resistant retrieval results, the stability of the KAG framework's reasoning paradigm and the rigor of its reasoning logic have been significantly improved under the guidance of iterative thinking paradigms. + * Expanded two modes: Private Knowledge Base (including structured & unstructured data) and Public Network Knowledge Base, supporting integration of LBS, WebSearch, and other public data sources via MCP protocol. + * Enhanced Private Knowledge Base indexing capabilities, with built-in fundamental index types such as Outline, Summary, KnowledgeUnit, AtomicQuery, Chunk, and Table. + * Decoupled knowledge bases from applications: Knowledge Bases manage private data (structured & unstructured) and public data; Applications can associate with multiple knowledge bases and automatically adapt corresponding retrievers for data recall based on index types established during knowledge base construction. + * Fully embraced MCP, enabling KAG-powered inference QA (via MCP protocol) within agent workflows. + * Completed adaptation for the KAG-Thinker model. Through optimizations in breadth-wise problem decomposition, depth-wise solution derivation, knowledge boundary determination, and noise-resistant retrieval results, the framework's reasoning paradigm stability and logical rigor have been improved under the guidance of multi-round iterative thinking frameworks. * 2025.04.17 : Released KAG 0.7 Version * First, we refactored the KAG-Solver framework. Added support for two task planning modes, static and iterative, while implementing a more rigorous knowledge layering mechanism for the reasoning phase. * Second, we optimized the product experience: introduced dual modes—"Simple Mode" and "Deep Reasoning"—during the reasoning phase, along with support for streaming inference output, automatic rendering of graph indexes, and linking generated content to original references.