OpenSPG / KAG

KAG: Knowledge Augmented Generation English | 简体中文 | 日本語版ドキュメント • What is KAG? KAG is a logical reasoning and Q&A framework based on the OpenSPG engine and large language models, which is used to build logical reasoning and Q&A solutions for vertical domain knowledge bases. KAG can effectively overcome the ambiguity of traditional RAG vector similarity calculation and the noise problem of GraphRAG introduced by OpenIE. KAG supports logical reasoning and multi-hop fact Q&A, etc., and is significantly better than the current SOTA method. The goal of KAG is to build a knowledge-enhanced LLM service framework in professional domains, supporting logical reasoning, factual Q&A, etc. KAG fully integrates the logical and factual characteristics of the KGs. Its core features include: • Knowledge and Chunk Mutual Indexing structure to integrate more complete contextual text information • Knowledge alignment using conceptual semantic reasoning to alleviate the noise problem caused by OpenIE • Schema-constrained knowledge construction to support the representation and construction of domain expert knowledge • Logical form-guided hybrid reasoning and retrieval to support logical reasoning and multi-hop reasoning Q&A ⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new releases! 🌟 • Core Features 2.1 Knowledge Representation In the context of private knowledge bases, unstructured data, structured information, and business expert experience often coexist. KAG references the DIKW hierarchy to upgrade SPG to a version that is friendly to LLMs. For unstructured data such as news, events, logs, and books, as well as structured data like transactions, statistics, and approvals, along with business experience and domain knowledge rules, KAG employs techniques such as layout analysis, knowledge extraction, property normalization, and semantic alignment to integrate raw business data and expert rules into a unified business knowledge graph. This makes it compatible with schema-free information extraction and schema-constrained expertise construction on the same knowledge type (e. G., entity type, event type), and supports the cross-index representation between the graph structure and the original text block. This mutual index representation is helpful to the construction of inverted index based on graph structure, and promotes the unified representation and reasoning of logical forms. 2.2 Mixed Reasoning Guided by Logic Forms KAG proposes a logically formal guided hybrid solution and inference engine. The engine includes three types of operators: planning, reasoning, and retrieval, which transform natural language problems into problem solving processes that combine language and notation. In this process, each step can use different operators, such as exact match retrieval, text retrieval, numerical calculation or semantic reasoning, so as to realize the integration of four different problem solving processes: Retrieval, Knowledge Graph reasoning, language reasoning and numerical calculation. • Release Notes 3.1 Latest Updates • 2025.06.27 : Released KAG 0.8.0 Version • 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. • Added an open_benchmark directory to the top level of the KAG repository, comparing various RAG methods under the same base to achieve state-of-the-art (SOTA) results. • Introduced a "Lightweight Build" mode, reducing knowledge construction token costs by 89%. • 2025.01.07 : Support domain knowledge injection, domain schema customization, QFS tasks support, Visual query analysis, enables schema-constraint mode for extraction, etc. • 2024.11.21 : Support Word docs upload, model invoke concurrency setting, User experience optimization, etc. • 2024.10.25 : KAG initial release 3.2 Future Plans • 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. • Quick Start 4.1 product-based (for ordinary users) 4.1.1 Engine & Dependent Image Installation • **…