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OpenSPG/KAG

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KAG

KAG is a graph-augmented retrieval augmented generation system and knowledge graph engine. It functions as a framework that integrates large language models with graph retrieval and numerical calculation to resolve natural language queries.

The system creates unified knowledge representations by aligning unstructured data and expert rules through semantic mapping. It maintains mutual indexing between graph structures and original text blocks to ensure that reasoning processes remain linked to verifiable source data.

The project provides capabilities for semantic information integration, graph-based data retrieval, and hybrid logical reasoning. It employs a pipeline that combines semantic graph search with numerical calculations and symbolic logic.

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Features

  • Graph-Based Retrieval Augmentation - Uses graph-based retrieval augmentation to connect structured representations to source text for verifiable AI generation.
  • Graph Reasoning Systems - Combines LLMs with graph retrieval and numerical calculation to facilitate complex information synthesis.
  • Graph Retrieval Augmented Generation - Implements a graph-augmented RAG architecture that links graph structures to source text for verifiable reasoning.
  • Hybrid Reasoning Engines - Combines semantic reasoning with numerical calculations and graph retrieval to resolve complex natural language queries.
  • Reasoning Pipelines - Implements a hybrid reasoning pipeline that chains semantic graph retrieval with numerical calculations and symbolic logic.
  • Hybrid Logical Reasoning - Combines semantic graph search with numerical calculations to solve complex natural language queries.
  • Knowledge Graph Construction Tools - Integrates unstructured data and expert rules via semantic alignment to build comprehensive knowledge bases.
  • Bidirectional Text-Graph Indexes - Maintains mutual indexing between graph structures and original text blocks to ensure reasoning is linked to verifiable source data.
  • Knowledge Graph Builders - Combines unstructured data and expert rules through semantic alignment to build comprehensive knowledge graphs.
  • Standardized Knowledge Abstractions - Provides a standardized knowledge graph abstraction to represent diverse data sources for uniform processing.
  • Source Text Linkers - Links graph structures to original text blocks to enable fast retrieval of source data during reasoning.
  • Knowledge Graph Indexing Engines - Provides a knowledge graph engine that integrates unstructured data and expert rules into semantic graphs.
  • Semantic Mapping Tools - Maps unstructured data and expert rules into a unified graph structure using semantic alignment.
  • Natural Language Querying - Translates natural language requests into a structured sequence of retrieval and reasoning operations via graph queries.
  • Logical Rule Expansion - Uses expert-defined logical rules to derive new relationships and facts within the knowledge graph.
  • Semantic Information Integration - Merges diverse data sources into a single unified format to improve the accuracy of information retrieval.
  • Knowledge Retrieval - Knowledge-enhanced generation framework for rigorous decision-making.
  • Retrieval Augmented Generation - Knowledge-enhanced generation framework for rigorous decision-making.
8,548 stars·659 forks·Python·apache-2.0·57 views

Star history

Star history chart for openspg/kagStar history chart for openspg/kag

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does openspg/kag do?

KAG is a graph-augmented retrieval augmented generation system and knowledge graph engine. It functions as a framework that integrates large language models with graph retrieval and numerical calculation to resolve natural language queries.

What are the main features of openspg/kag?

The main features of openspg/kag are: Graph-Based Retrieval Augmentation, Graph Reasoning Systems, Graph Retrieval Augmented Generation, Hybrid Reasoning Engines, Reasoning Pipelines, Hybrid Logical Reasoning, Knowledge Graph Construction Tools, Bidirectional Text-Graph Indexes.

Which projects share features with openspg/kag?

Projects with overlapping indexed features include: microsoft/graphrag — GraphRAG is a data processing pipeline and retrieval engine designed to transform unstructured text into… 1517005260/graph-rag-agent — This project is a comprehensive framework for constructing, managing, and evaluating knowledge graphs through… memgraph/memgraph — Memgraph is an in-memory, distributed graph database designed for high-performance labeled property graph management.… semantica-agi/semantica — Semantica is a Python framework for building context graphs and knowledge graphs that AI agents can query, reason… othmanadi/planning-with-files — Planning with files is an enterprise knowledge graph platform designed to transform unstructured organizational data… neo4j-labs/llm-graph-builder — llm-graph-builder is a tool for transforming unstructured data into structured Neo4j graph databases using large…

Projects sharing features with KAG

These projects share indexed features with KAG. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • microsoft/graphragmicrosoft avatar

    microsoft/graphrag

    36,590View on GitHub↗

    GraphRAG is a data processing pipeline and retrieval engine designed to transform unstructured text into interconnected knowledge graphs. By utilizing language models to extract entities and relationships, it builds structured representations of information that enable context-aware retrieval for downstream applications. The system distinguishes itself through hierarchical graph clustering and large-scale data synthesis, which organize massive document corpora into multi-level structures. This approach allows for both vector-based semantic searches and graph-based traversals, providing a comp

    Pythongptgpt-4gpt4
    View on GitHub↗36,590
  • 1517005260/graph-rag-agent1517005260 avatar

    1517005260/graph-rag-agent

    2,240View on GitHub↗

    This project is a comprehensive framework for constructing, managing, and evaluating knowledge graphs through multi-agent reasoning and deep search capabilities. It provides an end-to-end pipeline that ingests multi-format documents, extracts entities and relationships based on configurable schemas, and maintains structured knowledge bases to support evidence-based retrieval. The system distinguishes itself through its multi-agent orchestration, which decomposes complex queries into parallel research steps and synthesizes long-form reports. It leverages advanced graph-based techniques, includ

    Pythonagentic-ragchain-of-explorationdeepresearch
    View on GitHub↗2,240
  • memgraph/memgraphmemgraph avatar

    memgraph/memgraph

    4,163View on GitHub↗

    Memgraph is an in-memory, distributed graph database designed for high-performance labeled property graph management. It utilizes a Cypher query engine for declarative data retrieval and manipulation, providing a scalable knowledge graph backend that integrates vector search and graph traversals. The system distinguishes itself as a real-time graph analytics platform, employing native C++ and CUDA implementations to execute complex network analysis and dynamic community detection on streaming data. It provides specialized support for AI integration, including GraphRAG capabilities, the constr

    C++cyphergraphgraph-algorithms
    View on GitHub↗4,163
  • semantica-agi/semanticasemantica-agi avatar

    semantica-agi/semantica

    14,466View on GitHub↗

    Semantica is a Python framework for building context graphs and knowledge graphs that AI agents can query, reason over, and audit. It ingests documents and data from many sources, extracts entities and relations, merges duplicates, resolves conflicting facts, and stores the result as connected nodes and edges. Each AI decision can be recorded as a graph node with its reasoning, causal links, and policy checks, and every fact carries W3C PROV-O lineage linked by hashes so its origin can be verified and exported as an audit trail. Reasoning is rule-based and explainable: forward chaining, Rete

    Pythonagent-memoryaiai-governance
    View on GitHub↗14,466
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