6.4K stars! This knowledge enhancement framework is amazing for easily handling large model reasoning in professional fields!

Written by

Caleb Hayes

Updated on:June-27th-2025

"Large vertical models are difficult to implement and logical reasoning is always wrong. This open source framework from OpenSPG makes professional knowledge services as easy as building blocks!"

Project Introduction

KAGIt is a professional domain knowledge service framework based on OpenSPG knowledge engine and LLM , designed to solve the three major pain points of traditional RAG solutions in vertical field applications:

The "seemingly true" problem of vector retrieval (similar in meaning but logically wrong)
The "Noise Pollution" Problem of Open Information Extraction
Multi-hop reasoning problems in complex scenarios

The latest version already supports:
✅ Domain knowledge injection (finance/medical/law, etc.)
✅ Visual graph analysis query
✅ Hybrid reasoning engine (logic + semantic + numerical)
✅ Multimodal knowledge management (text/table/graph)

Core Features Highlights

Logical Reasoning Questions and Answers

Breaking through the keyword matching model of the traditional QA system, supporting:

Multi-condition combination reasoning ("Find listed companies whose revenue growth exceeded 20% in the past three years but whose debt ratio was less than 60%)
Temporal reasoning ("What is the possible cause of a patient experiencing symptom A before symptom B")
Conflict detection ("Does contract clause X conflict with industry norm Y?")

Knowledge Alignment Black Technology

Through conceptual semantic reasoning :

Automatic disambiguation ("apple->fruit/company" intelligent judgment)
Term alignment ("myocardial infarction = myocardial infarction = myocardial infarction" automatically associated)
Knowledge correction (detecting and correcting incorrect statements of fact)

Multimodal Knowledge Manager

Document ↔ Knowledge Graph Bidirectional Index
Support Word/PDF/Excel multi-format analysis
Expert experience is structured (through Schema constraints)

Hybrid Inference Engine

# Example of problem solving process
question =  "Has a new energy vehicle company's R&D investment in the past three years exceeded the industry average?"
Solution steps:
1.  Search → Obtain enterprise R&D data
2.  Calculation → Industry average calculation
3.  Reasoning → Trend Comparative Analysis
4.  Generate → Natural Language Conclusion

Enterprise-level knowledge security

Private deployment solution
Knowledge access rights control
Audit log tracking
Data encryption storage

Technical architecture analysis

Components	Core Technology	Advantages and features
kg-builder	LLMFriSPG framework, DIKW model, multimodal extraction	Compatible with structured/unstructured knowledge
kg-solver	Logical symbol guidance, mixed operators (planning/reasoning/retrieval)	Support seamless switching of four reasoning modes
kag-model	Domain adaptation fine-tuning, knowledge distillation, and prompt engineering optimization	Professional field effect improved by 40%+

Landing scene test

Financial risk control scenarios

User asked: Does Company A have any horizontal competition through its multi-level holding subsidiaries?
System execution:
1. Extracting the equity structure map
2. Analyze the overlap of business scope
3. Refer to regulatory rules for judgment
4. Generate risk assessment report

Medical diagnostic support

Medical record text → Information extraction → Symptom map → Diagnosis rules → Inference engine

Legal contract review

Traditional RAG: 68% accuracy (misunderstanding of terms)
KAG solution: 92% accuracy (accurate grasp of logical relationships)

Comparison with similar solutions

	Traditional RAG	GraphRAG	KAG
reasoning ability	❌	⚠️	✅ Logic + semantics
Knowledge accuracy	⚠️	❌	✅ Double verification
Multi-jump question and answer	❌	⚠️	✅ Automatic link
Deployment complexity	✅	❌	✅ Containerization solution
Domain Adaptation Cost	high	middle	Low

Quick Start Guide

Three-step deployment plan

# 1. Get the deployment file
curl -sSL https://raw.githubusercontent.com/... -o docker-compose.yml

# 2. Start the service
docker compose -f docker-compose.yml up -d

# 3. Access the system
Open the browser at http://127.0.0.1:8887
(Default account: openspg/openspg@kag)

Developer Extension Examples

from  kag  import  KnowledgeBuilder

# Customize the medical schema
medical_schema = {
    "Disease Type" : [ "Symptoms" ,  "Treatment Plan" ,  "Related Examinations" ],
    "drugs" : [ "indications" ,  "contraindications" ,  "interactions" ]
}

builder = KnowledgeBuilder(schema=medical_schema)
builder.add_document( "medical_report.docx" )
kg = builder.build()

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