KG_RAG: Empower Large Language Models using Knowledge Graph based Retrieval-Augmented Generation for knowledge

by BaranziniLab · Agent Tool · ★ 947

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Security audit verdict: SAFE · quality 67/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

bert-modelsbioinformaticsbioinformatics-algorithmsbiomedical-applicationsbiomedical-informaticscontext-awaregptgpt35turbogpt4knowledge-base

Quick Facts

Stars947
Forks115
LanguageJupyter Notebook
CategoryAgent Tool
LicenseApache-2.0
Quality Score67/100
Open Issues7
Last Updated2024-11-09
Created2023-11-11
Est. Tokens~18k

About KG_RAG

Table of Contents What is KG-RAG Example use case of KG-RAG Prompting GPT without KG-RAG Prompting GPT with KG-RAG Example notebook for KG-RAG with GPT How to run KG-RAG Step 1: Clone the repo Step 2: Create a virtual environment Step 3: Install dependencies Step 4: Update config.yaml Step 5: Run the setup script Step 6: Run KG-RAG from your terminal Using GPT [Using GPT interactive...

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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