RAG with knowledge graphs implemented from scratch

Haive AI Agent Framework — production-ready monorepo with 53+ agents, RAG, memory, KG, and games

Official implementation for the paper "KG-Infused RAG: Augmenting Corpus-Based RAG with External Knowledge Graphs"

Text-to-SQL with GraphRAG on Knowledge Graph Semantic Representation of SQL Databases

Multi-Channel Message Relay with AI Customer Service 🚀 A FastAPI-powered messaging system for Email ✉️, SMS 📲, and Voice 🎙️, featuring Redis scheduling ⏰, implemented with a custom Decorator Design Pattern 🎨 and IoC Container . It also includes an AI assistant leveraging LLM , RAG 📚, KG 🧠, and Speech-to-Text 🎤 throughout the project.

Official implementation of "LLaVA Needs More Knowledge: Retrieval-Augmented NLG with Knowledge Graph for Explaining Thoracic Pathologies" (AAAI 2025). This repo provides the KG-LLaVA framework, integrating KG-based Retrieval-Augmented Generation (KG-RAG) with Vision-Language Models to generate NLEs for medical imaging.

AI-powered adaptive learning platform combining Knowledge Graphs, Local LLMs, and Bayesian skill tracking. Privacy-first RAG with KG-enhanced retrieval, IRT/BKT mastery modeling, and real-time assessment generation.

[WWW 2026, Main Conference] KG-based RAG with relation-aware preference optimization for small language models

KG-RAG + ToT + multi-agent LLMs for evidence-grounded QA with Neo4j and fine-tuning; reproducible medical case study & evaluation.

Base code RAg with KG based NAGphormer

KG-RAG code associated with the thesis manuscript.

Retrieval-Augmented Generation (RAG) pipelines Knowledge Graph (KG) support (mocked for now) Agentic memory with planning and execution.

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A method for creating knowledge graphs with an LLM in a format amenable to using the KG to do RAG

KG2RAG is a novel framework integrating Knowledge Graphs (KG) with Retrieval-Augmented Generation (RAG) to improve response accuracy and reduces hallucinations in LLMs.

This project demonstrates a novel approach to automate and enhance key aspects of Requirements Engineering (RE) by integrating Retrieval-Augmented Generation (RAG)-Enhanced Large Language Models (LLMs) with dynamic Knowledge Graph (KG) construction, metaheuristic conflict resolution, and multi-criteria traceability optimization.

RAG with KG System

Combining RAG with Knowledge Graphs using Neo4j and Langchain

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Chat Bot Implemented with RAG and KG

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Practical Challenge chatbot with RAG + KG + MCP Server

an agentic RAG with KG for Intelligent Educational Question Answering

RAG with Co-occurrence, KG, and Ontology as Second Brain - Proof of Concept

Self-hosted memory MCP with structured KG and token-embeded DB. More than RAG.