Graph RAG Application

A Graph-based Retrieval-Augmented Generation (RAG) application designed to combine document retrieval with knowledge-graph relationships to provide more context-aware AI responses.

Architecture

Graph RAG Application
│
├── Backend
│   ├── API
│   ├── RAG Pipeline
│   ├── Document Processing
│   └── Knowledge Graph
│
└── Frontend
    └── AI Notes Agent

Project Structure

graph-rag-app/
│
├── backend/
│
├── frontend/
│   └── ai-notes-agent/
│
└── README.md

Core Concept

Traditional RAG systems primarily retrieve relevant document chunks before sending them to an LLM.

This project explores a Graph RAG approach, where information can also be represented through relationships between entities and concepts.

Documents
    │
    ▼
Processing
    │
    ├──────────────► Retrieved Context
    │
    ▼
Knowledge Graph
    │
    └──────────────► Related Entities
                         │
                         ▼
                    AI Generation
                         │
                         ▼
                       Answer

Key Goals

• Combine retrieval with relationship-aware context
• Improve contextual understanding of retrieved information
• Connect related entities and concepts
• Provide an AI-powered interface for working with knowledge
• Explore modern RAG and knowledge-graph architectures

Project Components

Backend

The backend contains the server-side logic responsible for processing information and supporting the Graph RAG pipeline.

Responsibilities include:

• API handling
• Data processing
• Retrieval
• Knowledge-graph operations
• AI/RAG workflow orchestration

Frontend

The frontend contains the AI Notes Agent, providing an interface for interacting with the application's AI capabilities.

Why Graph RAG?

Standard vector-based RAG can retrieve semantically similar information, but relationships between entities can sometimes be difficult to represent.

Graph RAG introduces explicit relationships:

Entity A
   │
   ├── related to ──► Entity B
   │
   ├── depends on ──► Entity C
   │
   └── mentioned in ─► Document D

This allows retrieval to consider both semantic similarity and relationships between information.

Future Improvements

• Advanced graph traversal
• Hybrid vector + graph retrieval
• Reranking
• Conversation memory
• Streaming AI responses
• Retrieval evaluation
• Observability and tracing
• Scalable vector and graph storage

Purpose

This project was built to explore Retrieval-Augmented Generation, knowledge graphs, information retrieval, and AI agent architecture, with a focus on understanding how structured relationships can improve context retrieval for LLM applications.