🧠 RAG & GraphRAG Project

This repository contains implementation and benchmark of retrieval-augmented generation (RAG), graph-based document analysis (GraphRAG).

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Project Structure

RAG_OpenAI/

This folder contains a complete implementation of a classic RAG system using OpenAI embeddings, ChromaDB, and LangChain.

• Loads .txt, .pdf (and optionally .md) documents
• Stores them in a vector database
• Enables conversational retrieval using GPT-4o

A detailed README is included in the folder to guide you step-by-step on usage, configuration, and extensions.

Link for a direct access detailed README for the RAG module.

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GraphRAG_Microsoft/ (from Microsoft)

This folder contains :

• The full instructions to implement your graphRAG
• A custom code to help visualize the Graph.

A detailed README is included in the folder to guide you step-by-step on usage, configuration, and extensions.

Link for a direct access detailed README for the GraphRAH module.

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Results excel/

This directory contains the evaluation results of the system using:

• Human annotations
• LLM-based scoring
  Stored in .xlsx format.

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Data location/

This folder includes .csv files listing the URLs of public articles used as data sources for the report, covering:

• The 2016 election of Donald Trump
• The Russia–Ukraine conflict

The data was retrieved using TheGuardian's official API.

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Summary

Folder	Description
RAG_OpenAI/	Full RAG pipeline with OpenAI + ChromaDB + LangChain
GraphRAG/	Microsoft GraphRAG integration + Gephi visualization support
Results excel/	XLSX files of human and LLM-based evaluation results
Data location/	CSV lists of article URLs used as data sources

Detailed explanation

Just read the Project_report.pdf to see the process and the results.

Link for a direct access Project_report.pdf.