graphrag and kg-rag
About graphrag
microsoft/graphrag
A modular graph-based Retrieval-Augmented Generation (RAG) system
Extracts knowledge graphs from unstructured text using LLMs, then uses those graph structures to improve retrieval and reasoning for private data. Implements a data indexing pipeline that transforms narrative documents into entity-relationship graphs, enabling more contextual and discovery-oriented query responses compared to standard vector retrieval. Supports prompt tuning workflows and integrates with major LLM providers through a configuration-driven architecture.
About kg-rag
VectorInstitute/kg-rag
This project implements a comprehensive framework for Knowledge Graph Retrieval Augmented Generation (KG-RAG). It focuses on financial data from SEC 10-Q filings and explores how knowledge graphs can improve information retrieval and question answering compared to baseline approaches.
Implements multiple retrieval strategies including entity-based embedding matching with beam search, Cypher queries against Neo4j, and hierarchical community detection (GraphRAG-style), enabling direct comparison of knowledge graph approaches versus traditional vector similarity and chain-of-thought baselines. Built as a modular Python package with Chroma vector stores, OpenAI LLM integration, and comprehensive evaluation pipelines including hyperparameter search across methods.
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