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.
Stars
26
Forks
11
Language
Python
License
—
Category
Last pushed
Mar 11, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/VectorInstitute/kg-rag"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Higher-rated alternatives
neo4j/neo4j-graphrag-python
Neo4j GraphRAG for Python
FalkorDB/GraphRAG-SDK
Build fast and accurate GenAI apps with GraphRAG SDK at scale.
microsoft/graphrag
A modular graph-based Retrieval-Augmented Generation (RAG) system
Hawksight-AI/semantica
Semantica 🧠— A framework for building semantic layers, context graphs, and decision...
gusye1234/nano-graphrag
A simple, easy-to-hack GraphRAG implementation