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.
This project helps financial analysts and researchers extract precise answers from complex financial documents like SEC 10-Q filings. You feed it a collection of these filings and ask questions in natural language, receiving accurate, context-rich answers. It's designed for anyone needing to quickly find specific financial information without manually sifting through dense reports.
Use this if you need to rapidly and accurately answer specific questions using data from a large collection of financial regulatory documents, improving on standard search methods.
Not ideal if your primary need is general document summarization or if your data is not structured financial text.
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.
Related tools
neo4j/neo4j-graphrag-python
Neo4j GraphRAG for Python
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...
FalkorDB/GraphRAG-SDK
Build fast and accurate GenAI apps with GraphRAG SDK at scale.
getzep/graphiti
Build Real-Time Knowledge Graphs for AI Agents