I created NewsGraphRAG as a personal deep-dive to explore the boundaries of hybrid local retrieval systems. Built entirely for free and running fully locally on your machine, this project demonstrates how graph databases unlock multi-hop reasoning that traditional flat vector databases simply can't handle. By combining spaCy and Ollama (llama3.2) for a two-stage NER pipeline and using Neo4j's built-in vector index, the system extracts interconnected entities from news articles and successfully traverses relationship paths to answer complex queries with high traceability.
Check out my latest project exploring local GraphRAG systems:
Building a Local Knowledge Graph RAG System for News Analysis with Neo4j and Ollama
How graph databases unlock multi-hop reasoning that vector databases simply can't do — built entirely for free, running entirely on your machine
The Problem with Traditional RAG
Imagine you ask your news analysis system: "What is the connection between Elon Musk and Sam Altman?"
A traditional RAG system backed by a vector database will search for the most semantically similar chunks and likely return one article — maybe the xAI launch, or maybe the OpenAI board crisis. But the full answer spans both, and more.
The complete picture is spread across multiple articles:
- Article 1: Musk and Altman co-founded OpenAI, Musk departed the board in 2018
- Article 2: Altman was fired and reinstated as CEO during the board crisis
- Article 3: Musk launched xAI and later sued OpenAI, alleging it had abandoned its non-profit mission
The…