Diving deeper into the GraphRAG rabbit hole, I explore how to transform real-world video data into knowledge graphs using RF-DETR for object detection and BLIP-2 for intelligent entity description - setting the foundation for context-aware retrieval systems.
After my initial experiment with GraphRAG using Qdrant, Neo4j, and Ollama, I took on a journey to build a more dynamic and context-aware system. This post dives into the details of how I constructed a dynamic ontology for NLP GraphRag.
I've been playing with a new approach to RAG systems - combining vector search with knowledge graphs for more contextual, relationship-aware answers. Here's what I've built, how it works, and why you might want to try it yourself.