How to build knowledge graphs with Llama 4 without any manual work

Photo by fabio on Unsplash

Some ideas are sexy on paper, but frustrating in practice.

If you’ve built a knowledge graph before a couple of years, it’s a task you’d call daunting — I did.

When I first learned about Neo4J, I was fascinated. It opens up many possibilities that weren’t available before. A friend of mine talked about knowledge graphs, which are incredibly useful for our organization.

After several weeks, we dropped the idea altogether.

Knowledge graphs require us to represent organizational documents as nodes and edges. Who would read through thousands of pages and PowerPoint slides to extract nodes and edges?

Indeed, it’s not practical. No one would invest their precious resources in R&D work for months like that.

But in today’s standards, this isn’t a big task.

We can use AI to build the knowledge graph. Human intervention is only needed to verify its accuracy.

LLMs can read through unimaginable lengths and create structured outputs. We can feed our PDFs, PPTs, Word Documents, etc., and effortlessly convert them into a knowledge graph.

In this post, I’ll walk you through the knowledge graph (KG) construction process I enjoyed working on. Note…