Mastering RAG with Knowledge Graphs

“RAG is difficult.”
“RAG doesn’t work.”
“I’ve tried a lot of solutions, and none of them worked.”

Or, at the very least, speak for yourself.
RAG works perfectly well, as long as you know how to do it. The key? Achieving what’s called semantic singularity through fine-grained granularity.

What Is RAG?

The process is always the same, regardless of the medium (JSON, graph, or vector database):

  • Break the source content into chunks.
  • Index the chunks.
  • Retrieve the segments by similarity or label.
  • Inject them into the Large Language Model’s (LLM) context.

Does It Work?

In some cases, vector approaches work well, provided that the chunks are clean and the indexing is suitable.

The Limits of Vector Approaches

The fundamental problem with vector approaches is informational noise. An LLM struggles to grasp specific relationships, whether they are business-specific (e.g., this standard applies to this component) or company-specific:

  • Product X is part of Range Y (company-specific).
  • Product X is manufactured at Plant Z (company-specific).
  • Product X’s communication is managed by Department W (company-specific).

These critical links, whether business-related or internal to the company, are often drowned out or misinterpreted in vectors. A Knowledge Graph (KG), on the other hand, explicitly encodes them (via precise chunks and labels), creating a contextual and accurate network of information that is nearly impossible for vectors alone to match.

The Power of Knowledge Graphs (KG)

A graph allows you to build a true reasoning schema where:

  • The information is chunked in a highly precise and succinct way (as short as possible).
  • Multi-labeling is applied with precision (granularity).
  • Clustering is optimized for context.
  • The relationships that link key information are what create the reasoning schema.

It’s not that vector approaches can’t theoretically reproduce these results, it’s that a graph eliminates noise and optimally structures the information, whereas LLMs have to imperfectly deduce it.

The Pillars of an Effective Knowledge Graph

  • ✅ Taxonomy: A clear hierarchical structure (in this case, the labels).
  • ✅ Ontology: The logic of the relationships (business/cognitive).

Roadmap for Building a High-Performance with Knowledge Graphs

  1. Structure the labels (Taxonomy).
  2. Create nodes with refined and relevant chunks.
  3. Link nodes and labels (more labels = better granularity = better singularity).
  4. Create the relationships (Ontology > see Episode 7: Knowledge Graph, storage and retrieval).

And if you use correctly RAG with Knowledge Graphs, you’ll build a reasoning schema tailored to your use case.

Tip: Visualization (using a tool like a Mind Map connected to a graph database, such as the Turing tool I created) is almost essential for properly modeling these complex relationships and achieving this singularity.

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