Knowledge Graphs for RAG

Adding structure to retrieval

Plain vector search finds passages that resemble a question but has no sense of how facts relate to each other. This course adds a knowledge graph to the retrieval step, so connected entities can be followed rather than only matched. Neo4j and its query language Cypher are used to store and fetch that structured data.

Who it suits

Developers who already understand basic RAG and whose questions depend on relationships between people, products or events will find the approach most relevant. It matters less if your documents are mostly independent passages with no shared entities.

Takeaways

• Graph queries can answer relationship questions that similarity search struggles with.
• Cypher is the practical skill here, since the value comes from querying the graph well.
• A graph adds modeling work up front, so it pays off most when relationships carry the meaning.