Relationship Extraction and Knowledge Graphs
GraphRAG-style relationship extraction turns documents into lightweight knowledge graphs for richer LLM context.
RAG is excellent at retrieving semantically similar chunks of text, but semantic similarity alone isn't always enough. Many questions depend on understanding how people, products, documents, APIs, and other concepts relate to one another.
To address this, I extract entities and relationships from documents to build a lightweight knowledge graph. Those nodes and edges provide additional context that complements vector search, helping the LLM reason over connected concepts instead of isolated text passages.
This work is inspired by Microsoft's 2024 paper, From Local to Global: A Graph RAG Approach to Query-Focused Summarization, which demonstrated how extracting relationships into a graph can improve retrieval and reasoning over large collections of information. My implementation uses those relationships to provide richer context for LLM responses in interactive AI applications.
