Effortlessly Build a Knowledge Graph RAG with LlamaIndex and Local PDF Documents

Enhance language models with Retrieval-Augmented Generation (RAG) and knowledge graphs for more accurate and efficient data retrieval and reasoning.

Retrieval-augmented generation (RAG) enhances language models by adding external knowledge sources, improving accuracy and context relevance.

However, RAG may miss relationships between entities when handling complex data. For example, a vector database might mistakenly associate "employee" more closely with "employer" than with "information."

The introduction of knowledge graphs solves this limitation. Using a structure of triples (nodes and edges), like "Employer — submits — claim," it clearly represents relationships between entities.

This structured approach makes knowledge graphs more precise and efficient for complex data retrieval.