A Neo4j Knowledge Graph for RAG Guidance Enforcement

Music City Center 

This session explores the demands of processing one or a few documents with absolute fidelity when presented with this scenario: given a guidance document, ensure that no sensitive data is present in a new dataset. In many NLP applications, processing numerous documents with high precision is often desirable, but not mandatory. However, our specific use case demands the processing of documents with nearly perfect precision. To achieve this, we have developed a knowledge graph implementation that checks for compliance with the guidance document. This knowledge graph must precisely mirror the content of the guidance document, necessitating the retention of the original text along with transformer-produced vector embeddings for Retrieval-Augmented Generation (RAG) interpretations of the database contents on each node. Our technique, leveraging RAG, is broadly applicable to any scenario requiring strict data compliance with a guidance document.

Knowledge Graph

RAG 

Section on Text Analysis