Knowledge Graph RAG & Entity Resolution for Enterprise Documents

Technical Challenge & Context

Standard vector RAG operates on localized document chunks. It fails when queries require aggregating relationships across disconnected documents - such as finding all contracts linked to a specific parent corporation across 500 PDF files.

System Architecture & Engineering Pattern

Graph RAG extracts named entities (Organizations, Clauses, Dates, Officers) and relationships into a Neo4j Knowledge Graph while storing text embeddings in Qdrant. Hybrid Graph-Vector retrieval queries graph paths alongside semantic similarity.

Production Code Blueprint

MATCH (c:Company {name: 'Acme Corp'})-[:HAS_CONTRACT]->(k:Contract)-[:CONTAINS_CLAUSE]->(cl:Clause)
WHERE cl.type = 'Indemnity'
RETURN c.name, k.title, cl.text, cl.expiration_date
ORDER BY cl.expiration_date ASC

Key Operational Takeaways

• Graph RAG solves multi-hop relational queries that vector-only RAG cannot answer.
• Entity resolution algorithms eliminate duplicate graph nodes (e.g. 'Acme Inc' vs 'Acme Corporation').
• Reduces global document processing time by pre-indexing entity topologies.

Author Overview

Rohit

Senior AI & Data Science Consultant

2+ Decades AI Experience

First built neural networks in C language in 2004 at IIT Roorkee under the mentorship of Dr. Sunil Padhi (HOD, Electrical Department) to predict annual sunspots. Today designing enterprise Agentic AI workflows, vLLM GPU clusters, and Custom RAG.