Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs

2026-05-31 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, long

Summary

A novel approach combining Proxy-Pointer RAG and Graphability Indexing optimizes Knowledge Graph (KG) ingestion by strategically reducing LLM token usage for entity and relation extraction. This method addresses the high cost and inefficiency of LLMs scanning entire long documents, such as 100+ page contracts exceeding 500k characters. By exploiting the structural predictability of legal documents, the system identifies and bypasses low-yield sections. Experiments on three corporate Credit Agreements (Emerson Electric Co. ~228,000 characters, AT&T Inc. ~214,000 characters, and Texas Roadhouse, Inc. ~434,000 characters) demonstrated significant LLM processing payload reductions: 16.10% for Emerson, 33.94% for AT&T, and 38% for Texas Roadhouse, without compromising KG integrity.

Key takeaway

For MLOps Engineers managing Knowledge Graph ingestion pipelines, this approach offers a direct solution to reduce LLM operational costs. By implementing Proxy-Pointer RAG with Graphability Indexing, you can strategically bypass low-value document sections, cutting token usage by up to 38% without sacrificing extraction integrity. Consider adopting this structure-aware ingestion to make large-scale KG construction more sustainable and efficient.

Key insights

Document structure can predict knowledge graph extraction yield, enabling targeted LLM processing.

Principles

• KG ingestion benefits from structural predictability.
• Relational density drives section value, not just entity count.
• Iterative index refinement improves bypass accuracy.

Method

Build a baseline Graphability Index for document types, create a structure tree, iteratively enrich the index with human review, then route high-yield sections to LLMs and bypass low-yield ones.

In practice

• Classify document sections by relational density.
• Maintain a graphability index per document type.
• Integrate Proxy-Pointer for structure-aware RAG.

Topics

• Knowledge Graphs
• LLM Cost Optimization
• Entity Relation Extraction
• Proxy-Pointer RAG
• Graphability Indexing
• Document Structure Analysis

Code references

• Proxy-Pointer/Proxy-Pointer-RAG

Best for: AI Architect, NLP Engineer, Machine Learning Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.