--- title: "Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs" url: https://daily.dev/posts/proxy-pointer-rag-eliminating-wasteful-entity-relations-extraction-in-knowledge-graphs-2706kwmyn source_url: https://towardsdatascience.com/proxy-pointer-rag-eliminating-wasteful-entity-relations-extraction-in-knowledge-graphs type: article source: "Towards Data Science" published: 2026-05-31T13:45:53.967Z updated: 2026-05-31T13:46:18.436Z tags: ["llm", "nlp", "rag"] reading_time: 19 upvotes: 2 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs **[Towards Data Science](https://daily.dev/sources/tds)** · 19 min read · 2 upvotes · 0 comments ## Summary Proxy-Pointer RAG introduces a 'Graphability Index' to reduce LLM token costs during Knowledge Graph ingestion from enterprise documents. Instead of sending entire documents to an LLM for NER and relation extraction, the approach classifies document sections by their predicted relational density before extraction begins. Sections rated Low or Very Low are bypassed entirely. Validated against three real-world corporate credit agreements (Emerson, AT&T, Texas Roadhouse), the index stabilized quickly and achieved payload reductions of 16%, 34%, and 38% respectively, with zero mismatches by the third document. The method leverages structural predictability of legal documents — treating them as semantic trees rather than flat text — and is available as an open-source MIT-licensed Python package. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Proxy-Pointer RAG: Solving Entity and Relationship Sprawl in Large Knowledge Graphs](https://daily.dev/posts/proxy-pointer-rag-solving-entity-and-relationship-sprawl-in-large-knowledge-graphs-6vg26wfhv) · Towards Data Science · 0 upvotes · 0 comments - [Proxy-Pointer RAG: Temporal Reasoning Without Semantic Precompilation](https://daily.dev/posts/proxy-pointer-rag-temporal-reasoning-without-semantic-precompilation-fvf5p9atw) · Towards Data Science · 1 upvotes · 0 comments - [Proxy-Pointer RAG: Achieving Vectorless Accuracy at Vector RAG Scale and Cost](https://daily.dev/posts/proxy-pointer-rag-achieving-vectorless-accuracy-at-vector-rag-scale-and-cost-robiobrdq) · Towards Data Science · 1 upvotes · 0 comments - [Proxy-Pointer RAG: Structure Meets Scale at 100% Accuracy with Smarter Retrieval](https://daily.dev/posts/proxy-pointer-rag-structure-meets-scale-at-100-accuracy-with-smarter-retrieval-mhzxjuu51) · Towards Data Science · 2 upvotes · 0 comments - [Proxy-Pointer RAG: Multimodal Answers Without Multimodal Embeddings](https://daily.dev/posts/proxy-pointer-rag-multimodal-answers-without-multimodal-embeddings-eyyeigz7i) · Towards Data Science · 1 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#nlp](https://daily.dev/tags/nlp), [#rag](https://daily.dev/tags/rag) [View this post on daily.dev](https://daily.dev/posts/proxy-pointer-rag-eliminating-wasteful-entity-relations-extraction-in-knowledge-graphs-2706kwmyn)