Stop Burning Tokens: How We Slashed Biomedical RAG Costs by 93% with GraphRAG

Every time you ask an LLM a complex, multi-hop question using traditional Vector RAG, you are burning tokens. Thousands of them.

For simple document retrieval, Basic RAG (ChromaDB + LLM) is fine. But when you ask a complex clinical question — like linking a drug to a disease, a side effect, and a genetic pathway — Basic RAG panics. It dumps chunks of tangentially related text into your context window, hoping the LLM will figure it out. At scale, this isn’t just a performance bottleneck; it’s a massive cost problem.

We built RxGR (Drug-Disease GraphRAG) for the TigerGraph GraphRAG Inference Hackathon to prove there is a vastly better way.

By swapping out naive vector similarity for structured, multi-hop graph traversals on TigerGraph, we didn’t just marginally improve performance. We obliterated the token count.

Here is how we achieved up to a 93.2% token reduction while maintaining semantic accuracy, and how you can run it on your machine in one command.

The Problem: The Vector RAG Token Explosion

Imagine asking your AI this clinical safety question:

“Which drugs treating Hypertension cause side effects that worsen Type 2 Diabetes through ACE pathways?”

To answer this, Basic RAG searches a vector database (like ChromaDB) and pulls the top 8 most “semantically similar” chunks from PubMed. That is 4,096 tokens of noisy, unstructured text dumped into your Gemini context window.

GraphRAG takes a completely different approach.

The RxGR Solution: Graph Beats Tokens

Instead of searching for similar words, RxGR traverses actual relationships.

We ingested the massive Drug Repurposing Knowledge Graph (DRKG) — 97,000 biomedical entities and 5.8 million relationships (like Drug treats Disease or SideEffect activates Gene)—into TigerGraph. We then enriched those entities with 10,000 pharmacology abstracts from PubMed.

When you ask the same clinical question, here is what RxGR does:

1. Dual-Model NER: A customized scispacy NLP pipeline extracts the exact entities from your prompt (e.g., "Hypertension" maps to MESH:D006973).
2. Multi-Hop Traversal: TigerGraph fires a 3-hop GSQL query starting from those seed nodes.
3. Structured Context: It follows the exact graph path: Lisinopril → treats → Hypertension → targets → ACE enzyme → kidney dysfunction.

The output isn’t a 4,000-token document dump. It is 280 tokens of highly precise, structured relationships.

The result? A 93.2% reduction in context tokens. Same LLM, same query, but 93% cheaper and radically more precise.

The Receipts: 50-Query Adversarial Benchmark

Anyone can optimize for a single demo query. We built a rigorous, automated evaluation engine to run 50 adversarial biomedical queries across three pipelines concurrently: LLM-Only, Basic RAG, and GraphRAG.

Here are the headline numbers from our benchmark dashboard:

• 📉 Average Token Reduction: 64% overall, scaling drastically with complexity. On Tier 3 (3-hop+) queries, GraphRAG averages an 84% reduction.
• 💰 Cost Savings: GraphRAG costs 54.5% less to run than Basic RAG across the 50-query suite.
• 🧠 Maintained Accuracy (BERTScore F1): Using BioBERT (a biomedical-specific model) to grade semantic similarity, GraphRAG scored an 0.871 F1, easily beating the 0.82 threshold required to prove semantic preservation.
• 🏆 LLM-as-a-Judge Win Rate: In a blind pairwise evaluation grading accuracy, clinical safety, and conciseness, GraphRAG won 82% of the time (and 90% of the time on the hardest Tier 3 queries).

The “Clinical Terminal” Experience

To prove it, we didn’t just build a backend script. We built a real-time React SPA with a dark “Clinical Terminal” aesthetic.

When you run a query, the dashboard streams the LLM responses side-by-side. You literally watch the real-time token counters tick up. You watch the Basic RAG counter explode into the thousands while the GraphRAG counter comfortably stops in the low hundreds, accompanied by a dynamic D3.js visualization of the exact TigerGraph traversal path that found the answer.

Bonus: Native AI Agent Integration

We didn’t stop at a web UI. RxGR ships with out-of-the-box support for the new TigerGraph Model Context Protocol (MCP).

This means you can point AI coding assistants like Cursor, GitHub Copilot, or Claude Desktop directly at your local instance. You can literally type into Cursor, “Find 5 drugs treating Hypertension from the graph,” and it will query the TigerGraph knowledge graph natively.

Try It Yourself

Graphs don’t just store data; they reason across it. If you are building AI for healthcare, finance, or Web3, relying on vector search alone is a massive liability.

You can run the entire RxGR system locally. The repo includes a pre-configured sample dataset that loads in minutes (instead of hours) and works on machines with standard RAM.

Run it in one command:

bash run_demo.sh

(This automatically spins up the FastAPI backend, ChromaDB, TigerGraph integration, and the React frontend).

Check out the code on GitHub: ShahiTechnovation/RxGR. Built for the 2026 TigerGraph GraphRAG Inference Hackathon.