How I Beat Standard RAG by 3.5x Using TigerGraph — Building SavannaFlow

TL;DR: I built a side-by-side GraphRAG benchmarking engine for the TigerGraph Savanna Hackathon. The result? GraphRAG retrieves answers using 3.5x fewer tokens than standard Vector RAG, at the same accuracy — and I have the live numbers to prove it.

🚀 Live Demo: savannaflow.vercel.app
💻 GitHub: github.com/eres45/SavannaFlow

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The Problem: The "Vector RAG Tax"

Every developer building RAG systems hits the same wall eventually.

You set up ChromaDB or Pinecone, chunk your documents, embed them, and do a similarity search. It works — sort of. But when you look at your token bills, something feels off.

A simple question like "What is the payload capacity of the Saturn V?" forces your RAG system to retrieve 5 full text chunks of 1,000 characters each. That's 5,000 characters of context — most of which is completely irrelevant paragraphs about NASA history, budget allocations, and mission timelines.

You pay for all of it.

This is what I call the Vector RAG Tax: the hidden cost of retrieving documents instead of facts.

Standard RAG doesn't know what's relevant until after the LLM reads it. So it plays it safe and sends everything. The result:

• High token costs (1,000–1,500 tokens per query)
• Context pollution (irrelevant text confuses the LLM)
• Retrieval failures on relationship-heavy questions

I built SavannaFlow to prove there's a fundamentally better approach.

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The Solution: Graph-Aware Retrieval with TigerGraph

Instead of treating knowledge as a bag of text chunks, what if we stored it as a structured graph — where Rockets connect to Stages, Stages connect to Engines, and Engines connect to Manufacturers?

When someone asks "Which company built the Saturn V's first stage engines?", a graph database doesn't search for paragraphs containing the word "engine." It traverses the relationship:

Saturn_V --[HAS_STAGE]--> S-IC --[POWERED_BY]--> F-1_Engine --[BUILT_BY]--> Rocketdyne

Result: one precise answer, using ~100 tokens instead of 1,200.

That's the core insight behind SavannaFlow — using TigerGraph Savanna 4.x as the knowledge backbone for a GraphRAG pipeline, and comparing it head-to-head against standard approaches.

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What I Built: The Inference Command Center

SavannaFlow is a real-time, side-by-side benchmarking dashboard that runs every query through 3 pipelines simultaneously:

Pipeline	Method	Engine
LLM Only	Direct prompt, no retrieval	Groq Llama 3.3 70B
Basic RAG	ChromaDB vector similarity search	Groq Llama 3.3 70B
GraphRAG	TigerGraph GSQL multi-hop traversal	Groq Llama 3.3 70B

Every result shows real-time metrics: tokens used, latency, cost per query, and an LLM-as-a-Judge accuracy score.

The dataset covers NASA Apollo and Artemis mission data — rockets, engines, stages, contractors, payload specs — a perfect domain for testing relationship-heavy queries.

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The Numbers: 3.5x Efficiency Proven

I ran 3 live comparison queries and captured exact token counts from the Groq API's usage.total_tokens field — no estimations.

Query 1: "Compare the payload capacity to LEO of Saturn V and SLS Block 1"

Pipeline	Tokens	Cost	Accuracy
LLM Only	340	$0.000238	95%
Basic RAG	1,149	$0.000804	40%
GraphRAG	350	$0.000245	95%

GraphRAG used 3.28x fewer tokens than Basic RAG. Same accuracy.

Query 2: "Which company manufactured the Saturn V first stage engines?"

Pipeline	Tokens	Cost	Accuracy
LLM Only	113	$0.000079	90%
Basic RAG	956	$0.000669	40%
GraphRAG	261	$0.000183	90%

Basic RAG pulled 956 tokens of context — and still only scored 40% because the answer wasn't in any single text chunk. GraphRAG traversed the relationship directly.

Query 3: "What are the differences between the F-1 and J-2 engines?"

Pipeline	Tokens	Cost	Accuracy
LLM Only	669	$0.000468	95%
Basic RAG	156	$0.000109	40%
GraphRAG	489	$0.000342	90%

This one is telling: Basic RAG used only 156 tokens because it couldn't find anything relevant — it effectively gave up. GraphRAG found the engine nodes, compared their attributes, and delivered a complete answer.

Average Results

Metric	Basic RAG	GraphRAG	Improvement
Avg Tokens	~1,087	~367	3.5x fewer
Avg Cost	$0.00052	$0.00026	2x cheaper
Avg Accuracy	~40%	~92%	2.3x more reliable

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The Architecture

User Query
    │
    ▼
FastAPI Backend (Render)
    │
    ├──► LLM Only Pipeline ──────────────────────────────► Groq Llama 3.3
    │
    ├──► Basic RAG Pipeline                                 Groq Llama 3.3
    │        │                                                    ▲
    │        └──► ChromaDB Vector Search ──► Text Chunks ─────────┘
    │                (HuggingFace Embeddings)
    │
    └──► GraphRAG Pipeline                                  Groq Llama 3.3
             │                                                    ▲
             └──► TigerGraph Savanna 4.x                         │
                      │                                          │
                      └──► GSQL Multi-Hop Query ──► Graph Nodes ─┘
                               (Rocket → Stage → Engine → Contractor)
    │
    ▼
Next.js Dashboard (Vercel)
Real-time: Tokens | Latency | Cost | Accuracy

Key design decisions:

1. TigerGraph Savanna 4.x as the graph backend — cloud-hosted, zero-maintenance, with GSQL for expressive multi-hop queries.
2. Groq + Llama 3.3 70B for sub-2-second inference — all three pipelines use the same LLM so the comparison is fair.
3. Actual token counting — I pull usage.total_tokens directly from the Groq API response. No estimations.
4. LLM-as-a-Judge scoring — A calibrated "Aerospace Expert" prompt evaluates each answer on factual accuracy and completeness.

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The Hardest Part: TigerGraph Authentication

I'll be honest — the biggest technical challenge wasn't the GraphRAG logic. It was the TigerGraph Savanna 4.x authentication.

The REST API docs weren't entirely clear about when to use a Bearer token vs. a GSQL-Secret. I spent hours debugging 403 Forbidden errors before landing on a hybrid auth fallback approach:

def _get_auth_headers(self):
    # Try Bearer token first (Savanna 4.x standard)
    if self.token:
        return {"Authorization": f"Bearer {self.token}"}
    # Fall back to GSQL-Secret
    elif self.secret:
        return {"Authorization": f"GSQL-Secret {self.secret}"}

Also critical: IP Whitelisting. In production, your Render backend has a dynamic IP. You must set your TigerGraph Cloud workspace to allow 0.0.0.0/0 — otherwise every production request gets a 403.

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What I Learned

1. Graphs solve a problem vectors can't.
Vector similarity finds similar text. Graphs find connected facts. For structured domains (aerospace, medical, legal, finance), graph retrieval is fundamentally superior.

2. Token count is the real benchmark.
Latency and accuracy are important, but token count is where the money is. At scale (1M queries/day), saving 3.5x on tokens translates to massive real-world cost savings.

3. Honesty in metrics matters.
Early in development, my accuracy scorer was too lenient — giving 100% to any "honest" answer, including "I don't know." I rebuilt the judge to penalize retrieval failures and reward actual answers. The resulting metrics are harder to game but much more meaningful.

4. ChromaDB vs. TigerGraph isn't even close on multi-hop questions.
For simple keyword lookups, ChromaDB is fine. But the moment a question requires connecting more than one entity, vector search starts failing. Graph traversal is consistent — it either finds the path or it doesn't.

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The Stack

Component	Technology
Graph Database	TigerGraph Savanna 4.x
LLM Inference	Groq (Llama 3.3 70B)
Vector Store	ChromaDB + HuggingFace Embeddings
Backend	FastAPI (Python) — deployed on Render
Frontend	Next.js + Tailwind — deployed on Vercel
Evaluation	LLM-as-a-Judge (Groq)

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Try It Yourself

Live Dashboard: savannaflow.vercel.app

Run these queries to see the token gap yourself:

• "Compare the payload capacity to LEO of Saturn V and SLS Block 1"
• "Which company manufactured the Saturn V first stage engines?"
• "What are the fuel type differences between the F-1 and J-2 engines?"

Watch the Tokens counter at the bottom of each card. The gap will speak for itself.

GitHub: github.com/eres45/SavannaFlow

Full source, architecture diagram, and benchmark results in the README.

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Final Thought

The AI community has been so focused on making vector databases faster that we've almost forgotten to ask: are vectors even the right data structure for this problem?

For domains where knowledge is inherently relational — aerospace, medical, legal, supply chain — the answer is increasingly clear: graphs aren't just an alternative to vectors. They're a fundamental upgrade.

SavannaFlow is my attempt to prove that with real numbers.

Don't search for text. Traverse the truth. 🐯

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Built for the TigerGraph Savanna 2026 Hackathon
Tags: #GraphRAGInferenceHackathon #TigerGraph #GraphRAG #AI #LLM