File size: 3,814 Bytes
d8904bf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | "use client";
export function Footer() {
return (
<footer style={{ background: "var(--color-surface-dark)", color: "var(--color-on-dark-soft)", padding: "64px 0 32px" }}>
<div className="container">
<div className="grid grid-cols-1 md:grid-cols-4 gap-8 mb-12">
{/* Brand */}
<div>
<div className="flex items-center gap-2 mb-4">
<svg width="24" height="24" viewBox="0 0 28 28" fill="none">
<circle cx="14" cy="14" r="13" stroke="#FF6B00" strokeWidth="2" />
<path d="M14 4L14 24M4 14L24 14M7 7L21 21M21 7L7 21" stroke="#FF6B00" strokeWidth="1.5" strokeLinecap="round" />
</svg>
<span className="title-md" style={{ color: "var(--color-on-dark)" }}>
Graph<span style={{ color: "#FF6B00" }}>RAG</span>
</span>
</div>
<p className="body-sm" style={{ color: "var(--color-on-dark-soft)", maxWidth: "280px" }}>
Proving that graphs make LLM inference faster, cheaper, and smarter —
with real numbers.
</p>
</div>
{/* Architecture */}
<div>
<div className="caption-uppercase mb-4" style={{ color: "var(--color-on-dark)" }}>Architecture</div>
{["Graph Layer (TigerGraph)", "Orchestration Layer", "LLM Layer (Claude)", "Evaluation Layer (RAGAS)"].map((item) => (
<div key={item} className="body-sm mb-2" style={{ color: "var(--color-on-dark-soft)" }}>{item}</div>
))}
</div>
{/* Novelties */}
<div>
<div className="caption-uppercase mb-4" style={{ color: "var(--color-on-dark)" }}>Novel Features</div>
{["🧠 Adaptive Query Router", "📋 Schema-Bounded Extraction", "🔑 Dual-Level Keywords", "🔗 Reasoning Paths", "📊 Cost Tracking"].map((item) => (
<div key={item} className="body-sm mb-2" style={{ color: "var(--color-on-dark-soft)" }}>{item}</div>
))}
</div>
{/* References */}
<div>
<div className="caption-uppercase mb-4" style={{ color: "var(--color-on-dark)" }}>References</div>
{[
{ label: "GraphRAG Paper", href: "https://arxiv.org/abs/2404.16130" },
{ label: "LightRAG Paper", href: "https://arxiv.org/abs/2410.05779" },
{ label: "HotpotQA Dataset", href: "https://hotpotqa.github.io/" },
{ label: "TigerGraph Cloud", href: "https://tgcloud.io" },
{ label: "Anthropic Claude", href: "https://anthropic.com" },
].map((link) => (
<a key={link.label} href={link.href} target="_blank" rel="noopener noreferrer"
className="body-sm block mb-2 hover:underline" style={{ color: "#FF6B00" }}>
{link.label} ↗
</a>
))}
</div>
</div>
<div style={{ borderTop: "1px solid rgba(255,255,255,0.08)", paddingTop: "24px" }}
className="flex flex-col md:flex-row justify-between items-center gap-4">
<span className="body-sm" style={{ color: "var(--color-on-dark-soft)" }}>
© 2025 GraphRAG Inference Hackathon by TigerGraph
</span>
<div className="flex items-center gap-4">
<span className="badge" style={{ background: "var(--color-surface-dark-elev)", color: "var(--color-on-dark-soft)", fontSize: "0.6875rem" }}>
TigerGraph × Claude
</span>
<span className="badge" style={{ background: "var(--color-surface-dark-elev)", color: "var(--color-on-dark-soft)", fontSize: "0.6875rem" }}>
Next.js + Recharts
</span>
</div>
</div>
</div>
</footer>
);
}
|