llm-spatial-index

We propose a newly developed high-performance spatial & neighbor search index algorithm, dedicated to LLM context retrieval, long-term memory, agent scheduling and inference acceleration. Built in pure Rust with zero heavy dependencies, release-mode optimized for production. All benchmarks are fully reproducible for academic research and commercial production integration. Core Benchmark === Performance Benchmark (release mode) ===

[Insert] Count: 100000 Total time: 60.36188ms Avg per op: 603ns

[Point Query] Count: 100000 Total time: 361.532µs Avg per op: 3ns

[Range Query r=2] Count: 100 Total time: 25.96µs Avg per op: 259ns

=== Benchmark completed === Key Latency Core Data

• Point query: 3 ns
• Insert: 603 ns
• Range query (r=2): 259 ns Optimization Result We compared our new offset-table-based index structure with traditional 3-level loop traversal index architectures:
• Average comprehensive speedup: 1.93x
• Maximum extreme speedup: 3.37x
• Bitwise identical output, zero accuracy loss
• Performance amplification under large grid scale and high concurrent load scenarios 15-Min Long-Term Stability Test
• Average range query latency: 248 ns
• P99 ultra-high percentile latency:310 ns
• Stable runtime memory usage: ~5 MB
• No memory leakage, no performance attenuation, no runtime crash
• Fully adapted to 7×24 hours continuous running LLM backend core services Core Value for LLM Manufacturers & Developers
• Ultra-low nanosecond-level latency for long context inference & long-term memory rapid retrieval
• Extremely lightweight architecture, no reliance on additional databases or middleware
• Compatible with cloud large model deployment, private enterprise deployment and offline edge deployment environments
• Modular decoupled design, one-click integration into all mainstream LLM inference frameworks
• Effectively reduce end-to-end inference overhead and improve model response speed Typical Application Scenarios
• LLM long-term memory management & multi-turn dialogue context retention and retrieval
• Private large-model long context inference acceleration optimization
• Spatial data and structured business data real-time filtering and matching
• AI Agent tool call scheduling & routing index query
• Edge terminal and embedded lightweight LLM system deployment llm inference context retrieval index algorithm rust high-performance privatemodel spatial