Algorithmic
Lexical Engine

Deterministic word resolution, sub-millisecond lookup, and optimized memory allocation for computational linguistics.

Avg Latency

0.03ms

Lexemes

15.2M

Tree Depth

O(log n)

❯sys.init(lexicon_core)

❯Loading 15M entries... [██████████] 100%

❯Trie indices built. Memory: 412MB

❯await ready()

Real-time allocation map
Nodes: 0 / Active: 0

Algorithm Playground

Execute lexical queries and observe resolution paths in real-time.

Query Controller

Time: O(m) Space: O(1) Hash: BKDR

Resolution Visualization

Step 0 / 0
Traversal: Trie + Hash

Core Architecture

Optimized data structures for high-throughput lexical processing.

Radix Trie Index

Compressed prefix tree enabling O(m) word traversal where m is key length. Shared prefixes reduce memory footprint by ~40%.

Lookup: O(m)Alloc: ~380MB

Double Hash Map

Primary BKDR hashing with quadratic probing collision resolution. Guarantees O(1) amortized access for direct lemmas.

Lookup: O(1)Alloc: ~96MB

Memory-Mapped Cache

OS-level page mapping for LRU hot entries. Bypasses serialization overhead for frequently accessed lexemes.

Cache Hit: 94%Latency: <0.01ms

API Reference

RESTful endpoints for algorithmic integration.

POST/v1/resolve

POST /v1/resolve
Content-Type: application/json

// Request Body
{
  "lexeme": "algorithm",
  "flags": ["etymology", "phonetic"],
  "lang": "en-US"
}
                        

GET/v1/lexicon/{id}/neighbors

GET /v1/lexicon/894021/neighbors?max_dist=2

// Response
{
  "status": "OK",
  "graph": [
    "algorithm", "algorithms", "rhythm", "lgorithm"
  ],
  "computed": 0.04
}
                        

AlgorithmicLexical Engine