Introduction

chaotic_semantic_memory is a Rust crate for AI memory systems built on:

• 10240-bit hyperdimensional vectors - Dense binary representations for semantic similarity
• Chaotic echo-state reservoirs - Temporal sequence processing with emergent dynamics
• libSQL persistence - Durable storage with SQLite compatibility

Why Chaotic Memory?

Traditional vector databases store static embeddings. Chaotic semantic memory adds:

1. Temporal processing - Sequences are compressed into single hypervectors
2. Emergent dynamics - Reservoir chaos enables associative recall
3. Sub-symbolic reasoning - Hypervector operations are neuro-symbolic

Vector Options

This crate uses Hyperdimensional Computing (HDC) for fast lexical similarity. For semantic similarity (synonyms, paraphrases), you can:

• External embeddings: Use sentence-transformers or similar, inject via inject_concept()
• Turso native vectors: Add F32_BLOB tables to the same libSQL/Turso database this crate uses
• Hybrid approach: HDC for lexical matching + Turso vector_top_k() for semantic search

See Text Encoding for details.

Features

• Native Rust with WASM target support
• SIMD-accelerated hypervector operations
• Sparse reservoir matrices for memory efficiency
• Async I/O with Tokio
• Connection pooling for remote databases
• Export/import for data migration

Quick Example

use chaotic_semantic_memory::prelude::*;

#[tokio::main]
async fn main() -> Result<()> {
    let framework = ChaoticSemanticFramework::builder()
        .without_persistence()
        .build()
        .await?;

    let concept = ConceptBuilder::new("cat".to_string()).build();
    framework.inject_concept("cat".to_string(), concept.vector.clone()).await?;

    let hits = framework.probe(concept.vector.clone(), 5).await?;
    println!("Found {} similar concepts", hits.len());
    Ok(())
}