Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/crates/ruvector-learning-wasm/src/lib.rs

@@ -0,0 +1,46 @@
+//! MicroLoRA WASM - Ultra-fast Low-Rank Adaptation for Edge AI
+//!
+//! This crate provides rank-2 LoRA (Low-Rank Adaptation) matrices optimized for
+//! WASM execution with <100us adaptation latency. Designed for real-time
+//! per-operator-type learning in query optimization systems.
+//!
+//! ## Key Features
+//!
+//! - **Rank-2 LoRA**: Minimal parameter count (2d parameters per adapter)
+//! - **Per-Operator Scoping**: Separate adapters for different operator types
+//! - **<100us Adaptation**: Instant weight updates for real-time learning
+//! - **WASM-Optimized**: no_std compatible, minimal allocations
+//!
+//! ## Architecture
+//!
+//! ```text
+//! Input Embedding (d-dim)
+//!        |
+//!        v
+//!   +---------+
+//!   | A: d x 2 |  Down projection
+//!   +---------+
+//!        |
+//!        v
+//!   +---------+
+//!   | B: 2 x d |  Up projection
+//!   +---------+
+//!        |
+//!        v
+//! Delta W = alpha * (A @ B)
+//!        |
+//!        v
+//! Output = Input + Delta W
+//! ```
+
+mod lora;
+mod operator_scope;
+mod trajectory;
+
+pub use lora::{LoRAConfig, LoRAPair, MicroLoRAEngine};
+pub use operator_scope::{OperatorScope, ScopedLoRA};
+pub use trajectory::{Trajectory, TrajectoryBuffer, TrajectoryStats};
+
+// Re-export core types for JS
+pub use lora::wasm_exports::*;
+pub use operator_scope::wasm_exports::*;