Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/crates/ruvector-gnn/src/lib.rs

@@ -0,0 +1,92 @@
+//! # RuVector GNN
+//!
+//! Graph Neural Network capabilities for RuVector, providing tensor operations,
+//! GNN layers, compression, and differentiable search.
+//!
+//! ## Forgetting Mitigation (Issue #17)
+//!
+//! This crate includes comprehensive forgetting mitigation for continual learning:
+//!
+//! - **Adam Optimizer**: Full implementation with momentum and bias correction
+//! - **Replay Buffer**: Experience replay with reservoir sampling for uniform coverage
+//! - **EWC (Elastic Weight Consolidation)**: Prevents catastrophic forgetting
+//! - **Learning Rate Scheduling**: Multiple strategies including warmup and plateau detection
+//!
+//! ### Usage Example
+//!
+//! ```rust,ignore
+//! use ruvector_gnn::{
+//!     training::{Optimizer, OptimizerType},
+//!     replay::ReplayBuffer,
+//!     ewc::ElasticWeightConsolidation,
+//!     scheduler::{LearningRateScheduler, SchedulerType},
+//! };
+//!
+//! // Create Adam optimizer
+//! let mut optimizer = Optimizer::new(OptimizerType::Adam {
+//!     learning_rate: 0.001,
+//!     beta1: 0.9,
+//!     beta2: 0.999,
+//!     epsilon: 1e-8,
+//! });
+//!
+//! // Create replay buffer for experience replay
+//! let mut replay = ReplayBuffer::new(10000);
+//!
+//! // Create EWC for preventing forgetting
+//! let mut ewc = ElasticWeightConsolidation::new(0.4);
+//!
+//! // Create learning rate scheduler
+//! let mut scheduler = LearningRateScheduler::new(
+//!     SchedulerType::CosineAnnealing { t_max: 100, eta_min: 1e-6 },
+//!     0.001
+//! );
+//! ```
+
+#![warn(missing_docs)]
+#![deny(unsafe_op_in_unsafe_fn)]
+
+pub mod compress;
+pub mod error;
+pub mod ewc;
+pub mod layer;
+pub mod query;
+pub mod replay;
+pub mod scheduler;
+pub mod search;
+pub mod tensor;
+pub mod training;
+
+#[cfg(all(not(target_arch = "wasm32"), feature = "mmap"))]
+pub mod mmap;
+
+#[cfg(all(feature = "cold-tier", not(target_arch = "wasm32")))]
+pub mod cold_tier;
+
+// Re-export commonly used types
+pub use compress::{CompressedTensor, CompressionLevel, TensorCompress};
+pub use error::{GnnError, Result};
+pub use ewc::ElasticWeightConsolidation;
+pub use layer::RuvectorLayer;
+pub use query::{QueryMode, QueryResult, RuvectorQuery, SubGraph};
+pub use replay::{DistributionStats, ReplayBuffer, ReplayEntry};
+pub use scheduler::{LearningRateScheduler, SchedulerType};
+pub use search::{cosine_similarity, differentiable_search, hierarchical_forward};
+pub use training::{
+    info_nce_loss, local_contrastive_loss, sgd_step, Loss, LossType, OnlineConfig, Optimizer,
+    OptimizerType, TrainConfig,
+};
+
+#[cfg(all(not(target_arch = "wasm32"), feature = "mmap"))]
+pub use mmap::{AtomicBitmap, MmapGradientAccumulator, MmapManager};
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_basic() {
+        // Basic smoke test to ensure the crate compiles
+        assert!(true);
+    }
+}