codanna 0.9.19

Code Intelligence for Large Language Models
Documentation 
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//! Vector search functionality for code intelligence.
//!
//! This module provides high-performance vector storage and search capabilities
//! designed to integrate with the existing Tantivy-based text search infrastructure.
//!
//! # Performance Targets
//! - Vector access: <1μs per vector
//! - Memory usage: ~100 bytes per symbol
//! - Indexing: 10,000+ files/second
//! - Search latency: <10ms for semantic search
//!
//! # Architecture
//! The vector search system uses IVFFlat (Inverted File with Flat vectors) indexing
//! with K-means clustering to achieve sub-linear search performance. Vectors are
//! stored in memory-mapped files for instant loading and minimal memory overhead.

mod clustering;
mod embedding;
mod engine;
mod storage;
mod types;

// Re-export core types for public API
pub use clustering::{
    ClusteringError, KMeansResult, assign_to_nearest_centroid, cosine_similarity, kmeans_clustering,
};
#[cfg(test)]
pub use embedding::MockEmbeddingGenerator;
pub use embedding::{
    EmbeddingGenerator, FastEmbedGenerator, create_symbol_text, model_to_string,
    parse_embedding_model,
};
pub use engine::VectorSearchEngine;
pub use storage::{ConcurrentVectorStorage, MmapVectorStorage, VectorStorageError};
pub use types::{
    ClusterId, Score, SegmentOrdinal, VECTOR_DIMENSION_384, VectorDimension, VectorError, VectorId,
};