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//! RAG (Retrieval-Augmented Generation) pipeline components.
//!
//! This module provides the text-processing and retrieval-quality infrastructure
//! that sits between raw search results and the final context window presented
//! to an LLM.
//!
//! # Components
//!
//! - **Chunkers** — split documents into retrievable pieces:
//! [`FixedLength`], [`TokenCount`], [`SentenceToken`], [`ParentChild`]
//! - **Rerankers** — reorder retrieved contexts by relevance:
//! [`NullReranker`] (pass-through), with cross-encoder support planned
//! - **Output filters** — post-process the context list:
//! [`MinScoreFilter`], [`DeduplicateFilter`], [`TruncateFilter`],
//! [`FilterPipeline`]
//! - **Query gap tracker** — surfaces corpus gaps from missed queries
//! - **Evaluation** — [`Evaluator`] with precision, recall, faithfulness,
//! and answer-relevance metrics
//!
//! # Quick start
//!
//! ```rust
//! use fornix::rag::chunkers::{Chunker, TokenCount};
//!
//! let chunker = TokenCount::new(200, 20);
//! let chunks = chunker.chunk("The quick brown fox jumps over the lazy dog.");
//! assert!(!chunks.is_empty());
//! ```
pub use ;
pub use ;
pub use ;
pub use QueryGapTracker;
pub use ;
pub use ;