/// The tool-calling research loop needs to reliably decide what to search
/// for, when a page is worth fetching, and when it has enough evidence —
/// that's a reasoning-heavy job best given to the largest local Gemma
/// variant. Turning gathered notes into prose (writing, summarizing) is
/// comparatively mechanical, so the smaller/faster variant handles those
/// passes instead.
pub(crate) const RESEARCHER_MODEL: &str = "gemma4:26b";
pub(crate) const WRITER_MODEL: &str = "gemma4-e4b:latest";

/// Deliberately a dedicated embedding model rather than reusing a chat model
/// for embeddings — it's trained for semantic similarity, not chat, and
/// Ollama's `nomic-embed-text` is a well-known identifier Rig already knows
/// the output dimensionality for.
pub(crate) const EMBEDDING_MODEL: &str = "nomic-embed-text";