use crate::affinity::TopicAffinities;

/// Inputs to the final blended relevance score for one article.
pub struct RelevanceInputs<'a> {
    /// Cosine similarity (0.0-1.0, already renormalized from [-1,1] if
    /// needed) between article and preference-profile embeddings.
    pub embedding_score: f32,
    /// LLM judgment (0.0-1.0), `None` if the article didn't clear the
    /// embedding shortlist threshold and so was never sent to the LLM.
    pub llm_score: Option<f32>,
    pub topics: &'a [String],
    pub affinities: &'a TopicAffinities,
}

/// Weights are deliberately conservative: the LLM judgment dominates when
/// present (it has read the actual content), the embedding score is a
/// fallback when the LLM stage was skipped, and topic affinity acts as a
/// bounded nudge rather than a veto — a strong LLM match should still
/// surface even for a topic the user historically skims.
const W_LLM: f32 = 0.6;
const W_EMBEDDING: f32 = 0.25;
const W_AFFINITY: f32 = 0.15;

pub fn score_article(inputs: RelevanceInputs) -> f32 {
    let affinity = inputs.affinities.score_topics(inputs.topics) as f32; // [-1, 1]
    let affinity_component = (affinity + 1.0) / 2.0; // renormalize to [0, 1]

    match inputs.llm_score {
        Some(llm) => {
            W_LLM * llm + W_EMBEDDING * inputs.embedding_score + W_AFFINITY * affinity_component
        }
        // No LLM score yet: redistribute its weight onto the embedding score.
        None => (W_LLM + W_EMBEDDING) * inputs.embedding_score + W_AFFINITY * affinity_component,
    }
    .clamp(0.0, 1.0)
}