car-search 0.15.1

//! LLM-based relevance scoring for mined `CodeReference`s.
//!
//! This is a post-processing step intentionally decoupled from the sources.
//! Sources return a cheap syntactic score; `score_with_llm` asks a model to
//! re-rank them against the original query with a structured JSON output.
//!
//! Callers apply this selectively — not every call to `MultiSource::search`
//! needs LLM scoring. That's why it lives as a free function, not inside
//! `MultiSource`.

use super::CodeReference;
use car_inference::{GenerateParams, GenerateRequest, InferenceEngine};
use serde::Deserialize;
use std::sync::Arc;

/// Re-score `refs` using an LLM. The original order is preserved when the
/// model returns fewer entries than were sent.
///
/// On model failure this returns the input unchanged — scoring is advisory,
/// not load-bearing. A failing scorer should not hide real hits.
pub async fn score_with_llm(
    engine: &Arc<InferenceEngine>,
    query: &str,
    mut refs: Vec<CodeReference>,
    model: Option<&str>,
) -> Vec<CodeReference> {
    if refs.is_empty() {
        return refs;
    }

    let prompt = build_prompt(query, &refs);
    let req = GenerateRequest {
        prompt,
        model: model.map(String::from),
        params: GenerateParams {
            temperature: 0.1,
            max_tokens: 1024,
            ..Default::default()
        },
        context: None,
        tools: None,
        images: None,
        messages: None,
        cache_control: false,
        response_format: None,
        intent: None,
    };

    let text = match engine.generate(req).await {
        Ok(t) => t,
        Err(e) => {
            tracing::warn!(error = %e, "score_with_llm: inference failed, keeping input order");
            return refs;
        }
    };

    let scores = match parse_scores(&text) {
        Some(s) => s,
        None => {
            tracing::warn!(
                raw = %truncate(&text, 200),
                "score_with_llm: could not parse scores, keeping input order"
            );
            return refs;
        }
    };

    // Blend LLM score (weight 0.7) with original source score (weight 0.3) so a
    // pathological model response can't fully erase the source signal.
    for entry in scores {
        if let Some(r) = refs.get_mut(entry.index) {
            let llm = entry.score.clamp(0.0, 1.0);
            r.score = (0.7 * llm + 0.3 * r.score).clamp(0.0, 1.0);
            if let Some(reason) = entry.why_relevant {
                if !reason.is_empty() {
                    r.why_relevant = reason;
                }
            }
        }
    }
    refs.sort_by(|a, b| {
        b.score
            .partial_cmp(&a.score)
            .unwrap_or(std::cmp::Ordering::Equal)
    });
    refs
}

fn build_prompt(query: &str, refs: &[CodeReference]) -> String {
    let mut buf = String::new();
    buf.push_str(
        "You are ranking code references by how relevant they are to a user's query.\n\
         For each reference, return a score in [0.0, 1.0] (1.0 = perfectly relevant) and a one-sentence reason.\n\
         Reply with ONLY a JSON array, no prose, no markdown fences.\n\
         Each element: {\"index\": <0-based>, \"score\": <float>, \"why_relevant\": \"...\"}.\n\n",
    );
    buf.push_str("Query: ");
    buf.push_str(query);
    buf.push_str("\n\nReferences:\n");
    for (i, r) in refs.iter().enumerate() {
        buf.push_str(&format!(
            "[{i}] repo={} path={}\n{}\n\n",
            r.repo,
            r.path,
            truncate(&r.snippet, 600)
        ));
    }
    buf.push_str("Return the JSON array now:");
    buf
}

#[derive(Deserialize)]
struct ScoreEntry {
    index: usize,
    score: f32,
    #[serde(default)]
    why_relevant: Option<String>,
}

fn parse_scores(text: &str) -> Option<Vec<ScoreEntry>> {
    // Be forgiving: the model may wrap output in fences or add a preamble.
    let trimmed = text.trim();
    if let Ok(v) = serde_json::from_str::<Vec<ScoreEntry>>(trimmed) {
        return Some(v);
    }
    // Strip common fence markers.
    let stripped = trimmed
        .trim_start_matches("```json")
        .trim_start_matches("```")
        .trim_end_matches("```")
        .trim();
    if let Ok(v) = serde_json::from_str::<Vec<ScoreEntry>>(stripped) {
        return Some(v);
    }
    // Last resort: find the first `[` and last `]` and try to parse that slice.
    let start = trimmed.find('[')?;
    let end = trimmed.rfind(']')?;
    if end <= start {
        return None;
    }
    serde_json::from_str::<Vec<ScoreEntry>>(&trimmed[start..=end]).ok()
}

fn truncate(s: &str, n: usize) -> String {
    if s.len() <= n {
        s.to_string()
    } else {
        // Find a valid UTF-8 boundary ≤ n.
        let mut end = n;
        while end > 0 && !s.is_char_boundary(end) {
            end -= 1;
        }
        format!("{}…", &s[..end])
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn parse_plain_array() {
        let v = parse_scores(r#"[{"index":0,"score":0.8,"why_relevant":"hits"}]"#).unwrap();
        assert_eq!(v.len(), 1);
        assert_eq!(v[0].index, 0);
    }

    #[test]
    fn parse_json_fence() {
        let v = parse_scores("```json\n[{\"index\":1,\"score\":0.3}]\n```").unwrap();
        assert_eq!(v[0].index, 1);
        assert!((v[0].score - 0.3).abs() < 1e-6);
    }

    #[test]
    fn parse_with_preamble() {
        let v = parse_scores("Sure! [{\"index\":0,\"score\":0.5}]. done").unwrap();
        assert_eq!(v.len(), 1);
    }

    #[test]
    fn parse_garbage_returns_none() {
        assert!(parse_scores("not json at all").is_none());
    }
}