use std::collections::HashMap;

use rusqlite::Connection;
use rusqlite::OptionalExtension;

use crate::core::error::Result;
use crate::embedding::chunk_ids::decode_rowid;

#[derive(Debug)]
pub struct VectorResult {
    pub document_id: i64,
    pub distance: f64,
}

fn max_chunks_per_document(conn: &Connection) -> Result<i64> {
    let stored: Option<i64> = conn
        .query_row(
            "SELECT MAX(chunk_count) FROM embedding_metadata
             WHERE chunk_index = 0 AND chunk_count IS NOT NULL",
            [],
            |row| row.get(0),
        )
        .optional()?
        .flatten();

    if let Some(max) = stored {
        return Ok(max);
    }

    Ok(conn
        .query_row(
            "SELECT COALESCE(MAX(cnt), 1) FROM (
            SELECT COUNT(*) as cnt FROM embedding_metadata
            WHERE last_error IS NULL GROUP BY document_id
        )",
            [],
            |row| row.get(0),
        )
        .optional()?
        .unwrap_or(1))
}

/// sqlite-vec hard limit for KNN `k` parameter.
const SQLITE_VEC_KNN_MAX: usize = 4_096;

/// Compute the KNN k value from the requested limit and the max chunks per
/// document. The result is guaranteed to never exceed [`SQLITE_VEC_KNN_MAX`].
fn compute_knn_k(limit: usize, max_chunks_per_doc: i64) -> usize {
    let max_chunks = max_chunks_per_doc.unsigned_abs().max(1) as usize;
    let multiplier = (max_chunks * 3 / 2 + 1).clamp(8, 200);
    (limit * multiplier).min(SQLITE_VEC_KNN_MAX)
}

pub fn search_vector(
    conn: &Connection,
    query_embedding: &[f32],
    limit: usize,
) -> Result<Vec<VectorResult>> {
    if query_embedding.is_empty() || limit == 0 {
        return Ok(Vec::new());
    }

    let embedding_bytes: Vec<u8> = query_embedding
        .iter()
        .flat_map(|f| f.to_le_bytes())
        .collect();

    let max_chunks = max_chunks_per_document(conn)?.max(1);
    let k = compute_knn_k(limit, max_chunks);

    let mut stmt = conn.prepare(
        "SELECT rowid, distance
         FROM embeddings
         WHERE embedding MATCH ?1
           AND k = ?2
         ORDER BY distance",
    )?;

    let rows: Vec<(i64, f64)> = stmt
        .query_map(rusqlite::params![embedding_bytes, k as i64], |row| {
            Ok((row.get(0)?, row.get(1)?))
        })?
        .collect::<std::result::Result<Vec<_>, _>>()?;

    let mut best: HashMap<i64, f64> = HashMap::new();
    for (rowid, distance) in rows {
        let (document_id, _chunk_index) = decode_rowid(rowid);
        best.entry(document_id)
            .and_modify(|d| {
                if distance < *d {
                    *d = distance;
                }
            })
            .or_insert(distance);
    }

    let mut results: Vec<VectorResult> = best
        .into_iter()
        .map(|(document_id, distance)| VectorResult {
            document_id,
            distance,
        })
        .collect();
    results.sort_by(|a, b| a.distance.total_cmp(&b.distance));
    results.truncate(limit);

    Ok(results)
}

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

    #[test]
    fn test_empty_returns_empty() {
        let result = search_vector_dedup(vec![], 10);
        assert!(result.is_empty());
    }

    #[test]
    fn test_dedup_keeps_best_distance() {
        let rows = vec![(1000_i64, 0.5_f64), (1001, 0.3), (2000, 0.4)];
        let results = search_vector_dedup(rows, 10);
        assert_eq!(results.len(), 2);
        assert_eq!(results[0].document_id, 1);
        assert!((results[0].distance - 0.3).abs() < f64::EPSILON);
        assert_eq!(results[1].document_id, 2);
    }

    #[test]
    fn test_dedup_respects_limit() {
        let rows = vec![(1000_i64, 0.1_f64), (2000, 0.2), (3000, 0.3)];
        let results = search_vector_dedup(rows, 2);
        assert_eq!(results.len(), 2);
    }

    #[test]
    fn test_knn_k_never_exceeds_sqlite_vec_limit() {
        // Brute-force: no combination of valid inputs should exceed 4096.
        for limit in [1, 10, 50, 100, 500, 1000, 1500, 2000, 5000] {
            for max_chunks in [1, 2, 5, 10, 50, 100, 200, 500, 1000] {
                let k = compute_knn_k(limit, max_chunks);
                assert!(
                    k <= SQLITE_VEC_KNN_MAX,
                    "k={k} exceeded limit for limit={limit}, max_chunks={max_chunks}"
                );
            }
        }
    }

    #[test]
    fn test_knn_k_reproduces_original_bug_scenario() {
        // The original bug: limit=1500 (RECALL_CAP) with multiplier >= 8
        // produced k=10_000 which exceeded sqlite-vec's 4096 cap.
        let k = compute_knn_k(1500, 1);
        assert!(k <= SQLITE_VEC_KNN_MAX, "k={k} exceeded 4096 at RECALL_CAP with 1 chunk");
    }

    #[test]
    fn test_knn_k_small_limit_uses_minimum_multiplier() {
        // With 1 chunk, multiplier clamps to minimum of 8.
        let k = compute_knn_k(10, 1);
        assert_eq!(k, 80);
    }

    #[test]
    fn test_knn_k_high_chunks_caps_multiplier() {
        // With 200 chunks, multiplier = (200*3/2 + 1) = 301 → clamped to 200.
        // limit=10 * 200 = 2000 < 4096.
        let k = compute_knn_k(10, 200);
        assert_eq!(k, 2000);
    }

    #[test]
    fn test_knn_k_zero_max_chunks_treated_as_one() {
        // max_chunks_per_doc=0 → unsigned_abs().max(1) = 1
        let k = compute_knn_k(10, 0);
        assert_eq!(k, 80); // multiplier clamp(8, 200) → 8
    }

    #[test]
    fn test_knn_k_negative_max_chunks_uses_absolute() {
        // Defensive: negative values use unsigned_abs
        let k = compute_knn_k(10, -5);
        assert_eq!(k, compute_knn_k(10, 5));
    }

    fn search_vector_dedup(rows: Vec<(i64, f64)>, limit: usize) -> Vec<VectorResult> {
        let mut best: HashMap<i64, f64> = HashMap::new();
        for (rowid, distance) in rows {
            let (document_id, _) = decode_rowid(rowid);
            best.entry(document_id)
                .and_modify(|d| {
                    if distance < *d {
                        *d = distance;
                    }
                })
                .or_insert(distance);
        }
        let mut results: Vec<VectorResult> = best
            .into_iter()
            .map(|(document_id, distance)| VectorResult {
                document_id,
                distance,
            })
            .collect();
        results.sort_by(|a, b| a.distance.total_cmp(&b.distance));
        results.truncate(limit);
        results
    }
}