vtcode-core 0.136.1

//! Production-grade algorithms for tool improvements.
//!
//! Provides: Jaro-Winkler string similarity, time-decay effectiveness scoring,
//! pattern detection over tool execution history, and ML-ready feature vectors.

use crate::utils::current_timestamp;
use serde::{Deserialize, Serialize};
use smallvec::SmallVec;

// ── String similarity ─────────────────────────────────────────────────────────

/// Jaro-Winkler string similarity in [0.0, 1.0].
///
/// Preferred over Levenshtein for short strings (tool arguments) because it
/// rewards matching prefixes, which is common in tool argument patterns.
/// Delegates to the battle-tested [`strsim`](https://docs.rs/strsim) implementation.
pub fn jaro_winkler_similarity(s1: &str, s2: &str) -> f32 {
    strsim::jaro_winkler(s1, s2) as f32
}

// ── Time-decay scoring ────────────────────────────────────────────────────────

/// Time-decay effectiveness score.
///
/// Recent successes are weighted higher. Decay follows:
/// `score × exp(−λ × age_hours)`, default λ = 0.1 per hour.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimeDecayedScore {
    /// Base score (0.0–1.0).
    pub base_score: f32,
    /// Age in seconds.
    pub age_seconds: u64,
    /// Decay constant.
    pub decay_lambda: f32,
    /// Decayed score (0.0–1.0).
    pub decayed_score: f32,
}

impl TimeDecayedScore {
    /// Calculate a time-decayed score for `base_score` recorded at `timestamp`.
    pub fn calculate(base_score: f32, timestamp: u64) -> Self {
        const DEFAULT_LAMBDA: f32 = 0.1;
        let now = current_timestamp();
        let age_seconds = now.saturating_sub(timestamp);
        let age_hours = age_seconds as f32 / 3600.0;
        let decayed_score = (base_score * (-DEFAULT_LAMBDA * age_hours).exp()).clamp(0.0, 1.0);

        Self {
            base_score,
            age_seconds,
            decay_lambda: DEFAULT_LAMBDA,
            decayed_score,
        }
    }

    /// Return a copy with a custom decay constant applied.
    pub fn with_decay(mut self, lambda: f32) -> Self {
        let age_hours = self.age_seconds as f32 / 3600.0;
        self.decayed_score = (self.base_score * (-lambda * age_hours).exp()).clamp(0.0, 1.0);
        self.decay_lambda = lambda;
        self
    }
}

// ── Pattern detection ─────────────────────────────────────────────────────────

/// Detected state in a tool-execution sequence.
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq)]
pub enum PatternState {
    /// Single execution.
    Single,
    /// Two identical executions.
    Duplicate,
    /// Multiple identical executions (3+).
    Loop,
    /// Executions with slight variation (fuzzy match).
    NearLoop,
    /// Sequential quality improvement.
    RefinementChain,
    /// Multiple tools converging to similar quality.
    Convergence,
    /// Quality degrading over iterations.
    Degradation,
}

/// A single tool-call record for pattern detection.
///
/// Shape: `(tool, args_hash, quality)` — named fields replace a bare tuple so
/// the dimensional structure of the data is explicit in the type system.
#[derive(Debug, Clone)]
pub struct ToolCallRecord {
    /// Tool name (e.g. `"grep"`, `"file_read"`).
    pub tool: String,
    /// Hash of the tool's arguments for fuzzy dedup.
    pub args_hash: String,
    /// Result quality in `[0, 1]`.
    pub quality: f32,
}

/// Detect a pattern in a history of tool-call records.
///
/// Only the last `window_size` entries are examined.
/// Returns [`PatternState::Single`] for an empty or single-entry history.
///
/// # Why a free function?
/// The logic is stateless — `window_size` is the only parameter. A wrapper
/// struct added no encapsulation and hurt discoverability (KISS).
pub fn detect_pattern(history: &[ToolCallRecord], window_size: usize) -> PatternState {
    if history.is_empty() {
        return PatternState::Single;
    }

    // Work on the most recent `window_size` entries; borrow the slice directly
    // (no intermediate Vec allocation).
    let start = history.len().saturating_sub(window_size);
    let recent = &history[start..];

    if recent.len() < 2 {
        return PatternState::Single;
    }

    let first = &recent[0];

    // --- Exact duplicates ---
    if recent.iter().all(|r| r.tool == first.tool && r.args_hash == first.args_hash) {
        return if recent.len() >= 3 {
            PatternState::Loop
        } else {
            PatternState::Duplicate
        };
    }

    // --- Single combined scan -------------------------------------------------
    // Collect qualities + same-tool flag + multi-tool flag in one pass.
    let mut qualities = SmallVec::<[f32; 32]>::with_capacity(recent.len());
    let mut same_tool = true;
    let mut multi_tool = false;

    for r in recent {
        qualities.push(r.quality);
        if r.tool != first.tool {
            same_tool = false;
            multi_tool = true;
        }
    }

    // --- Quality trends (≥3 points) ---
    if qualities.len() >= 3 {
        if qualities.windows(2).all(|w| w[1] > w[0] + 0.05) {
            return PatternState::RefinementChain;
        }
        if qualities.windows(2).all(|w| w[1] < w[0] - 0.05) {
            return PatternState::Degradation;
        }
    }

    // --- Near-loop: same tool, fuzzy args ---
    // Use `.all()` directly — no intermediate Vec<f32> needed.
    if same_tool
        && recent.len() >= 3
        && recent
            .windows(2)
            .all(|w| jaro_winkler_similarity(&w[0].args_hash, &w[1].args_hash) > 0.85)
    {
        return PatternState::NearLoop;
    }

    // --- Convergence: different tools, similar quality ---
    if multi_tool {
        let n = qualities.len() as f32;
        let avg = qualities.iter().sum::<f32>() / n;
        if qualities.iter().all(|&q| (q - avg).abs() < 0.1) {
            return PatternState::Convergence;
        }
    }

    PatternState::Single
}

// ── ML scoring ────────────────────────────────────────────────────────────────

/// ML-ready scoring components for tool effectiveness.
///
/// Can be used as a feature vector for training models.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MLScoreComponents {
    /// Success rate (0–1).
    pub success_rate: f32,
    /// Average execution time (ms).
    pub avg_execution_time: f32,
    /// Result quality (0–1).
    pub result_quality: f32,
    /// Number of failure modes observed.
    pub failure_count: usize,
    /// Time since last use (hours).
    pub age_hours: f32,
    /// Usage frequency (calls per hour).
    pub frequency: f32,
    /// Confidence in measurement (0–1).
    pub confidence: f32,
}

impl MLScoreComponents {
    /// Combined ML score before time decay.
    ///
    /// Weights: success 40% + quality 30% + speed 15% + frequency 15%.
    pub fn raw_score(&self) -> f32 {
        (self.success_rate * 0.40)
            + (self.result_quality * 0.30)
            + ((10_000.0 - self.avg_execution_time).max(0.0) / 10_000.0 * 0.15)
            + (self.frequency.min(1.0) * 0.15)
    }

    /// Apply confidence decay for older measurements (1-week half-life).
    pub fn with_age_decay(mut self) -> Self {
        // Older measurements are less reliable; decay confidence over time.
        self.confidence = (self.confidence * (-self.age_hours / 168.0).exp()).max(0.1);
        self
    }

    /// Return a 7-element feature vector, normalised for ML consumption.
    ///
    /// # Dimension key
    ///
    /// | Index | Name              | Meaning                          | Normalisation      |
    /// |-------|-------------------|----------------------------------|--------------------|
    /// | 0     | `success_rate`    | Fraction of successful calls     | `[0, 1]`           |
    /// | 1     | `avg_exec_time`   | Mean execution time (ms)         | `/ 10_000`         |
    /// | 2     | `result_quality`  | Mean quality of results          | `[0, 1]`           |
    /// | 3     | `failure_count`   | Distinct failure modes observed  | `min(10) / 10`     |
    /// | 4     | `age_hours`       | Time since last use (hours)      | `/ 168` (1 week)   |
    /// | 5     | `frequency`       | Calls per hour                   | `[0, ∞)`           |
    /// | 6     | `confidence`      | Measurement confidence           | `[0, 1]`           |
    pub fn to_feature_vector(&self) -> [f32; 7] {
        [
            self.success_rate,
            self.avg_execution_time / 10_000.0,
            self.result_quality,
            (self.failure_count as f32).min(10.0) / 10.0,
            self.age_hours / 168.0,
            self.frequency,
            self.confidence,
        ]
    }
}

// ── Tests ─────────────────────────────────────────────────────────────────────

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

    #[test]
    fn test_jaro_winkler_exact() {
        assert!((jaro_winkler_similarity("hello", "hello") - 1.0).abs() < f32::EPSILON);
    }

    #[test]
    fn test_jaro_winkler_partial() {
        let sim = jaro_winkler_similarity("pattern", "pattern_file");
        assert!(sim > 0.85 && sim < 1.0, "sim={sim}");
    }

    #[test]
    fn test_jaro_winkler_prefix_boost() {
        let with_prefix = jaro_winkler_similarity("test_one", "test_two");
        let without = jaro_winkler_similarity("one_test", "two_test");
        assert!(with_prefix > without, "prefix boost should be applied");
    }

    #[test]
    fn test_time_decay_ordering() {
        let now = current_timestamp();
        let recent = TimeDecayedScore::calculate(0.9, now);
        let old = TimeDecayedScore::calculate(0.9, now.saturating_sub(7 * 24 * 3600));
        assert!(old.decayed_score < recent.decayed_score);
    }

    #[test]
    fn test_detect_pattern_loop() {
        let history = vec![
            ToolCallRecord {
                tool: "grep".into(),
                args_hash: "pattern1".into(),
                quality: 0.5,
            },
            ToolCallRecord {
                tool: "grep".into(),
                args_hash: "pattern1".into(),
                quality: 0.5,
            },
            ToolCallRecord {
                tool: "grep".into(),
                args_hash: "pattern1".into(),
                quality: 0.5,
            },
        ];
        assert_eq!(detect_pattern(&history, 10), PatternState::Loop);
    }

    #[test]
    fn test_detect_pattern_refinement() {
        let history = vec![
            ToolCallRecord {
                tool: "grep".into(),
                args_hash: "pat1".into(),
                quality: 0.3,
            },
            ToolCallRecord {
                tool: "grep".into(),
                args_hash: "pat2".into(),
                quality: 0.5,
            },
            ToolCallRecord {
                tool: "grep".into(),
                args_hash: "pat3".into(),
                quality: 0.8,
            },
        ];
        assert_eq!(detect_pattern(&history, 10), PatternState::RefinementChain);
    }

    #[test]
    fn test_detect_pattern_near_loop_requires_three_entries() {
        let two_entries = vec![
            ToolCallRecord {
                tool: "grep".into(),
                args_hash: "pattern-one".into(),
                quality: 0.4,
            },
            ToolCallRecord {
                tool: "grep".into(),
                args_hash: "pattern-two".into(),
                quality: 0.45,
            },
        ];
        assert_eq!(detect_pattern(&two_entries, 10), PatternState::Single);

        let three_entries = vec![
            ToolCallRecord {
                tool: "grep".into(),
                args_hash: "pattern-one".into(),
                quality: 0.4,
            },
            ToolCallRecord {
                tool: "grep".into(),
                args_hash: "pattern-two".into(),
                quality: 0.45,
            },
            ToolCallRecord {
                tool: "grep".into(),
                args_hash: "pattern-three".into(),
                quality: 0.5,
            },
        ];
        assert_eq!(detect_pattern(&three_entries, 10), PatternState::NearLoop);
    }

    #[test]
    fn test_ml_raw_score() {
        let c = MLScoreComponents {
            success_rate: 0.9,
            avg_execution_time: 100.0,
            result_quality: 0.85,
            failure_count: 1,
            age_hours: 2.0,
            frequency: 0.5,
            confidence: 0.9,
        };
        let score = c.raw_score();
        assert!(score > 0.7 && score < 1.0, "score={score}");
    }

    #[test]
    fn test_ml_feature_vector_length() {
        let c = MLScoreComponents {
            success_rate: 0.9,
            avg_execution_time: 100.0,
            result_quality: 0.85,
            failure_count: 1,
            age_hours: 2.0,
            frequency: 0.5,
            confidence: 0.9,
        };
        assert_eq!(c.to_feature_vector().len(), 7);
    }
}