matrixcode_core/

memory.rs

//! Auto Memory system for MatrixCode.
//!
//! This module implements automatic memory accumulation inspired by Claude Code.
//! It captures user preferences, project decisions, key findings, and solutions
//! across sessions, providing persistent context that survives conversation compression.

use anyhow::Result;
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::{Path, PathBuf};
use std::fs;

use crate::providers::Message;

// Helper function to truncate strings (replaces ui::truncate_str)
fn truncate_str(s: &str, max_len: usize) -> String {
    if s.len() > max_len {
        format!("{}...", &s[..max_len.saturating_sub(3)])
    } else {
        s.to_string()
    }
}

fn truncate(s: &str, max_len: usize) -> String {
    if s.len() > max_len {
        s[..max_len].to_string()
    } else {
        s.to_string()
    }
}

// ============================================================================
// Constants
// ============================================================================

/// Maximum importance score ceiling (entries cannot exceed this).
pub const MAX_IMPORTANCE_CEILING: f64 = 100.0;

/// Minimum content length for similarity check (to avoid short words matching everything).
pub const MIN_SIMILARITY_LENGTH: usize = 10;

/// Similarity threshold for considering entries as duplicates (0.0-1.0).
pub const SIMILARITY_THRESHOLD: f64 = 0.7;

/// Minimum content length for memory detection (to avoid capturing too generic content).
pub const MIN_MEMORY_CONTENT_LENGTH: usize = 15;

/// Maximum entries to return from detection (to avoid overwhelming).
pub const MAX_DETECTED_ENTRIES: usize = 5;

/// Maximum length for memory content before truncation.
pub const MAX_MEMORY_CONTENT_LENGTH: usize = 200;

/// Maximum length for display (shorter for terminal readability).
pub const MAX_DISPLAY_LENGTH: usize = 60;

/// Topic overlap threshold for conflict detection.
pub const CONFLICT_OVERLAY_THRESHOLD: f64 = 0.5;

/// Lower topic overlap threshold when change signal is present.
pub const CONFLICT_OVERLAY_THRESHOLD_WITH_SIGNAL: f64 = 0.3;

/// Importance threshold for displaying star marker (⭐).
pub const IMPORTANCE_STAR_THRESHOLD: f64 = 80.0;

/// Weight for relevance in contextual summary (relevance vs importance trade-off).
pub const CONTEXT_RELEVANCE_WEIGHT: f64 = 0.6;

/// Weight for importance in contextual summary (1.0 - CONTEXT_RELEVANCE_WEIGHT).
pub const CONTEXT_IMPORTANCE_WEIGHT: f64 = 0.4;

/// Default model for cost-effective memory extraction.
pub const DEFAULT_MEMORY_EXTRACTOR_MODEL: &str = "claude-3-5-haiku-20241022";

/// Minimum keywords threshold for triggering AI fallback.
/// If rule-based extraction produces fewer keywords than this, AI is used.
pub const MIN_KEYWORDS_FOR_AI_FALLBACK: usize = 2;

/// AI keyword extraction mode.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
pub enum AiKeywordMode {
    /// Hybrid mode: rule-based first, AI fallback when keywords are insufficient (default).
    #[default]
    Auto,
    /// Always use AI for keyword extraction.
    Always,
    /// Never use AI, only rule-based extraction.
    Never,
}

impl AiKeywordMode {
    /// Parse from environment variable string.
    pub fn from_env() -> Self {
        match std::env::var("MEMORY_AI_KEYWORDS")
            .unwrap_or_default()
            .to_lowercase()
            .as_str()
        {
            "always" | "true" | "1" => AiKeywordMode::Always,
            "never" | "false" | "0" => AiKeywordMode::Never,
            "auto" | "" => AiKeywordMode::Auto,
            other => {
                log::warn!("Unknown MEMORY_AI_KEYWORDS value: '{}', using 'auto'", other);
                AiKeywordMode::Auto
            }
        }
    }
    
    /// Whether AI extraction should be used given the keyword count.
    pub fn should_use_ai(&self, keyword_count: usize) -> bool {
        match self {
            AiKeywordMode::Always => true,
            AiKeywordMode::Never => false,
            AiKeywordMode::Auto => keyword_count < MIN_KEYWORDS_FOR_AI_FALLBACK,
        }
    }
}

/// Default importance scores by category.
pub const DEFAULT_IMPORTANCE_DECISION: f64 = 90.0;
pub const DEFAULT_IMPORTANCE_SOLUTION: f64 = 85.0;
pub const DEFAULT_IMPORTANCE_PREF: f64 = 70.0;
pub const DEFAULT_IMPORTANCE_FINDING: f64 = 60.0;
pub const DEFAULT_IMPORTANCE_TECH: f64 = 50.0;
pub const DEFAULT_IMPORTANCE_STRUCTURE: f64 = 40.0;

// ============================================================================
// Memory Configuration
// ============================================================================

/// Configuration for the memory system.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryConfig {
    /// Maximum number of entries to keep.
    pub max_entries: usize,
    /// Minimum importance threshold to keep.
    pub min_importance: f64,
    /// Whether auto accumulation is enabled.
    pub enabled: bool,
    /// Days before time decay starts.
    pub decay_start_days: i64,
    /// Decay rate per period (0.0-1.0).
    pub decay_rate: f64,
    /// Importance increment per reference.
    pub reference_increment: f64,
    /// Maximum importance ceiling.
    pub max_importance_ceiling: f64,
}

impl Default for MemoryConfig {
    fn default() -> Self {
        Self {
            max_entries: 100,
            min_importance: 30.0,
            enabled: true,
            decay_start_days: 30,
            decay_rate: 0.5,
            reference_increment: 2.0,
            max_importance_ceiling: MAX_IMPORTANCE_CEILING,
        }
    }
}

impl MemoryConfig {
    /// Create a new config with custom max entries.
    pub fn with_max_entries(max: usize) -> Self {
        Self {
            max_entries: max,
            ..Self::default()
        }
    }
    
    /// Create a minimal config for low-memory environments.
    pub fn minimal() -> Self {
        Self {
            max_entries: 50,
            min_importance: 50.0,
            enabled: true,
            decay_start_days: 14,
            decay_rate: 0.6,
            reference_increment: 1.0,
            max_importance_ceiling: MAX_IMPORTANCE_CEILING,
        }
    }
    
    /// Create a config for long-term archival.
    pub fn archival() -> Self {
        Self {
            max_entries: 500,
            min_importance: 20.0,
            enabled: true,
            decay_start_days: 90,
            decay_rate: 0.3,
            reference_increment: 3.0,
            max_importance_ceiling: MAX_IMPORTANCE_CEILING,
        }
    }
}

// ============================================================================
// Memory Categories
// ============================================================================

/// Categories for memory entries.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize, Hash)]
#[serde(rename_all = "snake_case")]
pub enum MemoryCategory {
    /// User preferences (e.g., "I prefer vim over nano")
    Preference,
    /// Project decisions (e.g., "Decided to use PostgreSQL")
    Decision,
    /// Key findings (e.g., "API endpoint is at /api/v2")
    Finding,
    /// Problem solutions (e.g., "Fixed auth bug by adding token refresh")
    Solution,
    /// Technical notes (e.g., "React Query is used for data fetching")
    Technical,
    /// Project structure (e.g., "src/index.ts is entry point")
    Structure,
}

impl MemoryCategory {
    /// Get display name for the category.
    pub fn display_name(&self) -> &'static str {
        match self {
            MemoryCategory::Preference => "偏好",
            MemoryCategory::Decision => "决策",
            MemoryCategory::Finding => "发现",
            MemoryCategory::Solution => "解决方案",
            MemoryCategory::Technical => "技术",
            MemoryCategory::Structure => "结构",
        }
    }

    /// Get icon for the category.
    pub fn icon(&self) -> &'static str {
        match self {
            MemoryCategory::Preference => "👤",
            MemoryCategory::Decision => "🎯",
            MemoryCategory::Finding => "💡",
            MemoryCategory::Solution => "🔧",
            MemoryCategory::Technical => "📚",
            MemoryCategory::Structure => "🏗️",
        }
    }

    /// Get default importance score for the category.
    pub fn default_importance(&self) -> f64 {
        match self {
            MemoryCategory::Decision => DEFAULT_IMPORTANCE_DECISION,
            MemoryCategory::Solution => DEFAULT_IMPORTANCE_SOLUTION,
            MemoryCategory::Preference => DEFAULT_IMPORTANCE_PREF,
            MemoryCategory::Finding => DEFAULT_IMPORTANCE_FINDING,
            MemoryCategory::Technical => DEFAULT_IMPORTANCE_TECH,
            MemoryCategory::Structure => DEFAULT_IMPORTANCE_STRUCTURE,
        }
    }
}

// ============================================================================
// Memory Entry
// ============================================================================

/// A single memory entry.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryEntry {
    /// Unique identifier.
    pub id: String,
    /// When the memory was created.
    pub created_at: DateTime<Utc>,
    /// When the memory was last accessed/referenced.
    pub last_referenced: DateTime<Utc>,
    /// Category of the memory.
    pub category: MemoryCategory,
    /// The memory content.
    pub content: String,
    /// Source session ID (where this memory was created).
    pub source_session: Option<String>,
    /// Number of times this memory has been referenced.
    pub reference_count: u32,
    /// Importance score (0-100, higher = more important).
    pub importance: f64,
    /// Tags for searching/filtering.
    pub tags: Vec<String>,
    /// Whether this memory was manually added by user.
    pub is_manual: bool,
}

impl MemoryEntry {
    /// Create a new memory entry.
    pub fn new(category: MemoryCategory, content: String, source_session: Option<String>) -> Self {
        let id = uuid::Uuid::new_v4().to_string();
        Self {
            id,
            created_at: Utc::now(),
            last_referenced: Utc::now(),
            category,
            content,
            source_session,
            reference_count: 0,
            importance: category.default_importance(),
            tags: Vec::new(),
            is_manual: false,
        }
    }

    /// Create a manually added memory entry.
    pub fn manual(category: MemoryCategory, content: String) -> Self {
        let mut entry = Self::new(category, content, None);
        entry.is_manual = true;
        entry.importance = 95.0; // Manual entries are highly important
        entry
    }

    /// Mark this memory as referenced (increases importance over time).
    pub fn mark_referenced(&mut self) {
        self.mark_referenced_with_increment(2.0);
    }

    /// Mark this memory as referenced with custom importance increment.
    pub fn mark_referenced_with_increment(&mut self, increment: f64) {
        self.reference_count += 1;
        self.last_referenced = Utc::now();
        // Increase importance slightly with each reference (capped at ceiling)
        self.importance = (self.importance + increment).min(MAX_IMPORTANCE_CEILING);
    }

    /// Format for display.
    pub fn format_line(&self) -> String {
        let time = self.created_at.format("%Y-%m-%d %H:%M");
        let importance_marker = if self.importance >= IMPORTANCE_STAR_THRESHOLD { "⭐" } else { "" };
        let manual_marker = if self.is_manual { "📝" } else { "" };
        format!(
            "{} {} {}{}{} {}",
            self.category.icon(),
            time,
            importance_marker,
            manual_marker,
            self.category.display_name(),
            truncate_str(&self.content, MAX_DISPLAY_LENGTH)
        )
    }

    /// Format for inclusion in system prompt.
    pub fn format_for_prompt(&self) -> String {
        let category_name = self.category.display_name();
        if self.content.len() > MAX_MEMORY_CONTENT_LENGTH {
            format!("{}: {}...", category_name, truncate(&self.content, MAX_MEMORY_CONTENT_LENGTH - 3))
        } else {
            format!("{}: {}", category_name, self.content)
        }
    }
}

// ============================================================================
// Auto Memory Manager
// ============================================================================

/// Manager for automatic memory accumulation.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AutoMemory {
    /// All memory entries.
    pub entries: Vec<MemoryEntry>,
    /// Configuration for memory management.
    #[serde(default)]
    pub config: MemoryConfig,
    /// Legacy fields for backward compatibility (deprecated).
    #[serde(default = "default_max_entries")]
    pub max_entries: usize,
    #[serde(default = "default_min_importance")]
    pub min_importance: f64,
    #[serde(default = "default_enabled")]
    pub enabled: bool,
    /// Search index (not serialized, rebuilt on load).
    #[serde(skip)]
    search_index: Option<SearchIndex>,
}

/// Search index for fast lookups.
#[derive(Debug, Clone)]
struct SearchIndex {
    /// Lowercase content cache for each entry.
    content_lower: Vec<String>,
    /// Entries grouped by category.
    by_category: HashMap<MemoryCategory, Vec<usize>>,
    /// Entries sorted by importance (indices).
    by_importance: Vec<usize>,
    /// Total word frequency for relevance scoring (future use).
    #[allow(dead_code)]
    word_freq: HashMap<String, usize>,
}

impl SearchIndex {
    /// Build index from entries.
    fn build(entries: &[MemoryEntry]) -> Self {
        // Build lowercase cache
        let content_lower: Vec<String> = entries
            .iter()
            .map(|e| e.content.to_lowercase())
            .collect();
        
        // Build category index
        let mut by_category: HashMap<MemoryCategory, Vec<usize>> = HashMap::new();
        for (i, entry) in entries.iter().enumerate() {
            by_category.entry(entry.category).or_default().push(i);
        }
        
        // Build importance index (sorted descending)
        let mut by_importance: Vec<usize> = (0..entries.len()).collect();
        by_importance.sort_by(|a, b| {
            entries[*b].importance.partial_cmp(&entries[*a].importance)
                .unwrap_or(std::cmp::Ordering::Equal)
        });
        
        // Build word frequency
        let mut word_freq: HashMap<String, usize> = HashMap::new();
        for content in &content_lower {
            for word in content.split_whitespace() {
                *word_freq.entry(word.to_string()).or_default() += 1;
            }
        }
        
        Self {
            content_lower,
            by_category,
            by_importance,
            word_freq,
        }
    }
    
    /// Get lowercase content for entry.
    #[allow(dead_code)]
    fn get_lower(&self, idx: usize) -> &str {
        &self.content_lower[idx]
    }
    
    /// Search by query with optional limit.
    fn search(&self, _entries: &[MemoryEntry], query_lower: &str, limit: Option<usize>) -> Vec<usize> {
        // Use importance index to search in priority order
        let matches: Vec<usize> = self.by_importance
            .iter()
            .filter(|&idx| self.content_lower[*idx].contains(query_lower))
            .copied()
            .collect();
        
        if let Some(max) = limit {
            matches.into_iter().take(max).collect()
        } else {
            matches
        }
    }
    
    /// Multi-keyword search (matches any keyword).
    fn search_multi(&self, keywords_lower: &[String]) -> Vec<usize> {
        self.by_importance
            .iter()
            .filter(|&idx| {
                let content = &self.content_lower[*idx];
                keywords_lower.iter().any(|k| content.contains(k))
            })
            .copied()
            .collect()
    }
    
    /// Invalidate and rebuild index.
    #[allow(dead_code)]
    fn rebuild(&mut self, entries: &[MemoryEntry]) {
        *self = Self::build(entries);
    }
}

fn default_max_entries() -> usize { 100 }
fn default_min_importance() -> f64 { 30.0 }
fn default_enabled() -> bool { true }

impl Default for AutoMemory {
    fn default() -> Self {
        let config = MemoryConfig::default();
        Self {
            entries: Vec::new(),
            config: config.clone(),
            max_entries: config.max_entries,
            min_importance: config.min_importance,
            enabled: config.enabled,
            search_index: None,
        }
    }
}

impl AutoMemory {
    /// Create a new auto memory manager.
    pub fn new() -> Self {
        Self::default()
    }
    
    /// Ensure search index is built.
    fn ensure_index(&mut self) {
        if self.search_index.is_none() {
            self.rebuild_index();
        }
    }
    
    /// Rebuild search index.
    pub fn rebuild_index(&mut self) {
        self.search_index = Some(SearchIndex::build(&self.entries));
    }
    
    /// Invalidate search index (call after modifications).
    fn invalidate_index(&mut self) {
        self.search_index = None;
    }

    /// Create with custom configuration.
    pub fn with_config(config: MemoryConfig) -> Self {
        Self {
            entries: Vec::new(),
            config: config.clone(),
            max_entries: config.max_entries,
            min_importance: config.min_importance,
            enabled: config.enabled,
            search_index: None,
        }
    }

    /// Create a minimal memory manager (low-memory environments).
    pub fn minimal() -> Self {
        Self::with_config(MemoryConfig::minimal())
    }

    /// Create an archival memory manager (long-term storage).
    pub fn archival() -> Self {
        Self::with_config(MemoryConfig::archival())
    }

    /// Add a new memory entry.
    pub fn add(&mut self, entry: MemoryEntry) {
        self.entries.push(entry);
        self.invalidate_index();  // Index needs rebuild
        self.prune();
    }

    /// Add memory from detected content.
    pub fn add_memory(
        &mut self,
        category: MemoryCategory,
        content: String,
        source_session: Option<String>,
    ) {
        // Check for duplicates (similar content)
        if self.has_similar(&content) {
            return;
        }

        // Check for conflicts (same category, contradicting content)
        if let Some(conflict_idx) = self.find_conflict(&content, category) {
            // Replace the old conflicting entry with the new one
            let old_content = self.entries[conflict_idx].content.clone();
            log::debug!("Memory conflict detected: '{}' supersedes '{}'", content, old_content);
            self.entries.remove(conflict_idx);
            self.invalidate_index();
        }

        let entry = MemoryEntry::new(category, content, source_session);
        self.add(entry);
    }

    /// Find a conflicting memory entry.
    /// 
    /// A conflict is detected when:
    /// 1. Same category (e.g., both are Decision)
    /// 2. Same subject/topic (overlapping keywords)
    /// 3. Different conclusion (not similar enough to be a duplicate)
    /// 
    /// Example conflicts:
    /// - "决定使用 PostgreSQL" vs "决定使用 MySQL" (same topic: database choice)
    /// - "偏好 vim" vs "偏好 vscode" (same topic: editor preference)
    fn find_conflict(&self, new_content: &str, category: MemoryCategory) -> Option<usize> {
        let new_lower = new_content.to_lowercase();
        let new_words: std::collections::HashSet<&str> = new_lower.split_whitespace().collect();
        
        // If new content has explicit change signals, lower the threshold
        let has_change_signal = has_contradiction_signal("", &new_lower);
        let overlap_threshold = if has_change_signal { 
            CONFLICT_OVERLAY_THRESHOLD_WITH_SIGNAL 
        } else { 
            CONFLICT_OVERLAY_THRESHOLD 
        };
        
        // Only check entries in the same category
        for (i, entry) in self.entries.iter().enumerate() {
            if entry.category != category {
                continue;
            }
            
            let entry_lower = entry.content.to_lowercase();
            let entry_words: std::collections::HashSet<&str> = entry_lower.split_whitespace().collect();
            
            // Calculate topic overlap (shared words)
            let intersection = new_words.intersection(&entry_words).count();
            let min_len = new_words.len().min(entry_words.len());
            
            if min_len == 0 {
                continue;
            }
            
            let topic_overlap = intersection as f64 / min_len as f64;
            
            // High topic overlap but not a duplicate
            let jaccard = Self::calculate_similarity(&entry_lower, &new_lower);
            
            if topic_overlap > overlap_threshold && jaccard < SIMILARITY_THRESHOLD {
                // Check for contradiction patterns
                if has_contradiction_signal(&entry_lower, &new_lower) {
                    return Some(i);
                }
            }
            
            // Also check if new content explicitly references old content
            // e.g., "不再使用 vim" when old entry contains "vim"
            if has_change_signal {
                // Check if old entry's key terms appear in new content
                let old_key_terms: Vec<&str> = entry_words.iter()
                    .filter(|w| w.len() > 2)
                    .copied()
                    .collect();
                let referenced = old_key_terms.iter()
                    .any(|term| new_lower.contains(term));
                if referenced {
                    return Some(i);
                }
            }
        }
        
        None
    }

    /// Check if similar content already exists.
    /// Uses minimum length threshold to prevent short words from matching everything.
    pub fn has_similar(&self, content: &str) -> bool {
        let content_lower = content.to_lowercase();
        
        // Skip short content - they're likely too generic to be useful memories
        if content_lower.len() < MIN_SIMILARITY_LENGTH {
            return false;
        }
        
        self.entries.iter().any(|e| {
            let entry_lower = e.content.to_lowercase();
            
            // Exact match
            if entry_lower == content_lower {
                return true;
            }
            
            // Skip comparing with short entries
            if entry_lower.len() < MIN_SIMILARITY_LENGTH {
                return false;
            }
            
            // Calculate word-based similarity (Jaccard-like)
            let similarity = Self::calculate_similarity(&entry_lower, &content_lower);
            similarity >= SIMILARITY_THRESHOLD
        })
    }

/// Calculate word-based similarity between two strings.
    /// Returns a value between 0.0 (no similarity) and 1.0 (identical).
    fn calculate_similarity(a: &str, b: &str) -> f64 {
        use std::collections::HashSet;
        
        let a_words: HashSet<&str> = a.split_whitespace().collect();
        let b_words: HashSet<&str> = b.split_whitespace().collect();
        
        if a_words.is_empty() || b_words.is_empty() {
            return 0.0;
        }
        
        let intersection = a_words.intersection(&b_words).count();
        let union = a_words.union(&b_words).count();
        
        if union == 0 {
            0.0
        } else {
            intersection as f64 / union as f64
        }
    }

    /// Remove low-importance entries when exceeding max_entries.
    /// Strategy: preserve manual entries + high importance entries, sorted by importance.
    pub fn prune(&mut self) {
        if self.entries.len() <= self.max_entries {
            return;
        }

        // First, separate entries by priority
        // Manual entries are always kept (highest priority)
        let (manual_entries, auto_entries): (Vec<_>, Vec<_>) = self.entries
            .iter()
            .cloned()
            .partition(|e| e.is_manual);
        
        // Sort auto entries by importance (descending) + recency as tiebreaker
        let mut sorted_auto = auto_entries;
        sorted_auto.sort_by(|a, b| {
            // First compare by importance
            let importance_cmp = b.importance.partial_cmp(&a.importance)
                .unwrap_or(std::cmp::Ordering::Equal);
            
            // If equal importance, prefer more recently referenced
            if importance_cmp == std::cmp::Ordering::Equal {
                b.last_referenced.cmp(&a.last_referenced)
            } else {
                importance_cmp
            }
        });
        
        // Filter auto entries above min_importance threshold
        let kept_auto: Vec<_> = sorted_auto
            .into_iter()
            .filter(|e| e.importance >= self.min_importance)
            .take(self.max_entries.saturating_sub(manual_entries.len()))
            .collect();
        
        // Combine: manual entries first, then sorted auto entries
        self.entries = manual_entries.into_iter().chain(kept_auto).collect();
        
        // Final safety check: if still too many, truncate oldest/least important
        if self.entries.len() > self.max_entries {
            self.entries.sort_by(|a, b| {
                let importance_cmp = b.importance.partial_cmp(&a.importance)
                    .unwrap_or(std::cmp::Ordering::Equal);
                if importance_cmp == std::cmp::Ordering::Equal {
                    b.last_referenced.cmp(&a.last_referenced)
                } else {
                    importance_cmp
                }
            });
            self.entries.truncate(self.max_entries);
        }
        
        self.invalidate_index();  // Index needs rebuild after prune
    }

    /// Get entries by category.
    pub fn by_category(&self, category: MemoryCategory) -> Vec<&MemoryEntry> {
        self.entries.iter().filter(|e| e.category == category).collect()
    }
    
    /// Get entries by category using index (faster).
    pub fn by_category_fast(&mut self, category: MemoryCategory) -> Vec<&MemoryEntry> {
        self.ensure_index();
        if let Some(ref index) = self.search_index {
            index.by_category.get(&category)
                .map(|indices| indices.iter().map(|&i| &self.entries[i]).collect())
                .unwrap_or_default()
        } else {
            self.by_category(category)
        }
    }

    /// Get top N most important entries.
    pub fn top_n(&self, n: usize) -> Vec<&MemoryEntry> {
        let mut sorted: Vec<_> = self.entries.iter().collect();
        sorted.sort_by(|a, b| b.importance.partial_cmp(&a.importance).unwrap_or(std::cmp::Ordering::Equal));
        sorted.into_iter().take(n).collect()
    }
    
    /// Get top N using index (faster).
    pub fn top_n_fast(&mut self, n: usize) -> Vec<&MemoryEntry> {
        self.ensure_index();
        if let Some(ref index) = self.search_index {
            index.by_importance
                .iter()
                .take(n)
                .map(|&i| &self.entries[i])
                .collect()
        } else {
            self.top_n(n)
        }
    }

    /// Search entries by content or tags.
    pub fn search(&self, query: &str) -> Vec<&MemoryEntry> {
        self.search_with_limit(query, None)
    }

    /// Search entries with result limit.
    pub fn search_with_limit(&self, query: &str, limit: Option<usize>) -> Vec<&MemoryEntry> {
        let query_lower = query.to_lowercase();
        let mut results: Vec<_> = self.entries
            .iter()
            .filter(|e| {
                e.content.to_lowercase().contains(&query_lower) ||
                e.tags.iter().any(|t| t.to_lowercase().contains(&query_lower))
            })
            .collect();
        
        // Sort by relevance (importance) then apply limit
        results.sort_by(|a, b| b.importance.partial_cmp(&a.importance).unwrap_or(std::cmp::Ordering::Equal));
        
        if let Some(max) = limit {
            results.into_iter().take(max).collect()
        } else {
            results
        }
    }
    
    /// Search using index (faster).
    pub fn search_fast(&mut self, query: &str, limit: Option<usize>) -> Vec<&MemoryEntry> {
        self.ensure_index();
        let query_lower = query.to_lowercase();
        
        if let Some(ref index) = self.search_index {
            let indices = index.search(&self.entries, &query_lower, limit);
            indices.iter().map(|&i| &self.entries[i]).collect()
        } else {
            self.search_with_limit(query, limit)
        }
    }

    /// Multi-keyword search (matches any keyword).
    pub fn search_multi(&self, keywords: &[&str]) -> Vec<&MemoryEntry> {
        if keywords.is_empty() {
            return Vec::new();
        }
        
        let keywords_lower: Vec<String> = keywords.iter().map(|k| k.to_lowercase()).collect();
        
        self.entries
            .iter()
            .filter(|e| {
                let content_lower = e.content.to_lowercase();
                keywords_lower.iter().any(|k| content_lower.contains(k))
            })
            .collect()
    }
    
    /// Multi-keyword search using index (faster).
    pub fn search_multi_fast(&mut self, keywords: &[&str]) -> Vec<&MemoryEntry> {
        if keywords.is_empty() {
            return Vec::new();
        }
        
        self.ensure_index();
        let keywords_lower: Vec<String> = keywords.iter().map(|k| k.to_lowercase()).collect();
        
        if let Some(ref index) = self.search_index {
            let indices = index.search_multi(&keywords_lower);
            indices.iter().map(|&i| &self.entries[i]).collect()
        } else {
            self.search_multi(keywords)
        }
    }

    /// Batch add multiple entries efficiently.
    /// Only prunes once at the end instead of after each entry.
    pub fn add_batch(&mut self, entries: Vec<MemoryEntry>) {
        // Filter out duplicates first
        for entry in entries {
            if !self.has_similar(&entry.content) {
                self.entries.push(entry);
            }
        }
        // Single prune at the end
        self.prune();
    }

    /// Mark entries as referenced if they appear in the conversation.
    /// Optimized: pre-computes lowercase versions to avoid repeated conversions.
    pub fn update_references(&mut self, messages: &[Message]) {
        let increment = self.config.reference_increment;
        
        // Pre-compute all message texts in lowercase (optimization)
        let texts_lower: Vec<String> = messages
            .iter()
            .filter_map(Self::extract_message_text_lower)
            .collect();
        
        // Pre-compute all entry contents in lowercase
        let entry_contents_lower: Vec<String> = self.entries
            .iter()
            .map(|e| e.content.to_lowercase())
            .collect();
        
        // Check each entry against all texts
        for (i, entry) in self.entries.iter_mut().enumerate() {
            let entry_lower = &entry_contents_lower[i];
            if texts_lower.iter().any(|t| t.contains(entry_lower)) {
                entry.mark_referenced_with_increment(increment);
            }
        }
    }
    
    /// Extract lowercase text from a message for reference checking.
    fn extract_message_text_lower(msg: &Message) -> Option<String> {
        match &msg.content {
            crate::providers::MessageContent::Text(t) => Some(t.to_lowercase()),
            crate::providers::MessageContent::Blocks(blocks) => {
                let text = blocks
                    .iter()
                    .filter_map(|b| {
                        if let crate::providers::ContentBlock::Text { text } = b {
                            Some(text.as_str())
                        } else {
                            None
                        }
                    })
                    .collect::<Vec<_>>()
                    .join(" ");
                Some(text.to_lowercase())
            }
        }
    }

    /// Generate summary for system prompt.
    pub fn generate_prompt_summary(&self, max_entries: usize) -> String {
        if self.entries.is_empty() {
            return String::new();
        }

        let top_entries = self.top_n(max_entries);
        if top_entries.is_empty() {
            return String::new();
        }

        let mut summary = String::from("【自动记忆摘要】\n\n");
        
        // Group by category
        let mut by_cat: HashMap<MemoryCategory, Vec<&MemoryEntry>> = HashMap::new();
        for entry in top_entries {
            by_cat.entry(entry.category).or_default().push(entry);
        }

        for (cat, entries) in by_cat {
            summary.push_str(&format!("{} {}:\n", cat.icon(), cat.display_name()));
            for entry in entries {
                summary.push_str(&format!("  {}\n", entry.format_for_prompt()));
            }
            summary.push('\n');
        }

        summary
    }

    /// Generate context-aware summary for system prompt.
    /// 
    /// Unlike `generate_prompt_summary` which always returns top N by importance,
    /// this method selects memories that are relevant to the current conversation context.
    /// 
    /// Strategy:
    /// 1. Always include manual entries (user explicitly added)
    /// 2. Include entries whose content overlaps with recent conversation keywords
    /// 3. Fill remaining slots with top importance entries
    pub fn generate_contextual_summary(&self, context: &str, max_entries: usize) -> String {
        // Extract keywords internally
        let keywords = extract_context_keywords(context);
        self.generate_contextual_summary_with_keywords(&keywords, max_entries)
    }
    
    /// Generate context-aware summary with pre-extracted keywords.
    /// More efficient when keywords are already extracted (e.g., by AI).
    pub fn generate_contextual_summary_with_keywords(&self, context_keywords: &[String], max_entries: usize) -> String {
        if self.entries.is_empty() {
            return String::new();
        }

        // Score each entry by relevance to context keywords
        let mut scored: Vec<(&MemoryEntry, f64)> = self.entries
            .iter()
            .map(|entry| {
                let relevance = compute_relevance(entry, &context_keywords);
                (entry, relevance)
            })
            .collect();
        
        // Sort by: manual first, then relevance + importance combined
        scored.sort_by(|a, b| {
            // Manual entries always first
            if a.0.is_manual && !b.0.is_manual {
                return std::cmp::Ordering::Less;
            }
            if !a.0.is_manual && b.0.is_manual {
                return std::cmp::Ordering::Greater;
            }
            
            // Combined score: relevance weight + importance weight
            let score_a = a.1 * CONTEXT_RELEVANCE_WEIGHT + (a.0.importance / MAX_IMPORTANCE_CEILING) * CONTEXT_IMPORTANCE_WEIGHT;
            let score_b = b.1 * CONTEXT_RELEVANCE_WEIGHT + (b.0.importance / MAX_IMPORTANCE_CEILING) * CONTEXT_IMPORTANCE_WEIGHT;
            
            score_b.partial_cmp(&score_a).unwrap_or(std::cmp::Ordering::Equal)
        });
        
        // Take top entries
        let selected: Vec<&MemoryEntry> = scored
            .iter()
            .take(max_entries)
            .map(|(entry, _)| *entry)
            .collect();
        
        if selected.is_empty() {
            return String::new();
        }

        let mut summary = String::from("【跨会话记忆】\n\n");
        
        // Group by category
        let mut by_cat: HashMap<MemoryCategory, Vec<&MemoryEntry>> = HashMap::new();
        for entry in selected {
            by_cat.entry(entry.category).or_default().push(entry);
        }

        for (cat, entries) in by_cat {
            summary.push_str(&format!("{} {}:\n", cat.icon(), cat.display_name()));
            for entry in entries {
                summary.push_str(&format!("  {}\n", entry.format_for_prompt()));
            }
            summary.push('\n');
        }

        summary
    }

    /// Generate context-aware summary with AI-enhanced keyword extraction.
    /// 
    /// This is the async version that uses AI to extract keywords when
    /// rule-based extraction produces insufficient results.
    pub async fn generate_contextual_summary_async(
        &self,
        context: &str,
        max_entries: usize,
        fast_provider: Option<&dyn crate::providers::Provider>,
    ) -> String {
        if self.entries.is_empty() {
            return String::new();
        }

        // Extract keywords using hybrid approach (rule-based + AI fallback)
        let context_keywords = if let Some(provider) = fast_provider {
            extract_keywords_hybrid(context, Some(provider)).await
        } else {
            extract_context_keywords(context)
        };
        
        // Score each entry by relevance to context
        let mut scored: Vec<(&MemoryEntry, f64)> = self.entries
            .iter()
            .map(|entry| {
                let relevance = compute_relevance(entry, &context_keywords);
                (entry, relevance)
            })
            .collect();
        
        // Sort by: manual first, then relevance + importance combined
        scored.sort_by(|a, b| {
            // Manual entries always first
            if a.0.is_manual && !b.0.is_manual {
                return std::cmp::Ordering::Less;
            }
            if !a.0.is_manual && b.0.is_manual {
                return std::cmp::Ordering::Greater;
            }
            
            // Combined score: relevance weight + importance weight
            let score_a = a.1 * CONTEXT_RELEVANCE_WEIGHT + (a.0.importance / MAX_IMPORTANCE_CEILING) * CONTEXT_IMPORTANCE_WEIGHT;
            let score_b = b.1 * CONTEXT_RELEVANCE_WEIGHT + (b.0.importance / MAX_IMPORTANCE_CEILING) * CONTEXT_IMPORTANCE_WEIGHT;
            
            score_b.partial_cmp(&score_a).unwrap_or(std::cmp::Ordering::Equal)
        });
        
        // Take top entries
        let selected: Vec<&MemoryEntry> = scored
            .iter()
            .take(max_entries)
            .map(|(entry, _)| *entry)
            .collect();
        
        if selected.is_empty() {
            return String::new();
        }

        let mut summary = String::from("【跨会话记忆】\n\n");
        
        // Group by category
        let mut by_cat: HashMap<MemoryCategory, Vec<&MemoryEntry>> = HashMap::new();
        for entry in selected {
            by_cat.entry(entry.category).or_default().push(entry);
        }

        for (cat, entries) in by_cat {
            summary.push_str(&format!("{} {}:\n", cat.icon(), cat.display_name()));
            for entry in entries {
                summary.push_str(&format!("  {}\n", entry.format_for_prompt()));
            }
            summary.push('\n');
        }

        summary
    }

    /// Format all entries for display.
    pub fn format_all(&self) -> String {
        if self.entries.is_empty() {
            return "[no memories accumulated]".to_string();
        }

        let mut result = String::from("Accumulated memories:\n\n");
        
        // Sort by importance
        let mut sorted: Vec<_> = self.entries.iter().collect();
        sorted.sort_by(|a, b| b.importance.partial_cmp(&a.importance).unwrap_or(std::cmp::Ordering::Equal));

        for entry in sorted {
            result.push_str(&entry.format_line());
            result.push('\n');
        }

        result
    }

    /// Generate statistics summary for display.
    pub fn generate_statistics(&self) -> MemoryStatistics {
        let total = self.entries.len();
        let manual = self.entries.iter().filter(|e| e.is_manual).count();
        let auto = total - manual;
        
        // Count by category
        let by_category: HashMap<MemoryCategory, usize> = self.entries
            .iter()
            .fold(HashMap::new(), |mut acc, e| {
                *acc.entry(e.category).or_default() += 1;
                acc
            });
        
        // Calculate average importance
        let avg_importance = if total > 0 {
            self.entries.iter().map(|e| e.importance).sum::<f64>() / total as f64
        } else {
            0.0
        };
        
        // Find oldest and newest
        let oldest = self.entries
            .iter()
            .min_by_key(|e| e.created_at)
            .map(|e| e.created_at);
        let newest = self.entries
            .iter()
            .max_by_key(|e| e.created_at)
            .map(|e| e.created_at);
        
        // Count highly referenced
        let highly_referenced = self.entries
            .iter()
            .filter(|e| e.reference_count >= 3)
            .count();
        
        MemoryStatistics {
            total,
            manual,
            auto,
            by_category,
            avg_importance,
            oldest,
            newest,
            highly_referenced,
        }
    }

    /// Clear all memories.
    pub fn clear(&mut self) {
        self.entries.clear();
        self.invalidate_index();
    }

    /// Remove a specific memory by ID.
    pub fn remove(&mut self, id: &str) -> bool {
        let idx = self.entries.iter().position(|e| e.id == id);
        if let Some(i) = idx {
            self.entries.remove(i);
            self.invalidate_index();
            true
        } else {
            false
        }
    }

    /// Apply time decay to memory importance.
    /// Entries that haven't been referenced recently will have their importance reduced.
    pub fn apply_time_decay(&mut self) {
        let now = Utc::now();
        let decay_start_days = self.config.decay_start_days;
        let decay_rate = self.config.decay_rate;
        let decay_period_days = 30;  // Each decay period is 30 days
        
        for entry in &mut self.entries {
            // Skip manual entries - they should never decay
            if entry.is_manual {
                continue;
            }
            
            // Calculate days since last reference
            let days_since_reference = (now - entry.last_referenced)
                .num_days()
                .max(0);
            
            // Apply decay if older than threshold
            if days_since_reference > decay_start_days {
                // Calculate number of decay periods
                let decay_periods = (days_since_reference - decay_start_days) / decay_period_days;
                
                // Apply exponential decay
                let decay_factor = decay_rate.powi(decay_periods as i32);
                entry.importance *= decay_factor;
                
                // Ensure minimum importance (at least half of min_importance)
                entry.importance = entry.importance.max(self.min_importance * 0.5);
            }
        }
        
        // Re-prune after decay (low importance entries may now be removed)
        self.prune();
    }
}

/// Statistics about memory collection.
#[derive(Debug, Clone)]
pub struct MemoryStatistics {
    /// Total number of entries.
    pub total: usize,
    /// Number of manually added entries.
    pub manual: usize,
    /// Number of automatically detected entries.
    pub auto: usize,
    /// Count by category.
    pub by_category: HashMap<MemoryCategory, usize>,
    /// Average importance score.
    pub avg_importance: f64,
    /// Oldest entry creation time.
    pub oldest: Option<DateTime<Utc>>,
    /// Newest entry creation time.
    pub newest: Option<DateTime<Utc>>,
    /// Number of entries with high reference count (>= 3).
    pub highly_referenced: usize,
}

impl MemoryStatistics {
    /// Format statistics for display.
    pub fn format_summary(&self) -> String {
        use std::fmt::Write;
        
        let mut output = String::new();
        
        writeln!(output, "记忆统计：").unwrap();
        writeln!(output, "  总计: {} 条", self.total).unwrap();
        writeln!(output, "  ├─ 手动添加: {} 条", self.manual).unwrap();
        writeln!(output, "  └─ 自动检测: {} 条", self.auto).unwrap();
        writeln!(output).unwrap();
        
        writeln!(output, "分类统计：").unwrap();
        for (cat, count) in &self.by_category {
            writeln!(output, "  {} {}: {} 条", cat.icon(), cat.display_name(), count).unwrap();
        }
        writeln!(output).unwrap();
        
        writeln!(output, "质量指标：").unwrap();
        writeln!(output, "  平均重要性: {:.1} 分", self.avg_importance).unwrap();
        writeln!(output, "  高频引用: {} 条 (≥3次)", self.highly_referenced).unwrap();
        
        if let Some(oldest) = self.oldest {
            let days = (Utc::now() - oldest).num_days();
            writeln!(output, "  记忆跨度: {} 天", days).unwrap();
        }
        
        output
    }
}

// ============================================================================
// Memory Storage with File Lock
// ============================================================================

/// File lock for preventing concurrent access to memory storage.
/// Uses a simple lock file approach (.lock) with atomic operations.
pub struct MemoryFileLock {
    /// Path to the lock file.
    lock_path: PathBuf,
    /// Whether we currently hold the lock.
    locked: bool,
}

impl MemoryFileLock {
    /// Create a new file lock for the given directory.
    pub fn new(base_dir: &Path) -> Self {
        Self {
            lock_path: base_dir.join("memory.lock"),
            locked: false,
        }
    }
    
    /// Acquire the lock (blocking with timeout).
    /// Returns true if lock was acquired, false if timeout expired.
    pub fn acquire(&mut self, timeout_ms: u64) -> Result<bool> {
        if self.locked {
            return Ok(true);  // Already locked
        }
        
        let start = std::time::Instant::now();
        
        while start.elapsed().as_millis() < timeout_ms as u128 {
            // Try to create lock file atomically
            match fs::File::create_new(&self.lock_path) {
                Ok(_) => {
                    // Write lock info (PID + timestamp)
                    let lock_info = format!(
                        "{}:{}",
                        std::process::id(),
                        Utc::now().to_rfc3339()
                    );
                    fs::write(&self.lock_path, lock_info)?;
                    self.locked = true;
                    return Ok(true);
                }
                Err(e) if e.kind() == std::io::ErrorKind::AlreadyExists => {
                    // Lock file exists, check if it's stale
                    if self.is_stale_lock()? {
                        self.remove_stale_lock()?;
                    }
                    // Wait a bit before retrying
                    std::thread::sleep(std::time::Duration::from_millis(50));
                }
                Err(e) => {
                    return Err(e.into());
                }
            }
        }
        
        Ok(false)  // Timeout expired
    }
    
    /// Check if the existing lock is stale (older than 30 seconds).
    fn is_stale_lock(&self) -> Result<bool> {
        if !self.lock_path.exists() {
            return Ok(false);
        }
        
        // Check lock file age
        let metadata = fs::metadata(&self.lock_path)?;
        let modified = metadata.modified()?;
        let age = std::time::SystemTime::now()
            .duration_since(modified)
            .unwrap_or(std::time::Duration::ZERO);
        
        // Consider lock stale if older than 30 seconds
        Ok(age > std::time::Duration::from_secs(30))
    }
    
    /// Remove stale lock file.
    fn remove_stale_lock(&self) -> Result<()> {
        if self.lock_path.exists() {
            fs::remove_file(&self.lock_path)?;
        }
        Ok(())
    }
    
    /// Release the lock.
    pub fn release(&mut self) -> Result<()> {
        if self.locked {
            fs::remove_file(&self.lock_path)?;
            self.locked = false;
        }
        Ok(())
    }
}

impl Drop for MemoryFileLock {
    fn drop(&mut self) {
        // Auto-release lock on drop
        let _ = self.release();
    }
}

/// Storage for memory files (global and project-level) with file locking.
pub struct MemoryStorage {
    /// Base directory for global memory (~/.matrix).
    base_dir: PathBuf,
    /// Project root directory (optional).
    project_root: Option<PathBuf>,
    /// File lock for preventing concurrent writes.
    lock: MemoryFileLock,
}

impl MemoryStorage {
    /// Create a new memory storage.
    pub fn new(project_root: Option<&Path>) -> Result<Self> {
        let base_dir = Self::get_base_dir()?;
        let lock = MemoryFileLock::new(&base_dir);
        Ok(Self {
            base_dir,
            project_root: project_root.map(|p| p.to_path_buf()),
            lock,
        })
    }

    /// Create a new storage with explicit lock timeout.
    pub fn with_lock_timeout(project_root: Option<&Path>, timeout_ms: u64) -> Result<Self> {
        let mut storage = Self::new(project_root)?;
        storage.lock.acquire(timeout_ms)?;
        Ok(storage)
    }

    /// Get the base directory for memory storage.
    fn get_base_dir() -> Result<PathBuf> {
        let home = std::env::var_os("HOME")
            .or_else(|| std::env::var_os("USERPROFILE"))
            .ok_or_else(|| anyhow::anyhow!("HOME or USERPROFILE not set"))?;
        let mut p = PathBuf::from(home);
        p.push(".matrix");
        Ok(p)
    }

    /// Path to global memory file.
    pub fn global_memory_path(&self) -> PathBuf {
        self.base_dir.join("memory.json")
    }

    /// Path to project memory file.
    pub fn project_memory_path(&self) -> Option<PathBuf> {
        self.project_root.as_ref().map(|p| p.join(".matrix/memory.json"))
    }

    /// Path to config file.
    pub fn config_path(&self) -> PathBuf {
        self.base_dir.join("memory_config.json")
    }

    /// Ensure directories exist.
    fn ensure_dirs(&self) -> Result<()> {
        fs::create_dir_all(&self.base_dir)?;
        if let Some(root) = &self.project_root {
            let memory_dir = root.join(".matrix");
            fs::create_dir_all(memory_dir)?;
        }
        Ok(())
    }

    /// Acquire lock before write operations.
    fn acquire_lock(&mut self) -> Result<()> {
        self.lock.acquire(5000)?;  // 5 second timeout
        Ok(())
    }

    /// Release lock after write operations.
    fn release_lock(&mut self) -> Result<()> {
        self.lock.release()?;
        Ok(())
    }

    /// Load global memory (no lock needed for read).
    pub fn load_global(&self) -> Result<AutoMemory> {
        let path = self.global_memory_path();
        if !path.exists() {
            return Ok(AutoMemory::new());
        }
        let data = fs::read_to_string(&path)?;
        let memory: AutoMemory = serde_json::from_str(&data)?;
        Ok(memory)
    }

    /// Load project memory (no lock needed for read).
    pub fn load_project(&self) -> Result<Option<AutoMemory>> {
        let path = self.project_memory_path();
        match path {
            Some(p) if p.exists() => {
                let data = fs::read_to_string(&p)?;
                let memory: AutoMemory = serde_json::from_str(&data)?;
                Ok(Some(memory))
            }
            _ => Ok(None),
        }
    }

    /// Load combined memory (global + project).
    pub fn load_combined(&self) -> Result<AutoMemory> {
        let mut combined = self.load_global()?;
        
        if let Some(project) = self.load_project()? {
            // Merge project entries into global
            for entry in project.entries {
                // Tag as project-specific
                let mut tagged_entry = entry;
                if !tagged_entry.tags.contains(&"project".to_string()) {
                    tagged_entry.tags.push("project".to_string());
                }
                combined.entries.push(tagged_entry);
            }
            combined.prune();
        }

        Ok(combined)
    }

    /// Save global memory (with file lock).
    pub fn save_global(&mut self, memory: &AutoMemory) -> Result<()> {
        self.acquire_lock()?;
        self.ensure_dirs()?;
        
        let path = self.global_memory_path();
        let json = serde_json::to_string_pretty(memory)?;
        
        // Write to temp file then rename (atomic)
        let tmp = path.with_extension("json.tmp");
        fs::write(&tmp, json)?;
        fs::rename(&tmp, &path)?;
        
        self.release_lock()?;
        Ok(())
    }

    /// Save project memory (with file lock).
    pub fn save_project(&mut self, memory: &AutoMemory) -> Result<()> {
        self.acquire_lock()?;
        self.ensure_dirs()?;
        
        let path = self.project_memory_path()
            .ok_or_else(|| anyhow::anyhow!("no project root"))?;
        let json = serde_json::to_string_pretty(memory)?;
        
        let tmp = path.with_extension("json.tmp");
        fs::write(&tmp, json)?;
        fs::rename(&tmp, &path)?;
        
        self.release_lock()?;
        Ok(())
    }

    /// Save config to separate file.
    pub fn save_config(&mut self, config: &MemoryConfig) -> Result<()> {
        self.ensure_dirs()?;
        let path = self.config_path();
        let json = serde_json::to_string_pretty(config)?;
        fs::write(&path, json)?;
        Ok(())
    }

    /// Load config from file.
    pub fn load_config(&self) -> Result<MemoryConfig> {
        let path = self.config_path();
        if !path.exists() {
            return Ok(MemoryConfig::default());
        }
        let data = fs::read_to_string(&path)?;
        let config: MemoryConfig = serde_json::from_str(&data)?;
        Ok(config)
    }

    /// Add entry to appropriate storage (with file lock).
    pub fn add_entry(&mut self, entry: MemoryEntry, is_project_specific: bool) -> Result<()> {
        self.acquire_lock()?;
        
        if is_project_specific {
            let mut project = self.load_project()?.unwrap_or_else(AutoMemory::new);
            project.add(entry);
            self.save_project_locked(&project)?;
        } else {
            let mut global = self.load_global()?;
            global.add(entry);
            self.save_global_locked(&global)?;
        }
        
        self.release_lock()?;
        Ok(())
    }

    /// Remove entry from storage by ID (with file lock).
    pub fn remove_entry(&mut self, id: &str, is_project_specific: bool) -> Result<bool> {
        self.acquire_lock()?;
        
        let removed = if is_project_specific {
            if let Some(mut project) = self.load_project()? {
                let removed = project.remove(id);
                if removed {
                    self.save_project_locked(&project)?;
                }
                removed
            } else {
                false
            }
        } else {
            let mut global = self.load_global()?;
            let removed = global.remove(id);
            if removed {
                self.save_global_locked(&global)?;
            }
            removed
        };
        
        self.release_lock()?;
        Ok(removed)
    }
    
    /// Internal save methods that don't acquire lock (assumed already locked).
    fn save_global_locked(&self, memory: &AutoMemory) -> Result<()> {
        let path = self.global_memory_path();
        let json = serde_json::to_string_pretty(memory)?;
        let tmp = path.with_extension("json.tmp");
        fs::write(&tmp, json)?;
        fs::rename(&tmp, &path)?;
        Ok(())
    }
    
    fn save_project_locked(&self, memory: &AutoMemory) -> Result<()> {
        let path = self.project_memory_path()
            .ok_or_else(|| anyhow::anyhow!("no project root"))?;
        let json = serde_json::to_string_pretty(memory)?;
        let tmp = path.with_extension("json.tmp");
        fs::write(&tmp, json)?;
        fs::rename(&tmp, &path)?;
        Ok(())
    }
}

// ============================================================================
// Helper Functions (Global)
// ============================================================================

/// Calculate word-based similarity between two strings (Jaccard coefficient).
/// Returns a value between 0.0 (no similarity) and 1.0 (identical words).
/// This is the public version for external use.
pub fn calculate_similarity(a: &str, b: &str) -> f64 {
    AutoMemory::calculate_similarity(a, b)
}

/// Extract meaningful keywords from conversation context.
/// Filters out common stop words and short tokens.
/// Public for external use (e.g., TUI keyword display).
pub fn extract_context_keywords(context: &str) -> Vec<String> {
    use std::collections::HashSet;
    
    // Common stop words (Chinese + English)
    let stop_words: HashSet<&str> = [
        // Chinese stop words
        "的", "了", "是", "在", "我", "有", "和", "就", "不", "人", "都", "一", "一个",
        "上", "也", "很", "到", "说", "要", "去", "你", "会", "着", "没有", "看", "好",
        "自己", "这", "他", "她", "它", "们", "那", "些", "什么", "怎么", "如何", "请",
        "能", "可以", "需要", "应该", "可能", "因为", "所以", "但是", "然后", "还是",
        "已经", "正在", "将要", "曾经", "一下", "一点", "一些", "所有", "每个", "任何",
        // English stop words
        "the", "a", "an", "is", "are", "was", "were", "be", "been", "being",
        "have", "has", "had", "do", "does", "did", "will", "would", "could",
        "should", "may", "might", "can", "shall", "to", "of", "in", "for",
        "on", "with", "at", "by", "from", "as", "into", "through", "during",
        "before", "after", "above", "below", "between", "and", "but", "or",
        "not", "no", "so", "if", "then", "than", "too", "very", "just",
        "this", "that", "these", "those", "it", "its", "i", "me", "my",
        "we", "our", "you", "your", "he", "his", "she", "her", "they", "their",
        "please", "help", "need", "want", "make", "get", "let", "use",
    ].iter().copied().collect();
    
    // Technical/meaningful patterns to extract (Chinese + English)
    let tech_patterns: HashSet<&str> = [
        // Technical terms (keep these even if short)
        "api", "cli", "gui", "tui", "web", "http", "json", "xml", "sql", "db",
        "git", "npm", "cargo", "rust", "js", "ts", "py", "go", "java", "cpp",
        "cpu", "gpu", "io", "fs", "os", "ui", "ux", "ai", "ml", "dl",
        // File extensions
        "rs", "js", "ts", "py", "go", "java", "c", "h", "cpp", "hpp",
        "json", "yaml", "yml", "toml", "md", "txt", "html", "css", "scss",
        // Short meaningful words
        "bug", "fix", "add", "new", "old", "use", "run", "build", "test",
        "code", "data", "file", "dir", "path", "name", "type", "value",
    ].iter().copied().collect();
    
    let lower = context.to_lowercase();
    let mut keywords: HashSet<String> = HashSet::new();
    
    // 1. Extract English words (space-separated)
    for word in lower.split_whitespace() {
        let cleaned = word.trim_matches(|c: char| !c.is_alphanumeric()).to_string();
        if cleaned.len() >= 2 && !stop_words.contains(cleaned.as_str()) {
            keywords.insert(cleaned.clone());
        }
        // Keep technical short words
        if tech_patterns.contains(cleaned.as_str()) {
            keywords.insert(cleaned);
        }
    }
    
    // 2. Extract Chinese words/phrases (2-4 character sequences)
    // Chinese characters are typically 3 bytes in UTF-8
    let chinese_chars: Vec<char> = lower
        .chars()
        .filter(|c| *c >= '\u{4E00}' && *c <= '\u{9FFF}')  // Chinese Unicode range
        .collect();
    
    // Extract 2-4 character Chinese sequences
    for window_size in 2..=4 {
        if chinese_chars.len() >= window_size {
            for window in chinese_chars.windows(window_size) {
                let phrase: String = window.iter().collect();
                // Skip if contains stop words
                let has_stop = stop_words.iter().any(|sw| phrase.contains(sw));
                if !has_stop && phrase.len() >= window_size {
                    keywords.insert(phrase);
                }
            }
        }
    }
    
    // 3. Extract specific patterns (project names, file names, etc.)
    // Look for common project/file patterns
    let patterns = [
        // File paths
        r"[a-zA-Z_][a-zA-Z0-9_]*\.[a-zA-Z]{1,4}",  // file.ext
        r"[a-zA-Z_][a-zA-Z0-9_]*\.[a-zA-Z_][a-zA-Z0-9_]*",  // module.submodule
        // CamelCase/snake_case identifiers
        r"[A-Z][a-z]+[A-Z][a-zA-Z]*",  // CamelCase
        r"[a-z][a-z0-9]*_[a-z][a-z0-9_]*",  // snake_case
        // Numbers with units
        r"[0-9]+[kKmMgGtT][bB]?",  // 4K, 100MB
    ];
    
    for pattern in patterns {
        if let Ok(re) = regex::Regex::new(pattern) {
            for cap in re.find_iter(&lower) {
                keywords.insert(cap.as_str().to_string());
            }
        }
    }
    
    // Convert to vector and sort by length (prefer longer, more specific keywords)
    let mut result: Vec<String> = keywords.into_iter().collect();
    result.sort_by(|a, b| b.len().cmp(&a.len()));
    
    // Take top keywords (avoid too many)
    result.truncate(15);
    
    result
}

/// Compute relevance score of a memory entry to context keywords.
/// Returns 0.0-1.0 where 1.0 means highly relevant.
fn compute_relevance(entry: &MemoryEntry, context_keywords: &[String]) -> f64 {
    if context_keywords.is_empty() {
        return 0.0;
    }
    
    let content_lower = entry.content.to_lowercase();
    
    // Count how many context keywords appear in this entry
    let matches = context_keywords
        .iter()
        .filter(|kw| content_lower.contains(kw.as_str()))
        .count();
    
    // Normalize by total keywords (0.0-1.0)
    let keyword_score = matches as f64 / context_keywords.len() as f64;
    
    // Boost for tag matches
    let tag_matches = entry.tags
        .iter()
        .filter(|tag| {
            let tag_lower = tag.to_lowercase();
            context_keywords.iter().any(|kw| tag_lower.contains(kw.as_str()))
        })
        .count();
    
    let tag_score = if tag_matches > 0 { 0.2 } else { 0.0 };
    
    // Combined score (capped at 1.0)
    (keyword_score + tag_score).min(1.0)
}

/// Detect if two memory contents have contradiction signals.
/// 
/// Contradiction patterns:
/// - Same verb/action but different object ("使用 PostgreSQL" vs "使用 MySQL")
/// - Negation patterns ("不用 X" vs "使用 X")
/// - Replacement patterns ("改用", "换成", "替换为")
fn has_contradiction_signal(old: &str, new: &str) -> bool {
    // Check for replacement/change keywords in new content
    let change_signals = [
        "改用", "换成", "替换", "改为", "切换到", "迁移到",
        "不再使用", "弃用", "放弃", "取消",
        "switched to", "replaced", "migrated to", "changed to",
        "no longer", "deprecated", "abandoned",
    ];
    
    for signal in &change_signals {
        if new.contains(signal) {
            return true;
        }
    }
    
    // Check for same action verb but different object
    // e.g., "决定使用 PostgreSQL" vs "决定使用 MySQL"
    let action_verbs = [
        "决定使用", "选择使用", "采用", "使用",
        "decided to use", "chose", "using", "adopted",
    ];
    
    for verb in &action_verbs {
        if old.contains(verb) && new.contains(verb) {
            // Both have the same action verb - likely a conflict
            // (if they were the same thing, has_similar would have caught it)
            return true;
        }
    }
    
    // Check for preference conflicts
    let pref_verbs = ["偏好", "喜欢", "prefer", "like"];
    for verb in &pref_verbs {
        if old.contains(verb) && new.contains(verb) {
            return true;
        }
    }
    
    false
}

// ============================================================================
// AI-Based Memory Extraction
// ============================================================================

/// Trait for memory extraction implementations.
#[async_trait::async_trait]
pub trait MemoryExtractor: Send + Sync {
    /// Extract memories from conversation text using AI.
    async fn extract(&self, text: &str, session_id: Option<&str>) -> Result<Vec<MemoryEntry>>;
    
    /// Get the model name used for extraction.
    fn model_name(&self) -> &str;
}

/// AI-based memory extractor using a fast/cheap model.
pub struct AiMemoryExtractor {
    provider: Box<dyn crate::providers::Provider>,
    model: String,
}

impl AiMemoryExtractor {
    /// Create a new AI memory extractor.
    pub fn new(provider: Box<dyn crate::providers::Provider>, model: String) -> Self {
        Self { provider, model }
    }
}

/// System prompt for memory extraction.
const MEMORY_EXTRACT_SYSTEM_PROMPT: &str = r#"你是一个记忆提取助手。你的任务是从对话中识别并提取值得长期记忆的关键信息。

记忆类型：
1. Decision（决策）: 项目或技术选型的决定，如"决定使用 PostgreSQL"
2. Preference（偏好）: 用户习惯或偏好，如"我喜欢用 vim"
3. Solution（解决方案）: 解决问题的具体方法，如"通过添加 middleware 修复 bug"
4. Finding（发现）: 重要发现或信息，如"API 端点在 /api/v2"
5. Technical（技术）: 技术栈或框架信息，如"使用 React Query 做数据获取"
6. Structure（结构）: 项目结构信息，如"入口文件是 src/index.ts"

提取原则：
- 只提取有价值、可复用的信息
- 避免提取临时性、一次性信息
- 避免提取过于具体的代码细节
- 每条记忆应简洁明确（一句话）
- 最多提取 5 条记忆

输出格式（严格 JSON）：
```json
{
  "memories": [
    {
      "category": "decision",
      "content": "决定使用 PostgreSQL 作为主数据库",
      "importance": 90
    },
    {
      "category": "preference", 
      "content": "用户偏好 TypeScript 而非 JavaScript",
      "importance": 70
    }
  ]
}
```

如果没有值得记忆的内容，返回：
```json
{"memories": []}
```

直接输出 JSON，不要加代码块包裹。"#;

#[async_trait::async_trait]
impl MemoryExtractor for AiMemoryExtractor {
    async fn extract(&self, text: &str, session_id: Option<&str>) -> Result<Vec<MemoryEntry>> {
        use crate::providers::{ChatRequest, Message, MessageContent, Role};
        
        // Truncate text if too long (memory extraction focuses on key points)
        let truncated_text = if text.len() > 4000 {
            truncate_str(text, 4000)
        } else {
            text.to_string()
        };
        
        let request = ChatRequest {
            messages: vec![Message {
                role: Role::User,
                content: MessageContent::Text(format!(
                    "请从以下对话中提取值得记忆的关键信息：\n\n{}", 
                    truncated_text
                )),
            }],
            tools: vec![],  // No tools for memory extraction
            system: Some(MEMORY_EXTRACT_SYSTEM_PROMPT.to_string()),
            think: false,   // No extended thinking
            max_tokens: 512, // Short response
            server_tools: vec![],
            enable_caching: false,
        };
        
        let response = self.provider.chat(request).await?;
        
        // Extract text from response
        let response_text = response.content
            .iter()
            .filter_map(|block| {
                if let crate::providers::ContentBlock::Text { text } = block {
                    Some(text.clone())
                } else {
                    None
                }
            })
            .collect::<Vec<_>>()
            .join("");
        
        // Parse JSON response
        parse_memory_response(&response_text, session_id)
    }
    
    fn model_name(&self) -> &str {
        &self.model
    }
}

/// Parse AI response into memory entries.
fn parse_memory_response(json_text: &str, session_id: Option<&str>) -> Result<Vec<MemoryEntry>> {
    // Clean up response (remove possible markdown code blocks)
    let cleaned = json_text
        .trim()
        .trim_start_matches("```json")
        .trim_start_matches("```")
        .trim_end_matches("```")
        .trim();
    
    // Parse JSON
    #[derive(serde::Deserialize)]
    struct MemoryResponse {
        memories: Vec<MemoryItem>,
    }
    
    #[derive(serde::Deserialize)]
    struct MemoryItem {
        category: String,
        content: String,
        #[serde(default)]
        importance: f64,
    }
    
    let parsed: MemoryResponse = serde_json::from_str(cleaned)?;
    
    // Convert to MemoryEntry
    let entries = parsed.memories
        .into_iter()
        .filter_map(|item| {
            // Parse category
            let category = match item.category.to_lowercase().as_str() {
                "decision" => MemoryCategory::Decision,
                "preference" => MemoryCategory::Preference,
                "solution" => MemoryCategory::Solution,
                "finding" => MemoryCategory::Finding,
                "technical" => MemoryCategory::Technical,
                "structure" => MemoryCategory::Structure,
                _ => return None,  // Skip unknown categories
            };
            
            // Skip too short content
            if item.content.len() < MIN_MEMORY_CONTENT_LENGTH {
                return None;
            }
            
            // Create entry with AI-suggested importance or default
            let mut entry = MemoryEntry::new(
                category,
                item.content,
                session_id.map(|s| s.to_string()),
            );
            
            // Override importance if AI suggested a value
            if item.importance > 0.0 {
                entry.importance = item.importance.clamp(0.0, 100.0);
            }
            
            Some(entry)
        })
        .collect();
    
    // Deduplicate and limit
    Ok(deduplicate_entries(entries))
}

// ============================================================================
// AI-Based Keyword Extraction (for context-aware memory retrieval)
// ============================================================================

/// System prompt for AI keyword extraction.
const KEYWORD_EXTRACT_SYSTEM_PROMPT: &str = r#"你是一个关键词提取助手。你的任务是从用户输入中提取有意义的关键词，用于检索相关记忆。

提取原则：
1. 只提取有实际意义的词汇（技术名词、项目名、概念等）
2. 过滤掉常见的停用词（的、是、在、我、你、the、a、is 等）
3. 保留专有名词和技术术语
4. 中英文混合输入时，两种语言的关键词都提取
5. 提取 3-10 个关键词

输出格式（严格 JSON）：
```json
{
  "keywords": ["数据库", "PostgreSQL", "优化", "查询"]
}
```

如果没有有意义的关键词，返回：
```json
{"keywords": []}
```

直接输出 JSON，不要加代码块包裹。"#;

/// Extract keywords from context using AI (for context-aware memory retrieval).
/// 
/// This is used when the rule-based keyword extraction produces too few results
/// or when the context is complex and needs better understanding.
pub async fn extract_keywords_with_ai(
    context: &str,
    provider: &dyn crate::providers::Provider,
) -> Result<Vec<String>> {
    use crate::providers::{ChatRequest, Message, MessageContent, Role};
    
    // Truncate if too long
    let truncated = if context.len() > 1000 {
        truncate_str(context, 1000)
    } else {
        context.to_string()
    };
    
    let request = ChatRequest {
        messages: vec![Message {
            role: Role::User,
            content: MessageContent::Text(format!(
                "请从以下文本中提取关键词：\n\n{}", 
                truncated
            )),
        }],
        tools: vec![],
        system: Some(KEYWORD_EXTRACT_SYSTEM_PROMPT.to_string()),
        think: false,
        max_tokens: 256,
        server_tools: vec![],
        enable_caching: false,
    };
    
    let response = provider.chat(request).await?;
    
    // Extract text from response
    let response_text = response.content
        .iter()
        .filter_map(|block| {
            if let crate::providers::ContentBlock::Text { text } = block {
                Some(text.clone())
            } else {
                None
            }
        })
        .collect::<Vec<_>>()
        .join("");
    
    // Parse JSON response
    parse_keyword_response(&response_text)
}

/// Parse AI keyword extraction response.
fn parse_keyword_response(json_text: &str) -> Result<Vec<String>> {
    // Clean up response
    let cleaned = json_text
        .trim()
        .trim_start_matches("```json")
        .trim_start_matches("```")
        .trim_end_matches("```")
        .trim();
    
    #[derive(serde::Deserialize)]
    struct KeywordResponse {
        keywords: Vec<String>,
    }
    
    let parsed: KeywordResponse = serde_json::from_str(cleaned)?;
    
    // Filter out empty or too-short keywords
    Ok(parsed.keywords
        .into_iter()
        .filter(|k| k.len() >= 2)
        .collect())
}

/// Extract keywords from context with hybrid approach.
/// 
/// Strategy:
/// 1. First use rule-based stop word filtering (fast, zero cost)
/// 2. If result is insufficient (too few keywords), fall back to AI extraction
/// 3. Behavior controlled by MEMORY_AI_KEYWORDS env var (auto/always/never)
pub async fn extract_keywords_hybrid(
    context: &str,
    fast_provider: Option<&dyn crate::providers::Provider>,
) -> Vec<String> {
    // Get AI keyword extraction mode from environment
    let mode = AiKeywordMode::from_env();
    
    // If mode is Never, skip AI entirely
    if mode == AiKeywordMode::Never {
        return extract_context_keywords(context);
    }
    
    // Step 1: Try rule-based extraction first (unless mode is Always)
    let keywords = if mode == AiKeywordMode::Always {
        Vec::new()  // Skip rule-based when Always mode
    } else {
        extract_context_keywords(context)
    };
    
    // Step 2: Check if we should use AI based on mode and keyword count
    if !mode.should_use_ai(keywords.len()) {
        return keywords;
    }
    
    // Step 3: If we should use AI and have a provider, do AI extraction
    if let Some(provider) = fast_provider {
        match extract_keywords_with_ai(context, provider).await {
            Ok(ai_keywords) if !ai_keywords.is_empty() => {
                log::debug!("AI extracted {} keywords: {:?}", ai_keywords.len(), ai_keywords);
                // In Auto mode, merge AI keywords with rule-based ones
                if mode == AiKeywordMode::Auto && !keywords.is_empty() {
                    let merged = keywords
                        .into_iter()
                        .chain(ai_keywords.into_iter())
                        .collect::<std::collections::HashSet<_>>();
                    return merged.into_iter().collect();
                }
                return ai_keywords;
            }
            Ok(_) => {
                log::debug!("AI returned no keywords, keeping rule-based results");
            }
            Err(e) => {
                log::warn!("AI keyword extraction failed: {}, keeping rule-based results", e);
            }
        }
    }
    
    // Return whatever we have (rule-based results)
    keywords
}

// ============================================================================
// AI-Enhanced Memory Processing
// ============================================================================

/// System prompt for AI memory summarization.
const MEMORY_SUMMARY_SYSTEM_PROMPT: &str = r#"你是一个记忆摘要助手。你的任务是将多条相关记忆合并为一条精炼的摘要记忆。

摘要原则：
1. 保留核心信息，去除冗余细节
2. 使用简洁明确的一句话表达
3. 保留关键的技术名词和决策结论
4. 如果多条记忆主题相同，合并为一条综合性记忆
5. 优先保留高价值的决策和解决方案

输出格式（严格 JSON）：
```json
{
  "summary": "决定使用 PostgreSQL 作为主数据库，Redis 作为缓存层",
  "category": "decision",
  "importance": 90
}
```

如果没有值得保留的信息，返回：
```json
{"summary": "", "category": "", "importance": 0}
```

直接输出 JSON，不要加代码块包裹。"#;

/// System prompt for AI conflict detection.
const MEMORY_CONFLICT_SYSTEM_PROMPT: &str = r#"你是一个记忆冲突检测助手。你的任务是判断两条记忆是否矛盾或需要更新。

冲突类型：
1. 直接矛盾：两条记忆结论相反（如"使用 PostgreSQL" vs "使用 MySQL"）
2. 过时更新：新记忆明确替换旧记忆（如"改用 Redis" 替换 "使用 Memcached"）
3. 补充关系：新记忆补充旧记忆（如"PostgreSQL 版本为 15" 补充 "使用 PostgreSQL"）
4. 无关关系：两条记忆主题不同，不冲突

输出格式（严格 JSON）：
```json
{
  "conflict_type": "direct_conflict",
  "should_replace": true,
  "reason": "两条记忆都是数据库选型决策，但选择了不同的数据库",
  "winner": "new"
}
```

conflict_type 可选值：
- "direct_conflict": 直接矛盾，需要选择一条
- "outdated_update": 过时更新，新记忆替换旧记忆
- "supplement": 补充关系，两者可共存
- "no_conflict": 无关关系，不冲突

should_replace: true 表示需要替换旧记忆，false 表示保留两者
winner: "new" 表示新记忆胜出，"old" 表示旧记忆胜出（仅在 direct_conflict 时有意义）

直接输出 JSON，不要加代码块包裹。"#;

/// System prompt for AI memory quality assessment.
const MEMORY_QUALITY_SYSTEM_PROMPT: &str = r#"你是一个记忆质量评估助手。你的任务是评估记忆的长期价值和重要程度。

评估维度：
1. 复用价值：这条信息在未来的���话中会被引用吗？
2. 决策权重：这是重要的项目决策还是次要细节？
3. 时效性：这条信息会很快过时吗？
4. 独特性：这条信息是否足够独特，不与其他记忆重叠？

评分标准：
- 90-100: 核心决策，长期有效，高复用价值（如数据库选型、框架选择）
- 70-89: 重要偏好或解决方案，中等复用价值
- 50-69: 有用的技术信息或发现，时效性中等
- 30-49: 一般性信息，复用价值较低
- 0-29: 过时或过于具体的细节，建议丢弃

输出格式（严格 JSON）：
```json
{
  "quality_score": 85,
  "reason": "这是核心的技术选型决策，长期有效，高复用价值",
  "should_keep": true,
  "suggested_category": "decision"
}
```

直接输出 JSON，不要加代码块包裹。"#;

/// System prompt for AI memory merge.
const MEMORY_MERGE_SYSTEM_PROMPT: &str = r#"你是一个记忆合并助手。你的任务是将多条相似或相关的记忆合并为一条精炼的记忆。

合并原则：
1. 相同主题的记忆应合并为一条综合性记忆
2. 保留所有关键信息，去除重复内容
3. 使用简洁的一句话表达
4. 合并后的记忆应比原记忆更全面但更简洁
5. 如果记忆完全不相关，返回空结果表示不应合并

输出格式（严格 JSON）：
```json
{
  "merged_content": "使用 PostgreSQL 作为主数据库（版本15），Redis 作为缓存层，通过连接池优化性能",
  "category": "technical",
  "importance": 75,
  "merged_from_count": 3,
  "summary_reason": "三条记忆都与数据库和缓存技术栈相关，合并为一条综合性技术栈记忆"
}
```

如果不应合并，返回：
```json
{"merged_content": "", "category": "", "importance": 0, "merged_from_count": 0, "summary_reason": "记忆主题不同，不应合并"}
```

直接输出 JSON，不要加代码块包裹。"#;

/// Result of AI memory summarization.
#[derive(Debug, Clone, serde::Deserialize)]
pub struct MemorySummaryResult {
    pub summary: String,
    pub category: String,
    pub importance: f64,
}

/// Result of AI conflict detection.
#[derive(Debug, Clone, serde::Deserialize)]
pub struct MemoryConflictResult {
    pub conflict_type: String,
    pub should_replace: bool,
    pub reason: String,
    pub winner: Option<String>,
}

/// Result of AI quality assessment.
#[derive(Debug, Clone, serde::Deserialize)]
pub struct MemoryQualityResult {
    pub quality_score: f64,
    pub reason: String,
    pub should_keep: bool,
    pub suggested_category: Option<String>,
}

/// Result of AI memory merge.
#[derive(Debug, Clone, serde::Deserialize)]
pub struct MemoryMergeResult {
    pub merged_content: String,
    pub category: String,
    pub importance: f64,
    pub merged_from_count: usize,
    pub summary_reason: String,
}

/// AI-enhanced memory processor.
/// Provides advanced memory operations using AI.
pub struct AiMemoryProcessor {
    provider: Box<dyn crate::providers::Provider>,
    model: String,
}

impl AiMemoryProcessor {
    /// Create a new AI memory processor.
    pub fn new(provider: Box<dyn crate::providers::Provider>, model: String) -> Self {
        Self { provider, model }
    }
    
    /// Summarize multiple memories into one concise memory.
    pub async fn summarize_memories(&self, memories: &[&MemoryEntry]) -> Result<Option<MemoryEntry>> {
        if memories.is_empty() {
            return Ok(None);
        }
        
        // Build input from memories
        let memories_text = memories
            .iter()
            .map(|m| format!("[{}] {}", m.category.display_name(), m.content))
            .collect::<Vec<_>>()
            .join("\n");
        
        let request = build_ai_request(
            MEMORY_SUMMARY_SYSTEM_PROMPT,
            &format!("请将以下记忆合并为一条精炼的摘要：\n\n{}", memories_text),
        );
        
        let response = self.provider.chat(request).await?;
        let response_text = extract_response_text(&response);
        
        let result: MemorySummaryResult = parse_json_response(&response_text)?;
        
        if result.summary.is_empty() {
            return Ok(None);
        }
        
        let category = parse_category(&result.category)?;
        let mut entry = MemoryEntry::new(category, result.summary, None);
        entry.importance = result.importance.clamp(0.0, 100.0);
        
        Ok(Some(entry))
    }
    
    /// Detect if two memories conflict using AI.
    pub async fn detect_conflict(&self, old: &MemoryEntry, new: &MemoryEntry) -> Result<MemoryConflictResult> {
        let input = format!(
            "旧记忆：[{}] {}\n新记忆：[{}] {}\n\n请判断这两条记忆是否存在冲突。",
            old.category.display_name(),
            old.content,
            new.category.display_name(),
            new.content
        );
        
        let request = build_ai_request(MEMORY_CONFLICT_SYSTEM_PROMPT, &input);
        let response = self.provider.chat(request).await?;
        let response_text = extract_response_text(&response);
        
        parse_json_response(&response_text)
    }
    
    /// Assess memory quality using AI.
    pub async fn assess_quality(&self, memory: &MemoryEntry) -> Result<MemoryQualityResult> {
        let input = format!(
            "记忆内容：[{}] {}\n\n请评估这条记忆的质量和长期价值。",
            memory.category.display_name(),
            memory.content
        );
        
        let request = build_ai_request(MEMORY_QUALITY_SYSTEM_PROMPT, &input);
        let response = self.provider.chat(request).await?;
        let response_text = extract_response_text(&response);
        
        parse_json_response(&response_text)
    }
    
    /// Merge multiple memories using AI.
    pub async fn merge_memories(&self, memories: &[&MemoryEntry]) -> Result<Option<MemoryEntry>> {
        if memories.len() < 2 {
            return Ok(None);
        }
        
        let memories_text = memories
            .iter()
            .map(|m| format!("[{}] {}", m.category.display_name(), m.content))
            .collect::<Vec<_>>()
            .join("\n");
        
        let request = build_ai_request(
            MEMORY_MERGE_SYSTEM_PROMPT,
            &format!("请判断以下记忆是否应该合并，如果应该则生成合并后的记忆：\n\n{}", memories_text),
        );
        
        let response = self.provider.chat(request).await?;
        let response_text = extract_response_text(&response);
        
        let result: MemoryMergeResult = parse_json_response(&response_text)?;
        
        if result.merged_content.is_empty() || result.merged_from_count == 0 {
            return Ok(None);
        }
        
        let category = parse_category(&result.category)?;
        let mut entry = MemoryEntry::new(category, result.merged_content, None);
        entry.importance = result.importance.clamp(0.0, 100.0);
        
        Ok(Some(entry))
    }
    
    /// Get the model name.
    pub fn model_name(&self) -> &str {
        &self.model
    }
}

/// Build a standard AI request for memory processing.
fn build_ai_request(system_prompt: &str, user_input: &str) -> crate::providers::ChatRequest {
    use crate::providers::{ChatRequest, Message, MessageContent, Role};
    
    ChatRequest {
        messages: vec![Message {
            role: Role::User,
            content: MessageContent::Text(user_input.to_string()),
        }],
        tools: vec![],
        system: Some(system_prompt.to_string()),
        think: false,
        max_tokens: 512,
        server_tools: vec![],
        enable_caching: false,
    }
}

/// Extract text from AI response.
fn extract_response_text(response: &crate::providers::ChatResponse) -> String {
    response.content
        .iter()
        .filter_map(|block| {
            if let crate::providers::ContentBlock::Text { text } = block {
                Some(text.clone())
            } else {
                None
            }
        })
        .collect::<Vec<_>>()
        .join("")
}

/// Parse JSON response with cleanup.
fn parse_json_response<T: serde::de::DeserializeOwned>(json_text: &str) -> Result<T> {
    let cleaned = json_text
        .trim()
        .trim_start_matches("```json")
        .trim_start_matches("```")
        .trim_end_matches("```")
        .trim();
    
    serde_json::from_str(cleaned).map_err(|e| anyhow::anyhow!("JSON parse error: {}", e))
}

/// Parse category string to MemoryCategory.
fn parse_category(s: &str) -> Result<MemoryCategory> {
    match s.to_lowercase().as_str() {
        "decision" | "决策" => Ok(MemoryCategory::Decision),
        "preference" | "偏好" => Ok(MemoryCategory::Preference),
        "solution" | "解决方案" => Ok(MemoryCategory::Solution),
        "finding" | "发现" => Ok(MemoryCategory::Finding),
        "technical" | "技术" => Ok(MemoryCategory::Technical),
        "structure" | "结构" => Ok(MemoryCategory::Structure),
        _ => anyhow::bail!("Unknown category: {}", s),
    }
}

/// Configuration for AI-enhanced memory processing.
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct AiMemoryConfig {
    /// Enable AI summarization.
    pub enable_summarization: bool,
    /// Enable AI conflict detection.
    pub enable_conflict_detection: bool,
    /// Enable AI quality assessment.
    pub enable_quality_assessment: bool,
    /// Enable AI memory merging.
    pub enable_merging: bool,
    /// Minimum memories to trigger summarization.
    pub summarize_threshold: usize,
    /// Quality threshold for keeping memories.
    pub quality_threshold: f64,
    /// Similarity threshold for merging.
    pub merge_similarity_threshold: f64,
}

impl Default for AiMemoryConfig {
    fn default() -> Self {
        Self {
            enable_summarization: true,
            enable_conflict_detection: true,
            enable_quality_assessment: false,  // Optional, can be expensive
            enable_merging: true,
            summarize_threshold: 5,
            quality_threshold: 30.0,
            merge_similarity_threshold: 0.6,
        }
    }
}

impl AiMemoryConfig {
    /// Create a minimal config (disable all AI features).
    pub fn minimal() -> Self {
        Self {
            enable_summarization: false,
            enable_conflict_detection: false,
            enable_quality_assessment: false,
            enable_merging: false,
            summarize_threshold: 10,
            quality_threshold: 20.0,
            merge_similarity_threshold: 0.8,
        }
    }
    
    /// Create an aggressive config (enable all AI features).
    pub fn aggressive() -> Self {
        Self {
            enable_summarization: true,
            enable_conflict_detection: true,
            enable_quality_assessment: true,
            enable_merging: true,
            summarize_threshold: 3,
            quality_threshold: 40.0,
            merge_similarity_threshold: 0.5,
        }
    }
    
    /// Parse from environment variable.
    pub fn from_env() -> Self {
        let enable_all = std::env::var("MEMORY_AI_ALL")
            .map(|v| v == "true" || v == "1")
            .unwrap_or(false);
        
        if enable_all {
            return Self::aggressive();
        }
        
        Self {
            enable_summarization: std::env::var("MEMORY_AI_SUMMARY")
                .map(|v| v != "false" && v != "0")
                .unwrap_or(true),
            enable_conflict_detection: std::env::var("MEMORY_AI_CONFLICT")
                .map(|v| v != "false" && v != "0")
                .unwrap_or(true),
            enable_quality_assessment: std::env::var("MEMORY_AI_QUALITY")
                .map(|v| v == "true" || v == "1")
                .unwrap_or(false),
            enable_merging: std::env::var("MEMORY_AI_MERGE")
                .map(|v| v != "false" && v != "0")
                .unwrap_or(true),
            summarize_threshold: std::env::var("MEMORY_SUMMARY_THRESHOLD")
                .and_then(|v| v.parse().map_err(|_| std::env::VarError::NotPresent))
                .unwrap_or(5),
            quality_threshold: std::env::var("MEMORY_QUALITY_THRESHOLD")
                .and_then(|v| v.parse().map_err(|_| std::env::VarError::NotPresent))
                .unwrap_or(30.0),
            merge_similarity_threshold: std::env::var("MEMORY_MERGE_THRESHOLD")
                .and_then(|v| v.parse().map_err(|_| std::env::VarError::NotPresent))
                .unwrap_or(0.6),
        }
    }
}

/// Extended AutoMemory with AI-enhanced operations.
impl AutoMemory {
    /// Add memory with AI conflict detection.
    pub async fn add_memory_with_ai_conflict(
        &mut self,
        category: MemoryCategory,
        content: String,
        source_session: Option<String>,
        processor: Option<&AiMemoryProcessor>,
    ) -> Result<()> {
        // Check for duplicates first (rule-based, fast)
        if self.has_similar(&content) {
            return Ok(());
        }
        
        // Create new entry
        let new_entry = MemoryEntry::new(category, content.clone(), source_session);
        
        // Find potential conflicts (same category, similar topic)
        let potential_conflicts: Vec<(usize, &MemoryEntry)> = self.entries
            .iter()
            .enumerate()
            .filter(|(_, e)| {
                e.category == category && 
                Self::calculate_similarity(&e.content.to_lowercase(), &content.to_lowercase()) > 0.3
            })
            .collect();
        
        if let Some(processor) = processor {
            // Use AI to check each potential conflict
            for (idx, old_entry) in potential_conflicts {
                let result = processor.detect_conflict(old_entry, &new_entry).await?;
                
                if result.should_replace {
                    log::debug!("AI detected conflict: {} -> replacing '{}' with '{}'", 
                        result.conflict_type, old_entry.content, content);
                    self.entries.remove(idx);
                    self.invalidate_index();
                    break;
                }
            }
        } else {
            // Fallback to rule-based conflict detection
            if let Some(conflict_idx) = self.find_conflict(&content, category) {
                self.entries.remove(conflict_idx);
                self.invalidate_index();
            }
        }
        
        self.add(new_entry);
        Ok(())
    }
    
    /// Assess and filter memories by quality using AI.
    pub async fn assess_quality_with_ai(
        &mut self,
        processor: &AiMemoryProcessor,
        config: &AiMemoryConfig,
    ) -> Result<usize> {
        if !config.enable_quality_assessment {
            return Ok(0);
        }
        
        // Collect indices of non-manual entries first
        let indices_to_assess: Vec<usize> = self.entries
            .iter()
            .enumerate()
            .filter(|(_, entry)| !entry.is_manual)
            .map(|(i, _)| i)
            .collect();
        
        // Assess each entry and collect results
        let mut to_remove: Vec<usize> = Vec::new();
        let mut importance_updates: Vec<(usize, f64)> = Vec::new();
        
        for i in indices_to_assess {
            let entry = &self.entries[i];
            let result = processor.assess_quality(entry).await?;
            
            if !result.should_keep || result.quality_score < config.quality_threshold {
                log::debug!("AI quality assessment: removing '{}' (score: {:.1}, reason: {})",
                    entry.content, result.quality_score, result.reason);
                to_remove.push(i);
            } else {
                // Record importance update
                importance_updates.push((i, result.quality_score));
            }
        }
        
        // Apply importance updates
        for (i, score) in importance_updates {
            self.entries[i].importance = score;
        }
        
        let removed_count = to_remove.len();
        
        // Remove low-quality entries (in reverse order to preserve indices)
        for idx in to_remove.into_iter().rev() {
            self.entries.remove(idx);
        }
        
        if removed_count > 0 {
            self.invalidate_index();
            self.prune();
        }
        
        Ok(removed_count)
    }
    
    /// Merge similar memories using AI.
    pub async fn merge_similar_with_ai(
        &mut self,
        processor: &AiMemoryProcessor,
        config: &AiMemoryConfig,
    ) -> Result<usize> {
        if !config.enable_merging || self.entries.len() < 2 {
            return Ok(0);
        }
        
        let mut merged_count = 0;
        let mut to_remove: Vec<usize> = Vec::new();
        let mut new_entries: Vec<MemoryEntry> = Vec::new();
        
        // Find groups of similar memories
        let mut processed: std::collections::HashSet<usize> = std::collections::HashSet::new();
        
        for i in 0..self.entries.len() {
            if processed.contains(&i) {
                continue;
            }
            
            // Find similar entries to this one
            let mut similar_group: Vec<usize> = vec![i];
            
            for j in (i + 1)..self.entries.len() {
                if processed.contains(&j) {
                    continue;
                }
                
                let sim = Self::calculate_similarity(
                    &self.entries[i].content.to_lowercase(),
                    &self.entries[j].content.to_lowercase(),
                );
                
                if sim >= config.merge_similarity_threshold {
                    similar_group.push(j);
                }
            }
            
            // If we have a group, try to merge
            if similar_group.len() >= 2 {
                let group_entries: Vec<&MemoryEntry> = similar_group
                    .iter()
                    .map(|&idx| &self.entries[idx])
                    .collect();
                
                if let Some(merged) = processor.merge_memories(&group_entries).await? {
                    log::debug!("AI merged {} memories into: '{}'",
                        similar_group.len(), merged.content);
                    
                    new_entries.push(merged);
                    to_remove.extend(similar_group.iter().copied());
                    processed.extend(similar_group.iter().copied());
                    merged_count += similar_group.len() - 1;
                }
            }
        }
        
        // Remove merged entries (sorted and in reverse order)
        let mut sorted_remove: Vec<usize> = to_remove;
        sorted_remove.sort();
        for idx in sorted_remove.into_iter().rev() {
            self.entries.remove(idx);
        }
        
        // Add new merged entries
        for entry in new_entries {
            self.entries.push(entry);
        }
        
        if merged_count > 0 {
            self.invalidate_index();
            self.prune();
        }
        
        Ok(merged_count)
    }
    
    /// Generate AI-enhanced summary for prompt.
    pub async fn generate_ai_summary(
        &self,
        max_entries: usize,
        processor: Option<&AiMemoryProcessor>,
        config: Option<&AiMemoryConfig>,
    ) -> Result<String> {
        if self.entries.is_empty() {
            return Ok(String::new());
        }
        
        let default_config = AiMemoryConfig::default();
        let config = config.unwrap_or(&default_config);
        
        // If AI summarization is enabled and we have a processor
        if config.enable_summarization
            && let Some(processor) = processor
            && self.entries.len() >= config.summarize_threshold
        {
            
            // Group by category
            let mut by_category: HashMap<MemoryCategory, Vec<&MemoryEntry>> = HashMap::new();
            for entry in &self.entries {
                by_category.entry(entry.category).or_default().push(entry);
            }
            
            let mut summary = String::from("【跨会话记忆 (AI摘要)】\n\n");
            
            for (cat, entries) in by_category {
                if entries.is_empty() {
                    continue;
                }
                
                // Get top entries by importance
                let top_entries: Vec<&MemoryEntry> = entries
                    .iter()
                    .take(max_entries.min(entries.len()))
                    .copied()
                    .collect();
                
                // Try AI summarization for this category
                if let Some(ai_summary) = processor.summarize_memories(&top_entries).await? {
                    summary.push_str(&format!("{} {}:\n", cat.icon(), cat.display_name()));
                    summary.push_str(&format!("  {}\n\n", ai_summary.content));
                } else {
                    // Fallback to individual entries
                    summary.push_str(&format!("{} {}:\n", cat.icon(), cat.display_name()));
                    for entry in top_entries {
                        summary.push_str(&format!("  {}\n", entry.format_for_prompt()));
                    }
                    summary.push('\n');
                }
            }
            
            Ok(summary)
        } else {
            // Fallback to rule-based summary
            Ok(self.generate_contextual_summary("", max_entries))
        }
    }
}



// ============================================================================
// Memory Detection (Fallback - Rule-based)
// ============================================================================

/// Detect potential memory entries from conversation content.
/// This is the fallback method using rule-based detection (no AI).
/// For AI-based extraction, use AiMemoryExtractor.
pub fn detect_memories_fallback(text: &str, session_id: Option<&str>) -> Vec<MemoryEntry> {
    let mut entries = Vec::new();
    let text_lower = text.to_lowercase();

    // Detection patterns for each category (filtered to avoid too generic keywords)
    let patterns: Vec<(MemoryCategory, Vec<&str>)> = vec![
        (MemoryCategory::Decision, vec![
            "决定", "决定使用", "选择使用", "采用", "decided to", "decision to", 
            "chose to", "adopted", "选定", "最终选择",
        ]),
        (MemoryCategory::Preference, vec![
            "我喜欢", "我偏好", "prefer to", "i prefer", "my preference is",
            "习惯用", "我习惯", "usually prefer", "偏好使用",
        ]),
        (MemoryCategory::Solution, vec![
            "修复了", "解决了", "fixed by", "solved by", "resolved by", 
            "通过添加", "通过修改", "通过删除", "解决方法是",
        ]),
        (MemoryCategory::Finding, vec![
            "发现", "注意到", "found that", "noticed that", "discovered", 
            "观察到", "api 端点", "位于", "located at", "关键发现",
        ]),
        (MemoryCategory::Technical, vec![
            "使用框架", "using framework", "built with", "基于", 
            "框架是", "技术栈", "依赖库",
        ]),
        (MemoryCategory::Structure, vec![
            "入口文件", "entry point is", "主文件是", "main file", 
            "配置文件", "config file", "核心文件",
        ]),
    ];

    for (category, keywords) in patterns {
        for keyword in keywords {
            if text_lower.contains(keyword) {
                // Extract the relevant sentence or phrase
                let content = extract_memory_content(text, keyword);
                // Use higher threshold to avoid too generic content
                if !content.is_empty() && content.len() >= MIN_MEMORY_CONTENT_LENGTH {
                    let entry = MemoryEntry::new(
                        category,
                        content,
                        session_id.map(|s| s.to_string()),
                    );
                    entries.push(entry);
                }
            }
        }
    }

    // Deduplicate by content similarity
    deduplicate_entries(entries)
}

/// Detect memories from text using the rule-based fallback method.
/// This is kept for backward compatibility and for cases where AI is unavailable.
pub fn detect_memories_from_text(text: &str, session_id: Option<&str>) -> Vec<MemoryEntry> {
    detect_memories_fallback(text, session_id)
}

/// Detect memories asynchronously using AI extractor.
/// Falls back to rule-based detection if AI fails or is unavailable.
pub async fn detect_memories_with_ai(
    text: &str,
    session_id: Option<&str>,
    extractor: Option<&dyn MemoryExtractor>,
) -> Result<Vec<MemoryEntry>> {
    if let Some(ai_extractor) = extractor {
        // Try AI extraction first
        match ai_extractor.extract(text, session_id).await {
            Ok(entries) if !entries.is_empty() => {
                return Ok(entries);
            }
            Ok(_) => {
                // AI returned empty, try fallback (silent)
            }
            Err(_) => {
                // AI extraction failed, try fallback (silent)
            }
        }
    }
    
    // Fallback to rule-based detection
    Ok(detect_memories_fallback(text, session_id))
}

/// Deduplicate entries by content similarity.
/// Keeps longer (more detailed) entries when duplicates are found.
fn deduplicate_entries(entries: Vec<MemoryEntry>) -> Vec<MemoryEntry> {
    if entries.is_empty() {
        return entries;
    }
    
    // Sort by content length (longer first - keep more detailed entries)
    let mut sorted = entries;
    sorted.sort_by(|a, b| b.content.len().cmp(&a.content.len()));
    
    // Keep only unique entries
    let mut unique: Vec<MemoryEntry> = Vec::new();
    for entry in sorted {
        let entry_lower = entry.content.to_lowercase();
        
        // Check if already have similar entry
        let is_duplicate = unique.iter().any(|existing| {
            let existing_lower = existing.content.to_lowercase();
            
            // Exact match
            if existing_lower == entry_lower {
                return true;
            }
            
            // High similarity (same words mostly)
            let similarity = calculate_similarity(&existing_lower, &entry_lower);
            similarity >= 0.8
        });
        
        if !is_duplicate {
            unique.push(entry);
        }
        
        // Stop if we have enough entries
        if unique.len() >= MAX_DETECTED_ENTRIES {
            break;
        }
    }
    
    unique
}

/// Extract memory content around a keyword.
fn extract_memory_content(text: &str, keyword: &str) -> String {
    let text_lower = text.to_lowercase();
    let keyword_lower = keyword.to_lowercase();

    // Find keyword position
    let pos = match text_lower.find(&keyword_lower) {
        Some(p) => p,
        None => return String::new(),
    };

    // Find sentence boundaries (sentence end markers)
    const SENTENCE_END_MARKERS: [char; 3] = ['.', '\n', '。'];

    // For start: find the last sentence end marker before pos
    // Need to correctly find the next char boundary after multi-byte markers like '。'
    let start = text[..pos].rfind(SENTENCE_END_MARKERS)
        .map(|i| {
            // The marker char starts at byte position i
            // We need the byte position after this marker char
            // Use char_indices to find the next char's start position
            match text[i..].char_indices().nth(1) {
                Some((next_idx, _)) => i + next_idx,  // Next char starts at i + next_idx
                None => pos,  // Marker is at the end of prefix, start from keyword position
            }
        })
        .unwrap_or(0);

    // For end: find the first sentence end marker after pos
    let end = text[pos..].find(SENTENCE_END_MARKERS)
        .map(|i| {
            let marker_pos = pos + i;
            // Find the byte position after the marker char
            match text[marker_pos..].char_indices().nth(1) {
                Some((next_idx, _)) => marker_pos + next_idx,
                None => text.len(),  // Marker at end of text
            }
        })
        .unwrap_or_else(|| {
            // No marker found: use MAX_MEMORY_CONTENT_LENGTH, but ensure valid UTF-8 boundary
            let max_end = pos + MAX_MEMORY_CONTENT_LENGTH;
            // Find the nearest valid char boundary
            if max_end >= text.len() {
                text.len()
            } else {
                // Walk backwards to find a valid boundary
                let mut boundary = max_end;
                while boundary > pos && !text.is_char_boundary(boundary) {
                    boundary -= 1;
                }
                boundary
            }
        });

    // Ensure start and end are valid UTF-8 boundaries and start < end
    if start >= end || start > text.len() || end > text.len() {
        return String::new();
    }

    let content = text[start..end].trim();
    
    // Quality check: reject content that looks like formatting output
    if is_low_quality_memory(content) {
        return String::new();
    }
    
    // Clean up and truncate
    if content.len() > MAX_MEMORY_CONTENT_LENGTH {
        truncate_str(content, MAX_MEMORY_CONTENT_LENGTH - 3)
    } else {
        content.to_string()
    }
}

/// Check if extracted content is low quality (formatting artifacts, etc).
fn is_low_quality_memory(content: &str) -> bool {
    // Too short to be meaningful
    if content.len() < MIN_MEMORY_CONTENT_LENGTH {
        return true;
    }
    
    // Contains formatting characters (table borders, tree lines)
    let formatting_chars = ['│', '├', '└', '┌', '┐', '─', '═', '║', '╔', '╗', '╚', '╝'];
    if content.chars().any(|c| formatting_chars.contains(&c)) {
        return true;
    }
    
    // Starts with emoji (likely formatted output, not user intent)
    let first_char = content.chars().next().unwrap_or(' ');
    if !first_char.is_alphanumeric() && !first_char.is_ascii_punctuation() && first_char > '\u{FF}' {
        // Check if it's a common emoji prefix
        if content.starts_with("🎯") || content.starts_with("🔧") || content.starts_with("💡") ||
           content.starts_with("📚") || content.starts_with("🏗") || content.starts_with("👤") ||
           content.starts_with("⭐") || content.starts_with("📝") || content.starts_with("✅") ||
           content.starts_with("❌") || content.starts_with("⚠") {
            return true;
        }
    }
    
    // Contains memory system markers (self-referential)
    if content.contains("【自动记忆摘要】") || content.contains("[ACCUMULATED MEMORY]") ||
       content.contains("记忆统计") || content.contains("memory.json") {
        return true;
    }
    
    // Looks like a numbered list item without substance
    if content.starts_with("- ") && content.len() < 30 {
        return true;
    }
    
    // Contains mostly numbers/punctuation (likely code output)
    let alpha_count = content.chars().filter(|c| c.is_alphabetic()).count();
    let total_count = content.chars().count();
    if total_count > 0 && alpha_count < total_count / 4 {
        return true;
    }
    
    false
}

// ============================================================================
// Rewind / Summarize Up To Here
// ============================================================================

/// Result of a rewind/summarize operation.
#[derive(Debug, Clone)]
pub struct RewindResult {
    /// Original message count.
    pub original_count: usize,
    /// New message count after rewind.
    pub new_count: usize,
    /// Index where rewind was applied.
    pub rewind_index: usize,
    /// Summary generated for removed messages.
    pub summary: Option<String>,
    /// New message list (summary message + kept messages).
    pub new_messages: Vec<Message>,
}

/// Summarize messages up to a specific index, keeping recent ones.
/// Returns the new message list with summary + kept messages.
pub async fn summarize_up_to(
    messages: &[Message],
    index: usize,
    compressor: Option<&dyn crate::compress::Compressor>,
) -> Result<RewindResult> {
    if index >= messages.len() {
        anyhow::bail!("rewind index {} out of bounds (messages: {})", index, messages.len());
    }

    if index == 0 {
        // Nothing to summarize, return original messages
        return Ok(RewindResult {
            original_count: messages.len(),
            new_count: messages.len(),
            rewind_index: 0,
            summary: None,
            new_messages: messages.to_vec(),
        });
    }

    let to_summarize = &messages[..index];
    let to_keep = &messages[index..];

    // Generate summary
    let summary = if let Some(comp) = compressor {
        // Use AI compressor
        let segment = comp.summarize(to_summarize, &crate::compress::CompressionConfig::default()).await?;
        Some(segment.summary)
    } else {
        // Fallback to simple summary
        Some(generate_simple_summary(to_summarize))
    };

    // Build summary message
    let summary_msg = create_summary_message(&summary, to_summarize.len());

    // New message list: summary + kept messages
    let new_messages: Vec<Message> = std::iter::once(summary_msg)
        .chain(to_keep.iter().cloned())
        .collect();
    
    let new_count = new_messages.len();

    Ok(RewindResult {
        original_count: messages.len(),
        new_count,
        rewind_index: index,
        summary,
        new_messages,
    })
}

/// Create a summary message for injection.
fn create_summary_message(summary: &Option<String>, original_count: usize) -> Message {
    let content = match summary {
        Some(s) => format!("[对话摘要 - 原 {} 条消息]\n\n{}", original_count, s),
        None => format!("[对话摘要 - 原 {} 条消息已压缩]", original_count),
    };

    Message {
        role: crate::providers::Role::User,
        content: crate::providers::MessageContent::Text(content),
    }
}

/// Generate a simple summary without AI.
fn generate_simple_summary(messages: &[Message]) -> String {
    let mut parts: Vec<String> = Vec::new();
    
    // Extract key points from each message
    for msg in messages {
        if msg.role == crate::providers::Role::User {
            let text = match &msg.content {
                crate::providers::MessageContent::Text(t) => t,
                _ => continue,
            };
            // Take first significant line
            let first_line = text.lines().next().unwrap_or("");
            if first_line.len() > 20 {
                parts.push(truncate_str(first_line, 100));
            }
        }
    }

    if parts.is_empty() {
        "对话已压缩".to_string()
    } else if parts.len() <= 5 {
        parts.join(" | ")
    } else {
        format!("{} ... (共 {} 个话题)", parts[0], parts.len())
    }
}

// ============================================================================
// Semantic Search
// ============================================================================

/// Cosine similarity calculation utility.
/// Used for vector-based semantic search when embedding API is available.
pub struct SemanticUtils;

impl SemanticUtils {
    /// Calculate cosine similarity between two embeddings.
    /// 
    /// ## 余弦相似度公式
    /// 
    /// cos(A, B) = (A · B) / (|A| × |B|)
    /// 
    /// 取值范围：
    /// - 1.0 = 完全相同
    /// - 0.0 = 无关
    /// - -1.0 = 完全相反
    pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
        if a.len() != b.len() || a.is_empty() {
            return 0.0;
        }
        
        let dot_product = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum::<f32>();
        let norm_a = a.iter().map(|x| x * x).sum::<f32>().sqrt();
        let norm_b = b.iter().map(|x| x * x).sum::<f32>().sqrt();
        
        if norm_a == 0.0 || norm_b == 0.0 {
            return 0.0;
        }
        
        dot_product / (norm_a * norm_b)
    }
}


/// Semantic search without AI (using TF-IDF like approach).
/// 
/// ## TF-IDF 语义搜索
/// 
/// TF-IDF（Term Frequency-Inverse Document Frequency）是一种
/// 不需要 AI 模型的语义搜索方法。
/// 
/// ### 原理
/// 
/// 1. **TF（词频）**: 词在文档中出现的频率
///    TF(word, doc) = count(word in doc) / len(doc)
/// 
/// 2. **IDF（逆文档频率）**: 词在整个文档集合中的稀有程度
///    IDF(word) = log(total_docs / docs_containing_word)
/// 
/// 3. **TF-IDF**: TF × IDF
///    高 TF-IDF = 词在此文档中重要，但在其他文档中不常见
/// 
/// ### 示例
/// 
/// ```ignore
/// 文档1: "使用 PostgreSQL 数据库"
/// 文档2: "Redis 缓存配置"
/// 文档3: "数据库连接池设置"
/// 
/// 查询: "数据库"
/// 
/// TF-IDF("数据库", 文档1) = 1/3 × log(3/2) = 0.33 × 0.41 = 0.14
/// TF-IDF("数据库", 文档3) = 1/4 × log(3/2) = 0.25 × 0.41 = 0.10
/// 
/// 结果: 文档1 > 文档3 > 文档2
/// ```
pub struct TfIdfSearch {
    /// Word frequency in each document.
    doc_word_freq: HashMap<String, HashMap<String, f32>>,
    /// Total documents.
    total_docs: usize,
    /// IDF cache.
    idf_cache: HashMap<String, f32>,
}

impl TfIdfSearch {
    /// Create a new TF-IDF search instance.
    pub fn new() -> Self {
        Self {
            doc_word_freq: HashMap::new(),
            total_docs: 0,
            idf_cache: HashMap::new(),
        }
    }
    
    /// Index all memories for TF-IDF search.
    pub fn index(&mut self, memory: &AutoMemory) {
        self.clear();
        self.total_docs = memory.entries.len();
        
        for entry in &memory.entries {
            let words = self.tokenize(&entry.content);
            let word_freq = self.compute_word_freq(&words);
            self.doc_word_freq.insert(entry.content.clone(), word_freq);
        }
        
        // Compute IDF for all words
        self.compute_idf();
    }
    
    /// Tokenize text into words.
    /// Supports both space-separated languages and CJK characters.
    fn tokenize(&self, text: &str) -> Vec<String> {
        let lower = text.to_lowercase();
        let mut tokens = Vec::new();
        
        // Split by whitespace first
        for word in lower.split_whitespace() {
            let trimmed = word.trim_matches(|c: char| !c.is_alphanumeric());
            if trimmed.len() > 1 {
                tokens.push(trimmed.to_string());
            }
            
            // For CJK characters, also add individual characters and bigrams
            let chars: Vec<char> = trimmed.chars().collect();
            let has_cjk = chars.iter().any(|c| Self::is_cjk(*c));
            
            if has_cjk {
                // Add individual CJK characters
                for c in &chars {
                    if Self::is_cjk(*c) {
                        tokens.push(c.to_string());
                    }
                }
                // Add bigrams for CJK
                for window in chars.windows(2) {
                    if Self::is_cjk(window[0]) || Self::is_cjk(window[1]) {
                        tokens.push(window.iter().collect::<String>());
                    }
                }
            }
        }
        
        tokens
    }
    
    /// Check if a character is CJK (Chinese/Japanese/Korean).
    fn is_cjk(c: char) -> bool {
        matches!(c,
            '\u{4E00}'..='\u{9FFF}' |   // CJK Unified Ideographs
            '\u{3400}'..='\u{4DBF}' |   // CJK Extension A
            '\u{F900}'..='\u{FAFF}' |   // CJK Compatibility Ideographs
            '\u{3000}'..='\u{303F}' |   // CJK Symbols and Punctuation
            '\u{3040}'..='\u{309F}' |   // Hiragana
            '\u{30A0}'..='\u{30FF}'     // Katakana
        )
    }
    
    /// Compute word frequency in a document.
    fn compute_word_freq(&self, words: &[String]) -> HashMap<String, f32> {
        let total = words.len() as f32;
        let mut freq = HashMap::new();
        
        for word in words {
            *freq.entry(word.clone()).or_insert(0.0) += 1.0;
        }
        
        // Normalize by total words
        for (_, count) in freq.iter_mut() {
            *count /= total;
        }
        
        freq
    }
    
    /// Compute IDF for all words.
    fn compute_idf(&mut self) {
        // Count documents containing each word
        let mut word_doc_count: HashMap<String, usize> = HashMap::new();
        
        for word_freq in &self.doc_word_freq {
            for word in word_freq.1.keys() {
                *word_doc_count.entry(word.clone()).or_insert(0) += 1;
            }
        }
        
        // Compute IDF
        for (word, count) in word_doc_count {
            let idf = (self.total_docs as f32 / count as f32).ln();
            self.idf_cache.insert(word, idf);
        }
    }
    
    /// Search using TF-IDF similarity.
    pub fn search(&self, query: &str, limit: Option<usize>) -> Vec<(String, f32)> {
        let query_words = self.tokenize(query);
        let query_freq = self.compute_word_freq(&query_words);
        
        let mut results: Vec<(String, f32)> = Vec::new();
        
        for (doc, doc_freq) in &self.doc_word_freq {
            // Compute TF-IDF dot product similarity
            let similarity = self.compute_similarity(&query_freq, doc_freq);
            
            if similarity > 0.0 {
                results.push((doc.clone(), similarity));
            }
        }
        
        // Sort by similarity
        results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        
        // Apply limit
        if let Some(max) = limit {
            results.into_iter().take(max).collect()
        } else {
            results
        }
    }
    
    /// Compute TF-IDF similarity between query and document.
    fn compute_similarity(&self, query_freq: &HashMap<String, f32>, doc_freq: &HashMap<String, f32>) -> f32 {
        let mut similarity = 0.0;
        
        for (word, tf_query) in query_freq {
            if let Some(tf_doc) = doc_freq.get(word)
                && let Some(idf) = self.idf_cache.get(word) {
                    // TF-IDF(query) × TF-IDF(doc)
                    similarity += tf_query * idf * tf_doc * idf;
                }
        }
        
        similarity
    }
    
    /// Clear all indices.
    pub fn clear(&mut self) {
        self.doc_word_freq.clear();
        self.idf_cache.clear();
        self.total_docs = 0;
    }
}

impl Default for TfIdfSearch {
    fn default() -> Self {
        Self::new()
    }
}

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

    #[test]
    fn test_memory_entry_creation() {
        let entry = MemoryEntry::new(
            MemoryCategory::Decision,
            "Decided to use PostgreSQL for database".to_string(),
            Some("session-123".to_string()),
        );
        assert_eq!(entry.category, MemoryCategory::Decision);
        assert_eq!(entry.importance, 90.0);
        assert!(!entry.is_manual);
    }

    #[test]
    fn test_memory_reference_increase() {
        let mut entry = MemoryEntry::new(
            MemoryCategory::Finding,
            "API endpoint is at /api/v2".to_string(),
            None,
        );
        assert_eq!(entry.importance, 60.0);
        entry.mark_referenced();
        assert_eq!(entry.importance, 62.0);
        entry.mark_referenced();
        entry.mark_referenced();
        assert_eq!(entry.importance, 66.0);
    }

    #[test]
    fn test_auto_memory_add_and_prune() {
        let mut memory = AutoMemory::new();
        memory.max_entries = 5;

        for i in 0..10 {
            memory.add(MemoryEntry::new(
                MemoryCategory::Technical,
                format!("Note {}", i),
                None,
            ));
        }

        // Should have pruned to max_entries
        assert!(memory.entries.len() <= memory.max_entries);
    }

    #[test]
    fn test_duplicate_detection() {
        let mut memory = AutoMemory::new();
        memory.add_memory(
            MemoryCategory::Decision,
            "Use PostgreSQL".to_string(),
            None,
        );
        
        // Should not add duplicate
        memory.add_memory(
            MemoryCategory::Decision,
            "Use PostgreSQL".to_string(),
            None,
        );
        
        assert_eq!(memory.entries.len(), 1);
    }

    #[test]
    fn test_memory_detection() {
        // Test decision detection
        let text = "我决定使用 React 作为前端框架";
        let entries = detect_memories_from_text(text, None);
        assert!(!entries.is_empty());
        assert_eq!(entries[0].category, MemoryCategory::Decision);
        
        // Test solution detection (with more specific pattern)
        let text2 = "解决了认证问题，通过添加 token refresh 机制";
        let entries2 = detect_memories_from_text(text2, None);
        assert!(!entries2.is_empty());
        assert_eq!(entries2[0].category, MemoryCategory::Solution);
        
        // Test preference detection
        let text3 = "我偏好使用 TypeScript 进行开发";
        let entries3 = detect_memories_from_text(text3, None);
        assert!(!entries3.is_empty());
        assert_eq!(entries3[0].category, MemoryCategory::Preference);
    }

    #[test]
    fn test_category_importance() {
        assert!(MemoryCategory::Decision.default_importance() > MemoryCategory::Structure.default_importance());
        assert!(MemoryCategory::Solution.default_importance() > MemoryCategory::Technical.default_importance());
    }

    #[test]
    fn test_top_n_entries() {
        let mut memory = AutoMemory::new();
        
        // Add entries with different importance
        memory.add(MemoryEntry::new(MemoryCategory::Decision, "Decision 1".into(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Finding, "Finding 1".into(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Structure, "Structure 1".into(), None));

        let top = memory.top_n(2);
        assert_eq!(top.len(), 2);
        assert_eq!(top[0].category, MemoryCategory::Decision); // Highest importance
    }

    #[test]
    fn test_similarity_calculation() {
        // Test exact match
        let sim = AutoMemory::calculate_similarity("hello world", "hello world");
        assert_eq!(sim, 1.0);
        
        // Test no match
        let sim = AutoMemory::calculate_similarity("hello world", "foo bar");
        assert_eq!(sim, 0.0);
        
        // Test partial match (50% overlap)
        let sim = AutoMemory::calculate_similarity("hello world", "hello there");
        assert!(sim > 0.0 && sim < 1.0);
        
        // Test empty input
        let sim = AutoMemory::calculate_similarity("", "hello");
        assert_eq!(sim, 0.0);
    }
    
    #[test]
    fn test_similarity_threshold() {
        let mut memory = AutoMemory::new();
        
        // Add a long enough entry (>= MIN_SIMILARITY_LENGTH)
        memory.add(MemoryEntry::new(
            MemoryCategory::Decision,
            "We decided to use PostgreSQL for our database system".to_string(),
            None,
        ));
        
        // Should not add similar entry
        memory.add_memory(
            MemoryCategory::Decision,
            "We decided to use PostgreSQL for our database backend".to_string(),
            None,
        );
        
        // Should have only 1 entry (similar detected)
        assert_eq!(memory.entries.len(), 1);
    }
    
    #[test]
    fn test_short_content_skipped() {
        let mut memory = AutoMemory::new();
        
        // Short content should be skipped by has_similar
        memory.add(MemoryEntry::new(
            MemoryCategory::Technical,
            "short".to_string(),  // Only 5 chars, below MIN_SIMILARITY_LENGTH
            None,
        ));
        
        // Another short entry should be added (not detected as similar)
        memory.add_memory(
            MemoryCategory::Technical,
            "brief".to_string(),
            None,
        );
        
        assert_eq!(memory.entries.len(), 2);
    }
    
    #[test]
    fn test_prune_preserves_manual() {
        let mut memory = AutoMemory::new();
        memory.max_entries = 3;
        
        // Add manual entry (should always be preserved)
        let mut manual = MemoryEntry::manual(MemoryCategory::Decision, "Manual decision".into());
        manual.importance = 10.0; // Low importance but manual
        memory.add(manual);
        
        // Add high importance auto entries
        for i in 0..5 {
            let entry = MemoryEntry::new(
                MemoryCategory::Decision,
                format!("Auto decision {}", i),
                None,
            );
            memory.add(entry);
        }
        
        // Manual entry should still exist after prune
        assert!(memory.entries.iter().any(|e| e.is_manual));
        assert!(memory.entries.len() <= memory.max_entries);
    }
    
    #[test]
    fn test_deduplicate_entries() {
        // Use more similar entries (should have similarity >= 0.8)
        let entries = vec![
            MemoryEntry::new(MemoryCategory::Decision, "We chose PostgreSQL database system for our backend".into(), None),
            MemoryEntry::new(MemoryCategory::Decision, "We chose PostgreSQL database system backend".into(), None),
            MemoryEntry::new(MemoryCategory::Decision, "Using Redis for caching layer".into(), None),
        ];
        
        let deduped = deduplicate_entries(entries);
        
        // Should deduplicate similar entries
        assert!(deduped.len() >= 1);
        assert!(deduped.len() <= 3);
        
        // Should keep longer (more detailed) entry when similar
        let pg_entries: Vec<_> = deduped.iter()
            .filter(|e| e.content.to_lowercase().contains("postgresql"))
            .collect();
        
        if pg_entries.len() == 1 {
            // Correctly deduplicated to one PostgreSQL entry
            // Should be the longer one
            assert!(pg_entries[0].content.contains("backend"));
        }
    }
    
    #[test]
    fn test_memory_detection_edge_cases() {
        // Empty input
        let entries = detect_memories_from_text("", None);
        assert!(entries.is_empty());
        
        // Very short input (below MIN_MEMORY_CONTENT_LENGTH)
        let entries = detect_memories_from_text("决定", None);
        assert!(entries.is_empty());
        
        // Input with only generic keywords
        let entries = detect_memories_from_text("使用", None);
        assert!(entries.is_empty());
        
        // Multiple matches in same text
        let text = "我决定使用React，解决了性能问题通过添加缓存机制";
        let entries = detect_memories_from_text(text, None);
        assert!(entries.len() <= MAX_DETECTED_ENTRIES);
    }
    
    #[test]
    fn test_importance_ceiling() {
        let mut entry = MemoryEntry::new(
            MemoryCategory::Decision,
            "Important decision".into(),
            None,
        );
        
        // Start at 90 (Decision default)
        assert_eq!(entry.importance, 90.0);
        
        // Reference many times
        for _ in 0..10 {
            entry.mark_referenced();
        }
        
        // Should cap at 100
        assert!(entry.importance <= 100.0);
    }

    #[test]
    fn test_time_decay() {
        let mut memory = AutoMemory::new();
        memory.min_importance = 30.0;
        
        // Add manual entry (should never decay)
        let mut manual = MemoryEntry::manual(MemoryCategory::Decision, "Manual entry".into());
        manual.importance = 50.0;
        memory.add(manual);
        
        // Add auto entry with old reference date (simulate 60 days ago)
        let mut old_entry = MemoryEntry::new(
            MemoryCategory::Technical,
            "Old technical note".into(),
            None,
        );
        old_entry.importance = 60.0;
        // Set last_referenced to 60 days ago
        old_entry.last_referenced = Utc::now() - chrono::Duration::days(60);
        memory.add(old_entry);
        
        // Add recent entry (should not decay)
        let recent_entry = MemoryEntry::new(
            MemoryCategory::Finding,
            "Recent finding".into(),
            None,
        );
        memory.add(recent_entry);
        
        // Apply time decay
        memory.apply_time_decay();
        
        // Manual entry should not decay
        let manual_entry = memory.entries.iter().find(|e| e.is_manual);
        assert!(manual_entry.is_some());
        assert_eq!(manual_entry.unwrap().importance, 50.0);
        
        // Recent entry should not decay (still > 30 days threshold)
        let recent = memory.entries.iter().find(|e| e.content.contains("Recent"));
        assert!(recent.is_some());
        assert!(recent.unwrap().importance >= 60.0);  // Finding default
        
        // Old entry should have decayed
        let old = memory.entries.iter().find(|e| e.content.contains("Old"));
        if let Some(old_entry) = old {
            // Should have decayed (60 days - 30 days threshold = 30 days decay period)
            // Decay factor = 0.5^1 = 0.5, so importance = 60 * 0.5 = 30
            assert!(old_entry.importance < 60.0);
            // Should still be above minimum threshold
            assert!(old_entry.importance >= memory.min_importance * 0.5);
        }
    }

    #[test]
    fn test_parse_memory_response() {
        // Test valid JSON response
        let json = r#"{"memories": [{"category": "decision", "content": "决定使用 PostgreSQL 作为数据库", "importance": 90}, {"category": "preference", "content": "我偏好 TypeScript 而非 JavaScript", "importance": 70}]}"#;
        let entries = parse_memory_response(json, None).unwrap();
        assert_eq!(entries.len(), 2);
        
        // Check both entries exist (order may change due to deduplication sorting)
        let has_decision = entries.iter().any(|e| e.category == MemoryCategory::Decision);
        let has_preference = entries.iter().any(|e| e.category == MemoryCategory::Preference);
        assert!(has_decision);
        assert!(has_preference);
        
        // Check importance values
        let decision_entry = entries.iter().find(|e| e.category == MemoryCategory::Decision);
        assert!(decision_entry.is_some());
        assert_eq!(decision_entry.unwrap().importance, 90.0);
        
        // Test empty response
        let empty_json = r#"{"memories": []}"#;
        let empty_entries = parse_memory_response(empty_json, None).unwrap();
        assert!(empty_entries.is_empty());
        
        // Test JSON with markdown code blocks
        let markdown_json = r#"```json
{"memories": [{"category": "solution", "content": "通过添加 middleware 修复认证问题", "importance": 85}]}
```"#;
        let markdown_entries = parse_memory_response(markdown_json, None).unwrap();
        assert_eq!(markdown_entries.len(), 1);
        assert_eq!(markdown_entries[0].category, MemoryCategory::Solution);
        
        // Test unknown category (should be skipped)
        let unknown_json = r#"{"memories": [{"category": "unknown", "content": "This should be skipped content", "importance": 50}]}"#;
        let unknown_entries = parse_memory_response(unknown_json, None).unwrap();
        assert!(unknown_entries.is_empty());
        
        // Test short content (should be skipped)
        let short_json = r#"{"memories": [{"category": "finding", "content": "short", "importance": 60}]}"#;
        let short_entries = parse_memory_response(short_json, None).unwrap();
        assert!(short_entries.is_empty());
    }

    #[test]
    fn test_public_has_similar() {
        let mut memory = AutoMemory::new();
        
        // Add an entry
        memory.add(MemoryEntry::new(
            MemoryCategory::Decision,
            "We decided to use PostgreSQL for our main database system".to_string(),
            None,
        ));
        
        // Test exact match
        assert!(memory.has_similar("We decided to use PostgreSQL for our main database system"));
        
        // Test very similar content (high similarity > 0.7)
        // Original: "We decided to use PostgreSQL for our main database system"
        // Similar:  "We decided to use PostgreSQL for our main database backend"
        // Similarity = shared words / total unique words
        assert!(memory.has_similar("We decided to use PostgreSQL for our main database backend"));
        
        // Test moderately similar (should NOT match, < 0.7)
        assert!(!memory.has_similar("We decided to use Redis for caching"));
        
        // Test completely different content
        assert!(!memory.has_similar("The project uses React for frontend"));
        
        // Test short content (should return false)
        assert!(!memory.has_similar("short"));
    }

    #[test]
    fn test_public_prune() {
        let mut memory = AutoMemory::new();
        memory.max_entries = 5;
        memory.min_importance = 30.0;
        
        // Add entries exceeding max
        for i in 0..10 {
            memory.add(MemoryEntry::new(
                MemoryCategory::Technical,
                format!("Technical note number {} with sufficient length", i),
                None,
            ));
        }
        
        // Manually prune
        memory.prune();
        
        // Should be within limit
        assert!(memory.entries.len() <= memory.max_entries);
    }

    #[test]
    fn test_statistics() {
        let mut memory = AutoMemory::new();
        
        // Add various entries
        memory.add(MemoryEntry::new(MemoryCategory::Decision, "Decision one with enough content".to_string(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Preference, "Preference for TypeScript over JavaScript".to_string(), None));
        memory.add(MemoryEntry::manual(MemoryCategory::Technical, "Manual technical note".to_string()));
        
        // Reference some entries
        memory.entries[0].mark_referenced();
        memory.entries[0].mark_referenced();
        memory.entries[0].mark_referenced();
        
        let stats = memory.generate_statistics();
        
        assert_eq!(stats.total, 3);
        assert_eq!(stats.manual, 1);
        assert_eq!(stats.auto, 2);
        assert_eq!(stats.highly_referenced, 1);  // First entry has 3 references
        assert!(stats.by_category.contains_key(&MemoryCategory::Decision));
        assert!(stats.by_category.contains_key(&MemoryCategory::Preference));
        assert!(stats.by_category.contains_key(&MemoryCategory::Technical));
        assert!(stats.avg_importance > 0.0);
    }

    #[test]
    fn test_memory_config() {
        // Test default config
        let config = MemoryConfig::default();
        assert_eq!(config.max_entries, 100);
        assert_eq!(config.min_importance, 30.0);
        assert_eq!(config.decay_start_days, 30);
        assert_eq!(config.decay_rate, 0.5);
        
        // Test minimal config
        let minimal = MemoryConfig::minimal();
        assert_eq!(minimal.max_entries, 50);
        assert!(minimal.min_importance > config.min_importance);
        
        // Test archival config
        let archival = MemoryConfig::archival();
        assert_eq!(archival.max_entries, 500);
        assert!(archival.min_importance < config.min_importance);
        
        // Test with_max_entries
        let custom = MemoryConfig::with_max_entries(200);
        assert_eq!(custom.max_entries, 200);
        assert_eq!(custom.min_importance, 30.0);  // Other defaults preserved
    }

    #[test]
    fn test_auto_memory_with_config() {
        let config = MemoryConfig::minimal();
        let mut memory = AutoMemory::with_config(config);
        
        assert_eq!(memory.max_entries, 50);
        assert_eq!(memory.min_importance, 50.0);
        
        // Add entries
        for i in 0..60 {
            memory.add(MemoryEntry::new(
                MemoryCategory::Technical,
                format!("Technical note {} with enough length for detection", i),
                None,
            ));
        }
        
        // Should prune to config limit
        assert!(memory.entries.len() <= 50);
    }

    #[test]
    fn test_batch_add() {
        let mut memory = AutoMemory::new();
        
        // Batch add multiple entries
        let entries: Vec<MemoryEntry> = vec![
            MemoryEntry::new(MemoryCategory::Decision, "First decision with sufficient content".into(), None),
            MemoryEntry::new(MemoryCategory::Finding, "First finding with sufficient content".into(), None),
            MemoryEntry::new(MemoryCategory::Solution, "First solution with sufficient content".into(), None),
        ];
        
        memory.add_batch(entries);
        assert_eq!(memory.entries.len(), 3);
        
        // Batch add with duplicates
        let duplicate_entries: Vec<MemoryEntry> = vec![
            MemoryEntry::new(MemoryCategory::Decision, "First decision with sufficient content".into(), None),  // Duplicate
            MemoryEntry::new(MemoryCategory::Technical, "New technical note with sufficient content".into(), None),
        ];
        
        memory.add_batch(duplicate_entries);
        assert_eq!(memory.entries.len(), 4);  // Only 1 new entry added
    }

    #[test]
    fn test_search_with_limit() {
        let mut memory = AutoMemory::new();
        
        // Add multiple entries with same keyword
        for i in 0..10 {
            memory.add(MemoryEntry::new(
                MemoryCategory::Technical,
                format!("PostgreSQL technical note {} with details", i),
                None,
            ));
        }
        
        // Search without limit
        let all = memory.search("postgresql");
        assert_eq!(all.len(), 10);
        
        // Search with limit
        let limited = memory.search_with_limit("postgresql", Some(5));
        assert_eq!(limited.len(), 5);
        
        // Should return highest importance first
        assert!(limited[0].importance >= limited[limited.len() - 1].importance);
    }

    #[test]
    fn test_multi_keyword_search() {
        let mut memory = AutoMemory::new();
        
        memory.add(MemoryEntry::new(MemoryCategory::Decision, "Decided to use PostgreSQL".into(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Technical, "Using Redis for caching".into(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Solution, "Fixed by adding middleware".into(), None));
        
        // Search with multiple keywords
        let results = memory.search_multi(&["postgresql", "redis"]);
        assert_eq!(results.len(), 2);
        
        // Search with keyword that matches nothing
        let empty = memory.search_multi(&["mongodb"]);
        assert!(empty.is_empty());
    }

    #[test]
    fn test_mark_referenced_with_increment() {
        let mut entry = MemoryEntry::new(
            MemoryCategory::Finding,
            "API endpoint location".into(),
            None,
        );
        
        assert_eq!(entry.importance, 60.0);
        
        // Custom increment
        entry.mark_referenced_with_increment(5.0);
        assert_eq!(entry.importance, 65.0);
        
        // Default increment (2.0)
        entry.mark_referenced();
        assert_eq!(entry.importance, 67.0);
        
        // Should cap at 100
        for _ in 0..20 {
            entry.mark_referenced_with_increment(10.0);
        }
        assert!(entry.importance <= 100.0);
    }

    #[test]
    fn test_search_index() {
        let mut memory = AutoMemory::new();
        
        // Add multiple entries
        for i in 0..20 {
            memory.add(MemoryEntry::new(
                MemoryCategory::Technical,
                format!("PostgreSQL technical note {} with sufficient content length", i),
                None,
            ));
        }
        for i in 0..10 {
            memory.add(MemoryEntry::new(
                MemoryCategory::Decision,
                format!("Redis decision {} with sufficient content for testing", i),
                None,
            ));
        }
        
        // Rebuild index
        memory.rebuild_index();
        assert!(memory.search_index.is_some());
        
        // Test fast search
        let results = memory.search_fast("postgresql", Some(5));
        assert!(results.len() <= 5);
        assert!(results.iter().all(|e| e.content.to_lowercase().contains("postgresql")));
        
        // Test fast multi-keyword search
        let multi_results = memory.search_multi_fast(&["postgresql", "redis"]);
        assert!(multi_results.len() > 0);
        
        // Test fast category lookup
        let tech_entries = memory.by_category_fast(MemoryCategory::Technical);
        assert_eq!(tech_entries.len(), 20);
        
        let decision_entries = memory.by_category_fast(MemoryCategory::Decision);
        assert_eq!(decision_entries.len(), 10);
        
        // Test fast top_n
        let top = memory.top_n_fast(5);
        assert_eq!(top.len(), 5);
        // Results should be sorted by importance (Decision > Technical)
        assert!(top[0].importance >= top[top.len() - 1].importance);
    }

    #[test]
    fn test_index_auto_rebuild() {
        let mut memory = AutoMemory::new();
        
        // Index should be None initially
        assert!(memory.search_index.is_none());
        
        // Fast search should auto-build index
        memory.add(MemoryEntry::new(
            MemoryCategory::Decision,
            "Test decision with sufficient content length".into(),
            None,
        ));
        
        let results = memory.search_fast("test", None);
        assert!(results.len() > 0);
        assert!(memory.search_index.is_some());  // Index auto-built
        
        // Modify memory should invalidate index
        memory.clear();
        assert!(memory.search_index.is_none());
        
        // Add new entry should rebuild on next search
        memory.add(MemoryEntry::new(
            MemoryCategory::Finding,
            "New finding with sufficient content".into(),
            None,
        ));
        let _ = memory.search_fast("finding", None);
        assert!(memory.search_index.is_some());
    }

    #[test]
    fn test_cosine_similarity() {
        // Identical vectors
        let a = vec![1.0, 0.0, 0.0];
        let b = vec![1.0, 0.0, 0.0];
        assert_eq!(SemanticUtils::cosine_similarity(&a, &b), 1.0);
        
        // Orthogonal vectors (no similarity)
        let a = vec![1.0, 0.0, 0.0];
        let b = vec![0.0, 1.0, 0.0];
        assert!((SemanticUtils::cosine_similarity(&a, &b) - 0.0).abs() < 0.001);
        
        // Opposite vectors
        let a = vec![1.0, 0.0, 0.0];
        let b = vec![-1.0, 0.0, 0.0];
        assert!((SemanticUtils::cosine_similarity(&a, &b) - (-1.0)).abs() < 0.001);
        
        // Partial similarity
        let a = vec![1.0, 1.0, 0.0];
        let b = vec![1.0, 0.0, 0.0];
        let sim = SemanticUtils::cosine_similarity(&a, &b);
        assert!(sim > 0.0 && sim < 1.0);
        
        // Empty vectors
        let a: Vec<f32> = vec![];
        let b: Vec<f32> = vec![];
        assert_eq!(SemanticUtils::cosine_similarity(&a, &b), 0.0);
    }

    #[test]
    fn test_tfidf_search() {
        let mut memory = AutoMemory::new();
        
        memory.add(MemoryEntry::new(MemoryCategory::Decision, "使用 PostgreSQL 作为主数据库系统".into(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Technical, "Redis 缓存配置为 10 个连接".into(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Solution, "通过添加 middleware 修复认证问题".into(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Finding, "数据库连接池设置为 20".into(), None));
        
        let mut tfidf = TfIdfSearch::new();
        tfidf.index(&memory);
        
        // Search for "数据库" - should find PostgreSQL and 连接池 entries
        let results = tfidf.search("数据库", Some(5));
        assert!(!results.is_empty());
        // First result should contain "数据库"
        assert!(results[0].0.contains("数据库"));
        
        // Search for "Redis" - should find Redis entry
        let results = tfidf.search("redis", Some(5));
        assert!(!results.is_empty());
        assert!(results[0].0.to_lowercase().contains("redis"));
        
        // Search for something not in any entry
        let results = tfidf.search("mongodb", Some(5));
        assert!(results.is_empty());
    }

    #[test]
    fn test_tfidf_ranking() {
        let mut memory = AutoMemory::new();
        
        // Add entries with varying relevance to "数据库"
        memory.add(MemoryEntry::new(MemoryCategory::Decision, "使用 PostgreSQL 数据库 作为主数据库".into(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Technical, "数据库连接池配置".into(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Solution, "修复了前端样式问题".into(), None));
        
        let mut tfidf = TfIdfSearch::new();
        tfidf.index(&memory);
        
        let results = tfidf.search("数据库", None);
        
        // Should rank entries with more "数据库" mentions higher
        if results.len() >= 2 {
            assert!(results[0].1 >= results[1].1);
        }
    }

    #[test]
    fn test_conflict_detection() {
        let mut memory = AutoMemory::new();
        
        // Add initial decision
        memory.add_memory(
            MemoryCategory::Decision,
            "决定使用 PostgreSQL 作为主数据库".to_string(),
            None,
        );
        assert_eq!(memory.entries.len(), 1);
        assert!(memory.entries[0].content.contains("PostgreSQL"));
        
        // Add conflicting decision (same topic, different choice)
        memory.add_memory(
            MemoryCategory::Decision,
            "决定使用 MySQL 作为主数据库".to_string(),
            None,
        );
        
        // Should have replaced the old one
        assert_eq!(memory.entries.len(), 1);
        assert!(memory.entries[0].content.contains("MySQL"));
    }

    #[test]
    fn test_conflict_with_change_signal() {
        let mut memory = AutoMemory::new();
        
        // Add initial preference
        memory.add_memory(
            MemoryCategory::Preference,
            "偏好使用 vim 编辑器".to_string(),
            None,
        );
        assert_eq!(memory.entries.len(), 1);
        
        // Add replacement with change signal
        memory.add_memory(
            MemoryCategory::Preference,
            "改用 vscode 编辑器，不再使用 vim".to_string(),
            None,
        );
        
        // Should have replaced
        assert_eq!(memory.entries.len(), 1);
        assert!(memory.entries[0].content.contains("vscode"));
    }

    #[test]
    fn test_no_false_conflict() {
        let mut memory = AutoMemory::new();
        
        // Add two different decisions (different topics)
        memory.add_memory(
            MemoryCategory::Decision,
            "决定使用 PostgreSQL 作为主数据库".to_string(),
            None,
        );
        memory.add_memory(
            MemoryCategory::Decision,
            "决定使用 Redis 作为缓存系统".to_string(),
            None,
        );
        
        // Both should exist (different topics, no conflict)
        assert_eq!(memory.entries.len(), 2);
    }

    #[test]
    fn test_contextual_summary() {
        let mut memory = AutoMemory::new();
        
        // Add various memories
        memory.add(MemoryEntry::new(MemoryCategory::Decision, "决定使用 PostgreSQL 作为主数据库".into(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Technical, "前端使用 React 框架开发".into(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Solution, "通过添加 Redis 缓存解决性能问题".into(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Finding, "API 响应时间在 200ms 以内".into(), None));
        memory.add(MemoryEntry::new(MemoryCategory::Preference, "偏好使用 TypeScript 而非 JavaScript".into(), None));
        
        // Context about database - should prioritize database-related memories
        let db_summary = memory.generate_contextual_summary("数据库查询优化", 3);
        assert!(db_summary.contains("PostgreSQL"));
        
        // Context about frontend - should prioritize frontend-related memories
        let fe_summary = memory.generate_contextual_summary("React 组件开发", 3);
        assert!(fe_summary.contains("React"));
        
        // Empty context - should fall back to importance-based
        let empty_summary = memory.generate_contextual_summary("", 3);
        assert!(!empty_summary.is_empty());
    }

    #[test]
    fn test_low_quality_memory_filter() {
        // Formatting artifacts should be rejected
        assert!(is_low_quality_memory("│  🎯 决策: 决定使用 PostgreSQL."));
        assert!(is_low_quality_memory("├── Structure: 入口文件是 main."));
        assert!(is_low_quality_memory("🔧 解决方案: 通过添加 middleware."));
        assert!(is_low_quality_memory("【自动记忆摘要】"));
        assert!(is_low_quality_memory("short"));
        
        // Real content should pass
        assert!(!is_low_quality_memory("决定使用 PostgreSQL 作为主数据库系统"));
        assert!(!is_low_quality_memory("通过添加 Redis 缓存层解决了性能问题"));
        assert!(!is_low_quality_memory("用户偏好使用 TypeScript 进行开发"));
    }
}