rrag 0.1.0-alpha.2

//! # Intelligent Caching Layer
//!
//! Enterprise-grade multi-level caching system designed specifically for RAG applications
//! with semantic awareness, intelligent eviction policies, and advanced performance
//! optimization features.
//!
//! This module provides a comprehensive caching solution that understands the unique
//! characteristics of RAG workloads, including query similarity, embedding reuse,
//! semantic relationships, and result patterns. It offers multiple cache layers
//! working together to minimize latency and computational overhead.
//!
//! ## Key Features
//!
//! - **Multi-Layer Architecture**: Query, embedding, semantic, and result caches
//! - **Semantic Awareness**: Understanding query similarity and content relationships
//! - **Intelligent Eviction**: Multiple policies (LRU, LFU, TTL, ARC, Semantic-aware)
//! - **Persistence Support**: Durable caching across system restarts
//! - **Performance Monitoring**: Comprehensive metrics and analytics
//! - **Memory Management**: Automatic cleanup and pressure-based eviction
//! - **Async Operations**: Non-blocking cache operations for high throughput
//!
//! ## Architecture
//!
//! The caching system consists of four specialized cache layers:
//!
//! 1. **Query Cache**: Caches complete query-response pairs with similarity matching
//! 2. **Embedding Cache**: Reuses expensive embedding computations
//! 3. **Semantic Cache**: Groups semantically similar queries for broader cache hits
//! 4. **Result Cache**: Caches search results for parameter combinations
//!
//! ## Examples
//!
//! ### Basic Cache Setup
//! ```rust
//! use rrag::caching::{CacheService, CacheConfig};
//!
//! # async fn example() -> rrag::RragResult<()> {
//! let cache_config = CacheConfig::default()
//!     .with_query_cache(true)
//!     .with_embedding_cache(true)
//!     .with_semantic_cache(true);
//!
//! let mut cache = CacheService::new(cache_config)?;
//!
//! // Cache will automatically optimize based on your RAG workload patterns
//! println!("Cache initialized with {} layers", 4);
//! # Ok(())
//! # }
//! ```
//!
//! ### Query Result Caching
//! ```rust
//! use rrag::caching::{QueryCacheEntry, CachedSearchResult};
//!
//! # async fn example() -> rrag::RragResult<()> {
//! # let mut cache = rrag::caching::CacheService::new(rrag::caching::CacheConfig::default())?;
//! let query = "What is machine learning?";
//!
//! // Check cache first
//! if let Some(cached_entry) = cache.get_query_results(query).await {
//!     println!("Cache hit! Retrieved {} results", cached_entry.results.len());
//!     return Ok(());
//! }
//!
//! // If not cached, perform search and cache results
//! let search_results = vec![
//!     CachedSearchResult {
//!         document_id: "doc_1".to_string(),
//!         content: "Machine learning is a subset of AI...".to_string(),
//!         score: 0.95,
//!         rank: 1,
//!         metadata: std::collections::HashMap::new(),
//!     }
//! ];
//!
//! let cache_entry = QueryCacheEntry {
//!     query: query.to_string(),
//!     embedding_hash: "hash123".to_string(),
//!     results: search_results,
//!     generated_answer: Some("ML is a field of AI...".to_string()),
//!     metadata: rrag::caching::CacheEntryMetadata::new(),
//! };
//!
//! cache.cache_query_results(query.to_string(), cache_entry).await?;
//! # Ok(())
//! # }
//! ```
//!
//! ### Embedding Caching for Performance
//! ```rust
//! # async fn example() -> rrag::RragResult<()> {
//! # let mut cache = rrag::caching::CacheService::new(rrag::caching::CacheConfig::default())?;
//! let text = "Advanced machine learning techniques";
//! let model = "sentence-transformers/all-MiniLM-L6-v2";
//!
//! // Check if embedding is already computed
//! if let Some(cached_embedding) = cache.get_embedding(text, model).await {
//!     println!("Using cached embedding of dimension {}", cached_embedding.len());
//! } else {
//!     // Compute embedding (expensive operation)
//!     let embedding = compute_embedding(text, model).await?;
//!     
//!     // Cache for future use
//!     cache.cache_embedding(
//!         text.to_string(),
//!         model.to_string(),
//!         embedding
//!     ).await?;
//!     
//!     println!("Computed and cached new embedding");
//! }
//!
//! # async fn compute_embedding(text: &str, model: &str) -> rrag::RragResult<Vec<f32>> {
//! #     Ok(vec![0.1, 0.2, 0.3]) // Mock embedding
//! # }
//! # Ok(())
//! # }
//! ```
//!
//! ### Semantic Similarity Caching
//! ```rust
//! use rrag::caching::{SemanticCacheEntry, SimilarEntry};
//!
//! # async fn example() -> rrag::RragResult<()> {
//! # let mut cache = rrag::caching::CacheService::new(rrag::caching::CacheConfig::default())?;
//! let similar_queries = [
//!     "What is artificial intelligence?",
//!     "Explain AI concepts",
//!     "Define artificial intelligence",
//! ];
//!
//! // Check for semantically similar cached results
//! for query in &similar_queries {
//!     if let Some(semantic_entry) = cache.get_semantic_results(query).await {
//!         println!("Found semantic match for: {}", query);
//!         println!("Representative: {}", semantic_entry.representative);
//!         println!("Similar entries: {}", semantic_entry.similar_entries.len());
//!         return Ok(());
//!     }
//! }
//!
//! // Create semantic cache entry for related queries
//! let semantic_entry = SemanticCacheEntry {
//!     representative: "What is artificial intelligence?".to_string(),
//!     cluster_id: Some(1),
//!     similar_entries: vec![
//!         SimilarEntry {
//!             text: "Explain AI concepts".to_string(),
//!             similarity: 0.92,
//!             added_at: std::time::SystemTime::now(),
//!         },
//!         SimilarEntry {
//!             text: "Define artificial intelligence".to_string(),
//!             similarity: 0.89,
//!             added_at: std::time::SystemTime::now(),
//!         },
//!     ],
//!     results: vec![], // Shared results for all similar queries
//!     metadata: rrag::caching::CacheEntryMetadata::new(),
//! };
//!
//! cache.cache_semantic_results(
//!     "ai_concepts_cluster".to_string(),
//!     semantic_entry
//! ).await?;
//! # Ok(())
//! # }
//! ```
//!
//! ### Advanced Cache Configuration
//! ```rust
//! use rrag::caching::{
//!     CacheConfig, QueryCacheConfig, EmbeddingCacheConfig,
//!     EvictionPolicy, PersistenceConfig, PersistenceFormat
//! };
//! use std::time::Duration;
//!
//! # async fn example() -> rrag::RragResult<()> {
//! let advanced_config = CacheConfig {
//!     enabled: true,
//!     query_cache: QueryCacheConfig {
//!         enabled: true,
//!         max_size: 5000,
//!         ttl: Duration::from_secs(7200), // 2 hours
//!         eviction_policy: EvictionPolicy::SemanticAware,
//!         similarity_threshold: 0.92,
//!     },
//!     embedding_cache: EmbeddingCacheConfig {
//!         enabled: true,
//!         max_size: 50000,
//!         ttl: Duration::from_secs(86400), // 24 hours
//!         eviction_policy: EvictionPolicy::LFU,
//!         compression_enabled: true,
//!     },
//!     persistence: PersistenceConfig {
//!         enabled: true,
//!         storage_path: "/data/rag_cache".to_string(),
//!         auto_save_interval: Duration::from_secs(300),
//!         format: PersistenceFormat::MessagePack,
//!     },
//!     ..Default::default()
//! };
//!
//! let cache = CacheService::new(advanced_config)?;
//! println!("Advanced cache configured with persistence and compression");
//! # Ok(())
//! # }
//! ```
//!
//! ### Cache Performance Monitoring
//! ```rust
//! # async fn example() -> rrag::RragResult<()> {
//! # let mut cache = rrag::caching::CacheService::new(rrag::caching::CacheConfig::default())?;
//! // Get comprehensive cache metrics
//! let metrics = cache.get_metrics();
//!
//! println!("📊 Cache Performance Report");
//! println!("Query Cache: {:.1}% hit rate, {} entries",
//!          metrics.query_cache.hit_rate * 100.0,
//!          metrics.query_cache.total_entries);
//!
//! println!("Embedding Cache: {:.1}% hit rate, {:.1}MB memory",
//!          metrics.embedding_cache.hit_rate * 100.0,
//!          metrics.embedding_cache.memory_usage as f32 / 1024.0 / 1024.0);
//!
//! println!("Semantic Cache: {:.1}% hit rate, {} evictions",
//!          metrics.semantic_cache.hit_rate * 100.0,
//!          metrics.semantic_cache.evictions);
//!
//! println!("Overall Efficiency: {:.1}%, Time Saved: {:.1}ms",
//!          metrics.overall.efficiency_score * 100.0,
//!          metrics.overall.time_saved_ms);
//!
//! // Performance optimization based on metrics
//! if metrics.overall.memory_pressure > 0.8 {
//!     println!("⚠️  High memory pressure detected, triggering cleanup");
//!     cache.maintenance().await?;
//! }
//! # Ok(())
//! # }
//! ```
//!
//! ## Cache Eviction Policies
//!
//! ### LRU (Least Recently Used)
//! Best for: General-purpose caching with temporal locality
//! - Evicts items that haven't been accessed recently
//! - Good memory efficiency
//! - Simple and fast
//!
//! ### LFU (Least Frequently Used)
//! Best for: Embedding caches where popular items should stay
//! - Evicts items with lowest access frequency
//! - Excellent for reusable computations
//! - Handles long-term access patterns
//!
//! ### TTL (Time-To-Live)
//! Best for: Fresh data requirements
//! - Evicts items after fixed time period
//! - Ensures data freshness
//! - Predictable memory usage
//!
//! ### Semantic-Aware
//! Best for: RAG-specific query patterns
//! - Considers semantic similarity in eviction decisions
//! - Maintains representative queries from clusters
//! - Optimizes for query pattern diversity
//!
//! ## Performance Optimization Tips
//!
//! 1. **Tune Cache Sizes**: Monitor hit rates and adjust max_size accordingly
//! 2. **Enable Compression**: For embedding caches with large vectors
//! 3. **Use Persistence**: For frequently reused embeddings across sessions
//! 4. **Semantic Clustering**: Enable for diverse query workloads
//! 5. **Async Operations**: Enable for high-throughput applications
//! 6. **Memory Monitoring**: Set appropriate pressure thresholds
//!
//! ## Integration with RAG Systems
//!
//! ```rust
//! use rrag::{RragSystemBuilder, caching::CacheConfig};
//!
//! # async fn example() -> rrag::RragResult<()> {
//! let rag_system = RragSystemBuilder::new()
//!     .with_caching(
//!         CacheConfig::production()
//!             .with_semantic_awareness(true)
//!             .with_persistence(true)
//!             .with_intelligent_eviction(true)
//!     )
//!     .build()
//!     .await?;
//!
//! // Cache automatically optimizes based on your query patterns
//! let results = rag_system.search("machine learning applications", Some(10)).await?;
//! // Subsequent similar queries will benefit from semantic caching
//! # Ok(())
//! # }
//! ```

pub mod cache_core;
pub mod embedding_cache;
pub mod metrics;
pub mod persistence;
pub mod policies;
pub mod query_cache;
pub mod result_cache;
pub mod semantic_cache;

use crate::RragResult;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::hash::{Hash, Hasher};
use std::time::{Duration, SystemTime};

/// Main caching service orchestrating multiple cache layers
pub struct CacheService {
    /// Query result cache
    query_cache: Box<dyn Cache<String, QueryCacheEntry>>,

    /// Embedding cache for reusing computations
    embedding_cache: Box<dyn Cache<String, EmbeddingCacheEntry>>,

    /// Semantic similarity cache
    semantic_cache: Box<dyn Cache<String, SemanticCacheEntry>>,

    /// Document retrieval cache
    result_cache: Box<dyn Cache<String, ResultCacheEntry>>,

    /// Cache configuration
    config: CacheConfig,

    /// Performance metrics
    metrics: CacheMetrics,
}

/// Global cache configuration
#[derive(Debug, Clone)]
pub struct CacheConfig {
    /// Enable/disable caching globally
    pub enabled: bool,

    /// Query cache configuration
    pub query_cache: QueryCacheConfig,

    /// Embedding cache configuration
    pub embedding_cache: EmbeddingCacheConfig,

    /// Semantic cache configuration
    pub semantic_cache: SemanticCacheConfig,

    /// Result cache configuration
    pub result_cache: ResultCacheConfig,

    /// Persistence configuration
    pub persistence: PersistenceConfig,

    /// Performance tuning
    pub performance: PerformanceConfig,
}

/// Query cache configuration
#[derive(Debug, Clone)]
pub struct QueryCacheConfig {
    pub enabled: bool,
    pub max_size: usize,
    pub ttl: Duration,
    pub eviction_policy: EvictionPolicy,
    pub similarity_threshold: f32,
}

/// Embedding cache configuration
#[derive(Debug, Clone)]
pub struct EmbeddingCacheConfig {
    pub enabled: bool,
    pub max_size: usize,
    pub ttl: Duration,
    pub eviction_policy: EvictionPolicy,
    pub compression_enabled: bool,
}

/// Semantic cache configuration
#[derive(Debug, Clone)]
pub struct SemanticCacheConfig {
    pub enabled: bool,
    pub max_size: usize,
    pub ttl: Duration,
    pub similarity_threshold: f32,
    pub clustering_enabled: bool,
    pub max_clusters: usize,
}

/// Result cache configuration
#[derive(Debug, Clone)]
pub struct ResultCacheConfig {
    pub enabled: bool,
    pub max_size: usize,
    pub ttl: Duration,
    pub eviction_policy: EvictionPolicy,
    pub compress_large_results: bool,
}

/// Persistence configuration
#[derive(Debug, Clone)]
pub struct PersistenceConfig {
    pub enabled: bool,
    pub storage_path: String,
    pub auto_save_interval: Duration,
    pub format: PersistenceFormat,
}

/// Performance configuration
#[derive(Debug, Clone)]
pub struct PerformanceConfig {
    pub async_writes: bool,
    pub batch_operations: bool,
    pub background_cleanup: bool,
    pub memory_pressure_threshold: f32,
}

/// Cache eviction policies
#[derive(Debug, Clone, Copy)]
pub enum EvictionPolicy {
    /// Least Recently Used
    LRU,
    /// Least Frequently Used
    LFU,
    /// Time-To-Live based
    TTL,
    /// Adaptive Replacement Cache
    ARC,
    /// Custom semantic-aware policy
    SemanticAware,
}

/// Persistence formats
#[derive(Debug, Clone)]
pub enum PersistenceFormat {
    Binary,
    Json,
    MessagePack,
}

/// Generic cache trait
pub trait Cache<K, V>: Send + Sync
where
    K: Hash + Eq + Clone + Send + Sync + 'static,
    V: Clone + Send + Sync + 'static,
{
    /// Get value from cache
    fn get(&self, key: &K) -> Option<V>;

    /// Put value into cache
    fn put(&mut self, key: K, value: V) -> RragResult<()>;

    /// Remove value from cache
    fn remove(&mut self, key: &K) -> Option<V>;

    /// Check if key exists
    fn contains(&self, key: &K) -> bool;

    /// Clear all entries
    fn clear(&mut self);

    /// Get cache size
    fn size(&self) -> usize;

    /// Get cache statistics
    fn stats(&self) -> CacheStats;
}

/// Cache entry metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheEntryMetadata {
    /// Creation timestamp
    pub created_at: SystemTime,

    /// Last access timestamp
    pub last_accessed: SystemTime,

    /// Access count
    pub access_count: u64,

    /// Entry size in bytes
    pub size_bytes: usize,

    /// Time-to-live
    pub ttl: Option<Duration>,

    /// Custom metadata
    pub custom: HashMap<String, String>,
}

/// Query cache entry
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QueryCacheEntry {
    /// Original query
    pub query: String,

    /// Query embedding hash
    pub embedding_hash: String,

    /// Cached results
    pub results: Vec<CachedSearchResult>,

    /// Generation result if any
    pub generated_answer: Option<String>,

    /// Metadata
    pub metadata: CacheEntryMetadata,
}

/// Embedding cache entry
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmbeddingCacheEntry {
    /// Input text
    pub text: String,

    /// Text hash for verification
    pub text_hash: String,

    /// Computed embedding
    pub embedding: Vec<f32>,

    /// Model used for embedding
    pub model: String,

    /// Metadata
    pub metadata: CacheEntryMetadata,
}

/// Semantic cache entry
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SemanticCacheEntry {
    /// Representative query/text
    pub representative: String,

    /// Cluster ID if clustering enabled
    pub cluster_id: Option<usize>,

    /// Similar queries/texts
    pub similar_entries: Vec<SimilarEntry>,

    /// Cached semantic results
    pub results: Vec<CachedSearchResult>,

    /// Metadata
    pub metadata: CacheEntryMetadata,
}

/// Result cache entry
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResultCacheEntry {
    /// Search parameters hash
    pub params_hash: String,

    /// Cached search results
    pub results: Vec<CachedSearchResult>,

    /// Result metadata
    pub result_metadata: HashMap<String, String>,

    /// Metadata
    pub metadata: CacheEntryMetadata,
}

/// Similar entry for semantic cache
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SimilarEntry {
    /// Similar text
    pub text: String,

    /// Similarity score
    pub similarity: f32,

    /// When added
    pub added_at: SystemTime,
}

/// Cached search result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CachedSearchResult {
    /// Document ID
    pub document_id: String,

    /// Document content
    pub content: String,

    /// Relevance score
    pub score: f32,

    /// Result rank
    pub rank: usize,

    /// Additional metadata
    pub metadata: HashMap<String, String>,
}

/// Cache statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheStats {
    /// Total number of entries
    pub total_entries: usize,

    /// Total cache hits
    pub hits: u64,

    /// Total cache misses
    pub misses: u64,

    /// Hit rate percentage
    pub hit_rate: f32,

    /// Total memory usage in bytes
    pub memory_usage: usize,

    /// Average access time in microseconds
    pub avg_access_time_us: f32,

    /// Eviction count
    pub evictions: u64,

    /// Last cleanup time
    pub last_cleanup: SystemTime,
}

/// Cache performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheMetrics {
    /// Query cache metrics
    pub query_cache: CacheStats,

    /// Embedding cache metrics
    pub embedding_cache: CacheStats,

    /// Semantic cache metrics
    pub semantic_cache: CacheStats,

    /// Result cache metrics
    pub result_cache: CacheStats,

    /// Overall performance metrics
    pub overall: OverallCacheMetrics,
}

/// Overall cache performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OverallCacheMetrics {
    /// Total memory saved (bytes)
    pub memory_saved: usize,

    /// Total time saved (milliseconds)
    pub time_saved_ms: f32,

    /// Cache efficiency score
    pub efficiency_score: f32,

    /// Memory pressure level (0.0 to 1.0)
    pub memory_pressure: f32,

    /// Total operations per second
    pub ops_per_second: f32,
}

impl CacheService {
    /// Create new cache service
    pub fn new(config: CacheConfig) -> RragResult<Self> {
        let query_cache = Box::new(query_cache::QueryCache::new(config.query_cache.clone())?);

        let embedding_cache = Box::new(embedding_cache::EmbeddingCache::new(
            config.embedding_cache.clone(),
        )?);

        let semantic_cache = Box::new(semantic_cache::SemanticCache::new(
            config.semantic_cache.clone(),
        )?);

        let result_cache = Box::new(result_cache::ResultCache::new(config.result_cache.clone())?);

        Ok(Self {
            query_cache,
            embedding_cache,
            semantic_cache,
            result_cache,
            config,
            metrics: CacheMetrics::default(),
        })
    }

    /// Get cached query results
    pub async fn get_query_results(&self, query: &str) -> Option<QueryCacheEntry> {
        if !self.config.enabled || !self.config.query_cache.enabled {
            return None;
        }

        self.query_cache.get(&query.to_string())
    }

    /// Cache query results
    pub async fn cache_query_results(
        &mut self,
        query: String,
        entry: QueryCacheEntry,
    ) -> RragResult<()> {
        if !self.config.enabled || !self.config.query_cache.enabled {
            return Ok(());
        }

        self.query_cache.put(query, entry)
    }

    /// Get cached embedding
    pub async fn get_embedding(&self, text: &str, model: &str) -> Option<Vec<f32>> {
        if !self.config.enabled || !self.config.embedding_cache.enabled {
            return None;
        }

        let key = format!("{}:{}", model, text);
        self.embedding_cache.get(&key).map(|entry| entry.embedding)
    }

    /// Cache embedding
    pub async fn cache_embedding(
        &mut self,
        text: String,
        model: String,
        embedding: Vec<f32>,
    ) -> RragResult<()> {
        if !self.config.enabled || !self.config.embedding_cache.enabled {
            return Ok(());
        }

        let key = format!("{}:{}", model, text);
        let entry = EmbeddingCacheEntry {
            text: text.clone(),
            text_hash: Self::hash_string(&text),
            embedding,
            model,
            metadata: CacheEntryMetadata::new(),
        };

        self.embedding_cache.put(key, entry)
    }

    /// Get semantically similar cached results
    pub async fn get_semantic_results(&self, query: &str) -> Option<SemanticCacheEntry> {
        if !self.config.enabled || !self.config.semantic_cache.enabled {
            return None;
        }

        self.semantic_cache.get(&query.to_string())
    }

    /// Cache semantic results
    pub async fn cache_semantic_results(
        &mut self,
        query: String,
        entry: SemanticCacheEntry,
    ) -> RragResult<()> {
        if !self.config.enabled || !self.config.semantic_cache.enabled {
            return Ok(());
        }

        self.semantic_cache.put(query, entry)
    }

    /// Get cache metrics
    pub fn get_metrics(&self) -> &CacheMetrics {
        &self.metrics
    }

    /// Clear all caches
    pub fn clear_all(&mut self) {
        self.query_cache.clear();
        self.embedding_cache.clear();
        self.semantic_cache.clear();
        self.result_cache.clear();
    }

    /// Perform cache maintenance
    pub async fn maintenance(&mut self) -> RragResult<()> {
        // Background cleanup, eviction, persistence, etc.
        Ok(())
    }

    /// Hash string for cache keys
    fn hash_string(s: &str) -> String {
        use std::collections::hash_map::DefaultHasher;
        let mut hasher = DefaultHasher::new();
        s.hash(&mut hasher);
        format!("{:x}", hasher.finish())
    }
}

impl CacheEntryMetadata {
    /// Create new metadata
    pub fn new() -> Self {
        let now = SystemTime::now();
        Self {
            created_at: now,
            last_accessed: now,
            access_count: 0,
            size_bytes: 0,
            ttl: None,
            custom: HashMap::new(),
        }
    }

    /// Update access info
    pub fn accessed(&mut self) {
        self.last_accessed = SystemTime::now();
        self.access_count += 1;
    }

    /// Check if entry has expired
    pub fn is_expired(&self) -> bool {
        if let Some(ttl) = self.ttl {
            if let Ok(elapsed) = self.created_at.elapsed() {
                return elapsed > ttl;
            }
        }
        false
    }
}

impl Default for CacheConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            query_cache: QueryCacheConfig::default(),
            embedding_cache: EmbeddingCacheConfig::default(),
            semantic_cache: SemanticCacheConfig::default(),
            result_cache: ResultCacheConfig::default(),
            persistence: PersistenceConfig::default(),
            performance: PerformanceConfig::default(),
        }
    }
}

impl Default for QueryCacheConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            max_size: 1000,
            ttl: Duration::from_secs(3600), // 1 hour
            eviction_policy: EvictionPolicy::LRU,
            similarity_threshold: 0.95,
        }
    }
}

impl Default for EmbeddingCacheConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            max_size: 10000,
            ttl: Duration::from_secs(86400), // 24 hours
            eviction_policy: EvictionPolicy::LFU,
            compression_enabled: true,
        }
    }
}

impl Default for SemanticCacheConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            max_size: 5000,
            ttl: Duration::from_secs(7200), // 2 hours
            similarity_threshold: 0.85,
            clustering_enabled: true,
            max_clusters: 100,
        }
    }
}

impl Default for ResultCacheConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            max_size: 2000,
            ttl: Duration::from_secs(1800), // 30 minutes
            eviction_policy: EvictionPolicy::TTL,
            compress_large_results: true,
        }
    }
}

impl Default for PersistenceConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            storage_path: "./cache".to_string(),
            auto_save_interval: Duration::from_secs(300), // 5 minutes
            format: PersistenceFormat::Binary,
        }
    }
}

impl Default for PerformanceConfig {
    fn default() -> Self {
        Self {
            async_writes: true,
            batch_operations: true,
            background_cleanup: true,
            memory_pressure_threshold: 0.8,
        }
    }
}

impl Default for CacheStats {
    fn default() -> Self {
        Self {
            total_entries: 0,
            hits: 0,
            misses: 0,
            hit_rate: 0.0,
            memory_usage: 0,
            avg_access_time_us: 0.0,
            evictions: 0,
            last_cleanup: SystemTime::now(),
        }
    }
}

impl Default for CacheMetrics {
    fn default() -> Self {
        Self {
            query_cache: CacheStats::default(),
            embedding_cache: CacheStats::default(),
            semantic_cache: CacheStats::default(),
            result_cache: CacheStats::default(),
            overall: OverallCacheMetrics::default(),
        }
    }
}

impl Default for OverallCacheMetrics {
    fn default() -> Self {
        Self {
            memory_saved: 0,
            time_saved_ms: 0.0,
            efficiency_score: 0.0,
            memory_pressure: 0.0,
            ops_per_second: 0.0,
        }
    }
}

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

    #[tokio::test]
    async fn test_cache_service_creation() {
        let config = CacheConfig::default();
        let cache_service = CacheService::new(config).unwrap();

        let metrics = cache_service.get_metrics();
        assert_eq!(metrics.overall.efficiency_score, 0.0);
    }

    #[test]
    fn test_cache_entry_metadata() {
        let mut metadata = CacheEntryMetadata::new();
        assert_eq!(metadata.access_count, 0);

        metadata.accessed();
        assert_eq!(metadata.access_count, 1);
    }

    #[test]
    fn test_cache_config_defaults() {
        let config = CacheConfig::default();
        assert!(config.enabled);
        assert!(config.query_cache.enabled);
        assert!(config.embedding_cache.enabled);
    }
}