rrag 0.1.0-alpha.2

//! # Multi-Signal Reranking
//!
//! Combines multiple relevance signals beyond semantic similarity to improve
//! retrieval accuracy. Includes signals like freshness, authority, click-through
//! rates, document quality, and user preferences.

use crate::{RragResult, SearchResult};
use std::collections::HashMap;

/// Multi-signal reranker that combines various relevance signals
pub struct MultiSignalReranker {
    /// Configuration
    config: MultiSignalConfig,

    /// Signal extractors
    signal_extractors: HashMap<SignalType, Box<dyn SignalExtractor>>,

    /// Signal weights (learned or configured)
    signal_weights: HashMap<SignalType, f32>,

    /// Signal aggregation method
    aggregation: SignalAggregation,
}

/// Configuration for multi-signal reranking
#[derive(Debug, Clone)]
pub struct MultiSignalConfig {
    /// Enabled signal types
    pub enabled_signals: Vec<SignalType>,

    /// Signal weights
    pub signal_weights: HashMap<SignalType, SignalWeight>,

    /// Aggregation method
    pub aggregation_method: SignalAggregation,

    /// Normalization method
    pub normalization: SignalNormalization,

    /// Minimum signal confidence
    pub min_signal_confidence: f32,

    /// Enable adaptive weighting
    pub enable_adaptive_weights: bool,

    /// Learning rate for adaptive weights
    pub learning_rate: f32,
}

impl Default for MultiSignalConfig {
    fn default() -> Self {
        let mut signal_weights = HashMap::new();
        signal_weights.insert(SignalType::SemanticRelevance, SignalWeight::Fixed(0.3));
        signal_weights.insert(SignalType::TextualRelevance, SignalWeight::Fixed(0.25));
        signal_weights.insert(SignalType::DocumentFreshness, SignalWeight::Fixed(0.15));
        signal_weights.insert(SignalType::DocumentAuthority, SignalWeight::Fixed(0.1));
        signal_weights.insert(SignalType::DocumentQuality, SignalWeight::Fixed(0.1));
        signal_weights.insert(SignalType::UserPreference, SignalWeight::Fixed(0.05));
        signal_weights.insert(SignalType::ClickThroughRate, SignalWeight::Fixed(0.05));

        Self {
            enabled_signals: vec![
                SignalType::SemanticRelevance,
                SignalType::TextualRelevance,
                SignalType::DocumentFreshness,
                SignalType::DocumentQuality,
            ],
            signal_weights,
            aggregation_method: SignalAggregation::WeightedSum,
            normalization: SignalNormalization::MinMax,
            min_signal_confidence: 0.1,
            enable_adaptive_weights: false,
            learning_rate: 0.01,
        }
    }
}

/// Types of relevance signals
#[derive(Debug, Clone, Hash, PartialEq, Eq)]
pub enum SignalType {
    /// Semantic similarity between query and document
    SemanticRelevance,
    /// Textual/keyword relevance (BM25, TF-IDF)
    TextualRelevance,
    /// Document freshness/recency
    DocumentFreshness,
    /// Document authority/credibility
    DocumentAuthority,
    /// Document quality metrics
    DocumentQuality,
    /// User preference signals
    UserPreference,
    /// Click-through rates
    ClickThroughRate,
    /// Document popularity
    DocumentPopularity,
    /// Query-document interaction history
    InteractionHistory,
    /// Domain-specific signals
    DomainSpecific(String),
}

/// Signal weight configuration
pub enum SignalWeight {
    /// Fixed weight
    Fixed(f32),
    /// Query-dependent weight
    QueryDependent(Box<dyn Fn(&str) -> f32 + Send + Sync>),
    /// Learned weight (requires training data)
    Learned,
    /// Adaptive weight (updates based on feedback)
    Adaptive(f32), // Current weight
}

impl std::fmt::Debug for SignalWeight {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            Self::Fixed(w) => write!(f, "Fixed({})", w),
            Self::QueryDependent(_) => write!(f, "QueryDependent(<function>)"),
            Self::Learned => write!(f, "Learned"),
            Self::Adaptive(w) => write!(f, "Adaptive({})", w),
        }
    }
}

impl Clone for SignalWeight {
    fn clone(&self) -> Self {
        match self {
            Self::Fixed(w) => Self::Fixed(*w),
            Self::QueryDependent(_) => Self::Fixed(0.5), // Can't clone function, default to fixed
            Self::Learned => Self::Learned,
            Self::Adaptive(w) => Self::Adaptive(*w),
        }
    }
}

/// Methods for aggregating multiple signals
#[derive(Debug, Clone)]
pub enum SignalAggregation {
    /// Weighted sum of signals
    WeightedSum,
    /// Weighted average
    WeightedAverage,
    /// Maximum signal value
    Max,
    /// Minimum signal value
    Min,
    /// Learning-to-rank combination
    LearnedCombination,
    /// Custom aggregation function
    Custom(String),
}

/// Signal normalization methods
#[derive(Debug, Clone)]
pub enum SignalNormalization {
    /// Min-max normalization (0-1 range)
    MinMax,
    /// Z-score normalization
    ZScore,
    /// Rank normalization
    Rank,
    /// Sigmoid normalization
    Sigmoid,
    /// No normalization
    None,
}

/// A relevance signal extracted from query-document pair
#[derive(Debug, Clone)]
pub struct RelevanceSignal {
    /// Type of signal
    pub signal_type: SignalType,

    /// Signal value (typically 0-1)
    pub value: f32,

    /// Confidence in the signal (0-1)
    pub confidence: f32,

    /// Signal metadata
    pub metadata: SignalMetadata,
}

/// Metadata about a relevance signal
#[derive(Debug, Clone)]
pub struct SignalMetadata {
    /// Source of the signal
    pub source: String,

    /// Extraction time
    pub extraction_time_ms: u64,

    /// Features used
    pub features: HashMap<String, f32>,

    /// Warnings or notes
    pub warnings: Vec<String>,
}

/// Trait for extracting relevance signals
pub trait SignalExtractor: Send + Sync {
    /// Extract signal from query-document pair
    fn extract_signal(
        &self,
        query: &str,
        document: &SearchResult,
        context: &RetrievalContext,
    ) -> RragResult<RelevanceSignal>;

    /// Extract signals for multiple documents in batch
    fn extract_batch(
        &self,
        query: &str,
        documents: &[SearchResult],
        context: &RetrievalContext,
    ) -> RragResult<Vec<RelevanceSignal>> {
        documents
            .iter()
            .map(|doc| self.extract_signal(query, doc, context))
            .collect()
    }

    /// Get signal type
    fn signal_type(&self) -> SignalType;

    /// Get extractor configuration
    fn get_config(&self) -> SignalExtractorConfig;
}

/// Configuration for signal extractors
#[derive(Debug, Clone)]
pub struct SignalExtractorConfig {
    /// Extractor name
    pub name: String,

    /// Extractor version
    pub version: String,

    /// Supported features
    pub features: Vec<String>,

    /// Performance characteristics
    pub performance: PerformanceMetrics,
}

/// Performance metrics for signal extractors
#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    /// Average extraction time (ms)
    pub avg_extraction_time_ms: f32,

    /// Accuracy/precision of the signal
    pub accuracy: f32,

    /// Memory usage (MB)
    pub memory_usage_mb: f32,
}

/// Context for signal extraction
#[derive(Debug, Clone)]
pub struct RetrievalContext {
    /// User identifier (if available)
    pub user_id: Option<String>,

    /// Session information
    pub session_id: Option<String>,

    /// Query timestamp
    pub timestamp: chrono::DateTime<chrono::Utc>,

    /// Query type/intent
    pub query_intent: Option<String>,

    /// User preferences
    pub user_preferences: HashMap<String, f32>,

    /// Historical interactions
    pub interaction_history: Vec<InteractionRecord>,
}

/// Historical interaction record
#[derive(Debug, Clone)]
pub struct InteractionRecord {
    /// Document ID
    pub document_id: String,

    /// Interaction type (click, dwell, etc.)
    pub interaction_type: String,

    /// Interaction timestamp
    pub timestamp: chrono::DateTime<chrono::Utc>,

    /// Interaction value/strength
    pub value: f32,
}

impl MultiSignalReranker {
    /// Create a new multi-signal reranker
    pub fn new(config: MultiSignalConfig) -> Self {
        let mut reranker = Self {
            config: config.clone(),
            signal_extractors: HashMap::new(),
            signal_weights: HashMap::new(),
            aggregation: config.aggregation_method.clone(),
        };

        // Initialize signal extractors
        reranker.initialize_extractors();

        // Initialize weights
        reranker.initialize_weights();

        reranker
    }

    /// Initialize signal extractors based on configuration
    fn initialize_extractors(&mut self) {
        for signal_type in &self.config.enabled_signals {
            let extractor: Box<dyn SignalExtractor> = match signal_type {
                SignalType::SemanticRelevance => Box::new(SemanticRelevanceExtractor::new()),
                SignalType::TextualRelevance => Box::new(TextualRelevanceExtractor::new()),
                SignalType::DocumentFreshness => Box::new(DocumentFreshnessExtractor::new()),
                SignalType::DocumentAuthority => Box::new(DocumentAuthorityExtractor::new()),
                SignalType::DocumentQuality => Box::new(DocumentQualityExtractor::new()),
                SignalType::UserPreference => Box::new(UserPreferenceExtractor::new()),
                SignalType::ClickThroughRate => Box::new(ClickThroughRateExtractor::new()),
                SignalType::DocumentPopularity => Box::new(DocumentPopularityExtractor::new()),
                SignalType::InteractionHistory => Box::new(InteractionHistoryExtractor::new()),
                SignalType::DomainSpecific(domain) => {
                    Box::new(DomainSpecificExtractor::new(domain.clone()))
                }
            };

            self.signal_extractors
                .insert(signal_type.clone(), extractor);
        }
    }

    /// Initialize signal weights
    fn initialize_weights(&mut self) {
        for (signal_type, weight_config) in &self.config.signal_weights {
            let weight = match weight_config {
                SignalWeight::Fixed(w) => *w,
                SignalWeight::Adaptive(w) => *w,
                SignalWeight::Learned => 1.0 / self.config.signal_weights.len() as f32, // Default uniform
                SignalWeight::QueryDependent(_) => 1.0, // Will be computed per query
            };

            self.signal_weights.insert(signal_type.clone(), weight);
        }
    }

    /// Rerank search results using multiple signals
    pub async fn rerank(
        &self,
        query: &str,
        results: &[SearchResult],
    ) -> RragResult<HashMap<usize, f32>> {
        let context = RetrievalContext {
            user_id: None,
            session_id: None,
            timestamp: chrono::Utc::now(),
            query_intent: None,
            user_preferences: HashMap::new(),
            interaction_history: Vec::new(),
        };

        self.rerank_with_context(query, results, &context).await
    }

    /// Rerank with full context information
    pub async fn rerank_with_context(
        &self,
        query: &str,
        results: &[SearchResult],
        context: &RetrievalContext,
    ) -> RragResult<HashMap<usize, f32>> {
        let mut final_scores = HashMap::new();

        // Extract all signals for all documents
        let mut all_signals: HashMap<SignalType, Vec<RelevanceSignal>> = HashMap::new();

        for (signal_type, extractor) in &self.signal_extractors {
            match extractor.extract_batch(query, results, context) {
                Ok(signals) => {
                    all_signals.insert(signal_type.clone(), signals);
                }
                Err(e) => {
                    eprintln!("Warning: Failed to extract signal {:?}: {}", signal_type, e);
                    // Continue with other signals
                }
            }
        }

        // Normalize signals if needed
        let normalized_signals = self.normalize_signals(all_signals)?;

        // Compute final scores for each document
        for (doc_idx, _) in results.iter().enumerate() {
            let mut signal_values = Vec::new();
            let mut signal_weights = Vec::new();

            for (signal_type, signals) in &normalized_signals {
                if let Some(signal) = signals.get(doc_idx) {
                    if signal.confidence >= self.config.min_signal_confidence {
                        signal_values.push(signal.value);

                        let weight = self.get_signal_weight(signal_type, query, signal)?;
                        signal_weights.push(weight);
                    }
                }
            }

            // Aggregate signals
            let final_score = self.aggregate_signals(&signal_values, &signal_weights)?;
            final_scores.insert(doc_idx, final_score);
        }

        Ok(final_scores)
    }

    /// Normalize signals based on configuration
    fn normalize_signals(
        &self,
        signals: HashMap<SignalType, Vec<RelevanceSignal>>,
    ) -> RragResult<HashMap<SignalType, Vec<RelevanceSignal>>> {
        let mut normalized = HashMap::new();

        for (signal_type, signal_list) in signals {
            let normalized_list = match self.config.normalization {
                SignalNormalization::MinMax => self.normalize_min_max(&signal_list),
                SignalNormalization::ZScore => self.normalize_z_score(&signal_list),
                SignalNormalization::Rank => self.normalize_rank(&signal_list),
                SignalNormalization::Sigmoid => self.normalize_sigmoid(&signal_list),
                SignalNormalization::None => signal_list,
            };

            normalized.insert(signal_type, normalized_list);
        }

        Ok(normalized)
    }

    /// Min-max normalization
    fn normalize_min_max(&self, signals: &[RelevanceSignal]) -> Vec<RelevanceSignal> {
        let values: Vec<f32> = signals.iter().map(|s| s.value).collect();
        let min_val = values.iter().fold(f32::INFINITY, |a, &b| a.min(b));
        let max_val = values.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));

        let range = max_val - min_val;
        if range == 0.0 {
            return signals.to_vec(); // No normalization needed
        }

        signals
            .iter()
            .map(|signal| {
                let mut normalized = signal.clone();
                normalized.value = (signal.value - min_val) / range;
                normalized
            })
            .collect()
    }

    /// Z-score normalization
    fn normalize_z_score(&self, signals: &[RelevanceSignal]) -> Vec<RelevanceSignal> {
        let values: Vec<f32> = signals.iter().map(|s| s.value).collect();
        let mean = values.iter().sum::<f32>() / values.len() as f32;
        let variance = values.iter().map(|v| (v - mean).powi(2)).sum::<f32>() / values.len() as f32;
        let std_dev = variance.sqrt();

        if std_dev == 0.0 {
            return signals.to_vec();
        }

        signals
            .iter()
            .map(|signal| {
                let mut normalized = signal.clone();
                normalized.value = (signal.value - mean) / std_dev;
                // Convert to 0-1 range using sigmoid
                normalized.value = 1.0 / (1.0 + (-normalized.value).exp());
                normalized
            })
            .collect()
    }

    /// Rank normalization
    fn normalize_rank(&self, signals: &[RelevanceSignal]) -> Vec<RelevanceSignal> {
        let mut indexed_signals: Vec<(usize, &RelevanceSignal)> =
            signals.iter().enumerate().collect();

        indexed_signals.sort_by(|a, b| {
            b.1.value
                .partial_cmp(&a.1.value)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        let mut normalized = vec![signals[0].clone(); signals.len()];
        for (rank, (original_idx, signal)) in indexed_signals.iter().enumerate() {
            normalized[*original_idx] = (*signal).clone();
            normalized[*original_idx].value = 1.0 - (rank as f32 / signals.len() as f32);
        }

        normalized
    }

    /// Sigmoid normalization
    fn normalize_sigmoid(&self, signals: &[RelevanceSignal]) -> Vec<RelevanceSignal> {
        signals
            .iter()
            .map(|signal| {
                let mut normalized = signal.clone();
                normalized.value = 1.0 / (1.0 + (-signal.value).exp());
                normalized
            })
            .collect()
    }

    /// Get weight for a specific signal
    fn get_signal_weight(
        &self,
        signal_type: &SignalType,
        query: &str,
        _signal: &RelevanceSignal,
    ) -> RragResult<f32> {
        if let Some(weight_config) = self.config.signal_weights.get(signal_type) {
            match weight_config {
                SignalWeight::Fixed(w) => Ok(*w),
                SignalWeight::Adaptive(w) => Ok(*w),
                SignalWeight::Learned => {
                    Ok(self.signal_weights.get(signal_type).copied().unwrap_or(1.0))
                }
                SignalWeight::QueryDependent(func) => Ok(func(query)),
            }
        } else {
            Ok(1.0 / self.config.signal_weights.len() as f32) // Default uniform weight
        }
    }

    /// Aggregate multiple signals into final score
    fn aggregate_signals(&self, values: &[f32], weights: &[f32]) -> RragResult<f32> {
        if values.is_empty() {
            return Ok(0.0);
        }

        match &self.aggregation {
            SignalAggregation::WeightedSum => {
                Ok(values.iter().zip(weights.iter()).map(|(v, w)| v * w).sum())
            }
            SignalAggregation::WeightedAverage => {
                let weighted_sum: f32 = values.iter().zip(weights.iter()).map(|(v, w)| v * w).sum();
                let weight_sum: f32 = weights.iter().sum();
                Ok(if weight_sum > 0.0 {
                    weighted_sum / weight_sum
                } else {
                    0.0
                })
            }
            SignalAggregation::Max => Ok(values.iter().fold(0.0f32, |a, &b| a.max(b))),
            SignalAggregation::Min => Ok(values.iter().fold(1.0f32, |a, &b| a.min(b))),
            SignalAggregation::LearnedCombination => {
                // Would use a learned model - for now, use weighted average
                let weighted_sum: f32 = values.iter().zip(weights.iter()).map(|(v, w)| v * w).sum();
                let weight_sum: f32 = weights.iter().sum();
                Ok(if weight_sum > 0.0 {
                    weighted_sum / weight_sum
                } else {
                    0.0
                })
            }
            SignalAggregation::Custom(_) => {
                // Custom aggregation would be implemented here
                Ok(values.iter().sum::<f32>() / values.len() as f32)
            }
        }
    }
}

// Signal extractors implementation would go here...
// For brevity, I'll implement a few key ones:

/// Extractor for semantic relevance signals
struct SemanticRelevanceExtractor;

impl SemanticRelevanceExtractor {
    fn new() -> Self {
        Self
    }
}

impl SignalExtractor for SemanticRelevanceExtractor {
    fn extract_signal(
        &self,
        _query: &str,
        document: &SearchResult,
        _context: &RetrievalContext,
    ) -> RragResult<RelevanceSignal> {
        // Use the existing search score as semantic relevance
        Ok(RelevanceSignal {
            signal_type: SignalType::SemanticRelevance,
            value: document.score,
            confidence: 0.8,
            metadata: SignalMetadata {
                source: "search_engine".to_string(),
                extraction_time_ms: 1,
                features: HashMap::new(),
                warnings: Vec::new(),
            },
        })
    }

    fn signal_type(&self) -> SignalType {
        SignalType::SemanticRelevance
    }

    fn get_config(&self) -> SignalExtractorConfig {
        SignalExtractorConfig {
            name: "SemanticRelevanceExtractor".to_string(),
            version: "1.0".to_string(),
            features: vec!["vector_similarity".to_string()],
            performance: PerformanceMetrics {
                avg_extraction_time_ms: 1.0,
                accuracy: 0.8,
                memory_usage_mb: 0.1,
            },
        }
    }
}

/// Extractor for textual relevance (BM25-style)
struct TextualRelevanceExtractor;

impl TextualRelevanceExtractor {
    fn new() -> Self {
        Self
    }
}

impl SignalExtractor for TextualRelevanceExtractor {
    fn extract_signal(
        &self,
        query: &str,
        document: &SearchResult,
        _context: &RetrievalContext,
    ) -> RragResult<RelevanceSignal> {
        // Simple textual relevance based on term overlap
        let query_terms: std::collections::HashSet<&str> = query.split_whitespace().collect();
        let doc_terms: std::collections::HashSet<&str> =
            document.content.split_whitespace().collect();

        let intersection = query_terms.intersection(&doc_terms).count();
        let union = query_terms.union(&doc_terms).count();

        let jaccard = if union == 0 {
            0.0
        } else {
            intersection as f32 / union as f32
        };

        Ok(RelevanceSignal {
            signal_type: SignalType::TextualRelevance,
            value: jaccard,
            confidence: 0.7,
            metadata: SignalMetadata {
                source: "textual_analysis".to_string(),
                extraction_time_ms: 2,
                features: [
                    ("intersection".to_string(), intersection as f32),
                    ("union".to_string(), union as f32),
                ]
                .iter()
                .cloned()
                .collect(),
                warnings: Vec::new(),
            },
        })
    }

    fn signal_type(&self) -> SignalType {
        SignalType::TextualRelevance
    }

    fn get_config(&self) -> SignalExtractorConfig {
        SignalExtractorConfig {
            name: "TextualRelevanceExtractor".to_string(),
            version: "1.0".to_string(),
            features: vec!["term_overlap".to_string(), "jaccard_similarity".to_string()],
            performance: PerformanceMetrics {
                avg_extraction_time_ms: 2.0,
                accuracy: 0.7,
                memory_usage_mb: 0.05,
            },
        }
    }
}

/// Extractor for document freshness
struct DocumentFreshnessExtractor;

impl DocumentFreshnessExtractor {
    fn new() -> Self {
        Self
    }
}

impl SignalExtractor for DocumentFreshnessExtractor {
    fn extract_signal(
        &self,
        _query: &str,
        document: &SearchResult,
        context: &RetrievalContext,
    ) -> RragResult<RelevanceSignal> {
        // Extract timestamp from document metadata or use current time as fallback
        let doc_timestamp = document
            .metadata
            .get("timestamp")
            .and_then(|v| v.as_str())
            .and_then(|s| chrono::DateTime::parse_from_rfc3339(s).ok())
            .map(|dt| dt.with_timezone(&chrono::Utc))
            .unwrap_or_else(|| context.timestamp - chrono::Duration::days(30)); // Default: 30 days old

        let age_hours = (context.timestamp - doc_timestamp).num_hours() as f32;

        // Exponential decay: newer documents get higher scores
        let freshness = (-age_hours / (24.0 * 7.0)).exp().min(1.0); // 1 week half-life

        Ok(RelevanceSignal {
            signal_type: SignalType::DocumentFreshness,
            value: freshness,
            confidence: 0.9,
            metadata: SignalMetadata {
                source: "document_metadata".to_string(),
                extraction_time_ms: 1,
                features: [("age_hours".to_string(), age_hours)]
                    .iter()
                    .cloned()
                    .collect(),
                warnings: Vec::new(),
            },
        })
    }

    fn signal_type(&self) -> SignalType {
        SignalType::DocumentFreshness
    }

    fn get_config(&self) -> SignalExtractorConfig {
        SignalExtractorConfig {
            name: "DocumentFreshnessExtractor".to_string(),
            version: "1.0".to_string(),
            features: vec!["temporal_decay".to_string()],
            performance: PerformanceMetrics {
                avg_extraction_time_ms: 1.0,
                accuracy: 0.9,
                memory_usage_mb: 0.01,
            },
        }
    }
}

/// Extractor for document quality
struct DocumentQualityExtractor;

impl DocumentQualityExtractor {
    fn new() -> Self {
        Self
    }
}

impl SignalExtractor for DocumentQualityExtractor {
    fn extract_signal(
        &self,
        _query: &str,
        document: &SearchResult,
        _context: &RetrievalContext,
    ) -> RragResult<RelevanceSignal> {
        // Simple quality metrics based on document characteristics
        let length = document.content.len() as f32;
        let words = document.content.split_whitespace().count() as f32;
        let sentences = document.content.split('.').count() as f32;

        // Quality heuristics
        let length_score = if length > 100.0 && length < 5000.0 {
            1.0
        } else {
            0.5
        };
        let avg_word_length = if words > 0.0 { length / words } else { 0.0 };
        let word_length_score = if avg_word_length > 3.0 && avg_word_length < 15.0 {
            1.0
        } else {
            0.7
        };
        let sentence_length = if sentences > 0.0 {
            words / sentences
        } else {
            0.0
        };
        let sentence_score = if sentence_length > 5.0 && sentence_length < 30.0 {
            1.0
        } else {
            0.8
        };

        let quality_score = (length_score + word_length_score + sentence_score) / 3.0;

        Ok(RelevanceSignal {
            signal_type: SignalType::DocumentQuality,
            value: quality_score,
            confidence: 0.6,
            metadata: SignalMetadata {
                source: "quality_analysis".to_string(),
                extraction_time_ms: 3,
                features: [
                    ("length".to_string(), length),
                    ("word_count".to_string(), words),
                    ("sentence_count".to_string(), sentences),
                    ("avg_word_length".to_string(), avg_word_length),
                    ("avg_sentence_length".to_string(), sentence_length),
                ]
                .iter()
                .cloned()
                .collect(),
                warnings: Vec::new(),
            },
        })
    }

    fn signal_type(&self) -> SignalType {
        SignalType::DocumentQuality
    }

    fn get_config(&self) -> SignalExtractorConfig {
        SignalExtractorConfig {
            name: "DocumentQualityExtractor".to_string(),
            version: "1.0".to_string(),
            features: vec![
                "length_analysis".to_string(),
                "structural_analysis".to_string(),
            ],
            performance: PerformanceMetrics {
                avg_extraction_time_ms: 3.0,
                accuracy: 0.6,
                memory_usage_mb: 0.02,
            },
        }
    }
}

// Placeholder implementations for other extractors
macro_rules! impl_placeholder_extractor {
    ($name:ident, $signal_type:expr, $default_value:expr) => {
        struct $name;

        impl $name {
            fn new() -> Self {
                Self
            }
        }

        impl SignalExtractor for $name {
            fn extract_signal(
                &self,
                _query: &str,
                _document: &SearchResult,
                _context: &RetrievalContext,
            ) -> RragResult<RelevanceSignal> {
                Ok(RelevanceSignal {
                    signal_type: $signal_type,
                    value: $default_value,
                    confidence: 0.5,
                    metadata: SignalMetadata {
                        source: "placeholder".to_string(),
                        extraction_time_ms: 1,
                        features: HashMap::new(),
                        warnings: vec!["Placeholder implementation".to_string()],
                    },
                })
            }

            fn signal_type(&self) -> SignalType {
                $signal_type
            }

            fn get_config(&self) -> SignalExtractorConfig {
                SignalExtractorConfig {
                    name: stringify!($name).to_string(),
                    version: "0.1".to_string(),
                    features: vec!["placeholder".to_string()],
                    performance: PerformanceMetrics {
                        avg_extraction_time_ms: 1.0,
                        accuracy: 0.5,
                        memory_usage_mb: 0.01,
                    },
                }
            }
        }
    };
}

impl_placeholder_extractor!(
    DocumentAuthorityExtractor,
    SignalType::DocumentAuthority,
    0.5
);
impl_placeholder_extractor!(UserPreferenceExtractor, SignalType::UserPreference, 0.5);
impl_placeholder_extractor!(ClickThroughRateExtractor, SignalType::ClickThroughRate, 0.5);
impl_placeholder_extractor!(
    DocumentPopularityExtractor,
    SignalType::DocumentPopularity,
    0.5
);
impl_placeholder_extractor!(
    InteractionHistoryExtractor,
    SignalType::InteractionHistory,
    0.5
);

struct DomainSpecificExtractor {
    domain: String,
}

impl DomainSpecificExtractor {
    fn new(domain: String) -> Self {
        Self { domain }
    }
}

impl SignalExtractor for DomainSpecificExtractor {
    fn extract_signal(
        &self,
        _query: &str,
        _document: &SearchResult,
        _context: &RetrievalContext,
    ) -> RragResult<RelevanceSignal> {
        Ok(RelevanceSignal {
            signal_type: SignalType::DomainSpecific(self.domain.clone()),
            value: 0.5,
            confidence: 0.5,
            metadata: SignalMetadata {
                source: "domain_specific".to_string(),
                extraction_time_ms: 1,
                features: HashMap::new(),
                warnings: vec!["Placeholder implementation".to_string()],
            },
        })
    }

    fn signal_type(&self) -> SignalType {
        SignalType::DomainSpecific(self.domain.clone())
    }

    fn get_config(&self) -> SignalExtractorConfig {
        SignalExtractorConfig {
            name: format!("DomainSpecificExtractor({})", self.domain),
            version: "0.1".to_string(),
            features: vec!["domain_analysis".to_string()],
            performance: PerformanceMetrics {
                avg_extraction_time_ms: 1.0,
                accuracy: 0.5,
                memory_usage_mb: 0.01,
            },
        }
    }
}

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

    #[tokio::test]
    async fn test_multi_signal_reranking() {
        let config = MultiSignalConfig::default();
        let reranker = MultiSignalReranker::new(config);

        let results = vec![
            SearchResult {
                id: "doc1".to_string(),
                content: "Machine learning is a subset of artificial intelligence that focuses on algorithms".to_string(),
                score: 0.8,
                rank: 0,
                metadata: HashMap::new(),
                embedding: None,
            },
            SearchResult {
                id: "doc2".to_string(),
                content: "AI".to_string(), // Short, low quality
                score: 0.9,
                rank: 1,
                metadata: HashMap::new(),
                embedding: None,
            },
        ];

        let query = "What is machine learning in artificial intelligence?";
        let reranked_scores = reranker.rerank(query, &results).await.unwrap();

        assert!(!reranked_scores.is_empty());
        // Doc1 should rank higher due to better quality despite lower initial score
        assert!(reranked_scores.get(&0).unwrap_or(&0.0) > &0.0);
    }

    #[test]
    fn test_signal_normalization() {
        let config = MultiSignalConfig::default();
        let reranker = MultiSignalReranker::new(config);

        let signals = vec![
            RelevanceSignal {
                signal_type: SignalType::SemanticRelevance,
                value: 0.1,
                confidence: 1.0,
                metadata: SignalMetadata {
                    source: "test".to_string(),
                    extraction_time_ms: 0,
                    features: HashMap::new(),
                    warnings: Vec::new(),
                },
            },
            RelevanceSignal {
                signal_type: SignalType::SemanticRelevance,
                value: 0.9,
                confidence: 1.0,
                metadata: SignalMetadata {
                    source: "test".to_string(),
                    extraction_time_ms: 0,
                    features: HashMap::new(),
                    warnings: Vec::new(),
                },
            },
        ];

        let normalized = reranker.normalize_min_max(&signals);
        assert_eq!(normalized[0].value, 0.0); // Min becomes 0
        assert_eq!(normalized[1].value, 1.0); // Max becomes 1
    }
}