rrag 0.1.0-alpha.2

//! # Embedding Fusion
//!
//! Advanced multi-modal embedding fusion strategies for unified representation.

use super::{
    EmbeddingFusionStrategy, EmbeddingWeights, ExtractedTable, FusionStrategy, MultiModalDocument,
    MultiModalEmbeddings, ProcessedImage,
};
use crate::RragResult;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Default embedding fusion implementation
pub struct DefaultFusionStrategy {
    /// Fusion strategy
    strategy: FusionStrategy,

    /// Fusion configuration
    config: FusionConfig,

    /// Weight calculator
    weight_calculator: WeightCalculator,

    /// Dimension normalizer
    dimension_normalizer: DimensionNormalizer,

    /// Attention mechanism (for attention-based fusion)
    attention_mechanism: Option<AttentionMechanism>,
}

/// Fusion configuration
#[derive(Debug, Clone)]
pub struct FusionConfig {
    /// Target embedding dimension
    pub target_dimension: usize,

    /// Normalize embeddings before fusion
    pub normalize_embeddings: bool,

    /// Use adaptive weights
    pub adaptive_weights: bool,

    /// Minimum weight threshold
    pub min_weight: f32,

    /// Maximum weight threshold
    pub max_weight: f32,

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

/// Weight calculation strategies
pub struct WeightCalculator {
    /// Content analysis
    content_analyzer: ContentAnalyzer,

    /// Quality assessor
    quality_assessor: QualityAssessor,
}

/// Dimension normalization utility
pub struct DimensionNormalizer {
    /// Target dimension
    target_dim: usize,

    /// Normalization strategy
    strategy: NormalizationStrategy,
}

/// Attention mechanism for fusion
pub struct AttentionMechanism {
    /// Attention weights
    attention_weights: HashMap<String, Vec<f32>>,

    /// Query projection
    query_projection: AttentionProjection,

    /// Key projection
    key_projection: AttentionProjection,

    /// Value projection
    value_projection: AttentionProjection,
}

/// Content analyzer for weight calculation
pub struct ContentAnalyzer {
    /// Text importance scorer
    text_scorer: TextImportanceScorer,

    /// Visual importance scorer
    visual_scorer: VisualImportanceScorer,

    /// Table importance scorer
    table_scorer: TableImportanceScorer,
}

/// Quality assessment for embeddings
pub struct QualityAssessor {
    /// Embedding quality metrics
    quality_metrics: Vec<QualityMetric>,
}

/// Normalization strategies
#[derive(Debug, Clone, Copy)]
pub enum NormalizationStrategy {
    /// L2 normalization
    L2Norm,

    /// Min-Max scaling
    MinMax,

    /// Z-score normalization
    ZScore,

    /// Linear projection
    LinearProjection,

    /// PCA reduction
    PCA,
}

/// Attention projection layer
#[derive(Debug, Clone)]
pub struct AttentionProjection {
    /// Weight matrix
    pub weights: Vec<Vec<f32>>,

    /// Bias vector
    pub bias: Vec<f32>,
}

/// Text importance scoring
pub struct TextImportanceScorer {
    /// TF-IDF calculator
    tfidf_calculator: TfIdfCalculator,

    /// Named entity recognizer
    ner: NamedEntityRecognizer,
}

/// Visual importance scoring
pub struct VisualImportanceScorer {
    /// Saliency detector
    saliency_detector: SaliencyDetector,

    /// Aesthetic analyzer
    aesthetic_analyzer: AestheticAnalyzer,
}

/// Table importance scoring
pub struct TableImportanceScorer {
    /// Information density calculator
    density_calculator: InformationDensityCalculator,
}

/// Quality metrics for embeddings
#[derive(Debug, Clone)]
pub struct QualityMetric {
    /// Metric name
    pub name: String,

    /// Metric weight
    pub weight: f32,

    /// Metric function
    pub metric_type: QualityMetricType,
}

/// Quality metric types
#[derive(Debug, Clone, Copy)]
pub enum QualityMetricType {
    /// Embedding norm
    EmbeddingNorm,

    /// Variance
    Variance,

    /// Coherence
    Coherence,

    /// Distinctiveness
    Distinctiveness,
}

/// TF-IDF calculator
pub struct TfIdfCalculator {
    /// Document frequency map
    document_frequencies: HashMap<String, usize>,

    /// Total documents
    total_documents: usize,
}

/// Named entity recognizer (simplified)
pub struct NamedEntityRecognizer;

/// Saliency detection for images
pub struct SaliencyDetector;

/// Aesthetic analysis for images
pub struct AestheticAnalyzer;

/// Information density calculator for tables
pub struct InformationDensityCalculator;

/// Fusion result
#[derive(Debug, Clone)]
pub struct FusionResult {
    /// Fused embedding
    pub fused_embedding: Vec<f32>,

    /// Final weights used
    pub weights: EmbeddingWeights,

    /// Fusion confidence
    pub confidence: f32,

    /// Individual modality scores
    pub modality_scores: ModalityScores,
}

/// Scores for each modality
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModalityScores {
    /// Text quality score
    pub text_score: f32,

    /// Visual quality score
    pub visual_score: f32,

    /// Table quality score
    pub table_score: f32,

    /// Chart quality score
    pub chart_score: f32,
}

impl DefaultFusionStrategy {
    /// Create new fusion strategy
    pub fn new(strategy: FusionStrategy) -> RragResult<Self> {
        let config = FusionConfig::default();
        let weight_calculator = WeightCalculator::new()?;
        let dimension_normalizer = DimensionNormalizer::new(config.target_dimension);

        let attention_mechanism = if matches!(strategy, FusionStrategy::Attention) {
            Some(AttentionMechanism::new(config.target_dimension)?)
        } else {
            None
        };

        Ok(Self {
            strategy,
            config,
            weight_calculator,
            dimension_normalizer,
            attention_mechanism,
        })
    }

    /// Fuse embeddings with detailed analysis
    pub fn fuse_embeddings_detailed(
        &self,
        document: &MultiModalDocument,
    ) -> RragResult<FusionResult> {
        // Calculate optimal weights
        let weights = if self.config.adaptive_weights {
            self.calculate_weights(document)?
        } else {
            document.embeddings.weights.clone()
        };

        // Score individual modalities
        let modality_scores = self.calculate_modality_scores(document)?;

        // Normalize embeddings
        let normalized_embeddings = self.normalize_embeddings(&document.embeddings)?;

        // Perform fusion based on strategy
        let fused_embedding = match self.strategy {
            FusionStrategy::Average => self.fuse_average(&normalized_embeddings, &weights)?,
            FusionStrategy::Weighted => self.fuse_weighted(&normalized_embeddings, &weights)?,
            FusionStrategy::Concatenate => self.fuse_concatenate(&normalized_embeddings)?,
            FusionStrategy::Attention => self.fuse_attention(&normalized_embeddings, &weights)?,
            FusionStrategy::Learned => self.fuse_learned(&normalized_embeddings, &weights)?,
        };

        // Calculate fusion confidence
        let confidence = self.calculate_fusion_confidence(&fused_embedding, &modality_scores)?;

        Ok(FusionResult {
            fused_embedding,
            weights,
            confidence,
            modality_scores,
        })
    }

    /// Normalize embeddings to consistent dimensions
    fn normalize_embeddings(
        &self,
        embeddings: &MultiModalEmbeddings,
    ) -> RragResult<NormalizedEmbeddings> {
        let text_normalized = self
            .dimension_normalizer
            .normalize(&embeddings.text_embeddings)?;

        let visual_normalized = if let Some(ref visual) = embeddings.visual_embeddings {
            Some(self.dimension_normalizer.normalize(visual)?)
        } else {
            None
        };

        let table_normalized = if let Some(ref table) = embeddings.table_embeddings {
            Some(self.dimension_normalizer.normalize(table)?)
        } else {
            None
        };

        Ok(NormalizedEmbeddings {
            text: text_normalized,
            visual: visual_normalized,
            table: table_normalized,
        })
    }

    /// Average fusion
    fn fuse_average(
        &self,
        embeddings: &NormalizedEmbeddings,
        _weights: &EmbeddingWeights,
    ) -> RragResult<Vec<f32>> {
        let mut fused = embeddings.text.clone();
        let mut count = 1;

        if let Some(ref visual) = embeddings.visual {
            for (i, &val) in visual.iter().enumerate() {
                if i < fused.len() {
                    fused[i] += val;
                }
            }
            count += 1;
        }

        if let Some(ref table) = embeddings.table {
            for (i, &val) in table.iter().enumerate() {
                if i < fused.len() {
                    fused[i] += val;
                }
            }
            count += 1;
        }

        // Average
        for val in &mut fused {
            *val /= count as f32;
        }

        Ok(fused)
    }

    /// Weighted fusion
    fn fuse_weighted(
        &self,
        embeddings: &NormalizedEmbeddings,
        weights: &EmbeddingWeights,
    ) -> RragResult<Vec<f32>> {
        let mut fused = vec![0.0; self.config.target_dimension];

        // Weighted combination
        for (i, &val) in embeddings.text.iter().enumerate() {
            if i < fused.len() {
                fused[i] += val * weights.text_weight;
            }
        }

        if let Some(ref visual) = embeddings.visual {
            for (i, &val) in visual.iter().enumerate() {
                if i < fused.len() {
                    fused[i] += val * weights.visual_weight;
                }
            }
        }

        if let Some(ref table) = embeddings.table {
            for (i, &val) in table.iter().enumerate() {
                if i < fused.len() {
                    fused[i] += val * weights.table_weight;
                }
            }
        }

        // Normalize if configured
        if self.config.normalize_embeddings {
            self.l2_normalize(&mut fused);
        }

        Ok(fused)
    }

    /// Concatenation fusion
    fn fuse_concatenate(&self, embeddings: &NormalizedEmbeddings) -> RragResult<Vec<f32>> {
        let mut fused = embeddings.text.clone();

        if let Some(ref visual) = embeddings.visual {
            fused.extend_from_slice(visual);
        }

        if let Some(ref table) = embeddings.table {
            fused.extend_from_slice(table);
        }

        // Resize to target dimension if needed
        if fused.len() > self.config.target_dimension {
            fused.truncate(self.config.target_dimension);
        } else if fused.len() < self.config.target_dimension {
            fused.resize(self.config.target_dimension, 0.0);
        }

        Ok(fused)
    }

    /// Attention-based fusion
    fn fuse_attention(
        &self,
        embeddings: &NormalizedEmbeddings,
        _weights: &EmbeddingWeights,
    ) -> RragResult<Vec<f32>> {
        if let Some(ref attention) = self.attention_mechanism {
            attention.apply_attention(embeddings)
        } else {
            // Fallback to weighted fusion
            self.fuse_weighted(embeddings, _weights)
        }
    }

    /// Learned fusion (placeholder for ML model)
    fn fuse_learned(
        &self,
        embeddings: &NormalizedEmbeddings,
        weights: &EmbeddingWeights,
    ) -> RragResult<Vec<f32>> {
        // For now, use weighted fusion with learned weights
        self.fuse_weighted(embeddings, weights)
    }

    /// Calculate modality scores
    fn calculate_modality_scores(
        &self,
        document: &MultiModalDocument,
    ) -> RragResult<ModalityScores> {
        let text_score = self
            .weight_calculator
            .content_analyzer
            .text_scorer
            .calculate_text_score(&document.text_content)?;

        let visual_score = if !document.images.is_empty() {
            self.weight_calculator
                .content_analyzer
                .visual_scorer
                .calculate_visual_score(&document.images)?
        } else {
            0.0
        };

        let table_score = if !document.tables.is_empty() {
            self.weight_calculator
                .content_analyzer
                .table_scorer
                .calculate_table_score(&document.tables)?
        } else {
            0.0
        };

        let chart_score = if !document.charts.is_empty() {
            // Simplified chart scoring
            0.7
        } else {
            0.0
        };

        Ok(ModalityScores {
            text_score,
            visual_score,
            table_score,
            chart_score,
        })
    }

    /// Calculate fusion confidence
    fn calculate_fusion_confidence(
        &self,
        _fused_embedding: &[f32],
        scores: &ModalityScores,
    ) -> RragResult<f32> {
        // Confidence based on modality diversity and quality
        let mut confidence = 0.0;
        let mut active_modalities = 0;

        if scores.text_score > 0.0 {
            confidence += scores.text_score * 0.4;
            active_modalities += 1;
        }

        if scores.visual_score > 0.0 {
            confidence += scores.visual_score * 0.3;
            active_modalities += 1;
        }

        if scores.table_score > 0.0 {
            confidence += scores.table_score * 0.2;
            active_modalities += 1;
        }

        if scores.chart_score > 0.0 {
            confidence += scores.chart_score * 0.1;
            active_modalities += 1;
        }

        // Bonus for multi-modal content
        if active_modalities > 1 {
            confidence *= 1.0 + (active_modalities as f32 - 1.0) * 0.1;
        }

        Ok(confidence.min(1.0))
    }

    /// L2 normalization
    fn l2_normalize(&self, vector: &mut [f32]) {
        let norm: f32 = vector.iter().map(|x| x * x).sum::<f32>().sqrt();
        if norm > 0.0 {
            for val in vector {
                *val /= norm;
            }
        }
    }
}

impl EmbeddingFusionStrategy for DefaultFusionStrategy {
    fn fuse_embeddings(&self, embeddings: &MultiModalEmbeddings) -> RragResult<Vec<f32>> {
        match self.strategy {
            FusionStrategy::Average => {
                let mut fused = embeddings.text_embeddings.clone();
                let mut count = 1;

                if let Some(ref visual) = embeddings.visual_embeddings {
                    for (i, &val) in visual.iter().enumerate() {
                        if i < fused.len() {
                            fused[i] += val;
                        }
                    }
                    count += 1;
                }

                for val in &mut fused {
                    *val /= count as f32;
                }

                Ok(fused)
            }

            FusionStrategy::Weighted => {
                let mut fused = vec![0.0; embeddings.text_embeddings.len()];
                let weights = &embeddings.weights;

                for (i, &val) in embeddings.text_embeddings.iter().enumerate() {
                    fused[i] += val * weights.text_weight;
                }

                if let Some(ref visual) = embeddings.visual_embeddings {
                    for (i, &val) in visual.iter().enumerate() {
                        if i < fused.len() {
                            fused[i] += val * weights.visual_weight;
                        }
                    }
                }

                Ok(fused)
            }

            _ => {
                // Fallback to weighted
                self.fuse_embeddings(&MultiModalEmbeddings {
                    text_embeddings: embeddings.text_embeddings.clone(),
                    visual_embeddings: embeddings.visual_embeddings.clone(),
                    table_embeddings: embeddings.table_embeddings.clone(),
                    fused_embedding: vec![],
                    weights: EmbeddingWeights {
                        text_weight: 0.6,
                        visual_weight: 0.3,
                        table_weight: 0.1,
                        chart_weight: 0.0,
                    },
                })
            }
        }
    }

    fn calculate_weights(&self, document: &MultiModalDocument) -> RragResult<EmbeddingWeights> {
        self.weight_calculator.calculate_weights(document)
    }
}

/// Normalized embeddings container
#[derive(Debug, Clone)]
pub struct NormalizedEmbeddings {
    text: Vec<f32>,
    visual: Option<Vec<f32>>,
    table: Option<Vec<f32>>,
}

impl WeightCalculator {
    /// Create new weight calculator
    pub fn new() -> RragResult<Self> {
        Ok(Self {
            content_analyzer: ContentAnalyzer::new()?,
            quality_assessor: QualityAssessor::new(),
        })
    }

    /// Calculate optimal weights for document
    pub fn calculate_weights(&self, document: &MultiModalDocument) -> RragResult<EmbeddingWeights> {
        let scores = self.content_analyzer.analyze_content(document)?;
        let quality_scores = self.quality_assessor.assess_quality(&document.embeddings)?;

        // Combine content importance with quality scores
        let text_weight = scores.text_importance * quality_scores.text_quality;
        let visual_weight = scores.visual_importance * quality_scores.visual_quality;
        let table_weight = scores.table_importance * quality_scores.table_quality;
        let chart_weight = scores.chart_importance * quality_scores.chart_quality;

        // Normalize weights to sum to 1.0
        let total = text_weight + visual_weight + table_weight + chart_weight;

        if total > 0.0 {
            Ok(EmbeddingWeights {
                text_weight: text_weight / total,
                visual_weight: visual_weight / total,
                table_weight: table_weight / total,
                chart_weight: chart_weight / total,
            })
        } else {
            // Fallback to default weights
            Ok(EmbeddingWeights {
                text_weight: 0.6,
                visual_weight: 0.2,
                table_weight: 0.1,
                chart_weight: 0.1,
            })
        }
    }
}

impl DimensionNormalizer {
    /// Create new dimension normalizer
    pub fn new(target_dim: usize) -> Self {
        Self {
            target_dim,
            strategy: NormalizationStrategy::LinearProjection,
        }
    }

    /// Normalize embedding to target dimension
    pub fn normalize(&self, embedding: &[f32]) -> RragResult<Vec<f32>> {
        match self.strategy {
            NormalizationStrategy::LinearProjection => {
                if embedding.len() == self.target_dim {
                    Ok(embedding.to_vec())
                } else if embedding.len() > self.target_dim {
                    // Truncate
                    Ok(embedding[..self.target_dim].to_vec())
                } else {
                    // Pad with zeros
                    let mut normalized = embedding.to_vec();
                    normalized.resize(self.target_dim, 0.0);
                    Ok(normalized)
                }
            }

            NormalizationStrategy::L2Norm => {
                let mut normalized = embedding.to_vec();
                let norm: f32 = normalized.iter().map(|x| x * x).sum::<f32>().sqrt();
                if norm > 0.0 {
                    for val in &mut normalized {
                        *val /= norm;
                    }
                }

                // Resize to target dimension
                if normalized.len() != self.target_dim {
                    normalized.resize(self.target_dim, 0.0);
                }

                Ok(normalized)
            }

            _ => {
                // Fallback to linear projection
                self.normalize(embedding)
            }
        }
    }
}

impl AttentionMechanism {
    /// Create new attention mechanism
    pub fn new(dim: usize) -> RragResult<Self> {
        Ok(Self {
            attention_weights: HashMap::new(),
            query_projection: AttentionProjection::new(dim, dim)?,
            key_projection: AttentionProjection::new(dim, dim)?,
            value_projection: AttentionProjection::new(dim, dim)?,
        })
    }

    /// Apply attention to embeddings
    pub fn apply_attention(&self, embeddings: &NormalizedEmbeddings) -> RragResult<Vec<f32>> {
        // Simplified attention mechanism
        // In practice, this would implement proper multi-head attention

        let query = &embeddings.text;
        let mut attended = query.clone();

        if let Some(ref visual) = embeddings.visual {
            let attention_score = self.compute_attention_score(query, visual)?;
            for (i, &val) in visual.iter().enumerate() {
                if i < attended.len() {
                    attended[i] += val * attention_score;
                }
            }
        }

        if let Some(ref table) = embeddings.table {
            let attention_score = self.compute_attention_score(query, table)?;
            for (i, &val) in table.iter().enumerate() {
                if i < attended.len() {
                    attended[i] += val * attention_score;
                }
            }
        }

        Ok(attended)
    }

    /// Compute attention score between query and key
    fn compute_attention_score(&self, query: &[f32], key: &[f32]) -> RragResult<f32> {
        // Dot product attention
        let score: f32 = query.iter().zip(key.iter()).map(|(q, k)| q * k).sum();

        // Normalize by sqrt of dimension
        let normalized_score = score / (query.len() as f32).sqrt();

        // Apply softmax (simplified)
        Ok(normalized_score.exp() / (1.0 + normalized_score.exp()))
    }
}

impl AttentionProjection {
    /// Create new attention projection
    pub fn new(input_dim: usize, output_dim: usize) -> RragResult<Self> {
        // Initialize with small random values (simplified)
        let weights = vec![vec![0.01; input_dim]; output_dim];
        let bias = vec![0.0; output_dim];

        Ok(Self { weights, bias })
    }
}

impl ContentAnalyzer {
    /// Create new content analyzer
    pub fn new() -> RragResult<Self> {
        Ok(Self {
            text_scorer: TextImportanceScorer::new()?,
            visual_scorer: VisualImportanceScorer::new(),
            table_scorer: TableImportanceScorer::new(),
        })
    }

    /// Analyze content importance
    pub fn analyze_content(&self, document: &MultiModalDocument) -> RragResult<ContentScores> {
        let text_importance = self
            .text_scorer
            .calculate_text_score(&document.text_content)?;
        let visual_importance = self
            .visual_scorer
            .calculate_visual_score(&document.images)?;
        let table_importance = self.table_scorer.calculate_table_score(&document.tables)?;
        let chart_importance = if !document.charts.is_empty() {
            0.7
        } else {
            0.0
        };

        Ok(ContentScores {
            text_importance,
            visual_importance,
            table_importance,
            chart_importance,
        })
    }
}

/// Content importance scores
#[derive(Debug, Clone)]
pub struct ContentScores {
    pub text_importance: f32,
    pub visual_importance: f32,
    pub table_importance: f32,
    pub chart_importance: f32,
}

/// Quality scores for embeddings
#[derive(Debug, Clone)]
pub struct QualityScores {
    pub text_quality: f32,
    pub visual_quality: f32,
    pub table_quality: f32,
    pub chart_quality: f32,
}

impl TextImportanceScorer {
    pub fn new() -> RragResult<Self> {
        Ok(Self {
            tfidf_calculator: TfIdfCalculator::new(),
            ner: NamedEntityRecognizer,
        })
    }

    pub fn calculate_text_score(&self, text: &str) -> RragResult<f32> {
        let word_count = text.split_whitespace().count();
        let entity_score = self.ner.calculate_entity_score(text)?;

        // Combine length and entity density
        let length_score = (word_count as f32 / 1000.0).min(1.0);
        Ok(length_score * 0.7 + entity_score * 0.3)
    }
}

impl VisualImportanceScorer {
    pub fn new() -> Self {
        Self {
            saliency_detector: SaliencyDetector,
            aesthetic_analyzer: AestheticAnalyzer,
        }
    }

    pub fn calculate_visual_score(&self, images: &[ProcessedImage]) -> RragResult<f32> {
        if images.is_empty() {
            return Ok(0.0);
        }

        let mut total_score = 0.0;
        for image in images {
            let quality_score = image
                .features
                .as_ref()
                .map(|f| (f.quality.sharpness + f.quality.contrast) / 2.0)
                .unwrap_or(0.5);

            let aesthetic_score = self.aesthetic_analyzer.analyze_aesthetics(image)?;
            total_score += quality_score * 0.6 + aesthetic_score * 0.4;
        }

        Ok(total_score / images.len() as f32)
    }
}

impl TableImportanceScorer {
    pub fn new() -> Self {
        Self {
            density_calculator: InformationDensityCalculator,
        }
    }

    pub fn calculate_table_score(&self, tables: &[ExtractedTable]) -> RragResult<f32> {
        if tables.is_empty() {
            return Ok(0.0);
        }

        let mut total_score = 0.0;
        for table in tables {
            let size_score = (table.rows.len() * table.headers.len()) as f32 / 100.0;
            let density_score = self.density_calculator.calculate_density(table)?;
            total_score += size_score.min(1.0) * 0.5 + density_score * 0.5;
        }

        Ok(total_score / tables.len() as f32)
    }
}

impl QualityAssessor {
    pub fn new() -> Self {
        Self {
            quality_metrics: vec![
                QualityMetric {
                    name: "norm".to_string(),
                    weight: 0.3,
                    metric_type: QualityMetricType::EmbeddingNorm,
                },
                QualityMetric {
                    name: "variance".to_string(),
                    weight: 0.4,
                    metric_type: QualityMetricType::Variance,
                },
                QualityMetric {
                    name: "coherence".to_string(),
                    weight: 0.3,
                    metric_type: QualityMetricType::Coherence,
                },
            ],
        }
    }

    pub fn assess_quality(&self, embeddings: &MultiModalEmbeddings) -> RragResult<QualityScores> {
        let text_quality = self.calculate_embedding_quality(&embeddings.text_embeddings)?;

        let visual_quality = if let Some(ref visual) = embeddings.visual_embeddings {
            self.calculate_embedding_quality(visual)?
        } else {
            0.0
        };

        let table_quality = if let Some(ref table) = embeddings.table_embeddings {
            self.calculate_embedding_quality(table)?
        } else {
            0.0
        };

        Ok(QualityScores {
            text_quality,
            visual_quality,
            table_quality,
            chart_quality: 0.7, // Simplified
        })
    }

    fn calculate_embedding_quality(&self, embedding: &[f32]) -> RragResult<f32> {
        let mut quality_score = 0.0;

        for metric in &self.quality_metrics {
            let score = match metric.metric_type {
                QualityMetricType::EmbeddingNorm => {
                    let norm: f32 = embedding.iter().map(|x| x * x).sum::<f32>().sqrt();
                    (norm / embedding.len() as f32).min(1.0)
                }
                QualityMetricType::Variance => {
                    let mean = embedding.iter().sum::<f32>() / embedding.len() as f32;
                    let variance = embedding.iter().map(|x| (x - mean).powi(2)).sum::<f32>()
                        / embedding.len() as f32;
                    variance.min(1.0)
                }
                QualityMetricType::Coherence => 0.8, // Simplified
                QualityMetricType::Distinctiveness => 0.7, // Simplified
            };

            quality_score += score * metric.weight;
        }

        Ok(quality_score)
    }
}

// Simplified implementations for helper components
impl TfIdfCalculator {
    pub fn new() -> Self {
        Self {
            document_frequencies: HashMap::new(),
            total_documents: 0,
        }
    }
}

impl NamedEntityRecognizer {
    pub fn calculate_entity_score(&self, _text: &str) -> RragResult<f32> {
        // Simplified entity scoring
        Ok(0.6)
    }
}

impl SaliencyDetector {}

impl AestheticAnalyzer {
    pub fn analyze_aesthetics(&self, _image: &ProcessedImage) -> RragResult<f32> {
        // Simplified aesthetic analysis
        Ok(0.7)
    }
}

impl InformationDensityCalculator {
    pub fn calculate_density(&self, table: &ExtractedTable) -> RragResult<f32> {
        let total_cells = table.rows.len() * table.headers.len();
        let filled_cells = table
            .rows
            .iter()
            .flatten()
            .filter(|cell| !cell.value.trim().is_empty())
            .count();

        Ok(filled_cells as f32 / total_cells as f32)
    }
}

impl Default for FusionConfig {
    fn default() -> Self {
        Self {
            target_dimension: 768,
            normalize_embeddings: true,
            adaptive_weights: true,
            min_weight: 0.01,
            max_weight: 0.99,
            learning_rate: 0.001,
        }
    }
}

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

    #[test]
    fn test_fusion_strategy_creation() {
        let strategy = DefaultFusionStrategy::new(FusionStrategy::Weighted).unwrap();
        assert!(matches!(strategy.strategy, FusionStrategy::Weighted));
    }

    #[test]
    fn test_dimension_normalization() {
        let normalizer = DimensionNormalizer::new(512);

        let embedding = vec![1.0, 2.0, 3.0];
        let normalized = normalizer.normalize(&embedding).unwrap();

        assert_eq!(normalized.len(), 512);
        assert_eq!(&normalized[..3], &[1.0, 2.0, 3.0]);
    }

    #[test]
    fn test_weight_calculation() {
        let calculator = WeightCalculator::new().unwrap();

        // Create test document
        let document = MultiModalDocument {
            id: "test".to_string(),
            text_content: "Test content".to_string(),
            images: vec![],
            tables: vec![],
            charts: vec![],
            layout: super::super::DocumentLayout {
                pages: 1,
                sections: vec![],
                reading_order: vec![],
                columns: None,
                document_type: super::super::DocumentType::PlainText,
            },
            embeddings: MultiModalEmbeddings {
                text_embeddings: vec![0.1, 0.2, 0.3],
                visual_embeddings: None,
                table_embeddings: None,
                fused_embedding: vec![],
                weights: EmbeddingWeights {
                    text_weight: 1.0,
                    visual_weight: 0.0,
                    table_weight: 0.0,
                    chart_weight: 0.0,
                },
            },
            metadata: super::super::DocumentMetadata {
                title: None,
                author: None,
                creation_date: None,
                modification_date: None,
                page_count: 1,
                word_count: 2,
                language: "en".to_string(),
                format: super::super::DocumentType::PlainText,
            },
        };

        let weights = calculator.calculate_weights(&document).unwrap();
        assert!(weights.text_weight > 0.0);
    }
}