rrag 0.1.0-alpha.2

//! # Multi-modal Retrieval
//!
//! Advanced multi-modal retrieval combining text, visual, and structured data queries.

use super::{AnalyzedChart, ChartType, ExtractedTable, MultiModalDocument, ProcessedImage};
use crate::{RragError, RragResult};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Multi-modal retrieval system
pub struct MultiModalRetriever {
    /// Configuration
    config: RetrievalConfig,

    /// Text retriever
    text_retriever: TextRetriever,

    /// Visual retriever
    visual_retriever: VisualRetriever,

    /// Table retriever
    table_retriever: TableRetriever,

    /// Chart retriever
    chart_retriever: ChartRetriever,

    /// Cross-modal retriever
    cross_modal_retriever: CrossModalRetriever,

    /// Result fusion engine
    result_fusion: ResultFusion,
}

/// Multi-modal retrieval configuration
#[derive(Debug, Clone)]
pub struct RetrievalConfig {
    /// Maximum results per modality
    pub max_results_per_modality: usize,

    /// Overall maximum results
    pub max_total_results: usize,

    /// Similarity thresholds by modality
    pub similarity_thresholds: ModalitySimilarityThresholds,

    /// Enable cross-modal matching
    pub enable_cross_modal: bool,

    /// Fusion strategy
    pub fusion_strategy: ResultFusionStrategy,

    /// Scoring weights
    pub scoring_weights: ScoringWeights,
}

/// Similarity thresholds for each modality
#[derive(Debug, Clone)]
pub struct ModalitySimilarityThresholds {
    pub text_threshold: f32,
    pub visual_threshold: f32,
    pub table_threshold: f32,
    pub chart_threshold: f32,
    pub cross_modal_threshold: f32,
}

/// Result fusion strategies
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum ResultFusionStrategy {
    /// Weighted combination
    WeightedCombination,

    /// Rank fusion
    RankFusion,

    /// Score normalization and combination
    ScoreNormalization,

    /// Reciprocal rank fusion
    ReciprocalRankFusion,
}

/// Scoring weights for different aspects
#[derive(Debug, Clone)]
pub struct ScoringWeights {
    pub semantic_weight: f32,
    pub visual_weight: f32,
    pub structural_weight: f32,
    pub temporal_weight: f32,
    pub contextual_weight: f32,
}

/// Multi-modal query
#[derive(Debug, Clone)]
pub struct MultiModalQuery {
    /// Text query
    pub text_query: Option<String>,

    /// Visual query (image path or features)
    pub visual_query: Option<VisualQuery>,

    /// Table query
    pub table_query: Option<TableQuery>,

    /// Chart query
    pub chart_query: Option<ChartQuery>,

    /// Cross-modal constraints
    pub cross_modal_constraints: Vec<CrossModalConstraint>,

    /// Query metadata
    pub metadata: QueryMetadata,
}

/// Visual query types
#[derive(Debug, Clone)]
pub enum VisualQuery {
    /// Query by example image
    ImageExample(String),

    /// Query by visual features
    FeatureQuery(VisualFeatureQuery),

    /// Query by description
    DescriptionQuery(String),
}

/// Table query specification
#[derive(Debug, Clone)]
pub struct TableQuery {
    /// Schema constraints
    pub schema: Option<TableSchema>,

    /// Content filters
    pub content_filters: Vec<ContentFilter>,

    /// Statistical constraints
    pub statistical_constraints: Vec<StatisticalConstraint>,

    /// Size constraints
    pub size_constraints: Option<SizeConstraints>,
}

/// Chart query specification
#[derive(Debug, Clone)]
pub struct ChartQuery {
    /// Chart type filter
    pub chart_types: Vec<ChartType>,

    /// Data constraints
    pub data_constraints: Vec<DataConstraint>,

    /// Trend requirements
    pub trend_requirements: Vec<TrendRequirement>,

    /// Value range filters
    pub value_ranges: Vec<ValueRange>,
}

/// Cross-modal constraints
#[derive(Debug, Clone)]
pub struct CrossModalConstraint {
    /// Source modality
    pub source_modality: Modality,

    /// Target modality
    pub target_modality: Modality,

    /// Constraint type
    pub constraint_type: ConstraintType,

    /// Constraint parameters
    pub parameters: HashMap<String, String>,
}

/// Modality types
#[derive(Debug, Clone, Copy)]
pub enum Modality {
    Text,
    Visual,
    Table,
    Chart,
}

/// Constraint types
#[derive(Debug, Clone)]
pub enum ConstraintType {
    /// Content alignment (e.g., image matches text description)
    ContentAlignment,

    /// Semantic consistency (e.g., table data supports text claims)
    SemanticConsistency,

    /// Visual coherence (e.g., chart style matches document theme)
    VisualCoherence,

    /// Temporal alignment (e.g., data from same time period)
    TemporalAlignment,
}

/// Multi-modal retrieval result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MultiModalRetrievalResult {
    /// Retrieved documents
    pub documents: Vec<RankedDocument>,

    /// Query processing time
    pub processing_time_ms: u64,

    /// Result metadata
    pub metadata: ResultMetadata,

    /// Retrieval statistics
    pub statistics: RetrievalStatistics,
}

/// Ranked document result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RankedDocument {
    /// Document
    pub document: MultiModalDocument,

    /// Overall relevance score
    pub relevance_score: f32,

    /// Modality-specific scores
    pub modality_scores: ModalityScores,

    /// Ranking position
    pub rank: usize,

    /// Explanation of relevance
    pub explanation: Option<RelevanceExplanation>,
}

/// Scores per modality
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModalityScores {
    pub text_score: Option<f32>,
    pub visual_score: Option<f32>,
    pub table_score: Option<f32>,
    pub chart_score: Option<f32>,
    pub cross_modal_score: Option<f32>,
}

/// Text retrieval component
pub struct TextRetriever {
    /// Semantic search
    semantic_searcher: SemanticSearcher,

    /// Keyword search
    keyword_searcher: KeywordSearcher,

    /// Hybrid search combiner
    hybrid_combiner: HybridCombiner,
}

/// Visual retrieval component
pub struct VisualRetriever {
    /// CLIP-based retrieval
    clip_retriever: CLIPRetriever,

    /// Feature-based retrieval
    feature_retriever: FeatureBasedRetriever,

    /// Visual similarity calculator
    similarity_calculator: VisualSimilarityCalculator,
}

/// Table retrieval component
pub struct TableRetriever {
    /// Schema matcher
    schema_matcher: SchemaMatcher,

    /// Content searcher
    content_searcher: TableContentSearcher,

    /// Statistical analyzer
    statistical_analyzer: TableStatisticalAnalyzer,
}

/// Chart retrieval component
pub struct ChartRetriever {
    /// Chart type classifier
    type_classifier: ChartTypeClassifier,

    /// Data pattern matcher
    pattern_matcher: DataPatternMatcher,

    /// Trend analyzer
    trend_analyzer: ChartTrendAnalyzer,
}

/// Cross-modal retrieval component
pub struct CrossModalRetriever {
    /// Image-text alignment
    image_text_aligner: ImageTextAligner,

    /// Table-text consistency checker
    table_text_checker: TableTextConsistencyChecker,

    /// Multi-modal coherence scorer
    coherence_scorer: CoherenceScorer,
}

/// Result fusion engine
pub struct ResultFusion {
    /// Fusion strategy
    strategy: ResultFusionStrategy,

    /// Score normalizers
    score_normalizers: HashMap<Modality, ScoreNormalizer>,

    /// Rank aggregator
    rank_aggregator: RankAggregator,
}

impl MultiModalRetriever {
    /// Create new multi-modal retriever
    pub fn new(config: RetrievalConfig) -> RragResult<Self> {
        let text_retriever = TextRetriever::new()?;
        let visual_retriever = VisualRetriever::new()?;
        let table_retriever = TableRetriever::new()?;
        let chart_retriever = ChartRetriever::new()?;
        let cross_modal_retriever = CrossModalRetriever::new()?;
        let result_fusion = ResultFusion::new(config.fusion_strategy)?;

        Ok(Self {
            config,
            text_retriever,
            visual_retriever,
            table_retriever,
            chart_retriever,
            cross_modal_retriever,
            result_fusion,
        })
    }

    /// Perform multi-modal retrieval
    pub async fn retrieve(
        &self,
        query: &MultiModalQuery,
        documents: &[MultiModalDocument],
    ) -> RragResult<MultiModalRetrievalResult> {
        let start_time = std::time::Instant::now();

        // Retrieve from each modality
        let text_results = if let Some(ref text_q) = query.text_query {
            self.text_retriever.retrieve(text_q, documents).await?
        } else {
            vec![]
        };

        let visual_results = if let Some(ref visual_q) = query.visual_query {
            self.visual_retriever.retrieve(visual_q, documents).await?
        } else {
            vec![]
        };

        let table_results = if let Some(ref table_q) = query.table_query {
            self.table_retriever.retrieve(table_q, documents).await?
        } else {
            vec![]
        };

        let chart_results = if let Some(ref chart_q) = query.chart_query {
            self.chart_retriever.retrieve(chart_q, documents).await?
        } else {
            vec![]
        };

        // Cross-modal retrieval
        let cross_modal_results = if self.config.enable_cross_modal {
            self.cross_modal_retriever
                .retrieve(query, documents)
                .await?
        } else {
            vec![]
        };

        // Fuse results
        let fused_results = self.result_fusion.fuse_results(
            &text_results,
            &visual_results,
            &table_results,
            &chart_results,
            &cross_modal_results,
            &self.config.scoring_weights,
        )?;

        let processing_time = start_time.elapsed().as_millis() as u64;

        Ok(MultiModalRetrievalResult {
            documents: fused_results,
            processing_time_ms: processing_time,
            metadata: ResultMetadata {
                total_documents_searched: documents.len(),
                modalities_used: self.count_modalities_used(query),
                fusion_strategy_used: self.config.fusion_strategy,
            },
            statistics: RetrievalStatistics {
                text_results_count: text_results.len(),
                visual_results_count: visual_results.len(),
                table_results_count: table_results.len(),
                chart_results_count: chart_results.len(),
                cross_modal_results_count: cross_modal_results.len(),
            },
        })
    }

    /// Count modalities used in query
    fn count_modalities_used(&self, query: &MultiModalQuery) -> usize {
        let mut count = 0;
        if query.text_query.is_some() {
            count += 1;
        }
        if query.visual_query.is_some() {
            count += 1;
        }
        if query.table_query.is_some() {
            count += 1;
        }
        if query.chart_query.is_some() {
            count += 1;
        }
        count
    }

    /// Retrieve similar documents by embedding
    pub async fn retrieve_by_embedding(
        &self,
        embedding: &[f32],
        documents: &[MultiModalDocument],
    ) -> RragResult<Vec<RankedDocument>> {
        let mut scored_documents = Vec::new();

        for (idx, document) in documents.iter().enumerate() {
            let similarity = self
                .calculate_embedding_similarity(embedding, &document.embeddings.fused_embedding)?;

            if similarity >= self.config.similarity_thresholds.text_threshold {
                scored_documents.push(RankedDocument {
                    document: document.clone(),
                    relevance_score: similarity,
                    modality_scores: ModalityScores {
                        text_score: Some(similarity),
                        visual_score: None,
                        table_score: None,
                        chart_score: None,
                        cross_modal_score: None,
                    },
                    rank: idx,
                    explanation: None,
                });
            }
        }

        // Sort by relevance
        scored_documents.sort_by(|a, b| b.relevance_score.partial_cmp(&a.relevance_score).unwrap());

        // Update ranks
        for (idx, doc) in scored_documents.iter_mut().enumerate() {
            doc.rank = idx;
        }

        // Limit results
        scored_documents.truncate(self.config.max_total_results);

        Ok(scored_documents)
    }

    /// Calculate cosine similarity between embeddings
    fn calculate_embedding_similarity(&self, a: &[f32], b: &[f32]) -> RragResult<f32> {
        if a.len() != b.len() {
            return Err(RragError::validation(
                "embedding_dimensions",
                "matching dimensions",
                "mismatched dimensions",
            ));
        }

        let dot_product: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
        let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
        let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();

        if norm_a == 0.0 || norm_b == 0.0 {
            Ok(0.0)
        } else {
            Ok(dot_product / (norm_a * norm_b))
        }
    }
}

impl TextRetriever {
    pub fn new() -> RragResult<Self> {
        Ok(Self {
            semantic_searcher: SemanticSearcher::new(),
            keyword_searcher: KeywordSearcher::new(),
            hybrid_combiner: HybridCombiner::new(),
        })
    }

    pub async fn retrieve(
        &self,
        query: &str,
        documents: &[MultiModalDocument],
    ) -> RragResult<Vec<(usize, f32)>> {
        let semantic_results = self.semantic_searcher.search(query, documents)?;
        let keyword_results = self.keyword_searcher.search(query, documents)?;

        let combined_results = self
            .hybrid_combiner
            .combine(semantic_results, keyword_results)?;
        Ok(combined_results)
    }
}

impl VisualRetriever {
    pub fn new() -> RragResult<Self> {
        Ok(Self {
            clip_retriever: CLIPRetriever::new(),
            feature_retriever: FeatureBasedRetriever::new(),
            similarity_calculator: VisualSimilarityCalculator::new(),
        })
    }

    pub async fn retrieve(
        &self,
        query: &VisualQuery,
        documents: &[MultiModalDocument],
    ) -> RragResult<Vec<(usize, f32)>> {
        match query {
            VisualQuery::ImageExample(path) => {
                self.clip_retriever
                    .retrieve_by_example(path, documents)
                    .await
            }
            VisualQuery::FeatureQuery(features) => {
                self.feature_retriever
                    .retrieve_by_features(features, documents)
                    .await
            }
            VisualQuery::DescriptionQuery(description) => {
                self.clip_retriever
                    .retrieve_by_description(description, documents)
                    .await
            }
        }
    }
}

impl TableRetriever {
    pub fn new() -> RragResult<Self> {
        Ok(Self {
            schema_matcher: SchemaMatcher::new(),
            content_searcher: TableContentSearcher::new(),
            statistical_analyzer: TableStatisticalAnalyzer::new(),
        })
    }

    pub async fn retrieve(
        &self,
        query: &TableQuery,
        documents: &[MultiModalDocument],
    ) -> RragResult<Vec<(usize, f32)>> {
        let mut results = Vec::new();

        for (doc_idx, document) in documents.iter().enumerate() {
            if !document.tables.is_empty() {
                let mut table_score = 0.0;
                let mut matching_tables = 0;

                for table in &document.tables {
                    let mut score = 0.0;

                    // Schema matching
                    if let Some(ref schema) = query.schema {
                        score += self.schema_matcher.match_schema(schema, table)? * 0.3;
                    }

                    // Content filtering
                    for filter in &query.content_filters {
                        score += self.content_searcher.apply_filter(filter, table)? * 0.4;
                    }

                    // Statistical constraints
                    for constraint in &query.statistical_constraints {
                        score += self
                            .statistical_analyzer
                            .check_constraint(constraint, table)?
                            * 0.3;
                    }

                    if score > 0.0 {
                        table_score += score;
                        matching_tables += 1;
                    }
                }

                if matching_tables > 0 {
                    let avg_score = table_score / matching_tables as f32;
                    results.push((doc_idx, avg_score));
                }
            }
        }

        results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        Ok(results)
    }
}

impl ChartRetriever {
    pub fn new() -> RragResult<Self> {
        Ok(Self {
            type_classifier: ChartTypeClassifier::new(),
            pattern_matcher: DataPatternMatcher::new(),
            trend_analyzer: ChartTrendAnalyzer::new(),
        })
    }

    pub async fn retrieve(
        &self,
        query: &ChartQuery,
        documents: &[MultiModalDocument],
    ) -> RragResult<Vec<(usize, f32)>> {
        let mut results = Vec::new();

        for (doc_idx, document) in documents.iter().enumerate() {
            if !document.charts.is_empty() {
                let mut chart_score = 0.0;
                let mut matching_charts = 0;

                for chart in &document.charts {
                    let mut score = 0.0;

                    // Chart type matching
                    if query.chart_types.contains(&chart.chart_type) {
                        score += 0.3;
                    }

                    // Data constraints
                    for constraint in &query.data_constraints {
                        score += self.pattern_matcher.check_constraint(constraint, chart)? * 0.4;
                    }

                    // Trend requirements
                    if let Some(ref trends) = chart.trends {
                        for requirement in &query.trend_requirements {
                            score +=
                                self.trend_analyzer.check_requirement(requirement, trends)? * 0.3;
                        }
                    }

                    if score > 0.0 {
                        chart_score += score;
                        matching_charts += 1;
                    }
                }

                if matching_charts > 0 {
                    let avg_score = chart_score / matching_charts as f32;
                    results.push((doc_idx, avg_score));
                }
            }
        }

        results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        Ok(results)
    }
}

impl CrossModalRetriever {
    pub fn new() -> RragResult<Self> {
        Ok(Self {
            image_text_aligner: ImageTextAligner::new(),
            table_text_checker: TableTextConsistencyChecker::new(),
            coherence_scorer: CoherenceScorer::new(),
        })
    }

    pub async fn retrieve(
        &self,
        query: &MultiModalQuery,
        documents: &[MultiModalDocument],
    ) -> RragResult<Vec<(usize, f32)>> {
        let mut results = Vec::new();

        for (doc_idx, document) in documents.iter().enumerate() {
            let mut cross_modal_score = 0.0;
            let mut constraint_count = 0;

            for constraint in &query.cross_modal_constraints {
                let score = match constraint.constraint_type {
                    ConstraintType::ContentAlignment => self
                        .image_text_aligner
                        .calculate_alignment(&document.text_content, &document.images)?,
                    ConstraintType::SemanticConsistency => self
                        .table_text_checker
                        .check_consistency(&document.text_content, &document.tables)?,
                    ConstraintType::VisualCoherence => {
                        self.coherence_scorer.score_visual_coherence(document)?
                    }
                    ConstraintType::TemporalAlignment => {
                        0.7 // Simplified temporal alignment
                    }
                };

                cross_modal_score += score;
                constraint_count += 1;
            }

            if constraint_count > 0 {
                let avg_score = cross_modal_score / constraint_count as f32;
                results.push((doc_idx, avg_score));
            }
        }

        results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        Ok(results)
    }
}

impl ResultFusion {
    pub fn new(strategy: ResultFusionStrategy) -> RragResult<Self> {
        Ok(Self {
            strategy,
            score_normalizers: HashMap::new(),
            rank_aggregator: RankAggregator::new(),
        })
    }

    pub fn fuse_results(
        &self,
        text_results: &[(usize, f32)],
        visual_results: &[(usize, f32)],
        table_results: &[(usize, f32)],
        chart_results: &[(usize, f32)],
        cross_modal_results: &[(usize, f32)],
        weights: &ScoringWeights,
    ) -> RragResult<Vec<RankedDocument>> {
        match self.strategy {
            ResultFusionStrategy::WeightedCombination => self.weighted_fusion(
                text_results,
                visual_results,
                table_results,
                chart_results,
                cross_modal_results,
                weights,
            ),
            ResultFusionStrategy::RankFusion => self.rank_fusion(
                text_results,
                visual_results,
                table_results,
                chart_results,
                cross_modal_results,
            ),
            ResultFusionStrategy::ScoreNormalization => self.score_normalization_fusion(
                text_results,
                visual_results,
                table_results,
                chart_results,
                cross_modal_results,
                weights,
            ),
            ResultFusionStrategy::ReciprocalRankFusion => self.reciprocal_rank_fusion(
                text_results,
                visual_results,
                table_results,
                chart_results,
                cross_modal_results,
            ),
        }
    }

    fn weighted_fusion(
        &self,
        text_results: &[(usize, f32)],
        visual_results: &[(usize, f32)],
        _table_results: &[(usize, f32)],
        _chart_results: &[(usize, f32)],
        _cross_modal_results: &[(usize, f32)],
        weights: &ScoringWeights,
    ) -> RragResult<Vec<RankedDocument>> {
        let mut document_scores: HashMap<usize, f32> = HashMap::new();
        let mut modality_scores: HashMap<usize, ModalityScores> = HashMap::new();

        // Aggregate scores from each modality
        for &(doc_idx, score) in text_results {
            *document_scores.entry(doc_idx).or_insert(0.0) += score * weights.semantic_weight;
            modality_scores
                .entry(doc_idx)
                .or_insert(ModalityScores {
                    text_score: None,
                    visual_score: None,
                    table_score: None,
                    chart_score: None,
                    cross_modal_score: None,
                })
                .text_score = Some(score);
        }

        for &(doc_idx, score) in visual_results {
            *document_scores.entry(doc_idx).or_insert(0.0) += score * weights.visual_weight;
            modality_scores
                .entry(doc_idx)
                .or_insert(ModalityScores {
                    text_score: None,
                    visual_score: None,
                    table_score: None,
                    chart_score: None,
                    cross_modal_score: None,
                })
                .visual_score = Some(score);
        }

        // Convert to ranked documents (simplified)
        let mut ranked_docs: Vec<(usize, f32, ModalityScores)> = document_scores
            .into_iter()
            .map(|(doc_idx, score)| {
                let scores = modality_scores.remove(&doc_idx).unwrap_or(ModalityScores {
                    text_score: None,
                    visual_score: None,
                    table_score: None,
                    chart_score: None,
                    cross_modal_score: None,
                });
                (doc_idx, score, scores)
            })
            .collect();

        ranked_docs.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());

        // This would create proper RankedDocument instances in a real implementation
        // For now, return empty vector as placeholder
        Ok(vec![])
    }

    fn rank_fusion(
        &self,
        _text: &[(usize, f32)],
        _visual: &[(usize, f32)],
        _table: &[(usize, f32)],
        _chart: &[(usize, f32)],
        _cross: &[(usize, f32)],
    ) -> RragResult<Vec<RankedDocument>> {
        // Placeholder for rank fusion implementation
        Ok(vec![])
    }

    fn score_normalization_fusion(
        &self,
        _text: &[(usize, f32)],
        _visual: &[(usize, f32)],
        _table: &[(usize, f32)],
        _chart: &[(usize, f32)],
        _cross: &[(usize, f32)],
        _weights: &ScoringWeights,
    ) -> RragResult<Vec<RankedDocument>> {
        // Placeholder for score normalization fusion
        Ok(vec![])
    }

    fn reciprocal_rank_fusion(
        &self,
        _text: &[(usize, f32)],
        _visual: &[(usize, f32)],
        _table: &[(usize, f32)],
        _chart: &[(usize, f32)],
        _cross: &[(usize, f32)],
    ) -> RragResult<Vec<RankedDocument>> {
        // Placeholder for reciprocal rank fusion
        Ok(vec![])
    }
}

// Simplified implementations for helper components
impl SemanticSearcher {
    pub fn new() -> Self {
        Self
    }
    pub fn search(
        &self,
        _query: &str,
        _documents: &[MultiModalDocument],
    ) -> RragResult<Vec<(usize, f32)>> {
        Ok(vec![(0, 0.8), (1, 0.6), (2, 0.4)])
    }
}

impl KeywordSearcher {
    pub fn new() -> Self {
        Self
    }
    pub fn search(
        &self,
        _query: &str,
        _documents: &[MultiModalDocument],
    ) -> RragResult<Vec<(usize, f32)>> {
        Ok(vec![(0, 0.7), (2, 0.5), (3, 0.3)])
    }
}

impl HybridCombiner {
    pub fn new() -> Self {
        Self
    }
    pub fn combine(
        &self,
        semantic: Vec<(usize, f32)>,
        keyword: Vec<(usize, f32)>,
    ) -> RragResult<Vec<(usize, f32)>> {
        let mut combined = HashMap::new();

        for (idx, score) in semantic {
            combined.insert(idx, score * 0.7);
        }

        for (idx, score) in keyword {
            *combined.entry(idx).or_insert(0.0) += score * 0.3;
        }

        let mut results: Vec<(usize, f32)> = combined.into_iter().collect();
        results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());

        Ok(results)
    }
}

// Additional helper implementations...
impl CLIPRetriever {
    pub fn new() -> Self {
        Self
    }
    pub async fn retrieve_by_example(
        &self,
        _path: &str,
        _documents: &[MultiModalDocument],
    ) -> RragResult<Vec<(usize, f32)>> {
        Ok(vec![(0, 0.9), (1, 0.7)])
    }
    pub async fn retrieve_by_description(
        &self,
        _description: &str,
        _documents: &[MultiModalDocument],
    ) -> RragResult<Vec<(usize, f32)>> {
        Ok(vec![(0, 0.8), (2, 0.6)])
    }
}

impl FeatureBasedRetriever {
    pub fn new() -> Self {
        Self
    }
    pub async fn retrieve_by_features(
        &self,
        _features: &VisualFeatureQuery,
        _documents: &[MultiModalDocument],
    ) -> RragResult<Vec<(usize, f32)>> {
        Ok(vec![(1, 0.85), (3, 0.5)])
    }
}

impl VisualSimilarityCalculator {
    pub fn new() -> Self {
        Self
    }
}

impl SchemaMatcher {
    pub fn new() -> Self {
        Self
    }
    pub fn match_schema(&self, _schema: &TableSchema, _table: &ExtractedTable) -> RragResult<f32> {
        Ok(0.8)
    }
}

impl TableContentSearcher {
    pub fn new() -> Self {
        Self
    }
    pub fn apply_filter(
        &self,
        _filter: &ContentFilter,
        _table: &ExtractedTable,
    ) -> RragResult<f32> {
        Ok(0.7)
    }
}

impl TableStatisticalAnalyzer {
    pub fn new() -> Self {
        Self
    }
    pub fn check_constraint(
        &self,
        _constraint: &StatisticalConstraint,
        _table: &ExtractedTable,
    ) -> RragResult<f32> {
        Ok(0.6)
    }
}

impl ChartTypeClassifier {
    pub fn new() -> Self {
        Self
    }
}

impl DataPatternMatcher {
    pub fn new() -> Self {
        Self
    }
    pub fn check_constraint(
        &self,
        _constraint: &DataConstraint,
        _chart: &AnalyzedChart,
    ) -> RragResult<f32> {
        Ok(0.7)
    }
}

impl ChartTrendAnalyzer {
    pub fn new() -> Self {
        Self
    }
    pub fn check_requirement(
        &self,
        _requirement: &TrendRequirement,
        _trends: &super::TrendAnalysis,
    ) -> RragResult<f32> {
        Ok(0.8)
    }
}

impl ImageTextAligner {
    pub fn new() -> Self {
        Self
    }
    pub fn calculate_alignment(&self, _text: &str, _images: &[ProcessedImage]) -> RragResult<f32> {
        Ok(0.75)
    }
}

impl TableTextConsistencyChecker {
    pub fn new() -> Self {
        Self
    }
    pub fn check_consistency(&self, _text: &str, _tables: &[ExtractedTable]) -> RragResult<f32> {
        Ok(0.8)
    }
}

impl CoherenceScorer {
    pub fn new() -> Self {
        Self
    }
    pub fn score_visual_coherence(&self, _document: &MultiModalDocument) -> RragResult<f32> {
        Ok(0.7)
    }
}

impl RankAggregator {
    pub fn new() -> Self {
        Self
    }
}

impl ScoreNormalizer {
    pub fn new() -> Self {
        Self
    }
}

// Supporting types (simplified)
#[derive(Debug, Clone)]
pub struct VisualFeatureQuery {
    pub colors: Option<Vec<String>>,
    pub objects: Option<Vec<String>>,
    pub scene_type: Option<String>,
}

#[derive(Debug, Clone)]
pub struct TableSchema {
    pub columns: Vec<ColumnSchema>,
    pub constraints: Vec<SchemaConstraint>,
}

#[derive(Debug, Clone)]
pub struct ColumnSchema {
    pub name: String,
    pub data_type: super::DataType,
    pub required: bool,
}

#[derive(Debug, Clone)]
pub struct SchemaConstraint {
    pub constraint_type: String,
    pub parameters: HashMap<String, String>,
}

#[derive(Debug, Clone)]
pub struct ContentFilter {
    pub column: String,
    pub operator: FilterOperator,
    pub value: String,
}

#[derive(Debug, Clone)]
pub enum FilterOperator {
    Equals,
    Contains,
    GreaterThan,
    LessThan,
    Between,
}

#[derive(Debug, Clone)]
pub struct StatisticalConstraint {
    pub metric: StatisticalMetric,
    pub operator: FilterOperator,
    pub value: f64,
}

#[derive(Debug, Clone)]
pub enum StatisticalMetric {
    Mean,
    Median,
    StandardDeviation,
    Count,
}

#[derive(Debug, Clone)]
pub struct SizeConstraints {
    pub min_rows: Option<usize>,
    pub max_rows: Option<usize>,
    pub min_cols: Option<usize>,
    pub max_cols: Option<usize>,
}

#[derive(Debug, Clone)]
pub struct DataConstraint {
    pub constraint_type: String,
    pub parameters: HashMap<String, String>,
}

#[derive(Debug, Clone)]
pub struct TrendRequirement {
    pub trend_type: String,
    pub strength: Option<f32>,
}

#[derive(Debug, Clone)]
pub struct ValueRange {
    pub min: f64,
    pub max: f64,
}

#[derive(Debug, Clone)]
pub struct QueryMetadata {
    pub query_id: String,
    pub timestamp: String,
    pub user_id: Option<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResultMetadata {
    pub total_documents_searched: usize,
    pub modalities_used: usize,
    pub fusion_strategy_used: ResultFusionStrategy,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RetrievalStatistics {
    pub text_results_count: usize,
    pub visual_results_count: usize,
    pub table_results_count: usize,
    pub chart_results_count: usize,
    pub cross_modal_results_count: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RelevanceExplanation {
    pub primary_matches: Vec<String>,
    pub cross_modal_connections: Vec<String>,
    pub confidence_factors: HashMap<String, f32>,
}

// Component structs for compilation
pub struct SemanticSearcher;
pub struct KeywordSearcher;
pub struct HybridCombiner;
pub struct CLIPRetriever;
pub struct FeatureBasedRetriever;
pub struct VisualSimilarityCalculator;
pub struct SchemaMatcher;
pub struct TableContentSearcher;
pub struct TableStatisticalAnalyzer;
pub struct ChartTypeClassifier;
pub struct DataPatternMatcher;
pub struct ChartTrendAnalyzer;
pub struct ImageTextAligner;
pub struct TableTextConsistencyChecker;
pub struct CoherenceScorer;
pub struct RankAggregator;
pub struct ScoreNormalizer;

impl Default for RetrievalConfig {
    fn default() -> Self {
        Self {
            max_results_per_modality: 50,
            max_total_results: 100,
            similarity_thresholds: ModalitySimilarityThresholds {
                text_threshold: 0.5,
                visual_threshold: 0.6,
                table_threshold: 0.4,
                chart_threshold: 0.5,
                cross_modal_threshold: 0.7,
            },
            enable_cross_modal: true,
            fusion_strategy: ResultFusionStrategy::WeightedCombination,
            scoring_weights: ScoringWeights {
                semantic_weight: 0.4,
                visual_weight: 0.3,
                structural_weight: 0.2,
                temporal_weight: 0.05,
                contextual_weight: 0.05,
            },
        }
    }
}

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

    #[test]
    fn test_retriever_creation() {
        let config = RetrievalConfig::default();
        let retriever = MultiModalRetriever::new(config).unwrap();

        assert_eq!(retriever.config.max_total_results, 100);
        assert!(retriever.config.enable_cross_modal);
    }

    #[test]
    fn test_embedding_similarity() {
        let config = RetrievalConfig::default();
        let retriever = MultiModalRetriever::new(config).unwrap();

        let emb1 = vec![1.0, 0.0, 0.0];
        let emb2 = vec![1.0, 0.0, 0.0];
        let emb3 = vec![0.0, 1.0, 0.0];

        let sim1 = retriever
            .calculate_embedding_similarity(&emb1, &emb2)
            .unwrap();
        let sim2 = retriever
            .calculate_embedding_similarity(&emb1, &emb3)
            .unwrap();

        assert!((sim1 - 1.0).abs() < 1e-6);
        assert!((sim2 - 0.0).abs() < 1e-6);
    }
}