rrag 0.1.0-alpha.2

//! # Multi-Modal RAG Processing
//!
//! Advanced multi-modal processing capabilities for handling diverse content types
//! in RAG systems including images, tables, charts, PDFs, and structured documents.
//!
//! This module enables RAG systems to process and understand content beyond plain text,
//! making it possible to build applications that can reason over visual content,
//! extract information from tables, analyze charts, and process complex document layouts.
//!
//! ## Features
//!
//! - **Image Processing**: Extract features, generate captions, detect objects
//! - **Table Processing**: Extract structured data from HTML, CSV, and PDF tables
//! - **Chart Analysis**: Understand charts, graphs, and visualizations
//! - **OCR Integration**: Extract text from images and scanned documents
//! - **Layout Analysis**: Understand document structure and reading order
//! - **Embedding Fusion**: Combine embeddings from different modalities
//! - **Multi-Modal Retrieval**: Search across text, images, and structured data
//!
//! ## Supported Formats
//!
//! - **Images**: PNG, JPEG, GIF, WebP, SVG
//! - **Documents**: PDF, Word, PowerPoint, HTML
//! - **Tables**: HTML tables, CSV, TSV, Excel
//! - **Charts**: PNG/JPEG charts, SVG graphics
//! - **Mixed Content**: Documents with embedded images and tables
//!
//! ## Examples
//!
//! ### Basic Multi-Modal Document Processing
//! ```rust
//! use rrag::multimodal::{MultiModalService, MultiModalConfig, MultiModalDocument};
//!
//! # async fn example() -> rrag::RragResult<()> {
//! let service = MultiModalService::new(
//!     MultiModalConfig::default()
//!         .enable_image_processing(true)
//!         .enable_table_extraction(true)
//!         .enable_chart_analysis(true)
//! ).await?;
//!
//! // Process a document with mixed content
//! let document = MultiModalDocument::new()
//!     .add_text("Q4 2024 Revenue Report")
//!     .add_image("charts/revenue_chart.png")
//!     .add_table("data/quarterly_results.csv");
//!
//! let processed = service.process_document(document).await?;
//! println!("Extracted {} text chunks, {} images, {} tables",
//!          processed.text_chunks.len(),
//!          processed.images.len(),
//!          processed.tables.len());
//! # Ok(())
//! # }
//! ```
//!
//! ### Image Analysis and Captioning
//! ```rust
//! use rrag::multimodal::image_processor::{ImageProcessor, ProcessingConfig};
//!
//! # async fn example() -> rrag::RragResult<()> {
//! let processor = ImageProcessor::new(
//!     ProcessingConfig::default()
//!         .enable_object_detection(true)
//!         .enable_captioning(true)
//!         .enable_ocr(true)
//! );
//!
//! let image_path = "images/product_diagram.png";
//! let analysis = processor.analyze_image(image_path).await?;
//!
//! println!("Caption: {}", analysis.caption);
//! println!("Detected {} objects", analysis.objects.len());
//! println!("Extracted text: {}", analysis.text);
//! # Ok(())
//! # }
//! ```
//!
//! ### Table Extraction and Analysis
//! ```rust
//! use rrag::multimodal::table_processor::{TableProcessor, TableConfig};
//!
//! # async fn example() -> rrag::RragResult<()> {
//! let processor = TableProcessor::new(TableConfig::default());
//!
//! // Extract tables from HTML
//! let html = r#"<table><tr><th>Product</th><th>Revenue</th></tr>..."#;
//! let tables = processor.extract_from_html(html).await?;
//!
//! for table in tables {
//!     println!("Table: {} rows, {} columns",
//!              table.rows.len(),
//!              table.headers.len());
//! }
//! # Ok(())
//! # }
//! ```
//!
//! ### Chart Analysis
//! ```rust
//! use rrag::multimodal::chart_processor::{ChartProcessor, ChartConfig};
//!
//! # async fn example() -> rrag::RragResult<()> {
//! let processor = ChartProcessor::new(ChartConfig::default());
//!
//! let chart_path = "charts/sales_trend.png";
//! let analysis = processor.analyze_chart(chart_path).await?;
//!
//! println!("Chart type: {:?}", analysis.chart_type);
//! println!("Description: {}", analysis.description);
//! println!("Key insights: {:?}", analysis.insights);
//! # Ok(())
//! # }
//! ```
//!
//! ### Multi-Modal Search
//! ```rust
//! use rrag::multimodal::retrieval::{MultiModalRetriever, SearchOptions};
//!
//! # async fn example() -> rrag::RragResult<()> {
//! let retriever = MultiModalRetriever::new().await?;
//!
//! // Search across text, images, and tables
//! let results = retriever.search_multi_modal(
//!     "revenue trends Q4 2024",
//!     SearchOptions::new()
//!         .include_text(true)
//!         .include_images(true)
//!         .include_tables(true)
//! ).await?;
//!
//! for result in results {
//!     match result.content_type {
//!         ContentType::Text => println!("Text: {}", result.content),
//!         ContentType::Image => println!("Image: {}", result.path),
//!         ContentType::Table => println!("Table: {} rows", result.metadata["rows"]),
//!     }
//! }
//! # Ok(())
//! # }
//! ```
//!
//! ## Performance Considerations
//!
//! - **Batch Processing**: Process multiple items together for efficiency
//! - **Caching**: Cache embeddings and analysis results
//! - **Parallel Processing**: Use multiple threads for CPU-intensive tasks
//! - **GPU Acceleration**: Leverage CUDA for deep learning models (when available)
//! - **Memory Management**: Stream large files to avoid memory issues
//!
//! ## Model Integration
//!
//! The module supports integration with various pre-trained models:
//!
//! - **Vision Models**: CLIP, BLIP, ViT for image understanding
//! - **OCR Models**: Tesseract, EasyOCR, TrOCR
//! - **Layout Models**: LayoutLM, DiT for document layout
//! - **Table Models**: TableNet, TableTransformer
//! - **Chart Models**: ChartQA, PlotQA for chart understanding

pub mod chart_processor;
pub mod document_parser;
pub mod embedding_fusion;
pub mod image_processor;
pub mod layout_analysis;
pub mod ocr;
pub mod retrieval;
pub mod table_processor;

use crate::RragResult;
use serde::{Deserialize, Serialize};
use std::path::Path;

/// Multi-modal processing service
pub struct MultiModalService {
    /// Configuration
    config: MultiModalConfig,

    /// Image processor
    image_processor: Box<dyn ImageProcessor>,

    /// Table processor
    table_processor: Box<dyn TableProcessor>,

    /// Chart processor
    chart_processor: Box<dyn ChartProcessor>,

    /// OCR engine
    ocr_engine: Box<dyn OCREngine>,

    /// Layout analyzer
    layout_analyzer: Box<dyn LayoutAnalyzer>,

    /// Embedding fusion strategy
    fusion_strategy: Box<dyn EmbeddingFusionStrategy>,
}

/// Multi-modal configuration
#[derive(Debug, Clone)]
pub struct MultiModalConfig {
    /// Enable image processing
    pub process_images: bool,

    /// Enable table extraction
    pub process_tables: bool,

    /// Enable chart analysis
    pub process_charts: bool,

    /// Image processing config
    pub image_config: ImageProcessingConfig,

    /// Table extraction config
    pub table_config: TableExtractionConfig,

    /// Chart analysis config
    pub chart_config: ChartAnalysisConfig,

    /// OCR configuration
    pub ocr_config: OCRConfig,

    /// Layout analysis config
    pub layout_config: LayoutAnalysisConfig,

    /// Fusion strategy
    pub fusion_strategy: FusionStrategy,
}

/// Image processing configuration
#[derive(Debug, Clone)]
pub struct ImageProcessingConfig {
    /// Maximum image dimensions
    pub max_width: u32,
    pub max_height: u32,

    /// Image formats to process
    pub supported_formats: Vec<ImageFormat>,

    /// Enable CLIP embeddings
    pub use_clip: bool,

    /// Enable image captioning
    pub generate_captions: bool,

    /// Extract visual features
    pub extract_features: bool,

    /// Compression quality (0-100)
    pub compression_quality: u8,
}

/// Table extraction configuration
#[derive(Debug, Clone)]
pub struct TableExtractionConfig {
    /// Minimum rows for valid table
    pub min_rows: usize,

    /// Minimum columns for valid table
    pub min_cols: usize,

    /// Extract headers
    pub extract_headers: bool,

    /// Infer data types
    pub infer_types: bool,

    /// Generate summaries
    pub generate_summaries: bool,

    /// Output format
    pub output_format: TableOutputFormat,
}

/// Chart analysis configuration
#[derive(Debug, Clone)]
pub struct ChartAnalysisConfig {
    /// Chart types to recognize
    pub chart_types: Vec<ChartType>,

    /// Extract data points
    pub extract_data: bool,

    /// Generate descriptions
    pub generate_descriptions: bool,

    /// Analyze trends
    pub analyze_trends: bool,
}

/// OCR configuration
#[derive(Debug, Clone)]
pub struct OCRConfig {
    /// OCR engine to use
    pub engine: OCREngineType,

    /// Languages to recognize
    pub languages: Vec<String>,

    /// Confidence threshold
    pub confidence_threshold: f32,

    /// Enable spell correction
    pub spell_correction: bool,

    /// Preserve formatting
    pub preserve_formatting: bool,
}

/// Layout analysis configuration
#[derive(Debug, Clone)]
pub struct LayoutAnalysisConfig {
    /// Detect document structure
    pub detect_structure: bool,

    /// Identify sections
    pub identify_sections: bool,

    /// Extract reading order
    pub extract_reading_order: bool,

    /// Detect columns
    pub detect_columns: bool,
}

/// Multi-modal document representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MultiModalDocument {
    /// Document ID
    pub id: String,

    /// Text content
    pub text_content: String,

    /// Images in document
    pub images: Vec<ProcessedImage>,

    /// Tables in document
    pub tables: Vec<ExtractedTable>,

    /// Charts in document
    pub charts: Vec<AnalyzedChart>,

    /// Document layout
    pub layout: DocumentLayout,

    /// Combined embeddings
    pub embeddings: MultiModalEmbeddings,

    /// Metadata
    pub metadata: DocumentMetadata,
}

/// Processed image data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProcessedImage {
    /// Image ID
    pub id: String,

    /// Original path or URL
    pub source: String,

    /// Image caption
    pub caption: Option<String>,

    /// OCR text if applicable
    pub ocr_text: Option<String>,

    /// Visual features
    pub features: Option<VisualFeatures>,

    /// CLIP embedding
    pub clip_embedding: Option<Vec<f32>>,

    /// Image metadata
    pub metadata: ImageMetadata,
}

/// Extracted table data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExtractedTable {
    /// Table ID
    pub id: String,

    /// Table headers
    pub headers: Vec<String>,

    /// Table data rows
    pub rows: Vec<Vec<TableCell>>,

    /// Table summary
    pub summary: Option<String>,

    /// Column types
    pub column_types: Vec<DataType>,

    /// Table embedding
    pub embedding: Option<Vec<f32>>,

    /// Statistics
    pub statistics: Option<TableStatistics>,
}

/// Analyzed chart data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnalyzedChart {
    /// Chart ID
    pub id: String,

    /// Chart type
    pub chart_type: ChartType,

    /// Chart title
    pub title: Option<String>,

    /// Axis labels
    pub axes: ChartAxes,

    /// Data points
    pub data_points: Vec<DataPoint>,

    /// Trend analysis
    pub trends: Option<TrendAnalysis>,

    /// Description
    pub description: Option<String>,

    /// Chart embedding
    pub embedding: Option<Vec<f32>>,
}

/// Document layout information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DocumentLayout {
    /// Page count
    pub pages: usize,

    /// Document sections
    pub sections: Vec<DocumentSection>,

    /// Reading order
    pub reading_order: Vec<String>,

    /// Column layout
    pub columns: Option<ColumnLayout>,

    /// Document type
    pub document_type: DocumentType,
}

/// Multi-modal embeddings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MultiModalEmbeddings {
    /// Text embeddings
    pub text_embeddings: Vec<f32>,

    /// Visual embeddings (averaged)
    pub visual_embeddings: Option<Vec<f32>>,

    /// Table embeddings (averaged)
    pub table_embeddings: Option<Vec<f32>>,

    /// Fused embedding
    pub fused_embedding: Vec<f32>,

    /// Embedding weights
    pub weights: EmbeddingWeights,
}

/// Image processor trait
pub trait ImageProcessor: Send + Sync {
    /// Process image
    fn process_image(&self, image_path: &Path) -> RragResult<ProcessedImage>;

    /// Extract features
    fn extract_features(&self, image_path: &Path) -> RragResult<VisualFeatures>;

    /// Generate caption
    fn generate_caption(&self, image_path: &Path) -> RragResult<String>;

    /// Generate CLIP embedding
    fn generate_clip_embedding(&self, image_path: &Path) -> RragResult<Vec<f32>>;
}

/// Table processor trait
pub trait TableProcessor: Send + Sync {
    /// Extract table from document
    fn extract_table(&self, content: &str) -> RragResult<Vec<ExtractedTable>>;

    /// Parse table structure
    fn parse_structure(&self, table_html: &str) -> RragResult<ExtractedTable>;

    /// Generate table summary
    fn generate_summary(&self, table: &ExtractedTable) -> RragResult<String>;

    /// Calculate statistics
    fn calculate_statistics(&self, table: &ExtractedTable) -> RragResult<TableStatistics>;
}

/// Chart processor trait
pub trait ChartProcessor: Send + Sync {
    /// Analyze chart
    fn analyze_chart(&self, image_path: &Path) -> RragResult<AnalyzedChart>;

    /// Extract data points
    fn extract_data_points(&self, chart_image: &Path) -> RragResult<Vec<DataPoint>>;

    /// Identify chart type
    fn identify_type(&self, chart_image: &Path) -> RragResult<ChartType>;

    /// Analyze trends
    fn analyze_trends(&self, data_points: &[DataPoint]) -> RragResult<TrendAnalysis>;
}

/// OCR engine trait
pub trait OCREngine: Send + Sync {
    /// Perform OCR on image
    fn ocr(&self, image_path: &Path) -> RragResult<OCRResult>;

    /// Get text with confidence
    fn get_text_with_confidence(&self, image_path: &Path) -> RragResult<Vec<(String, f32)>>;

    /// Get text layout
    fn get_layout(&self, image_path: &Path) -> RragResult<TextLayout>;
}

/// Layout analyzer trait
pub trait LayoutAnalyzer: Send + Sync {
    /// Analyze document layout
    fn analyze_layout(&self, document_path: &Path) -> RragResult<DocumentLayout>;

    /// Detect sections
    fn detect_sections(&self, content: &str) -> RragResult<Vec<DocumentSection>>;

    /// Extract reading order
    fn extract_reading_order(&self, layout: &DocumentLayout) -> RragResult<Vec<String>>;
}

/// Embedding fusion strategy trait
pub trait EmbeddingFusionStrategy: Send + Sync {
    /// Fuse multi-modal embeddings
    fn fuse_embeddings(&self, embeddings: &MultiModalEmbeddings) -> RragResult<Vec<f32>>;

    /// Calculate optimal weights
    fn calculate_weights(&self, document: &MultiModalDocument) -> RragResult<EmbeddingWeights>;
}

// Supporting types

/// Image formats
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ImageFormat {
    JPEG,
    PNG,
    GIF,
    BMP,
    WEBP,
    SVG,
    TIFF,
}

/// Table output formats
#[derive(Debug, Clone, Copy)]
pub enum TableOutputFormat {
    CSV,
    JSON,
    Markdown,
    HTML,
}

/// Chart types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum ChartType {
    Line,
    Bar,
    Pie,
    Scatter,
    Area,
    Histogram,
    HeatMap,
    Box,
    Unknown,
}

/// OCR engine types
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum OCREngineType {
    Tesseract,
    EasyOCR,
    PaddleOCR,
    CloudVision,
}

/// Document types
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq)]
pub enum DocumentType {
    PDF,
    Word,
    PowerPoint,
    HTML,
    Markdown,
    PlainText,
    Mixed,
}

/// Data types for table columns
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq, Hash)]
pub enum DataType {
    String,
    Number,
    Date,
    Boolean,
    Mixed,
}

/// Fusion strategies
#[derive(Debug, Clone, Copy)]
pub enum FusionStrategy {
    /// Simple averaging
    Average,

    /// Weighted average based on content
    Weighted,

    /// Concatenation
    Concatenate,

    /// Attention-based fusion
    Attention,

    /// Learned fusion
    Learned,
}

/// Visual features extracted from images
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VisualFeatures {
    /// Dominant colors
    pub colors: Vec<Color>,

    /// Detected objects
    pub objects: Vec<DetectedObject>,

    /// Scene classification
    pub scene: Option<String>,

    /// Image quality metrics
    pub quality: ImageQuality,

    /// Spatial layout
    pub layout: SpatialLayout,
}

/// Table cell
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TableCell {
    /// Cell value
    pub value: String,

    /// Cell type
    pub data_type: DataType,

    /// Cell formatting
    pub formatting: Option<CellFormatting>,
}

/// Table statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TableStatistics {
    /// Row count
    pub row_count: usize,

    /// Column count
    pub column_count: usize,

    /// Null percentage per column
    pub null_percentages: Vec<f32>,

    /// Column statistics
    pub column_stats: Vec<ColumnStatistics>,
}

/// Column statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ColumnStatistics {
    /// Column name
    pub name: String,

    /// For numeric columns
    pub numeric_stats: Option<NumericStatistics>,

    /// For text columns
    pub text_stats: Option<TextStatistics>,

    /// Unique values count
    pub unique_count: usize,
}

/// Numeric statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NumericStatistics {
    pub min: f64,
    pub max: f64,
    pub mean: f64,
    pub median: f64,
    pub std_dev: f64,
}

/// Text statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TextStatistics {
    pub min_length: usize,
    pub max_length: usize,
    pub avg_length: f32,
    pub most_common: Vec<(String, usize)>,
}

/// Chart axes information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChartAxes {
    pub x_label: Option<String>,
    pub y_label: Option<String>,
    pub x_range: Option<(f64, f64)>,
    pub y_range: Option<(f64, f64)>,
}

/// Data point in chart
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataPoint {
    pub x: f64,
    pub y: f64,
    pub label: Option<String>,
    pub series: Option<String>,
}

/// Trend analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrendAnalysis {
    /// Trend direction
    pub direction: TrendDirection,

    /// Trend strength (0-1)
    pub strength: f32,

    /// Seasonality detected
    pub seasonality: Option<Seasonality>,

    /// Outliers
    pub outliers: Vec<DataPoint>,

    /// Forecast
    pub forecast: Option<Vec<DataPoint>>,
}

/// Trend directions
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq)]
pub enum TrendDirection {
    Increasing,
    Decreasing,
    Stable,
    Volatile,
}

/// Seasonality patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Seasonality {
    pub period: f64,
    pub amplitude: f64,
    pub phase: f64,
}

/// OCR result
#[derive(Debug, Clone)]
pub struct OCRResult {
    /// Extracted text
    pub text: String,

    /// Overall confidence
    pub confidence: f32,

    /// Word-level results
    pub words: Vec<OCRWord>,

    /// Detected languages
    pub languages: Vec<String>,
}

/// OCR word result
#[derive(Debug, Clone)]
pub struct OCRWord {
    pub text: String,
    pub confidence: f32,
    pub bounding_box: BoundingBox,
}

/// Bounding box
#[derive(Debug, Clone)]
pub struct BoundingBox {
    pub x: u32,
    pub y: u32,
    pub width: u32,
    pub height: u32,
}

/// Text layout from OCR
#[derive(Debug, Clone)]
pub struct TextLayout {
    /// Text blocks
    pub blocks: Vec<TextBlock>,

    /// Reading order
    pub reading_order: Vec<usize>,

    /// Detected columns
    pub columns: Option<Vec<Column>>,
}

/// Text block
#[derive(Debug, Clone)]
pub struct TextBlock {
    pub id: usize,
    pub text: String,
    pub bounding_box: BoundingBox,
    pub block_type: BlockType,
}

/// Block types
#[derive(Debug, Clone, Copy)]
pub enum BlockType {
    Title,
    Heading,
    Paragraph,
    Caption,
    Footer,
    Header,
}

/// Column in layout
#[derive(Debug, Clone)]
pub struct Column {
    pub index: usize,
    pub blocks: Vec<usize>,
    pub width: u32,
}

/// Document section
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DocumentSection {
    pub id: String,
    pub title: Option<String>,
    pub content: String,
    pub section_type: SectionType,
    pub level: usize,
    pub page_range: (usize, usize),
}

/// Section types
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq, Hash)]
pub enum SectionType {
    Title,
    Abstract,
    Introduction,
    Body,
    Conclusion,
    References,
    Appendix,
}

/// Column layout
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ColumnLayout {
    pub column_count: usize,
    pub column_widths: Vec<f32>,
    pub gutter_width: f32,
}

/// Document metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DocumentMetadata {
    pub title: Option<String>,
    pub author: Option<String>,
    pub creation_date: Option<String>,
    pub modification_date: Option<String>,
    pub page_count: usize,
    pub word_count: usize,
    pub language: String,
    pub format: DocumentType,
}

/// Image metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ImageMetadata {
    pub width: u32,
    pub height: u32,
    pub format: String,
    pub size_bytes: usize,
    pub dpi: Option<u32>,
    pub color_space: Option<String>,
}

/// Color information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Color {
    pub rgb: (u8, u8, u8),
    pub percentage: f32,
    pub name: Option<String>,
}

/// Detected object in image
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DetectedObject {
    pub class: String,
    pub confidence: f32,
    pub bounding_box: (f32, f32, f32, f32),
}

/// Image quality metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ImageQuality {
    pub sharpness: f32,
    pub contrast: f32,
    pub brightness: f32,
    pub noise_level: f32,
}

/// Spatial layout of image
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SpatialLayout {
    pub composition_type: CompositionType,
    pub focal_points: Vec<(f32, f32)>,
    pub balance: f32,
}

/// Composition types
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum CompositionType {
    RuleOfThirds,
    Centered,
    Diagonal,
    Symmetrical,
    Asymmetrical,
}

/// Cell formatting
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CellFormatting {
    pub bold: bool,
    pub italic: bool,
    pub color: Option<String>,
    pub background: Option<String>,
}

/// Embedding weights for fusion
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmbeddingWeights {
    pub text_weight: f32,
    pub visual_weight: f32,
    pub table_weight: f32,
    pub chart_weight: f32,
}

impl MultiModalService {
    /// Create new multi-modal service
    pub fn new(config: MultiModalConfig) -> RragResult<Self> {
        Ok(Self {
            config: config.clone(),
            image_processor: Box::new(image_processor::DefaultImageProcessor::new(
                config.image_config,
            )?),
            table_processor: Box::new(table_processor::DefaultTableProcessor::new(
                config.table_config,
            )?),
            chart_processor: Box::new(chart_processor::DefaultChartProcessor::new(
                config.chart_config,
            )?),
            ocr_engine: Box::new(ocr::DefaultOCREngine::new(config.ocr_config)?),
            layout_analyzer: Box::new(layout_analysis::DefaultLayoutAnalyzer::new(
                config.layout_config,
            )?),
            fusion_strategy: Box::new(embedding_fusion::DefaultFusionStrategy::new(
                config.fusion_strategy,
            )?),
        })
    }

    /// Process multi-modal document
    pub async fn process_document(&self, _document_path: &Path) -> RragResult<MultiModalDocument> {
        // Implementation would process all modalities
        todo!("Implement multi-modal document processing")
    }

    /// Extract all modalities
    pub async fn extract_modalities(&self, _content: &[u8]) -> RragResult<MultiModalDocument> {
        // Implementation would extract different modalities
        todo!("Implement modality extraction")
    }
}

impl Default for MultiModalConfig {
    fn default() -> Self {
        Self {
            process_images: true,
            process_tables: true,
            process_charts: true,
            image_config: ImageProcessingConfig::default(),
            table_config: TableExtractionConfig::default(),
            chart_config: ChartAnalysisConfig::default(),
            ocr_config: OCRConfig::default(),
            layout_config: LayoutAnalysisConfig::default(),
            fusion_strategy: FusionStrategy::Weighted,
        }
    }
}

impl Default for ImageProcessingConfig {
    fn default() -> Self {
        Self {
            max_width: 1920,
            max_height: 1080,
            supported_formats: vec![ImageFormat::JPEG, ImageFormat::PNG, ImageFormat::WEBP],
            use_clip: true,
            generate_captions: true,
            extract_features: true,
            compression_quality: 85,
        }
    }
}

impl Default for TableExtractionConfig {
    fn default() -> Self {
        Self {
            min_rows: 2,
            min_cols: 2,
            extract_headers: true,
            infer_types: true,
            generate_summaries: true,
            output_format: TableOutputFormat::JSON,
        }
    }
}

impl Default for ChartAnalysisConfig {
    fn default() -> Self {
        Self {
            chart_types: vec![
                ChartType::Line,
                ChartType::Bar,
                ChartType::Pie,
                ChartType::Scatter,
            ],
            extract_data: true,
            generate_descriptions: true,
            analyze_trends: true,
        }
    }
}

impl Default for OCRConfig {
    fn default() -> Self {
        Self {
            engine: OCREngineType::Tesseract,
            languages: vec!["eng".to_string()],
            confidence_threshold: 0.7,
            spell_correction: true,
            preserve_formatting: true,
        }
    }
}

impl Default for LayoutAnalysisConfig {
    fn default() -> Self {
        Self {
            detect_structure: true,
            identify_sections: true,
            extract_reading_order: true,
            detect_columns: true,
        }
    }
}

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

    #[test]
    fn test_multimodal_config() {
        let config = MultiModalConfig::default();
        assert!(config.process_images);
        assert!(config.process_tables);
        assert!(config.process_charts);
    }

    #[test]
    fn test_image_config() {
        let config = ImageProcessingConfig::default();
        assert_eq!(config.max_width, 1920);
        assert_eq!(config.max_height, 1080);
        assert!(config.use_clip);
    }
}