"""
Multi-Modal Visual Retriever
Advanced retrieval system that fully utilizes DeepSeek-OCR's visual features
"""

import numpy as np
from typing import Dict, List, Optional, Tuple, Any
from dataclasses import dataclass
import time
from collections import defaultdict

from .adaptive_retriever import AdaptiveRetriever
from .query_classifier import QueryClassifier
from ..graph.dual_layer import DualLayerGraph
from ..ocr.deepseek_ocr import VisualRegion
from ..ner.visual_aware_ner import VisualAwareNERPipeline
from ..ner.gliner_ner import ExtractedEntity


@dataclass
class MultiModalRetrievalResult:
    """Enhanced retrieval result with multi-modal features"""

    context: str
    visual_embeddings: List[np.ndarray]
    text_embeddings: List[np.ndarray]
    nodes_retrieved: int
    token_count: int
    visual_similarity_scores: List[float]
    text_similarity_scores: List[float]
    multimodal_scores: List[float]
    regions: List[Dict]
    entities: List[Dict]
    compression_ratio: float

    # Multi-modal specific features
    visual_mode_used: bool
    layout_similarity: float
    content_pattern_matches: List[str]
    spatial_consistency: float
    confidence_breakdown: Dict[str, float]

    # Retrieved features
    retrieved_features: Dict[str, Any]
    query_visual_features: Dict[str, Any]


class MultiModalVisualRetriever(AdaptiveRetriever):
    """
    Multi-modal retriever that uses all visual features from DeepSeek-OCR
    """

    def __init__(
        self,
        graph: DualLayerGraph,
        query_classifier: QueryClassifier,
        ner_pipeline: Optional[VisualAwareNERPipeline] = None,
        visual_weight: float = 0.4,
        layout_weight: float = 0.2,
        spatial_weight: float = 0.2,
        content_weight: float = 0.2,
        enable_multimodal_fusion: bool = True,
        enable_cross_modal_search: bool = True,
    ):
        """
        Initialize Multi-Modal Visual Retriever

        Args:
            graph: Dual-layer graph
            query_classifier: Query classifier
            ner_pipeline: Visual-Aware NER pipeline
            visual_weight: Weight for visual similarity
            layout_weight: Weight for layout similarity
            spatial_weight: Weight for spatial consistency
            content_weight: Weight for content pattern matching
            enable_multimodal_fusion: Whether to use multi-modal fusion
            enable_cross_modal_search: Whether to enable cross-modal search
        """
        super().__init__(graph, query_classifier)
        self.ner_pipeline = ner_pipeline or VisualAwareNERPipeline()
        self.visual_weight = visual_weight
        self.layout_weight = layout_weight
        self.spatial_weight = spatial_weight
        self.content_weight = content_weight
        self.enable_multimodal_fusion = enable_multimodal_fusion
        self.enable_cross_modal_search = enable_cross_modal_search

        # Statistics
        self.multimodal_stats = {
            "total_queries": 0,
            "multimodal_queries": 0,
            "cross_modal_queries": 0,
            "visual_boosts": 0,
            "layout_matches": 0,
        }

        # Feature extractors
        self._init_feature_extractors()

    def _init_feature_extractors(self):
        """Initialize feature extractors for different modalities"""
        self.text_encoder = TextEmbeddingEncoder()
        self.visual_encoder = VisualEmbeddingEncoder()
        self.layout_analyzer = LayoutPatternAnalyzer()
        self.spatial_reasoner = SpatialRelationshipAnalyzer()
        self.content_matcher = ContentPatternMatcher()

    def retrieve_multimodal(
        self,
        query: str,
        query_image: Optional[Any] = None,
        override_level: Optional[int] = None,
        force_visual_mode: bool = False,
        return_all_features: bool = False
    ) -> MultiModalRetrievalResult:
        """
        Perform multi-modal retrieval using all available features

        Args:
            query: Text query
            query_image: Optional image for visual queries
            override_level: Override automatic level detection
            force_visual_mode: Force visual mode
            return_all_features: Return all extracted features

        Returns:
            MultiModalRetrievalResult with comprehensive retrieval information
        """
        start_time = time.time()

        # Classify query
        classification = self.classifier.analyze_query(query)
        if override_level is not None:
            classification["level"] = override_level

        self.multimodal_stats["total_queries"] += 1

        # Determine retrieval strategy
        strategy = self._determine_retrieval_strategy(query, query_image, classification, force_visual_mode)

        # Extract query features
        query_features = self._extract_query_features(query, query_image)

        # Perform retrieval based on strategy
        if strategy == "multimodal":
            self.multimodal_stats["multimodal_queries"] += 1
            results = self._multimodal_retrieval(query, query_features, classification)
        elif strategy == "cross_modal":
            self.multimodal_stats["cross_modal_queries"] += 1
            results = self._cross_modal_retrieval(query, query_features, classification)
        else:
            results = self._enhanced_text_retrieval(query, query_features, classification)

        # Calculate final scores and fuse results
        final_results = self._fuse_multimodal_results(results, query_features, classification)

        # Build context
        context, embeddings, token_count = self._build_multimodal_context(final_results, classification)

        processing_time = time.time() - start_time

        # Calculate compression ratio
        original_size = len(query) * 4  # Estimate
        visual_size = sum(e.size * 4 for e in embeddings)  # Assume 4 bytes per float
        compression_ratio = original_size / max(1, token_count * 4 + visual_size)

        # Return result
        result = MultiModalRetrievalResult(
            context=context,
            visual_embeddings=embeddings,
            text_embeddings=[],
            nodes_retrieved=len(final_results["regions"]),
            token_count=token_count,
            visual_similarity_scores=[r.get("visual_score", 0) for r in final_results["regions"]],
            text_similarity_scores=[r.get("text_score", 0) for r in final_results["regions"]],
            multimodal_scores=[r.get("final_score", 0) for r in final_results["regions"]],
            regions=final_results["regions"],
            entities=final_results.get("entities", []),
            compression_ratio=compression_ratio,
            visual_mode_used=len(embeddings) > 0,
            layout_similarity=final_results.get("layout_similarity", 0),
            content_pattern_matches=final_results.get("content_matches", []),
            spatial_consistency=final_results.get("spatial_consistency", 0),
            confidence_breakdown=final_results.get("confidence_breakdown", {}),
            retrieved_features=final_results.get("features", {}) if return_all_features else {},
            query_visual_features=query_features,
        )

        return result

    def _determine_retrieval_strategy(
        self,
        query: str,
        query_image: Optional[Any],
        classification: Dict,
        force_visual: bool
    ) -> str:
        """Determine optimal retrieval strategy"""
        if force_visual:
            return "multimodal"

        # Check for visual query elements
        visual_keywords = {
            "figure", "chart", "graph", "table", "diagram", "image",
            "picture", "layout", "structure", "visual", "format", "design",
            "show", "illustrate", "display", "plot", "graphical"
        }

        has_visual_keywords = any(kw in query.lower() for kw in visual_keywords)
        has_image = query_image is not None
        is_complex = classification.get("level", 1) >= 3

        if has_image or (has_visual_keywords and is_complex):
            return "multimodal"
        elif has_visual_keywords or is_complex:
            return "cross_modal"
        else:
            return "text"

    def _extract_query_features(self, query: str, query_image: Optional[Any]) -> Dict[str, Any]:
        """Extract comprehensive query features"""
        features = {
            "text": query,
            "text_embedding": self.text_encoder.encode(query),
            "query_type": self._classify_query_type(query),
            "entities": self._extract_query_entities(query),
            "intent": self._determine_query_intent(query),
            "complexity": self._calculate_query_complexity(query),
        }

        # Add visual features if image provided
        if query_image:
            features.update({
                "image": query_image,
                "visual_embedding": self.visual_encoder.encode(query_image),
                "visual_features": self._analyze_query_image(query_image),
            })

        return features

    def _multimodal_retrieval(
        self,
        query: str,
        query_features: Dict[str, Any],
        classification: Dict
    ) -> Dict[str, Any]:
        """Perform multi-modal retrieval"""
        max_tokens = classification.get("max_tokens", 2000)

        # 1. Text-based retrieval
        text_results = self._retrieve_by_text(query_features, max_tokens // 2)

        # 2. Visual-based retrieval
        visual_results = self._retrieve_by_visual(query_features, max_tokens // 2)

        # 3. Layout-based retrieval
        layout_results = self._retrieve_by_layout(query_features, max_tokens // 3)

        # 4. Content pattern matching
        content_results = self._retrieve_by_content_patterns(query_features, max_tokens // 3)

        # Combine results
        combined = {
            "regions": [],
            "entities": [],
            "features": {
                "text_results": text_results,
                "visual_results": visual_results,
                "layout_results": layout_results,
                "content_results": content_results,
            }
        }

        # Merge regions with their scores
        for result_set in [text_results, visual_results, layout_results, content_results]:
            for region in result_set.get("regions", []):
                combined["regions"].append(region)

        # Merge entities
        for result_set in [text_results, visual_results, layout_results, content_results]:
            combined["entities"].extend(result_set.get("entities", []))

        return combined

    def _cross_modal_retrieval(
        self,
        query: str,
        query_features: Dict[str, Any],
        classification: Dict
    ) -> Dict[str, Any]:
        """Perform cross-modal retrieval"""
        max_tokens = classification.get("max_tokens", 2000)

        # 1. Primary modality (usually text)
        primary_results = self._retrieve_by_text(query_features, max_tokens)

        # 2. Cross-modal enhancement using visual features
        if query_features.get("visual_embedding") is not None:
            enhanced_visual = self._enhance_text_results_with_visual(
                primary_results, query_features
            )
            return enhanced_visual

        # 3. Cross-modal enhancement using layout features
        if query_features.get("query_type") in ["layout", "structure"]:
            enhanced_layout = self._enhance_with_layout_patterns(primary_results, query_features)
            return enhanced_layout

        return primary_results

    def _enhanced_text_retrieval(
        self,
        query: str,
        query_features: Dict[str, Any],
        classification: Dict
    ) -> Dict[str, Any]:
        """Enhanced text retrieval with visual context"""
        # Standard text retrieval
        text_results = self._retrieve_by_text(query_features, classification.get("max_tokens", 2000))

        # Enhance with visual context if available
        if query_features.get("visual_features"):
            text_results = self._add_visual_context_to_text_results(text_results, query_features)

        return text_results

    def _retrieve_by_text(
        self, query_features: Dict[str, Any], max_tokens: int
    ) -> Dict[str, Any]:
        """Retrieve regions using text similarity"""
        query_embedding = query_features["text_embedding"]

        candidates = []
        token_count = 0

        # Search through graph nodes
        for node_id, node in self.graph.visual_spatial.nodes.items():
            if node.region:
                # Get text embedding for region
                text_embedding = self._get_text_embedding(node.region)
                if text_embedding is not None:
                    similarity = self._cosine_similarity(query_embedding, text_embedding)

                    candidate = {
                        "node_id": node_id,
                        "region": node.region,
                        "text_score": similarity,
                        "visual_score": 0,
                        "layout_score": 0,
                        "content_score": 0,
                    }

                    candidates.append(candidate)
                    token_count += node.region.token_count

        # Sort and filter by token budget
        candidates.sort(key=lambda x: x["text_score"], reverse=True)

        selected = []
        selected_tokens = 0

        for candidate in candidates:
            if selected_tokens + candidate["region"].token_count <= max_tokens:
                selected.append(candidate)
                selected_tokens += candidate["region"].token_count

        return {
            "regions": selected,
            "entities": [],
            "method": "text",
        }

    def _retrieve_by_visual(
        self, query_features: Dict[str, Any], max_tokens: int
    ) -> Dict[str, Any]:
        """Retrieve regions using visual similarity"""
        if "visual_embedding" not in query_features:
            return {"regions": [], "entities": [], "method": "visual"}

        query_visual_embedding = query_features["visual_embedding"]

        candidates = []
        token_count = 0

        # Search through graph nodes
        for node_id, node in self.graph.visual_spatial.nodes.items():
            if node.region and node.region.region_embedding is not None:
                # Get visual embedding
                visual_embedding = node.region.region_embedding
                similarity = self._cosine_similarity(query_visual_embedding, visual_embedding)

                candidate = {
                    "node_id": node_id,
                    "region": node.region,
                    "text_score": 0,
                    "visual_score": similarity,
                    "layout_score": 0,
                    "content_score": 0,
                }

                candidates.append(candidate)
                token_count += node.region.token_count

        # Sort and filter
        candidates.sort(key=lambda x: x["visual_score"], reverse=True)

        selected = []
        selected_tokens = 0

        for candidate in candidates:
            if selected_tokens + candidate["region"].token_count <= max_tokens:
                selected.append(candidate)
                selected_tokens += candidate["region"].token_count

        return {
            "regions": selected,
            "entities": [],
            "method": "visual",
        }

    def _retrieve_by_layout(
        self, query_features: Dict[str, Any], max_tokens: int
    ) -> Dict[str, Any]:
        """Retrieve regions using layout similarity"""
        query_type = query_features.get("query_type", "general")

        candidates = []
        token_count = 0

        # Search for layout matches
        for node_id, node in self.graph.visual_spatial.nodes.items():
            if node.region:
                layout_score = self._calculate_layout_similarity(
                    query_features, node.region, query_type
                )

                candidate = {
                    "node_id": node_id,
                    "region": node.region,
                    "text_score": 0,
                    "visual_score": 0,
                    "layout_score": layout_score,
                    "content_score": 0,
                }

                candidates.append(candidate)
                token_count += node.region.token_count

        # Sort and filter
        candidates.sort(key=lambda x: x["layout_score"], reverse=True)

        selected = []
        selected_tokens = 0

        for candidate in candidates:
            if selected_tokens + candidate["region"].token_count <= max_tokens:
                selected.append(candidate)
                selected_tokens += candidate["region"].token_count

        return {
            "regions": selected,
            "entities": [],
            "method": "layout",
        }

    def _retrieve_by_content_patterns(
        self, query_features: Dict[str, Any], max_tokens: int
    ) -> Dict[str, Any]:
        """Retrieve regions using content pattern matching"""
        query_patterns = self._extract_query_patterns(query_features)

        candidates = []
        token_count = 0

        # Search for pattern matches
        for node_id, node in self.graph.visual_spatial.nodes.items():
            if node.region:
                content_score = self._calculate_content_pattern_match(
                    query_patterns, node.region
                )

                candidate = {
                    "node_id": node_id,
                    "region": node.region,
                    "text_score": 0,
                    "visual_score": 0,
                    "layout_score": 0,
                    "content_score": content_score,
                }

                candidates.append(candidate)
                token_count += node.region.token_count

        # Sort and filter
        candidates.sort(key=lambda x: x["content_score"], reverse=True)

        selected = []
        selected_tokens = 0

        for candidate in candidates:
            if selected_tokens + candidate["region"].token_count <= max_tokens:
                selected.append(candidate)
                selected_tokens += candidate["region"].token_count

        return {
            "regions": selected,
            "entities": [],
            "method": "content",
        }

    def _fuse_multimodal_results(
        self,
        results: Dict[str, Any],
        query_features: Dict[str, Any],
        classification: Dict
    ) -> Dict[str, Any]:
        """Fuse results from multiple retrieval modalities"""
        regions = results.get("regions", [])
        if not regions:
            return results

        # Calculate final scores for each region
        for region in regions:
            final_score = (
                region["text_score"] * self.content_weight +
                region["visual_score"] * self.visual_weight +
                region["layout_score"] * self.layout_weight +
                region["content_score"] * self.spatial_weight
            )

            region["final_score"] = final_score

        # Re-sort by final score
        regions.sort(key=lambda x: x["final_score"], reverse=True)

        # Calculate breakdown statistics
        confidence_breakdown = {
            "text_weight": self.content_weight,
            "visual_weight": self.visual_weight,
            "layout_weight": self.layout_weight,
            "spatial_weight": self.spatial_weight,
        }

        # Calculate overall metrics
        if regions:
            avg_visual = np.mean([r["visual_score"] for r in regions])
            if avg_visual > 0.3:
                self.multimodal_stats["visual_boosts"] += 1

            avg_layout = np.mean([r["layout_score"] for r in regions])
            if avg_layout > 0.3:
                self.multimodal_stats["layout_matches"] += 1

        # Calculate spatial consistency
        spatial_consistency = self._calculate_spatial_consistency(regions[:5])  # Top 5 results

        # Find content pattern matches
        content_matches = self._find_content_pattern_matches(regions, query_features)

        # Calculate layout similarity
        layout_similarity = np.mean([r["layout_score"] for r in regions]) if regions else 0

        return {
            "regions": regions,
            "entities": results.get("entities", []),
            "features": results.get("features", {}),
            "confidence_breakdown": confidence_breakdown,
            "spatial_consistency": spatial_consistency,
            "content_matches": content_matches,
            "layout_similarity": layout_similarity,
        }

    def _build_multimodal_context(
        self,
        results: Dict[str, Any],
        classification: Dict
    ) -> Tuple[str, List[np.ndarray], int]:
        """Build context from multi-modal results"""
        context_parts = []
        embeddings = []
        token_count = 0

        regions = results.get("regions", [])

        for region_data in regions[:10]:  # Limit to top 10
            region = region_data["region"]

            # Add context with visual information
            if region.should_use_visual_mode():
                context_part = f"### Page {region.page_num} [{region.block_type}] (Visual Mode)\n"
                context_part += f"**Visual Content**: {region.text_content[:200]}...\n"
                context_part += f"**Visual Score**: {region_data['visual_score']:.2f}\n"
                context_part += f"**Layout Match**: {region_data['layout_score']:.2f}\n"
                context_part += f"**Content Match**: {region_data['content_score']:.2f}\n"
                context_part += f"**Final Score**: {region_data.get('final_score', 0):.2f}\n"

                # Add embedding reference
                if region.region_embedding is not None:
                    embeddings.append(region.region_embedding)
            else:
                context_part = f"### Page {region.page_num} [{region.block_type}]\n"
                context_part += f"{region.markdown_content}\n"

            context_parts.append(context_part)
            token_count += len(context_part) // 4  # Rough estimate

        # Add entities if available
        entities = results.get("entities", [])
        if entities:
            context_parts.append("\n\n### Key Entities\n")
            for entity in entities[:5]:  # Top 5 entities
                context_parts.append(f"- **{entity.get('name', '')}** ({entity.get('type', '')})")

        context = "\n\n".join(context_parts)

        return context, embeddings, token_count

    def _get_text_embedding(self, region: VisualRegion) -> Optional[np.ndarray]:
        """Get text embedding for a region"""
        # Try to get semantic embedding
        if hasattr(region, 'semantic_embedding') and region.semantic_embedding is not None:
            return region.semantic_embedding

        # Fall back to creating from text content
        if region.text_content:
            return self.text_encoder.encode(region.text_content[:500])  # First 500 chars

        return None

    def _cosine_similarity(self, vec1: np.ndarray, vec2: np.ndarray) -> float:
        """Calculate cosine similarity between vectors"""
        dot_product = np.dot(vec1, vec2)
        norm1 = np.linalg.norm(vec1)
        norm2 = np.linalg.norm(vec2)

        if norm1 == 0 or norm2 == 0:
            return 0.0

        return dot_product / (norm1 * norm2)

    # Helper methods for feature extraction and analysis
    def _classify_query_type(self, query: str) -> str:
        """Classify the type of query"""
        query_lower = query.lower()

        if any(kw in query_lower for kw in ["show", "figure", "chart", "graph", "image"]):
            return "visual"
        elif any(kw in query_lower for kw in ["table", "row", "column", "cell"]):
            return "table"
        elif any(kw in query_lower for kw in ["layout", "structure", "format", "design"]):
            return "layout"
        elif any(kw in query_lower for kw in ["formula", "equation", "math", "symbol"]):
            return "mathematical"
        else:
            return "general"

    def _extract_query_entities(self, query: str) -> List[Dict]:
        """Extract entities from query"""
        # Simple entity extraction (in practice, would use NER)
        import re

        # Look for capitalized words (potential entities)
        entities = []
        for match in re.finditer(r'\b[A-Z][a-zA-Z]+(?:\s+[A-Z][a-zA-Z]+)*\b', query):
            entities.append({
                "text": match.group(),
                "type": "UNKNOWN",  # Would be classified
                "position": match.start(),
            })

        return entities

    def _determine_query_intent(self, query: str) -> str:
        """Determine the intent of the query"""
        query_lower = query.lower()

        if any(kw in query_lower for kw in ["what", "define", "explain", "describe"]):
            return "definition"
        elif any(kw in query_lower for kw in ["how", "way", "method", "process"]):
            return "process"
        elif any(kw in query_lower for kw in ["find", "locate", "where", "position"]):
            return "location"
        elif any(kw in query_lower for kw in ["compare", "difference", "versus", "vs"]):
            return "comparison"
        else:
            return "search"

    def _calculate_query_complexity(self, query: str) -> float:
        """Calculate query complexity score"""
        words = query.split()
        avg_word_length = np.mean([len(w) for w in words]) if words else 0
        unique_words = len(set(words.lower()))
        word_count = len(words)

        # Complexity factors
        length_complexity = min(1.0, word_count / 20)
        vocabulary_complexity = min(1.0, unique_words / word_count if word_count > 0 else 0)
        structure_complexity = min(1.0, avg_word_length / 10)

        return (length_complexity + vocabulary_complexity + structure_complexity) / 3

    def _analyze_query_image(self, image: Any) -> Dict[str, Any]:
        """Analyze query image features"""
        # Placeholder for image analysis
        return {
            "has_text": False,
            "dominant_colors": [],
            "edge_density": 0.5,
            "complexity": 0.5,
        }

    def _enhance_text_results_with_visual(
        self, text_results: Dict[str, Any], query_features: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Enhance text results with visual features"""
        # This would use visual features to boost text retrieval scores
        return text_results

    def _enhance_with_layout_patterns(
        self, text_results: Dict[str, Any], query_features: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Enhance results with layout pattern matching"""
        # This would boost results that match layout patterns
        return text_results

    def _add_visual_context_to_text_results(
        self, text_results: Dict[str, Any], query_features: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Add visual context to text-only results"""
        return text_results

    def _calculate_layout_similarity(
        self, query_features: Dict[str, Any], region: VisualRegion, query_type: str
    ) -> float:
        """Calculate layout similarity score"""
        # Simplified layout matching
        if query_type == "visual" and region.block_type in ["figure", "table", "formula"]:
            return 0.8
        elif query_type == "table" and region.block_type == "table":
            return 0.9
        elif query_type == "layout" and region.block_type == "header":
            return 0.7
        else:
            return 0.3

    def _extract_query_patterns(self, query_features: Dict[str, Any]) -> List[str]:
        """Extract patterns from query features"""
        # Simplified pattern extraction
        query = query_features["text"]
        patterns = []

        # Look for numeric patterns
        if re.search(r'\d+%|\d+\.\d+', query):
            patterns.append("numeric")

        # Look for date patterns
        if re.search(r'\b\d{4}[-/]\d{1,2}[-/]\d{1,2}\b|\b\d{1,2}[-/]\d{1,2}[-/]\d{4}\b', query):
            patterns.append("date")

        # Look for patterns in entities
        for entity in query_features.get("entities", []):
            if entity["text"].isupper():
                patterns.append("proper_noun")

        return patterns

    def _calculate_content_pattern_match(
        self, patterns: List[str], region: VisualRegion
    ) -> float:
        """Calculate how well region matches query patterns"""
        if not patterns:
            return 0

        text = region.text_content.lower()
        matches = 0

        for pattern in patterns:
            if pattern == "numeric" and re.search(r'\d+\.?\d*', text):
                matches += 1
            elif pattern == "date" and re.search(r'\b(jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)|\b\d{1,2}[-/]\d{1,2}[-/]\d{2,4}', text):
                matches += 1
            elif pattern == "proper_noun" and sum(1 for c in text if c.isupper()) > 0:
                matches += 1

        return matches / len(patterns) if patterns else 0

    def _calculate_spatial_consistency(self, regions: List[Dict]) -> float:
        """Calculate spatial consistency of retrieved regions"""
        if len(regions) < 2:
            return 1.0

        # Check if regions are in reading order
        positions = [(r["region"].bbox.y1, r["region"].bbox.x1) for r in regions]
        sorted_positions = sorted(positions)

        # Count how many are in order
        in_order = sum(1 for i, pos in enumerate(positions) if pos == sorted_positions[i])
        return in_order / len(positions)

    def _find_content_pattern_matches(
        self, regions: List[Dict], query_features: Dict[str, Any]
    ) -> List[str]:
        """Find content pattern matches in retrieved regions"""
        matches = []

        for region_data in regions:
            region = region_data["region"]
            text = region.text_content.lower()
            query = query_features["text"].lower()

            # Simple pattern matching
            if query in text:
                matches.append(f"Direct match: {query}")

            # Partial matches
            words = query.split()
            for word in words:
                if word in text:
                    matches.append(f"Partial match: {word}")

        return matches

    def get_multimodal_stats(self) -> Dict[str, Any]:
        """Get multi-modal retrieval statistics"""
        stats = dict(self.multimodal_stats)

        if stats["total_queries"] > 0:
            stats.update({
                "multimodal_percentage": (stats["multimodal_queries"] / stats["total_queries"]) * 100,
                "cross_modal_percentage": (stats["cross_modal_queries"] / stats["total_queries"]) * 100,
                "visual_boost_rate": (stats["visual_boosts"] / stats["total_queries"]) * 100,
                "layout_match_rate": (stats["layout_matches"] / stats["total_queries"]) * 100,
            })

        return stats


class TextEmbeddingEncoder:
    """Encoder for text embeddings"""

    def __init__(self):
        pass  # Would initialize actual embedding model

    def encode(self, text: str) -> np.ndarray:
        """Encode text to embedding vector"""
        # Placeholder - would use actual embedding model
        # Return simple TF-IDF-like encoding
        words = text.lower().split()
        vocab = set(words)

        # Create simple feature vector
        features = [
            len(words) / 100,  # Length
            len(vocab) / len(words),  # Vocabulary diversity
            sum(1 for w in words if w.isdigit()) / len(words),  # Number density
            sum(1 for w in words if w[0].isupper()) / len(words),  # Case ratio
        ]

        # Pad to 256 dimensions
        while len(features) < 256:
            features.append(0.0)

        return np.array(features[:256], dtype=np.float32)


class VisualEmbeddingEncoder:
    """Encoder for visual embeddings"""

    def __init__(self):
        pass  # Would initialize actual visual model

    def encode(self, image) -> np.ndarray:
        """Encode image to embedding vector"""
        # Placeholder - would use actual visual model
        # Return simple visual features
        return np.random.rand(256).astype(np.float32)


class LayoutPatternAnalyzer:
    """Analyzer for layout patterns"""

    def analyze(self, image: Any) -> Dict[str, Any]:
        """Analyze layout patterns in image"""
        return {"pattern": "unknown", "confidence": 0.5}


class SpatialRelationshipAnalyzer:
    """Analyzer for spatial relationships"""

    def analyze(self, regions: List[VisualRegion]) -> Dict[str, Any]:
        """Analyze spatial relationships between regions"""
        return {"relationships": [], "consistency": 0.5}


class ContentPatternMatcher:
    """Matcher for content patterns"""

    def match(self, query: str, content: str) -> float:
        """Match query pattern against content"""
        return 0.5 if query.lower() in content.lower() else 0.0