Session 8: Multi-Modal & Advanced RAG Variants - Expanding Beyond Text Intelligence¶

# MRAG 1.0: Pseudo-Multimodal (Lossy Translation Approach)

class MRAG_1_0_System:
    """Demonstrates the limitations of text-centric multimodal processing."""

    def __init__(self, image_captioner, text_rag_system):
        self.image_captioner = image_captioner  # Converts images to text descriptions
        self.text_rag_system = text_rag_system   # Traditional text-only RAG

    def process_multimodal_content(self, content_items):
        """MRAG 1.0: Convert everything to text, lose multimodal information."""

        text_representations = []
        information_loss = {}

        for item in content_items:
            if item['type'] == 'text':
                # Direct text processing - no loss
                text_representations.append({
                    'content': item['content'],
                    'source_type': 'text',
                    'information_loss': 0.0
                })

            elif item['type'] == 'image':
                # LOSSY: Image → Text Caption
                caption = self.image_captioner.caption(item['content'])
                loss_analysis = self._analyze_image_information_loss(item['content'], caption)

                text_representations.append({
                    'content': caption,  # LOSSY CONVERSION
                    'source_type': 'image_to_text',
                    'information_loss': loss_analysis['loss_percentage'],
                    'lost_information': loss_analysis['lost_elements']
                })

            elif item['type'] == 'audio':
                # LOSSY: Audio → Text Transcript (loses tone, emotion, audio cues)
                transcript = self._transcribe_audio(item['content'])
                loss_analysis = self._analyze_audio_information_loss(item['content'], transcript)

                text_representations.append({
                    'content': transcript,  # LOSSY CONVERSION
                    'source_type': 'audio_to_text',
                    'information_loss': loss_analysis['loss_percentage'],
                    'lost_information': loss_analysis['lost_elements']
                })

            elif item['type'] == 'video':
                # EXTREME LOSS: Video → Text Summary (loses visual sequences, audio, timing)
                summary = self._video_to_text_summary(item['content'])
                loss_analysis = self._analyze_video_information_loss(item['content'], summary)

                text_representations.append({
                    'content': summary,  # EXTREME LOSSY CONVERSION
                    'source_type': 'video_to_text',
                    'information_loss': loss_analysis['loss_percentage'],  # Often 70-90%
                    'lost_information': loss_analysis['lost_elements']
                })

        # Process through traditional text-only RAG
        text_contents = [rep['content'] for rep in text_representations]
        rag_result = self.text_rag_system.process(text_contents)

        return {
            'result': rag_result,
            'total_information_loss': self._calculate_total_loss(text_representations),
            'processing_approach': 'MRAG_1_0_lossy_translation',
            'limitations': self._document_mrag_1_limitations(text_representations)
        }

    def _analyze_image_information_loss(self, image, caption):
        """Demonstrate information lost in image-to-text conversion."""

        # Analyze what's lost when converting images to text captions
        lost_elements = {
            'spatial_relationships': 'Object positioning, layout, composition',
            'visual_details': 'Colors, textures, fine details, visual aesthetics',
            'contextual_clues': 'Environmental context, situational nuances',
            'non_describable_elements': 'Artistic elements, emotional visual cues',
            'quantitative_visual_info': 'Precise measurements, quantities, scales'
        }

        # Estimate information loss (caption typically captures 20-40% of image content)
        loss_percentage = 0.70  # 70% information loss is typical

        return {
            'loss_percentage': loss_percentage,
            'lost_elements': lost_elements,
            'caption_limitations': [
                'Cannot capture spatial relationships accurately',
                'Subjective interpretation of visual content',
                'Limited vocabulary for visual descriptions',
                'Inability to describe complex visual patterns'
            ]
        }

    def _document_mrag_1_limitations(self, text_representations):
        """Document the fundamental limitations of MRAG 1.0 approach."""

        return {
            'semantic_degradation': 'Multimodal semantics reduced to text approximations',
            'information_bottleneck': 'Text descriptions become information bottlenecks',
            'context_loss': 'Cross-modal contextual relationships destroyed',
            'query_limitations': 'Cannot handle native multimodal queries',
            'retrieval_constraints': 'Limited to text-similarity matching',
            'response_quality': 'Cannot provide authentic multimodal responses'
        }

# Demonstration of MRAG 1.0 limitations with concrete example

def demonstrate_mrag_1_limitations():
    """Show concrete example of information loss in MRAG 1.0."""

    # Example: Medical X-ray analysis
    original_image_content = {
        'type': 'medical_xray',
        'visual_information': {
            'bone_density_variations': 'Subtle gradients indicating osteoporosis risk',
            'spatial_relationships': 'Precise positioning of fracture relative to joint',
            'texture_patterns': 'Specific trabecular patterns indicating bone health',
            'contrast_differences': 'Minute variations critical for diagnosis',
            'measurement_precision': 'Exact angles and distances for surgical planning'
        },
        'diagnostic_value': 'High - contains critical diagnostic information'
    }

    # MRAG 1.0 conversion result
    mrag_1_caption = "X-ray image showing bone structure with some irregularities"

    information_loss_analysis = {
        'lost_diagnostic_info': [
            'Precise bone density measurements',
            'Exact fracture positioning and angles',
            'Subtle texture patterns indicating pathology',
            'Quantitative measurements for surgical planning',
            'Fine-grained contrast variations'
        ],
        'clinical_impact': 'Insufficient information for accurate diagnosis',
        'loss_percentage': 0.85,  # 85% of diagnostic information lost
        'consequence': 'MRAG 1.0 system cannot support clinical decision-making'
    }

    return {
        'original_content': original_image_content,
        'mrag_1_result': mrag_1_caption,
        'information_loss': information_loss_analysis,
        'lesson': 'MRAG 1.0 cannot preserve critical multimodal information'
    }

# Multi-modal RAG system with comprehensive content processing

import cv2
import whisper
from PIL import Image
from typing import List, Dict, Any, Union, Optional
import base64
import io
import numpy as np
from dataclasses import dataclass
from enum import Enum

class ContentType(Enum):
    TEXT = "text"
    IMAGE = "image"
    AUDIO = "audio"
    VIDEO = "video"
    DOCUMENT = "document"
    TABLE = "table"

@dataclass
class MultiModalContent:
    """Structured representation of multi-modal content."""
    content_id: str
    content_type: ContentType
    raw_content: Any
    extracted_text: Optional[str] = None
    visual_description: Optional[str] = None
    audio_transcript: Optional[str] = None
    structured_data: Optional[Dict] = None
    embeddings: Optional[Dict[str, np.ndarray]] = None
    metadata: Optional[Dict[str, Any]] = None

class MultiModalProcessor:
    """Comprehensive processor for multi-modal content."""

    def __init__(self, config: Dict[str, Any]):
        self.config = config

        # Initialize specialized models
        self.vision_model = self._initialize_vision_model(config)
        self.audio_model = self._initialize_audio_model(config)
        self.text_embedding_model = self._initialize_text_embeddings(config)
        self.vision_embedding_model = self._initialize_vision_embeddings(config)

        # Content processors
        self.processors = {
            ContentType.TEXT: self._process_text_content,
            ContentType.IMAGE: self._process_image_content,
            ContentType.AUDIO: self._process_audio_content,
            ContentType.VIDEO: self._process_video_content,
            ContentType.DOCUMENT: self._process_document_content,
            ContentType.TABLE: self._process_table_content
        }

    def process_multi_modal_content(self, content_items: List[Dict]) -> List[MultiModalContent]:
        """Process multiple content items of different types."""

        processed_items = []

        for item in content_items:
            try:
                content_type = ContentType(item['type'])

                # Process using appropriate processor
                if content_type in self.processors:
                    processed_item = self.processors[content_type](item)
                    processed_items.append(processed_item)
                else:
                    print(f"Unsupported content type: {content_type}")

            except Exception as e:
                print(f"Error processing content item: {e}")
                continue

        return processed_items

    def demonstrate_mrag_2_0_advantages(self) -> Dict[str, Any]:
        """Demonstrate MRAG 2.0 advantages over MRAG 1.0."""

        return {
            'semantic_preservation': {
                'mrag_1_0': 'Lossy text conversion, 60-90% information loss',
                'mrag_2_0': 'Native multimodal processing, <5% information loss',
                'improvement': 'Preserves visual, audio, and contextual semantics'
            },
            'query_capabilities': {
                'mrag_1_0': 'Text queries only, limited to caption matching',
                'mrag_2_0': 'Native multimodal queries (image+text, audio+text)',
                'improvement': 'True cross-modal understanding and retrieval'
            },
            'response_quality': {
                'mrag_1_0': 'Text-only responses, cannot reference visual details',
                'mrag_2_0': 'Multimodal responses with authentic visual understanding',
                'improvement': 'Maintains multimodal context in responses'
            }
        }

# MRAG 3.0: Autonomous Multimodal Intelligence with Dynamic Reasoning

class MRAG_3_0_AutonomousSystem:
    """MRAG 3.0: Autonomous multimodal RAG with intelligent control and dynamic reasoning."""

    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.mrag_version = MRAGVersion.MRAG_3_0

        # MRAG 3.0: Autonomous intelligence components
        self.multimodal_reasoning_engine = self._initialize_reasoning_engine(config)
        self.autonomous_search_planner = self._initialize_search_planner(config)
        self.dynamic_strategy_selector = self._initialize_strategy_selector(config)

        # Integration with Session 7 reasoning capabilities
        self.cognitive_reasoning_system = self._initialize_cognitive_reasoning(config)

        # MRAG 3.0: Self-improving multimodal intelligence
        self.multimodal_learning_system = self._initialize_multimodal_learning(config)

        # Built on MRAG 2.0 foundation
        self.mrag_2_0_base = MRAG_2_0_Processor(config)

        # MRAG 3.0: Autonomous decision-making capabilities
        self.autonomous_capabilities = {
            'intelligent_parsing': self._autonomous_query_parsing,
            'dynamic_strategy_selection': self._dynamic_strategy_selection,
            'self_correcting_reasoning': self._self_correcting_multimodal_reasoning,
            'adaptive_response_generation': self._adaptive_multimodal_response_generation
        }

    async def autonomous_multimodal_processing(self, query: str,
                                             multimodal_content: List[Dict] = None,
                                             context: Dict = None) -> Dict[str, Any]:
        """MRAG 3.0: Autonomous processing with intelligent multimodal reasoning."""

        # MRAG 3.0: Autonomous query analysis and planning
        autonomous_plan = await self._create_autonomous_processing_plan(
            query, multimodal_content, context
        )

        # MRAG 3.0: Execute intelligent multimodal processing
        processing_results = await self._execute_autonomous_plan(autonomous_plan)

        # MRAG 3.0: Self-correcting validation and improvement
        validated_results = await self._autonomous_validation_and_improvement(
            processing_results, autonomous_plan
        )

        return {
            'query': query,
            'autonomous_plan': autonomous_plan,
            'processing_results': processing_results,
            'validated_results': validated_results,
            'mrag_version': MRAGVersion.MRAG_3_0,
            'autonomous_intelligence_metrics': self._calculate_autonomous_metrics(validated_results)
        }

    async def _create_autonomous_processing_plan(self, query: str,
                                               multimodal_content: List[Dict],
                                               context: Dict) -> Dict[str, Any]:
        """MRAG 3.0: Autonomously plan optimal multimodal processing strategy."""

        # MRAG 3.0: Intelligent query analysis
        query_analysis = await self.autonomous_capabilities['intelligent_parsing'](
            query, multimodal_content, context
        )

        # MRAG 3.0: Dynamic strategy selection based on content and query analysis
        optimal_strategy = await self.autonomous_capabilities['dynamic_strategy_selection'](
            query_analysis
        )

        # Integration with Session 7: Cognitive reasoning planning
        cognitive_reasoning_plan = await self.cognitive_reasoning_system.plan_multimodal_reasoning(
            query_analysis, optimal_strategy
        )

        return {
            'query_analysis': query_analysis,
            'optimal_strategy': optimal_strategy,
            'cognitive_reasoning_plan': cognitive_reasoning_plan,
            'autonomous_intelligence_level': 'high',
            'processing_approach': 'fully_autonomous'
        }

    async def _autonomous_query_parsing(self, query: str, multimodal_content: List[Dict],
                                      context: Dict) -> Dict[str, Any]:
        """MRAG 3.0: Autonomously parse and understand complex multimodal queries."""

        # MRAG 3.0: Intelligent multimodal query understanding
        multimodal_intent = await self.multimodal_reasoning_engine.analyze_multimodal_intent(query)

        # Autonomous parsing of query requirements
        parsing_analysis = {
            'query_complexity': self._assess_query_complexity(query),
            'multimodal_requirements': self._identify_multimodal_requirements(query),
            'reasoning_requirements': self._identify_reasoning_requirements(query),
            'cross_modal_relationships': self._identify_cross_modal_relationships(query),
            'autonomous_processing_needs': self._identify_autonomous_processing_needs(query)
        }

        # MRAG 3.0: Dynamic adaptation based on content analysis
        content_adaptation = await self._autonomous_content_adaptation(
            multimodal_content, parsing_analysis
        )

        return {
            'multimodal_intent': multimodal_intent,
            'parsing_analysis': parsing_analysis,
            'content_adaptation': content_adaptation,
            'autonomous_confidence': self._calculate_autonomous_confidence(parsing_analysis)
        }

    async def _dynamic_strategy_selection(self, query_analysis: Dict) -> Dict[str, Any]:
        """MRAG 3.0: Dynamically select optimal processing strategy."""

        # MRAG 3.0: Analyze available strategies and their suitability
        strategy_options = {
            'native_multimodal_processing': self._assess_native_processing_suitability(query_analysis),
            'cross_modal_reasoning': self._assess_cross_modal_reasoning_needs(query_analysis),
            'sequential_multimodal': self._assess_sequential_processing_needs(query_analysis),
            'parallel_multimodal': self._assess_parallel_processing_needs(query_analysis),
            'hybrid_approach': self._assess_hybrid_approach_benefits(query_analysis)
        }

        # MRAG 3.0: Autonomous strategy selection using intelligent decision-making
        optimal_strategy = await self.dynamic_strategy_selector.select_optimal_strategy(
            strategy_options, query_analysis
        )

        return {
            'selected_strategy': optimal_strategy,
            'strategy_reasoning': self._explain_strategy_selection(optimal_strategy, strategy_options),
            'expected_performance': self._predict_strategy_performance(optimal_strategy),
            'adaptability_level': 'fully_autonomous'
        }

    async def _self_correcting_multimodal_reasoning(self, intermediate_results: Dict) -> Dict[str, Any]:
        """MRAG 3.0: Self-correcting reasoning with autonomous validation."""

        # MRAG 3.0: Autonomous validation of multimodal reasoning
        reasoning_validation = await self.multimodal_reasoning_engine.validate_reasoning_chain(
            intermediate_results
        )

        # Self-correction if issues detected
        if reasoning_validation['requires_correction']:
            corrected_results = await self._autonomous_reasoning_correction(
                intermediate_results, reasoning_validation
            )
            return corrected_results

        return {
            'reasoning_results': intermediate_results,
            'validation_passed': True,
            'autonomous_confidence': reasoning_validation['confidence_score']
        }

    def demonstrate_mrag_3_0_capabilities(self) -> Dict[str, Any]:
        """Demonstrate MRAG 3.0 autonomous intelligence capabilities."""

        return {
            'autonomous_intelligence': {
                'query_understanding': 'Intelligent parsing of complex multimodal queries',
                'strategy_selection': 'Dynamic selection of optimal processing strategies',
                'self_correction': 'Autonomous validation and improvement of results',
                'adaptive_learning': 'Continuous improvement from multimodal interactions'
            },
            'integration_with_session_7': {
                'cognitive_reasoning': 'Multimodal reasoning chains with logical validation',
                'autonomous_planning': 'Intelligent planning of multimodal processing workflows',
                'self_improving': 'Learning optimal multimodal reasoning patterns',
                'contextual_adaptation': 'Dynamic adaptation to multimodal context requirements'
            },
            'advanced_capabilities': {
                'cross_modal_intelligence': 'Seamless reasoning across multiple modalities',
                'dynamic_adaptation': 'Real-time strategy adaptation based on content analysis',
                'autonomous_optimization': 'Self-optimizing multimodal processing performance',
                'intelligent_error_handling': 'Autonomous detection and correction of processing errors'
            }
        }

# Complete MRAG Evolution Demonstration

def demonstrate_mrag_evolution_comparison():
    """Educational demonstration of MRAG 1.0 → 2.0 → 3.0 evolution."""

    # Example: Complex multimodal query
    complex_query = "Analyze this medical imaging data and explain the relationship between the visual abnormalities in the X-ray and the patient's symptoms described in the audio recording, considering the historical context from the patient's text records."

    multimodal_content = {
        'medical_xray': {'type': 'image', 'content': 'chest_xray.jpg'},
        'patient_interview': {'type': 'audio', 'content': 'patient_symptoms.wav'},
        'medical_history': {'type': 'text', 'content': 'patient_history.txt'}
    }

    # MRAG 1.0 Processing
    mrag_1_0_result = {
        'approach': 'Convert all to text, process through text-only RAG',
        'xray_processing': 'X-ray → "Medical image showing chest area" (95% information loss)',
        'audio_processing': 'Audio → "Patient mentions chest pain" (70% information loss)',
        'limitations': [
            'Cannot analyze visual abnormalities in detail',
            'Loses audio nuances (tone, urgency, specific symptoms)',
            'Cannot establish cross-modal relationships',
            'Response quality severely limited by information loss'
        ],
        'information_retention': '20%',
        'clinical_utility': 'Low - insufficient for medical decision-making'
    }

    # MRAG 2.0 Processing
    mrag_2_0_result = {
        'approach': 'Preserve multimodal content, use MLLMs for native processing',
        'xray_processing': 'Native visual analysis with detailed abnormality detection',
        'audio_processing': 'Rich audio analysis preserving tone, emotion, specific symptoms',
        'capabilities': [
            'Detailed visual abnormality analysis',
            'Comprehensive audio symptom extraction',
            'Cross-modal semantic understanding',
            'High-quality multimodal responses'
        ],
        'information_retention': '90%',
        'clinical_utility': 'High - suitable for clinical decision support'
    }

    # MRAG 3.0 Processing
    mrag_3_0_result = {
        'approach': 'Autonomous intelligent reasoning across all modalities',
        'intelligent_analysis': [
            'Autonomous identification of key visual abnormalities',
            'Intelligent correlation of symptoms with visual findings',
            'Dynamic reasoning about medical relationships',
            'Self-correcting diagnostic reasoning'
        ],
        'autonomous_capabilities': [
            'Intelligent parsing of complex medical queries',
            'Dynamic selection of optimal analysis strategies',
            'Self-correcting multimodal reasoning',
            'Autonomous quality validation and improvement'
        ],
        'information_retention': '95%+',
        'clinical_utility': 'Expert-level - autonomous medical reasoning support'
    }

    return {
        'query': complex_query,
        'mrag_1_0': mrag_1_0_result,
        'mrag_2_0': mrag_2_0_result,
        'mrag_3_0': mrag_3_0_result,
        'evolution_benefits': {
            '1.0_to_2.0': 'Elimination of information loss, true multimodal processing',
            '2.0_to_3.0': 'Addition of autonomous intelligence and dynamic reasoning',
            'overall_transformation': 'From lossy translation to autonomous multimodal intelligence'
        }
    }

    def _process_image_content(self, item: Dict) -> MultiModalContent:
        """Process image content with comprehensive analysis."""

        image_path = item['path']
        content_id = item.get('id', f"img_{hash(image_path)}")

        # Load and preprocess image
        image = Image.open(image_path)
        image_array = np.array(image)

        # Extract visual features and descriptions
        visual_analysis = self._analyze_image_content(image)

        # Generate text embeddings from visual description
        text_embedding = None
        if visual_analysis['description']:
            text_embedding = self.text_embedding_model.encode([visual_analysis['description']])[0]

        # Generate vision embeddings
        vision_embedding = self._generate_vision_embedding(image)

        return MultiModalContent(
            content_id=content_id,
            content_type=ContentType.IMAGE,
            raw_content=image_array,
            visual_description=visual_analysis['description'],
            structured_data={
                'objects_detected': visual_analysis['objects'],
                'scene_type': visual_analysis['scene'],
                'colors': visual_analysis['colors'],
                'text_in_image': visual_analysis.get('ocr_text', '')
            },
            embeddings={
                'text': text_embedding,
                'vision': vision_embedding
            },
            metadata={
                'image_size': image.size,
                'format': image.format,
                'path': image_path,
                'analysis_confidence': visual_analysis.get('confidence', 0.8)
            }
        )

    def _analyze_image_content(self, image: Image.Image) -> Dict[str, Any]:
        """Comprehensive image analysis including objects, scenes, and text."""

        # Vision-language model analysis
        if self.vision_model:
            # Generate detailed description
            description_prompt = "Describe this image in detail, including objects, people, setting, actions, and any visible text."
            description = self._vision_model_query(image, description_prompt)

            # Object detection
            objects_prompt = "List all objects visible in this image."
            objects_text = self._vision_model_query(image, objects_prompt)
            objects = [obj.strip() for obj in objects_text.split(',') if obj.strip()]

            # Scene classification
            scene_prompt = "What type of scene or environment is this? (indoor/outdoor, specific location type)"
            scene = self._vision_model_query(image, scene_prompt)

            # Color analysis
            colors = self._extract_dominant_colors(image)

            # OCR for text in images
            ocr_text = self._extract_text_from_image(image)

            return {
                'description': description,
                'objects': objects,
                'scene': scene,
                'colors': colors,
                'ocr_text': ocr_text,
                'confidence': 0.85
            }
        else:
            # Fallback analysis without vision model
            return {
                'description': "Image content (vision model not available)",
                'objects': [],
                'scene': 'unknown',
                'colors': self._extract_dominant_colors(image),
                'confidence': 0.3
            }

    def _vision_model_query(self, image: Image.Image, prompt: str) -> str:
        """Query vision-language model with image and prompt."""

        try:
            # Convert image to base64 for API call
            buffered = io.BytesIO()
            image.save(buffered, format="PNG")
            img_str = base64.b64encode(buffered.getvalue()).decode()

            # Use vision model API (implementation depends on your chosen model)
            # This is a placeholder - implement with your chosen vision-language model
            response = self.vision_model.query(img_str, prompt)
            return response

        except Exception as e:
            print(f"Vision model query error: {e}")
            return "Unable to analyze image"

    def _process_audio_content(self, item: Dict) -> MultiModalContent:
        """Process audio content with transcription and analysis."""

        audio_path = item['path']
        content_id = item.get('id', f"audio_{hash(audio_path)}")

        # Transcribe audio using Whisper
        transcript = self._transcribe_audio(audio_path)

        # Analyze audio characteristics
        audio_analysis = self._analyze_audio_features(audio_path)

        # Generate embeddings from transcript
        text_embedding = None
        if transcript:
            text_embedding = self.text_embedding_model.encode([transcript])[0]

        return MultiModalContent(
            content_id=content_id,
            content_type=ContentType.AUDIO,
            raw_content=audio_path,  # Store path, not raw audio data
            audio_transcript=transcript,
            structured_data={
                'duration': audio_analysis['duration'],
                'language': audio_analysis.get('language', 'unknown'),
                'speaker_count': audio_analysis.get('speakers', 1),
                'audio_quality': audio_analysis.get('quality_score', 0.8)
            },
            embeddings={
                'text': text_embedding
            },
            metadata={
                'file_path': audio_path,
                'transcription_confidence': audio_analysis.get('transcription_confidence', 0.8)
            }
        )

    def _process_video_content(self, item: Dict) -> MultiModalContent:
        """Process video content by extracting frames and audio."""

        video_path = item['path']
        content_id = item.get('id', f"video_{hash(video_path)}")

        # Extract key frames
        key_frames = self._extract_key_frames(video_path)

        # Extract and process audio track
        audio_path = self._extract_audio_from_video(video_path)
        audio_transcript = self._transcribe_audio(audio_path) if audio_path else ""

        # Analyze visual content from key frames
        visual_descriptions = []
        frame_embeddings = []

        for frame in key_frames:
            frame_analysis = self._analyze_image_content(Image.fromarray(frame))
            visual_descriptions.append(frame_analysis['description'])

            frame_embedding = self._generate_vision_embedding(Image.fromarray(frame))
            frame_embeddings.append(frame_embedding)

        # Create combined description
        combined_description = self._create_video_description(
            visual_descriptions, audio_transcript
        )

        # Generate combined embeddings
        text_embedding = self.text_embedding_model.encode([combined_description])[0]

        # Average frame embeddings for video-level visual embedding
        avg_visual_embedding = np.mean(frame_embeddings, axis=0) if frame_embeddings else None

        return MultiModalContent(
            content_id=content_id,
            content_type=ContentType.VIDEO,
            raw_content=video_path,
            audio_transcript=audio_transcript,
            visual_description=combined_description,
            structured_data={
                'frame_count': len(key_frames),
                'duration': self._get_video_duration(video_path),
                'frame_descriptions': visual_descriptions,
                'has_audio': bool(audio_transcript)
            },
            embeddings={
                'text': text_embedding,
                'vision': avg_visual_embedding
            },
            metadata={
                'file_path': video_path,
                'key_frames_extracted': len(key_frames)
            }
        )

# Multi-modal vector storage and retrieval system

class MultiModalVectorStore:
    """Advanced vector store for multi-modal content."""

    def __init__(self, config: Dict[str, Any]):
        self.config = config

        # Separate vector stores for different embedding types
        self.text_store = self._initialize_text_vector_store(config)
        self.vision_store = self._initialize_vision_vector_store(config)
        self.hybrid_store = self._initialize_hybrid_vector_store(config)

        # Multi-modal fusion strategies
        self.fusion_strategies = {
            'early_fusion': self._early_fusion_search,
            'late_fusion': self._late_fusion_search,
            'cross_modal': self._cross_modal_search,
            'adaptive_fusion': self._adaptive_fusion_search
        }

    def store_multi_modal_content(self, content_items: List[MultiModalContent]) -> Dict[str, Any]:
        """Store multi-modal content with appropriate indexing."""

        storage_results = {
            'text_stored': 0,
            'vision_stored': 0,
            'hybrid_stored': 0,
            'total_items': len(content_items)
        }

        for item in content_items:
            # Store text embeddings
            if item.embeddings and 'text' in item.embeddings:
                text_doc = self._create_text_document(item)
                self.text_store.add_documents([text_doc])
                storage_results['text_stored'] += 1

            # Store vision embeddings
            if item.embeddings and 'vision' in item.embeddings:
                vision_doc = self._create_vision_document(item)
                self.vision_store.add_documents([vision_doc])
                storage_results['vision_stored'] += 1

            # Store hybrid representation
            if self._should_create_hybrid_representation(item):
                hybrid_doc = self._create_hybrid_document(item)
                self.hybrid_store.add_documents([hybrid_doc])
                storage_results['hybrid_stored'] += 1

        return storage_results

    async def multi_modal_search(self, query: str, query_image: Optional[Image.Image] = None,
                                search_config: Dict = None) -> Dict[str, Any]:
        """Perform multi-modal search across content types."""

        config = search_config or {
            'fusion_strategy': 'adaptive_fusion',
            'content_types': [ContentType.TEXT, ContentType.IMAGE, ContentType.VIDEO],
            'top_k': 10,
            'rerank_results': True
        }

        # Determine search strategy based on query inputs
        if query and query_image:
            search_type = 'multi_modal_query'
        elif query_image:
            search_type = 'visual_query'
        else:
            search_type = 'text_query'

        print(f"Performing {search_type} search...")

        # Execute search using configured fusion strategy
        fusion_strategy = config.get('fusion_strategy', 'adaptive_fusion')
        search_results = await self.fusion_strategies[fusion_strategy](
            query, query_image, config
        )

        # Post-process results
        processed_results = self._post_process_search_results(
            search_results, config
        )

        return {
            'search_type': search_type,
            'fusion_strategy': fusion_strategy,
            'results': processed_results,
            'metadata': {
                'total_results': len(processed_results),
                'content_types_found': list(set(r['content_type'].value for r in processed_results)),
                'search_time': search_results.get('search_time', 0)
            }
        }

    async def _adaptive_fusion_search(self, query: str, query_image: Optional[Image.Image],
                                    config: Dict) -> Dict[str, Any]:
        """Adaptive fusion that selects optimal strategy based on query characteristics."""

        import time
        start_time = time.time()

        # Analyze query characteristics to select fusion approach
        fusion_analysis = self._analyze_fusion_requirements(query, query_image)

        if fusion_analysis['preferred_strategy'] == 'cross_modal':
            results = await self._cross_modal_search(query, query_image, config)
        elif fusion_analysis['preferred_strategy'] == 'late_fusion':
            results = await self._late_fusion_search(query, query_image, config)
        else:
            results = await self._early_fusion_search(query, query_image, config)

        results['search_time'] = time.time() - start_time
        results['fusion_analysis'] = fusion_analysis

        return results

    async def _cross_modal_search(self, query: str, query_image: Optional[Image.Image],
                                config: Dict) -> Dict[str, Any]:
        """Cross-modal search that finds content across different modalities."""

        cross_modal_results = []

        # Text query to find relevant visual content
        if query:
            visual_results = self._search_visual_content_with_text(query, config)
            cross_modal_results.extend(visual_results)

        # Visual query to find relevant text content
        if query_image:
            text_results = self._search_text_content_with_image(query_image, config)
            cross_modal_results.extend(text_results)

        # Multi-modal to multi-modal matching
        if query and query_image:
            hybrid_results = self._search_hybrid_content(query, query_image, config)
            cross_modal_results.extend(hybrid_results)

        # Remove duplicates and rank
        unique_results = self._deduplicate_cross_modal_results(cross_modal_results)
        ranked_results = self._rank_cross_modal_results(
            unique_results, query, query_image
        )

        return {
            'results': ranked_results,
            'cross_modal_matches': len(cross_modal_results),
            'unique_results': len(unique_results)
        }

    def _search_visual_content_with_text(self, query: str, config: Dict) -> List[Dict]:
        """Search visual content using text query."""

        # Generate text embedding for query
        query_embedding = self.text_embedding_model.encode([query])[0]

        # Search vision store using text embedding similarity
        # This requires cross-modal embedding space or learned mapping
        vision_results = self.vision_store.similarity_search_by_vector(
            query_embedding, k=config.get('top_k', 10)
        )

        # Convert to standardized format
        formatted_results = []
        for result in vision_results:
            formatted_results.append({
                'content_id': result.metadata['content_id'],
                'content_type': ContentType(result.metadata['content_type']),
                'content': result.page_content,
                'similarity_score': result.metadata.get('similarity_score', 0.0),
                'cross_modal_type': 'text_to_visual'
            })

        return formatted_results

# MRAG 3.0: Autonomous Multimodal RAG-Fusion implementation

class MultimodalRAGFusionSystem:
    """MRAG 3.0: Autonomous multimodal RAG-Fusion with intelligent cross-modal reasoning."""

    def __init__(self, llm_model, multimodal_vector_stores: Dict[str, Any],
                 mrag_processor, reranker=None):
        self.llm_model = llm_model
        self.multimodal_vector_stores = multimodal_vector_stores
        self.mrag_processor = mrag_processor  # MRAG 3.0 processor
        self.reranker = reranker

        # MRAG 3.0: Autonomous multimodal capabilities
        self.autonomous_query_planner = self._initialize_autonomous_planner()
        self.multimodal_reasoning_engine = self._initialize_multimodal_reasoning()

        # Integration with Session 7: Cognitive reasoning
        self.cognitive_fusion_system = self._initialize_cognitive_fusion()

        # MRAG 3.0: Multimodal query generation strategies
        self.multimodal_query_generators = {
            'cross_modal_perspective': self._generate_cross_modal_perspective_queries,
            'multimodal_decomposition': self._generate_multimodal_decomposed_queries,
            'semantic_bridging': self._generate_semantic_bridging_queries,
            'autonomous_expansion': self._autonomous_multimodal_expansion,
            'cognitive_reasoning_queries': self._generate_cognitive_reasoning_queries
        }

        # MRAG 3.0: Autonomous multimodal fusion methods
        self.autonomous_fusion_methods = {
            'semantic_integrity_fusion': self._semantic_integrity_fusion,
            'cross_modal_reciprocal_fusion': self._cross_modal_reciprocal_fusion,
            'autonomous_weighted_fusion': self._autonomous_weighted_fusion,
            'cognitive_reasoning_fusion': self._cognitive_reasoning_fusion,
            'adaptive_multimodal_fusion': self._adaptive_multimodal_fusion
        }

    async def autonomous_multimodal_fusion_search(self, original_query: str,
                                                 multimodal_context: Dict = None,
                                                 fusion_config: Dict = None) -> Dict[str, Any]:
        """MRAG 3.0: Perform autonomous multimodal RAG-Fusion with intelligent reasoning."""

        config = fusion_config or {
            'num_multimodal_variants': 7,
            'query_strategies': ['cross_modal_perspective', 'autonomous_expansion'],
            'fusion_method': 'adaptive_multimodal_fusion',
            'preserve_semantic_integrity': True,
            'enable_cognitive_reasoning': True,
            'top_k_per_modality': 15,
            'final_top_k': 12,
            'use_autonomous_reranking': True
        }

        print(f"MRAG 3.0 Autonomous Multimodal Fusion search for: {original_query[:100]}...")

        # MRAG 3.0 Step 1: Autonomous multimodal query analysis and planning
        autonomous_query_plan = await self.autonomous_query_planner.analyze_and_plan(
            original_query, multimodal_context, config
        )

        # MRAG 3.0 Step 2: Generate intelligent multimodal query variants
        multimodal_variants = await self._generate_multimodal_query_variants(
            original_query, autonomous_query_plan, config
        )

        # MRAG 3.0 Step 3: Execute intelligent multimodal retrieval
        multimodal_retrieval_results = await self._execute_autonomous_multimodal_retrieval(
            original_query, multimodal_variants, autonomous_query_plan, config
        )

        # MRAG 3.0 Step 4: Apply autonomous semantic-preserving fusion
        fusion_method = config.get('fusion_method', 'adaptive_multimodal_fusion')
        fused_results = await self.autonomous_fusion_methods[fusion_method](
            multimodal_retrieval_results, autonomous_query_plan, config
        )

        # MRAG 3.0 Step 5: Apply autonomous cognitive reranking
        if config.get('use_autonomous_reranking', True):
            fused_results = await self._apply_autonomous_cognitive_reranking(
                original_query, fused_results, autonomous_query_plan, config
            )

        # MRAG 3.0 Step 6: Generate autonomous multimodal response with reasoning
        autonomous_response = await self._generate_autonomous_multimodal_response(
            original_query, fused_results, autonomous_query_plan, config
        )

        return {
            'original_query': original_query,
            'autonomous_query_plan': autonomous_query_plan,
            'multimodal_variants': multimodal_variants,
            'multimodal_retrieval_results': multimodal_retrieval_results,
            'fused_results': fused_results,
            'autonomous_response': autonomous_response,
            'mrag_3_0_metadata': {
                'autonomous_intelligence_level': 'high',
                'multimodal_variants_generated': len(multimodal_variants),
                'fusion_method': fusion_method,
                'semantic_integrity_preserved': config.get('preserve_semantic_integrity', True),
                'cognitive_reasoning_applied': config.get('enable_cognitive_reasoning', True),
                'total_multimodal_candidates': sum(
                    len(r.get('results', [])) for r in multimodal_retrieval_results.values()
                ),
                'final_results': len(fused_results)
            }
        }

    async def _generate_query_variants(self, original_query: str,
                                     config: Dict) -> List[str]:
        """Generate diverse query variants using multiple strategies."""

        num_variants = config.get('num_query_variants', 5)
        strategies = config.get('query_strategies', ['perspective_shift', 'decomposition'])

        all_variants = []
        variants_per_strategy = max(1, num_variants // len(strategies))

        for strategy in strategies:
            if strategy in self.query_generators:
                strategy_variants = await self.query_generators[strategy](
                    original_query, variants_per_strategy
                )
                all_variants.extend(strategy_variants)

        # Remove duplicates and limit to requested number
        unique_variants = list(set(all_variants))
        return unique_variants[:num_variants]

    async def _generate_perspective_queries(self, query: str, count: int) -> List[str]:
        """Generate queries from different perspectives and viewpoints."""

        perspective_prompt = f"""
        Generate {count} alternative versions of this query from different perspectives or viewpoints:

        Original Query: {query}

        Create variations that:
        1. Approach the topic from different angles
        2. Consider different stakeholder perspectives
        3. Focus on different aspects of the topic
        4. Use different terminology while maintaining intent

        Return only the query variations, one per line:
        """

        try:
            response = await self._async_llm_predict(perspective_prompt, temperature=0.7)
            variants = [
                line.strip().rstrip('?') + '?' if not line.strip().endswith('?') else line.strip()
                for line in response.strip().split('\n')
                if line.strip() and len(line.strip()) > 10
            ]
            return variants[:count]

        except Exception as e:
            print(f"Perspective query generation error: {e}")
            return []

    async def _generate_decomposed_queries(self, query: str, count: int) -> List[str]:
        """Decompose complex query into focused sub-queries."""

        decomposition_prompt = f"""
        Break down this complex query into {count} focused sub-questions that together would comprehensively address the original question:

        Original Query: {query}

        Create sub-queries that:
        1. Each focus on a specific aspect
        2. Are independently searchable
        3. Together provide comprehensive coverage
        4. Avoid redundancy

        Sub-queries:
        """

        try:
            response = await self._async_llm_predict(decomposition_prompt, temperature=0.5)
            variants = [
                line.strip().rstrip('?') + '?' if not line.strip().endswith('?') else line.strip()
                for line in response.strip().split('\n')
                if line.strip() and '?' in line
            ]
            return variants[:count]

        except Exception as e:
            print(f"Decomposition query generation error: {e}")
            return []

    def _reciprocal_rank_fusion(self, retrieval_results: Dict[str, Any],
                              config: Dict) -> List[Dict[str, Any]]:
        """Apply Reciprocal Rank Fusion to combine multiple retrieval results."""

        k = config.get('rrf_k', 60)  # RRF parameter

        # Collect all documents with their ranks from each query
        document_scores = {}

        for query, query_results in retrieval_results.items():
            for rank, result in enumerate(query_results['results']):
                doc_id = result.get('id', result.get('content', '')[:100])

                if doc_id not in document_scores:
                    document_scores[doc_id] = {
                        'document': result,
                        'rrf_score': 0.0,
                        'query_ranks': {},
                        'original_scores': {}
                    }

                # Add RRF score: 1 / (k + rank)
                rrf_score = 1.0 / (k + rank + 1)
                document_scores[doc_id]['rrf_score'] += rrf_score
                document_scores[doc_id]['query_ranks'][query] = rank + 1
                document_scores[doc_id]['original_scores'][query] = result.get('score', 0.0)

        # Sort by RRF score
        fused_results = sorted(
            document_scores.values(),
            key=lambda x: x['rrf_score'],
            reverse=True
        )

        # Format results
        formatted_results = []
        for item in fused_results:
            result = item['document'].copy()
            result['fusion_score'] = item['rrf_score']
            result['fusion_metadata'] = {
                'queries_found_in': len(item['query_ranks']),
                'best_rank': min(item['query_ranks'].values()),
                'average_rank': sum(item['query_ranks'].values()) / len(item['query_ranks']),
                'query_ranks': item['query_ranks']
            }
            formatted_results.append(result)

        return formatted_results[:config.get('final_top_k', 10)]

# Ensemble RAG system with multiple models and strategies

class EnsembleRAGSystem:
    """Ensemble RAG system combining multiple models and strategies."""

    def __init__(self, rag_systems: Dict[str, Any], ensemble_config: Dict):
        self.rag_systems = rag_systems
        self.ensemble_config = ensemble_config

        # Ensemble strategies
        self.ensemble_methods = {
            'voting': self._voting_ensemble,
            'weighted_average': self._weighted_average_ensemble,
            'learned_combination': self._learned_combination_ensemble,
            'cascading': self._cascading_ensemble,
            'adaptive_selection': self._adaptive_selection_ensemble
        }

        # Performance tracking for adaptive weighting
        self.system_performance = {name: {'correct': 0, 'total': 0} for name in rag_systems.keys()}

    async def ensemble_generate(self, query: str,
                              ensemble_config: Dict = None) -> Dict[str, Any]:
        """Generate response using ensemble of RAG systems."""

        config = ensemble_config or self.ensemble_config
        ensemble_method = config.get('method', 'weighted_average')

        print(f"Ensemble RAG generation using {ensemble_method}...")

        # Generate responses from all systems
        system_responses = await self._generate_all_system_responses(query, config)

        # Apply ensemble method
        ensemble_response = await self.ensemble_methods[ensemble_method](
            query, system_responses, config
        )

        # Calculate ensemble confidence
        ensemble_confidence = self._calculate_ensemble_confidence(
            system_responses, ensemble_response
        )

        return {
            'query': query,
            'system_responses': system_responses,
            'ensemble_response': ensemble_response,
            'ensemble_confidence': ensemble_confidence,
            'ensemble_metadata': {
                'method': ensemble_method,
                'systems_used': len(system_responses),
                'systems_agreed': self._count_system_agreement(system_responses),
                'confidence_variance': self._calculate_confidence_variance(system_responses)
            }
        }

    async def _generate_all_system_responses(self, query: str,
                                           config: Dict) -> Dict[str, Dict]:
        """Generate responses from all RAG systems."""

        system_responses = {}

        # Generate responses concurrently for efficiency
        tasks = []
        for system_name, rag_system in self.rag_systems.items():
            task = self._generate_single_system_response(system_name, rag_system, query)
            tasks.append((system_name, task))

        # Collect results
        import asyncio
        results = await asyncio.gather(*[task for _, task in tasks], return_exceptions=True)

        for (system_name, _), result in zip(tasks, results):
            if isinstance(result, Exception):
                system_responses[system_name] = {
                    'success': False,
                    'error': str(result),
                    'response': '',
                    'confidence': 0.0
                }
            else:
                system_responses[system_name] = result

        return system_responses

    async def _weighted_average_ensemble(self, query: str,
                                       system_responses: Dict[str, Dict],
                                       config: Dict) -> Dict[str, Any]:
        """Combine responses using weighted averaging based on system performance."""

        # Calculate dynamic weights based on performance and confidence
        system_weights = self._calculate_dynamic_weights(system_responses, config)

        # Extract responses and confidences
        responses = []
        confidences = []
        weights = []

        for system_name, response_data in system_responses.items():
            if response_data.get('success', False):
                responses.append(response_data['response'])
                confidences.append(response_data.get('confidence', 0.5))
                weights.append(system_weights.get(system_name, 0.1))