# Multimodal Analysis Agent with Vision and Text Processing

## Overview
Demonstrates advanced multimodal capabilities combining text and image analysis. This agent can process documents with images, analyze visual content alongside textual information, and provide comprehensive insights across multiple modalities.

## Use Case
- Document analysis with embedded images and diagrams
- Medical report analysis with X-rays and scans
- Product catalog analysis with images and descriptions
- Educational content processing with visual aids

## Agent Specification

### Core Functionality
- **Input**: Text documents with embedded images or separate image/text pairs
- **Processing**: Simultaneous text and visual analysis with cross-modal reasoning
- **Output**: Comprehensive insights combining both modalities
- **Memory**: Visual pattern recognition and text-image relationship learning

### Signature Pattern
```python
class MultimodalAnalysisSignature(dspy.Signature):
    """Analyze and correlate information from text and visual content."""
    text_content: str = dspy.InputField(desc="Text content for analysis")
    image_paths: str = dspy.InputField(desc="Comma-separated paths to images")
    analysis_type: str = dspy.InputField(desc="Type of analysis required")
    context: str = dspy.InputField(desc="Domain context and requirements")

    text_insights: str = dspy.OutputField(desc="Key insights from text analysis")
    visual_insights: str = dspy.OutputField(desc="Key insights from image analysis")
    cross_modal_correlations: str = dspy.OutputField(desc="Relationships between text and images")
    comprehensive_summary: str = dspy.OutputField(desc="Integrated analysis summary")
    confidence_scores: str = dspy.OutputField(desc="Confidence for each modality and correlation")
```

## Expected Execution Flow

### Phase 1: Content Ingestion (0-500ms)
```
[00:00:000] Text content parsed and preprocessed
[00:00:150] Image files loaded and validated
[00:00:300] Content type classification completed
[00:00:400] Multimodal processing strategy determined
[00:00:500] Analysis pipeline configured
```

### Phase 2: Text Analysis (500ms-2s)
```
[00:00:500] Text processing initiated
[00:00:800] Entity extraction and classification
[00:01:100] Sentiment and topic analysis
[00:01:400] Key concept identification
[00:01:700] Text structure and relationships mapped
[00:02:000] Text analysis completed
```

### Phase 3: Visual Analysis (2s-5s)
```
[00:02:000] Image preprocessing and normalization
[00:02:500] Object detection and recognition
[00:03:000] Scene understanding and context analysis
[00:03:500] Text extraction from images (OCR)
[00:04:000] Visual pattern and feature extraction
[00:04:500] Image content categorization
[00:05:000] Visual analysis completed
```

### Phase 4: Cross-Modal Integration (5s-7s)
```
[00:05:000] Text-image correspondence analysis
[00:05:500] Semantic alignment between modalities
[00:06:000] Contextual relationship identification
[00:06:500] Integrated reasoning and inference
[00:07:000] Cross-modal insights synthesized
```

### Phase 5: Comprehensive Report Generation (7s-8s)
```
[00:07:000] Individual modality summaries compiled
[00:07:300] Cross-modal correlations documented
[00:07:600] Comprehensive analysis report generated
[00:07:800] Confidence scores calculated and validated
[00:08:000] Final multimodal analysis completed
```

## Technical Requirements

### Dependencies
```python
# Core Kailash SDK
from kailash.workflow.builder import WorkflowBuilder
from kailash.runtime.local import LocalRuntime
from kailash.nodes.llm_agent import LLMAgentNode
from kailash.nodes.vision import VisionAnalysisNode
from kailash.nodes.file_operations import FileOperationsNode

# Multimodal processing
import dspy
from PIL import Image
import cv2
import numpy as np
import pytesseract
import torch
import transformers
from typing import List, Dict, Optional, Tuple
import base64
import json
```

### Configuration
```yaml
multimodal_config:
  max_images: 10
  max_image_size_mb: 25
  supported_formats: ["jpg", "png", "pdf", "tiff", "webp"]
  ocr_enabled: true
  object_detection_enabled: true

vision_models:
  object_detection: "yolov8n"
  scene_classification: "clip-vit-base-patch32"
  ocr_engine: "tesseract"
  face_detection: "mtcnn"

text_processing:
  entity_recognition: true
  sentiment_analysis: true
  topic_modeling: true
  relationship_extraction: true

llm_config:
  provider: "openai"
  model: "gpt-4-vision-preview"
  temperature: 0.3
  max_tokens: 1500
```

### Memory Requirements
- **Runtime Memory**: ~2GB (includes vision models)
- **Image Processing**: ~500MB per large image
- **Model Cache**: ~1GB for pre-trained models
- **Text Processing**: ~200MB for NLP models

## Architecture Overview

### Agent Coordination Pattern
```
Input Documents → Text Processor   → Cross-Modal Analyzer → Report Generator
                ↘               ↗                        ↗
                  Image Processor → Correlation Engine →
```

### Data Flow
1. **Content Ingestion**: Load and validate text and image inputs
2. **Parallel Processing**: Simultaneous text and visual analysis
3. **Feature Extraction**: Extract relevant features from both modalities
4. **Correlation Analysis**: Identify relationships between text and images
5. **Integration**: Combine insights from all modalities
6. **Report Generation**: Create comprehensive multimodal analysis

### Processing Pipeline
```python
class MultimodalPipeline:
    def __init__(self):
        self.text_processor = TextAnalyzer()
        self.vision_processor = VisionAnalyzer()
        self.correlation_engine = CrossModalCorrelator()
        self.report_generator = MultimodalReporter()

    async def process(self, text, images):
        # Parallel processing of modalities
        text_features = await self.text_processor.analyze(text)
        image_features = await self.vision_processor.analyze(images)

        # Cross-modal correlation
        correlations = self.correlation_engine.correlate(
            text_features, image_features
        )

        # Integrated report
        return self.report_generator.generate(
            text_features, image_features, correlations
        )
```

## Success Criteria

### Functional Requirements
- ✅ Processes text and images simultaneously with high accuracy
- ✅ Identifies relevant correlations between modalities
- ✅ Generates coherent integrated analysis reports
- ✅ Handles various image formats and text structures

### Accuracy Metrics
- ✅ Text analysis accuracy >90% (entity recognition, sentiment)
- ✅ Visual analysis accuracy >85% (object detection, scene understanding)
- ✅ Cross-modal correlation precision >80%
- ✅ OCR accuracy >95% for clear text in images

### Performance Requirements
- ✅ Total processing time <15 seconds for typical documents
- ✅ Concurrent multimodal analysis for up to 5 documents
- ✅ Memory usage <3GB peak per analysis
- ✅ Image processing latency <2 seconds per image

## Enterprise Considerations

### Privacy and Security
- Image content encryption and secure storage
- PII detection and redaction in both text and images
- Secure processing pipeline with data isolation
- Compliance with image privacy regulations

### Scalability
- Distributed processing across multiple GPU nodes
- Efficient caching of vision model inference results
- Batch processing capabilities for large document sets
- Load balancing for concurrent multimodal requests

### Integration
- Support for enterprise document management systems
- API integration with existing content analysis workflows
- Custom model fine-tuning for domain-specific content
- Integration with enterprise security and compliance systems

## Error Scenarios

### Image Processing Failure
```python
# Response when image analysis fails
{
  "text_insights": "Complete text analysis results",
  "visual_insights": "IMAGE_PROCESSING_FAILED",
  "cross_modal_correlations": "Text-only analysis available",
  "comprehensive_summary": "Analysis based on text content only",
  "processing_status": "PARTIAL_SUCCESS",
  "failed_images": ["corrupted_image.jpg"]
}
```

### OCR Extraction Error
```python
# Handling poor quality images with unreadable text
{
  "text_insights": "Primary text analysis completed",
  "visual_insights": "Visual objects detected, text extraction failed",
  "ocr_confidence": 0.23,
  "alternative_description": "Image contains text that appears to be a form or document",
  "recommendation": "Provide higher resolution image for text extraction"
}
```

### Memory Limitation
```python
# Response when processing exceeds memory limits
{
  "processing_status": "MEMORY_OPTIMIZED",
  "images_processed": 7,
  "images_skipped": 3,
  "optimization_applied": "Large images downsampled to 1024x1024",
  "analysis_completeness": "85%"
}
```

### Model Loading Failure
```python
# Fallback when vision models fail to load
{
  "fallback_mode": "TEXT_ONLY_WITH_BASIC_VISION",
  "available_features": ["OCR", "basic_object_detection"],
  "unavailable_features": ["advanced_scene_understanding", "face_detection"],
  "recommendation": "Restart analysis when full models are available"
}
```

## Testing Strategy

### Unit Tests
- Individual modality processing accuracy
- Cross-modal correlation algorithm validation
- Error handling and fallback behavior testing
- Memory usage and performance optimization

### Integration Tests
- End-to-end multimodal workflow validation
- Various document type and format handling
- Concurrent processing capability verification
- API integration and response format validation

### Accuracy Tests
- Human-labeled test dataset validation
- Cross-modal correlation accuracy assessment
- Domain-specific content analysis validation
- Bias detection and mitigation testing

### Performance Tests
- Large document set processing benchmarks
- Memory usage profiling under load
- GPU utilization optimization validation
- Concurrent analysis capacity testing

## Implementation Details

### Key Components
1. **Text Analyzer**: NLP processing with entity recognition and topic modeling
2. **Vision Analyzer**: Computer vision with object detection and scene understanding
3. **OCR Engine**: Text extraction from images with confidence scoring
4. **Cross-Modal Correlator**: Identifies relationships between text and visual content
5. **Report Generator**: Creates comprehensive integrated analysis reports

### Vision Processing Capabilities
- **Object Detection**: Identification and localization of objects in images
- **Scene Understanding**: Context and environment classification
- **Text Recognition**: OCR with multiple language support
- **Face Detection**: Privacy-aware face detection and anonymization
- **Document Analysis**: Layout understanding for structured documents

### Cross-Modal Correlation Techniques
- **Semantic Matching**: Align text concepts with visual elements
- **Spatial Reasoning**: Relate text descriptions to image locations
- **Temporal Analysis**: Sequence understanding in multi-page documents
- **Contextual Inference**: Domain-specific relationship identification
- **Confidence Scoring**: Reliability assessment for each correlation
