Multimodal Capabilities

Overview

Who is this for? Developers building AI applications that need to process and understand multiple types of content simultaneously - text, images, audio, video, and documents - in a single unified interaction.

What you'll achieve: Build sophisticated AI applications that can analyze, understand, and generate responses combining multiple content modalities, creating richer and more comprehensive AI interactions.

The AI Proxy provides comprehensive multimodal capabilities that enable AI models to process and understand multiple content types simultaneously, creating more natural and powerful AI interactions.

Supported Modalities

Text + Images (Vision)

Process text queries with accompanying images for visual understanding.

Supported Formats: JPG, PNG, WEBP, GIF, HEIC
Use Cases: Visual question answering, image analysis, content moderation

Text + Documents (PDF)

Analyze documents while providing textual context and questions.

Supported Formats: PDF
Use Cases: Document analysis, contract review, research assistance

Text + Audio (Speech)

Process text with audio content for comprehensive understanding.

Supported Formats: MP3, WAV, M4A, FLAC
Use Cases: Transcription, audio analysis, meeting summaries

Combined Modalities

Process multiple content types in a single request.

Combinations: Text + Images + Documents, Text + Audio + Images
Use Cases: Comprehensive content analysis, multi-source research

Provider Support Matrix

Basic Multimodal Usage

Text + Image Analysis

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Text + PDF Processing

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Multiple Images Analysis

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Mixed Content Processing

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Advanced Multimodal Features

Cross-Modal Understanding

Enable AI models to understand relationships between different content types.

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Contextual Analysis

Provide context from one modality to enhance understanding of another.

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Sequential Processing

Process multiple modalities in sequence with maintained context.

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Implementation Examples

Node.js Multimodal Processor

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Python Multimodal Analysis

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React Multimodal Upload Component

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Use Cases by Industry

Healthcare

• Medical Imaging: Analyze X-rays, MRIs with accompanying patient history
• Clinical Documentation: Process medical records with diagnostic images
• Telemedicine: Analyze symptoms described in text with photos
• Research: Combine research papers with data visualizations

Education

• Content Analysis: Analyze textbooks with diagrams and images
• Assignment Grading: Grade submissions with text, images, and documents
• Accessibility: Convert visual content to text descriptions
• Interactive Learning: Create rich educational content

Legal & Compliance

• Evidence Analysis: Analyze legal documents with photographic evidence
• Contract Review: Process contracts with accompanying visual materials
• Compliance Checking: Verify documents against visual standards
• Case Preparation: Organize mixed evidence types

E-commerce & Retail

• Product Analysis: Analyze product descriptions with images and manuals
• Quality Assurance: Check products against specifications and photos
• Customer Support: Handle inquiries with product images and documentation
• Inventory Management: Catalog products with multiple content types

Media & Content

• Content Moderation: Review posts with text, images, and videos
• Asset Management: Organize media files with metadata and descriptions
• Content Creation: Generate content combining multiple media types
• Archive Processing: Digitize and analyze historical content

Content Processing Workflows

Sequential Analysis

Process content types in sequence, building context progressively.

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Parallel Processing

Analyze multiple content types simultaneously for efficiency.

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Contextual Enhancement

Use one modality to enhance understanding of another.

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Response Formats

Unified Analysis Response

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Modality-Specific Insights

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Cross-Modal Relationships

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Quality and Performance

Content Quality Guidelines

• Image Resolution: Minimum 512x512 for optimal analysis
• Document Quality: Clear, high-contrast PDFs preferred
• Audio Quality: 16kHz+ sample rate recommended
• File Size Limits: Respect provider-specific limits

Performance Optimization

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Quality Assurance

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Best Practices

Content Preparation

• Standardize Formats: Use consistent file formats across modalities
• Optimize Sizes: Balance quality with processing speed
• Organize Context: Structure content logically for better analysis
• Validate Inputs: Ensure all content is accessible and valid

Request Structure

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Error Handling

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Cost Optimization

• Token Management: Different modalities consume different token amounts
• Provider Selection: Choose providers based on multimodal pricing
• Content Filtering: Process only necessary content
• Caching: Cache results for repeated multimodal analysis

Advanced Features

Template-Based Processing

Define reusable templates for common multimodal workflows.

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Batch Multimodal Processing

Process multiple multimodal requests efficiently.

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Real-Time Multimodal Streaming

Stream responses for multimodal content analysis.

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Integration Patterns

Workflow Integration

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API Gateway Integration

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Microservices Architecture

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Security Considerations

Content Security

• Input Validation: Validate all content types before processing
• Malware Scanning: Scan uploaded files for security threats
• Content Filtering: Apply appropriate content policies
• Access Control: Implement proper authentication for content access

Privacy Protection

• Data Encryption: Encrypt content in transit and at rest
• Content Retention: Define clear policies for content storage
• Anonymization: Remove or mask sensitive information
• Compliance: Ensure GDPR, HIPAA, and other regulatory compliance

Troubleshooting

Common Issues

Content Format Errors
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Size Limit Exceeded
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Context Loss Between Modalities
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Performance Issues

• Large File Processing: Break down large files into smaller chunks
• Memory Usage: Monitor memory consumption for large multimodal requests
• Network Timeouts: Increase timeouts for complex multimodal analysis
• Provider Limits: Respect individual provider limitations

Scaling Multimodal Applications

Horizontal Scaling

• Content Distribution: Distribute content processing across instances
• Load Balancing: Balance multimodal requests across providers
• Caching Strategies: Implement effective caching for multimodal results
• Queue Management: Manage processing queues for different content types

Vertical Scaling

• Resource Allocation: Allocate sufficient resources for content processing
• Memory Management: Optimize memory usage for large files
• Storage Optimization: Efficiently store and retrieve multimodal content
• Processing Optimization: Optimize algorithms for multimodal analysis

Next Steps

• Vision: Deep dive into image processing capabilities
• PDF Input: Explore document processing features
• Structured Outputs: Structure multimodal analysis results
• Tool Calling: Combine multimodal analysis with function calls