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Multi-Modal AI Pattern

An integration pattern that combines multiple types of AI models to process different data modalities (text, images, audio, video) within a single application. This pattern enables more comprehensive understanding and generation across various media types.

When to Use

Use this pattern when applications need to understand or generate content across multiple media types, when context spans different modalities, or when you need to provide rich, multimedia responses. Essential for content creation platforms, educational systems, and comprehensive document analysis applications.

Architecture Drawing

The architecture demonstrates multiple specialized models for different modalities coordinated through a central fusion engine that combines insights and generates multi-modal responses.

Components: - User Interface (300, 50) - Modality Router (300, 100) - Text Processing Engine (150, 200) - Image Processing Engine (300, 200) - Audio Processing Engine (450, 200) - Multi-Modal Fusion Engine (300, 300) - Response Generator (300, 400) - Vector Store (500, 250) - Context Manager (100, 300)

Edges: - User Interface → Modality Router: "Multi-Modal Input" - Modality Router → Text Processing Engine: "Text Data" - Modality Router → Image Processing Engine: "Image Data" - Modality Router → Audio Processing Engine: "Audio Data" - Text Processing Engine → Multi-Modal Fusion Engine: "Text Features" - Image Processing Engine → Multi-Modal Fusion Engine: "Image Features" - Audio Processing Engine → Multi-Modal Fusion Engine: "Audio Features" - Multi-Modal Fusion Engine → Response Generator: "Fused Understanding" - Response Generator → User Interface: "Multi-Modal Response" - Vector Store → Multi-Modal Fusion Engine: "Cross-Modal Context" - Context Manager → Multi-Modal Fusion Engine: "Session Context"

RAG Pattern, Content Generation Pipeline Pattern, Unified Embedding Pattern

References

  1. Flamingo: a Visual Language Model for Few-Shot Learning - 2022 - arXiv - Breakthrough model demonstrating effective multi-modal learning with few-shot capabilities
  2. CLIP: Learning Transferable Visual Representations from Natural Language Supervision - 2021 - arXiv - Foundational work on joint vision-language understanding and cross-modal retrieval
  3. Make-A-Video: Text-to-Video Generation without Text-Video Data - 2022 - arXiv - Advanced multi-modal generation combining text understanding with video creation capabilities