Standard RAG works with text. But what about extracting knowledge from PowerPoint slides, retrieving information from video content, or enabling customer service through image uploads? That’s where multimodal RAG comes in.

Why Multimodal RAG Matters

Compelling applications include:

  • Extracting knowledge from presentations for multilingual scripts
  • Retrieving specific information from video content with captions
  • Customer service through image uploads
  • Multimodal agents for purchase assistance

Core Challenges

Three fundamental obstacles:

  1. Representation — Learning efficient multimodal representations while exploiting complementary information across modalities
  2. Alignment — Integrating information from different modalities by identifying relationships (e.g., matching images with captions)
  3. Translation — Converting one modality to another, such as generating textual descriptions from images

The Solution: Multimodal Embeddings

Multimodal embeddings convert data from different modalities (images, text, audio) into a unified vector embedding space. This shared space:

  • Preserves semantic similarities within each modality
  • Ensures alignment across modalities
  • Facilitates translation between them

System Architecture

Three critical components:

LLM Backbone

Models like Claude 3 or GPT Omni serve as the reasoning engine. They process the retrieved multimodal context and generate coherent responses.

Specialized Encoders

Process modality-specific data through techniques like patch processing (for images) and positional embeddings (for sequences). Each modality needs its own encoder that understands its unique structure.

Adapter Modules

Bridge modality-specific encoders and the LLM using MLPs or attention mechanisms. These are the glue that makes different modalities speak the same language.

Training Adapter Modules

Training requires paired data (images with descriptions) and employs cosine similarity with contrastive loss. The objective: maximize alignment between matching pairs while minimizing similarity for mismatches.

Search and Retrieval Infrastructure

Vector databases power retrieval using various indexing methods:

  • KNN — Exact nearest neighbors (accurate but slow at scale)
  • HNSW — Hierarchical Navigable Small World (fast approximate search)
  • IVF — Inverted File Index (partitioned search)
  • ANN — Approximate Nearest Neighbors (balances speed and accuracy)

The complete system chains these components for end-to-end multimodal question-answering.