What is Multimodal RAG
Multimodal RAG - Retrieval-augmented generation across multiple content types
Multimodal RAG extends traditional text-based retrieval-augmented generation to include images, videos, audio, and documents as retrievable context. Instead of limiting an LLM to text passages, multimodal RAG retrieves relevant video clips, image regions, document pages, and audio segments, then provides them as grounded evidence for generation.
How It Works
Multimodal RAG operates in two phases. First, a retrieval system searches across a unified embedding space that contains text, image, video, and audio representations. Relevant chunks from any modality are retrieved based on semantic similarity to the user query. Second, these multimodal chunks are formatted with source citations (timestamps, page numbers, image references) and passed as context to a large language model, which generates a grounded answer referencing the retrieved evidence.
Technical Details
The retrieval component uses multimodal embeddings (such as CLIP or Vertex AI embeddings) that project different content types into a shared vector space. Documents are chunked modality-appropriately: videos by scene boundaries, documents by layout sections, and audio by speaker turns. At query time, the system performs approximate nearest neighbor search across all modalities, optionally reranks with a cross-encoder, and assembles a context window that respects the LLM's token limits while maximizing information density.
Best Practices
- Use modality-appropriate chunking strategies rather than uniform character-based splitting
- Include source metadata (timestamps, page numbers, file names) in the context so the LLM can cite its sources
- Apply cross-encoder reranking after initial retrieval to improve the relevance of context passed to the LLM
- Set a maximum context window size and prioritize higher-scored chunks to avoid overwhelming the generator
- Evaluate retrieval quality independently from generation quality to diagnose pipeline issues
Common Pitfalls
- Treating all modalities identically during chunking, leading to incoherent context fragments
- Ignoring the retrieval step and assuming LLM quality alone determines answer quality
- Overloading the context window with too many low-relevance chunks, diluting the signal
- Failing to provide source attribution, making it impossible to verify generated answers
- Not testing retrieval recall separately, which masks the root cause of bad generations
Advanced Tips
- Use iterative retrieval where the LLM reformulates the query based on initial results for deeper exploration
- Implement modality-aware prompt templates that instruct the LLM how to interpret visual vs textual context
- Consider late fusion approaches where each modality is retrieved and scored independently before combining
- Cache frequently retrieved chunks to reduce latency for common queries
- Use agentic RAG patterns where the LLM decides which collections or modalities to search based on the query