Multimodal RAG Developments: Combining Vector and Graph Search

RAG is no longer purely text-based. In early 2026, the strongest momentum is coming from multimodal systems that combine vector similarity with graph relationships to improve accuracy and traceability.

Signals from the Field

• Unified retrieval across text, images, and audio.
• Hybrid ranking that blends vector score with graph connectivity.
• Retrieval quality treated as a first-class product metric.

Technical Notes

• Multi-embedding strategy: separate embeddings per modality with shared alignment.
• Chunking techniques: region-based chunks for images, semantic chunks for text.
• Hybrid retrieval: enrich vector results with graph-based relationships.
• Source transparency: citations and provenance as core UX elements.

Product Impact

• More accurate answers through broader context.
• Better exploration via relationship maps and knowledge graphs.
• Stronger enterprise search across diverse knowledge assets.

Implementation Tips

• Classify data modalities early and test embedding choices independently.
• Build a simple A/B evaluation set for hybrid retrieval.
• Put citations in the center of the user experience.

Summary

Multimodal RAG is becoming a baseline capability. The fusion of vector and graph search is lifting enterprise discovery to a new level in 2026.