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Interactive 3D/Multimodal RAG Pipeline
Multimodal RAG Pipeline
Image + text query · CLIP · late fusion · top-3
Incoming Query
🐱
Query Image
Text query:
"What are the behavioral characteristics of orange tabby cats?"
Text-only vs Multimodal Retrieval Quality
Text-only Precision67%
Multimodal Precision100%
Controls
Query Type
Retrieval Top-K
138
Embedding Model
Fusion Strategy
Multimodal RAG retrieves from both image and text indexes, then merges results before generation.

CLIP embeds images and text in the same vector space - enabling cross-modal similarity search.

Multimodal RAG Pipeline - Interactive Visualization

Multimodal RAG extends standard text RAG by retrieving from both image and text indexes using a joint embedding model like CLIP or ALIGN. The query - which may itself include an image - is embedded in the same vector space as the corpus items, enabling cross-modal similarity search. Retrieved image and text results are then fused (either late fusion via reciprocal rank fusion, or early fusion via concatenation and reranking) before being passed to a vision-language generator.

  • Step-through pipeline: query → embed → retrieve → fuse → generate
  • Separate image and text retrieval paths that merge at the fusion stage
  • Similarity scores for retrieved items - see which are relevant vs noise
  • Precision@k comparison: text-only vs multimodal retrieval quality
  • Late fusion (RRF) vs early fusion (concat) strategy toggle
  • Embedding model selector: CLIP, ALIGN, ImageBind

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.