Multi-Modal Vector Indexes (EXPLAINED)
Shared embedding spaces, separate indexes, CLIP-style models, metadata routing, and fusion strategies.
TL;DR — Quick Answer
Use joint embedding models (e.g., CLIP) for cross-modal search in one index, or separate modality-specific indexes with a router query classifier. Store modality metadata, normalize metrics per space, and fusion/rerank at application layer when models differ. Multi-modal RAG requires careful chunking — images need captions or OCR text for LLM context.
The Interview Question
How do you design vector indexes for multi-modal retrieval (text, image, audio)?
Deep Explanation
Use cases
Sign in to unlock full answer
Get deep explanations, PDF export & all Vector Databases questions
- 23 more sections of deep explanation
- Real-world examples
- Common mistakes
- Interviewer expectations
- Follow-up questions
Multi-ModalCLIPImage SearchEmbeddingsGoogleOpenAI