"Find the screenshot of the billing error."
Standard RAG (Retrieval Augmented Generation) only sees text. If your knowledge base is full of screenshots, PDFs with charts, or architectural diagrams, it's blind.
Multimodal RAG solves this by using models like CLIP to understand that an image of a cat and the word "feline" are mathematically identical.
02. Joint Embedding Space
We use a multimodal embedding model. We pass images through a Vision Encoder and text through a Text Encoder. They project vectors into the same n-dimensional space.
Deep Dive: The Modal Gap
You cannot use OpenAI's text-embedding-3-small for text and a ResNet for images. The vectors would be in completely different coordinate systems.
You must use a model trained with Contrastive Learning (like CLIP or SigLIP) which forces "A photo of a dog" and the text "A photo of a dog" to be close in vector space.
id: 'chart-1',
values: imgVector,
metadata: {'{'} type: 'image' {'}'}
{'}'});
04. The Senior Engineer's Take
Cost vs Value
Multimodal embeddings are large (high dimensionality) and expensive to index.
Pro Tip: Don't embed every frame of a video. Use keyframe extraction (every 5s) to capture the semantic meaning without blowing up your vector storage bill.
ColBERT & Late Interaction
If standard cosine similarity isn't precise enough, look into ColBERT (Late Interaction). It keeps all token vectors rather than compressing them into one, allowing for much finer-grained matching at the cost of higher storage and compute.
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