What is multimodal RAG and how to create an index for images?

Multimodal RAG (Retrieval-Augmented Generation) is an AI approach that combines generative models with search and information retrieval mechanisms from various sources and modalities (text, images, audio, etc.). In multimodal RAG, the model not only generates responses but also consults external indexes with multimodal data to improve quality and relevance.

Image indexing typically involves the following steps:

1. Feature extraction: using pre-trained computer vision models (e.g., ResNet, CLIP), a vector representation is obtained from each image.

2. Index building: the obtained vectors are added to a vector index (e.g., FAISS, Annoy), which allows for quick search of similar images based on proximity in the vector space.

3. Search and matching: when a query is made, a query vector (textual or visual) is extracted, and a search for nearest neighbors in the index is performed.

This approach effectively integrates visual information into the RAG system, enhancing answer quality, especially in tasks where image context is important.