Handling multiple images in Retrieval-Augmented Generation (RAG) systems requires techniques that efficiently process, index, and contextualize visual data alongside text. The core challenge is integrating images into a framework designed primarily for text retrieval and generation. Here are three key strategies to address this:
1. Image Embedding and Indexing Start by converting images into numerical representations (embeddings) using vision models like CLIP, ResNet, or ViT. These models encode visual features into vectors that capture semantic meaning, enabling similarity comparisons. For example, CLIP embeddings align images and text in a shared space, allowing you to retrieve images based on textual queries. Store these embeddings in a vector database (e.g., FAISS, Milvus) alongside text embeddings for unified retrieval. To handle multiple images, group related images (e.g., product photos from different angles) using metadata or clustering. For instance, an e-commerce RAG system could index product images by category, color, or style, ensuring the retriever fetches relevant visual and textual data together.
2. Contextual Linking and Multi-Modal Attention When processing queries involving multiple images, establish relationships between images and text using cross-modal attention mechanisms. For example, in a medical RAG system analyzing X-rays and lab reports, the model must link specific images to corresponding patient notes. Use architectures like Flamingo or BLIP-2, which combine vision and language transformers to fuse image and text features. During retrieval, prioritize images that share contextual cues with the text (e.g., timestamps, captions). If a user asks, “Compare the MRI scans from January and April,” the system should retrieve both images and their associated diagnostic texts, then use attention layers to highlight differences in the generator phase.
3. Hierarchical Processing and Caching For systems with large image datasets, break the workflow into stages. First, retrieve a broad set of candidate images using lightweight filters (e.g., metadata tags), then refine results using detailed embeddings. For example, a satellite imagery RAG tool might filter images by location and date before applying CLIP to identify specific features like “forest fires.” Cache frequently accessed images or precompute embeddings to reduce latency. Additionally, use hybrid retrieval—combining text-based Elasticsearch queries with vector search—to balance precision and recall. In a news summarization system, this approach could fetch images of a protest event using keywords (“2023 Paris strike”) and then refine results using visual similarity to exclude irrelevant photos.
By combining these techniques, developers can build RAG systems that handle multiple images efficiently while maintaining coherence between visual and textual data. Practical implementation often involves trade-offs—like balancing embedding quality with computational cost—but modular design allows iterative improvements.