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Join our new short course, Multi-Vector Image Retrieval! Learn from Kacper Łukawski, Senior Developer Advocate at Qdrant.
Most retrieval systems represent each image with a single vector, but multi-vector techniques represent images as collections of smaller vectors—one for each patch. This detailed representation enables fine-grained matching between text query tokens and image patches, delivering higher-quality search on complex documents that combine text, images, and diagrams.
In this course, you’ll learn multi-vector retrieval concepts, implement ColPali for image search, and apply optimization techniques to make these systems production-ready. You’ll work with real course materials to build a complete multi-modal RAG system.