Multi-Vector Image Retrieval

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.

In detail, you’ll: 

• Learn multi-vector text retrieval with ColBERT – Understand how multi-vector retrieval works by implementing ColBERT for text, then explore the computational and memory challenges of late interaction search.
• Implement ColPali for multi-vector image retrieval – Apply multi-vector concepts to images using ColPali, which adapts vision language models to create patch-level embeddings for fine-grained visual search.
• Optimize ColPali’s memory footprint – Apply scalar and binary quantization, plus row, column, and hierarchical pooling techniques to dramatically reduce memory usage while preserving search quality.
• Enable fast search with MUVERA embeddings – Convert multi-vector representations into high-dimensional single vectors using MUVERA, unlocking HNSW search for significantly faster retrieval.
• Build a multi-modal RAG system with ColPali – Combine ColPali retrieval, memory optimizations, and MUVERA search into a complete RAG pipeline that retrieves and reasons over visual documents.

Start building retrieval systems that understand images at the patch level and deliver accurate multi-modal search.

AI builders working with multi-modal data who want to implement advanced image retrieval. Familiarity with Python and vector embeddings is recommended.