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Learn more: https://bit.ly/44b5kHS Our new short course, Multi-Vector Image Retrieval, created in collaboration with Qdrant, is now live! Most retrieval systems represent an image with a single vector. This course shows how multi-vector methods represent images as collections of patch-level embeddings, enabling much more precise matching between text queries and visual content, especially in documents that mix images, diagrams, and text. You’ll learn how to: - Implement ColBERT to understand multi-vector text retrieval and late-interaction search. - Apply ColPali to extract patch-level image embeddings for fine-grained visual search. - Optimize memory with quantization and pooling techniques. - Speed up retrieval by converting multi-vector outputs into MUVERA embeddings for fast HNSW search. - Build a multi-modal RAG pipeline that retrieves and reasons over complex visual documents. This course is taught by Kacper Łukawski, Senior Developer Advocate at Qdrant. By the end, you’ll know how to implement production-ready multi-vector retrieval systems that understand images at the patch level and support accurate multi-modal search. Enroll now: https://bit.ly/44b5kHS
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