🚀 New short course with Qdrant: Multi-vector Image Retrieval. Taught by Kacper Łukawski, Senior Developer Advocate at Qdrant, the course shows how multi-vector techniques outperform single-vector methods by matching text tokens to image patches directly. You’ll implement ColBERT to understand multi-vector search, apply ColPali for patch-level image retrieval, reduce memory with quantization and pooling, and use MUVERA to enable fast HNSW search. The course concludes with a full multi-modal RAG

Learn about tokenization and vector search optimization for large-scale customer-facing RAG applications in our new short course, Retrieval Optimization: From Tokenization to Vector Quantization. In this course, created in collaboration with Qdrant and taught by Kacper Łukawski, you will explore the technical details of how vector search works and learn techniques to optimize it for better performance. Gain practical knowledge in: 🧩 Techniques like Byte-Pair Encoding, WordPiece, and Unigram. 📊