Using ColPali and Binary Quantization for Efficient Multimodal Retrieval

In this webinar, we explored the technical details of ColPali, an advanced multimodal retrieval approach that uses Vision Language Models (VLMs) to handle visually complex documents.

Learn how ColPali uses multivectors to represent document images, capturing both local and global context.

Key topics:

• Multivectors: Representing documents as multiple embeddings to capture both local and global context, enhancing search accuracy.
• Late Interaction: Performing token-level comparisons between queries and document patches for precise relevance scoring.
• MaxSim Pooling: Aggregating the highest similarity scores from these comparisons to identify the most relevant document sections.
• Binary Quantization: Compressing vector data to optimize memory usage and accelerate search with minimal accuracy loss.

Learn how these techniques can be applied for efficient multimodal retrieval for complex, visually-rich documents.

Qdrant Speakers

Atita Arora, Solutions Architect

Atita Arora is a solution architect with 17+ years in information retrieval, driving AI innovation at Qdrant. She specializes in vector and hybrid search, focusing on Retrieval-Augmented Generation (RAG) and LLMs. An avid open-source contributor, Atita also champions diversity in tech as co-leader of Women in Search.

Sabrina Aquino, Developer Relations

Jenny Sukhodolskaya, Developer Advocate

Jenny Sukhodolskaya has 7 years of IT experience across software engineering, machine learning, and technical management, and 3 years in Developer Relations. She holds a Master’s in Machine Learning, Data Analytics, and Data Engineering, and is passionate about NLP, data-centric AI, and the role of vector databases in advancing AI technologies.