ColPali makes us rethink how we approach document processing.

ColPali revolutionizes visual document search by combining late interaction scoring with visual language models. This approach eliminates the need for extensive text extraction and preprocessing, handling messy real-world data more effectively than traditional methods.

In this episode, Jo Bergum, chief scientist at Vespa, shares his insights on how ColPali is changing the way we approach complex document formats like PDFs and HTML pages.

Introduction to ColPali:

• Combines late interaction scoring from Colbert with visual language model (PoliGemma)
• Represents screenshots of documents as multi-vector representations
• Enables searching across complex document formats (PDFs, HTML)
• Eliminates need for extensive text extraction and preprocessing

Advantages of ColPali:

• Handles messy, real-world data better than traditional methods
• Considers both textual and visual elements in documents
• Potential applications in various domains (finance, medical, legal)
• Scalable to large document collections with proper optimization

Jo Bergum:

• LinkedIn
• Vespa
• X (Twitter)
• PDF Retrieval with Vision Language Models
• Scaling ColPali to billions of PDFs with Vespa

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)

00:00 Messy Data in AI 01:19 Challenges in Search Systems 03:41 Understanding Representational Approaches 08:18 Dense vs Sparse Representations 19:49 Advanced Retrieval Models and ColPali 30:59 Exploring Image-Based AI Progress 32:25 Challenges and Innovations in OCR 33:45 Understanding ColPali and MaxSim 38:13 Scaling and Practical Applications of ColPali 44:01 Future Directions and Use Cases