Module 2: Multi-Vector Representations for Multi-Modal Data on Qdrant - Vector Search Engine

How ColPali Models Work

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Module 2

How ColPali Models Work

ColPali extends the late interaction paradigm from text to visual documents. It can process PDFs, images, and scanned documents, generating multi-vector representations that capture both textual and visual information.

Understanding ColPali’s architecture helps you leverage its full potential for multi-modal document retrieval.

Follow along in Colab:

From Text to Visual Documents

What about documents that aren’t just text? PDFs often contain diagrams, tables, charts, equations, and complex layouts where the visual presentation carries as much meaning as the text itself.

ColPali Family Overview

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Module 2

ColPali Family Overview

The ColPali is not only the name of a model. Still, it is also often used to refer to an entire family of models that convert images and text into multi-vector representations, based on Vision Language Models.

Let’s explore what the options are and which model to choose depending on the data you work with.

The ColPali family includes several model variants. When selecting a model for your application, you’ll need to consider factors like model size, supported languages, computational requirements, and licensing constraints - each variant offers different trade-offs along these dimensions.

Visual Interpretability of ColPali

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Module 2

Visual Interpretability of ColPali

Why did this document match my query? Unlike traditional black-box embedding models that produce a single opaque vector, ColPali’s multi-vector architecture offers something remarkable: you can see exactly where the model “looks” when matching a query to a document.

This visual interpretability is invaluable for building trust in multi-modal search systems, debugging unexpected results, and understanding model behavior and limitations.

Follow along in Colab: