---
title: "Multi-Vector Embeddings in Qdrant"
description: Configure Qdrant collections for multi-vector embeddings and learn how to index and query multi-vector data.
weight: 4
---

{{< date >}} Module 2 {{< /date >}}

# Multi-Vector Embeddings in Qdrant

Qdrant provides first-class support for multi-vector embeddings through its multi-vector configuration. This lesson covers creating collections, indexing documents, and querying with MaxSim distance.

By the end, you'll have a working multi-modal search system powered by ColPali and Qdrant.

---

<div class="video">
<iframe
  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
  frameborder="0"
  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
  referrerpolicy="strict-origin-when-cross-origin"
  allowfullscreen>
</iframe>
</div>

---

You now have the tools to build multi-modal search systems. In Module 3, we'll tackle the scalability challenges and optimize for production deployment.