landing_page/qdrant-landing/content/course/multi-vector-search/module-1/multi-vector-in-qdrant.md

title, description, weight

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Multi-Vector Embeddings in Qdrant	Configure Qdrant collections for multi-vector embeddings and learn how to index and query multi-vector data.	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.

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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.