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Remove MaxSim mentions

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Kacper Łukawski
2025-12-19 15:06:03 +01:00
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qdrant-landing/content/course/multi-vector-search/module-1/late-interaction-basics.md
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# Late Interaction Basics
When building a search system, one fundamental question emerges: **when should a query and document interact?** The answer to this question profoundly affects both the quality of search results and the system's scalability.
When building a search system, one fundamental question emerges: **when should a query and document interact?** The answer to this question may affect both the quality of search results and the system's scalability.
This lesson introduces the late interaction paradigm - the foundation of multi-vector search - and explores how it compares to other approaches.
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---
**Follow along in Colab:** <a href="https://colab.research.google.com/github/qdrant/examples/blob/master/course/multi-vector-search/module-1/late-interaction-basics.ipynb">
**Follow along in Colab:** <a href="https://colab.research.google.com/github/qdrant/examples/blob/master/course-multi-vector-search/module-1/late-interaction-basics.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" style="display:inline; margin:0;" alt="Open In Colab"/>
</a>
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# TODO: implement the code snippet
```
**Key observation:** Unlike single-vector search, each document is represented by **multiple vectors** (typically 32-512 depending on document length). The similarity computation (MaxSim) happens at search time, comparing each query token against all document tokens.
**Key observation:** Unlike single-vector search, each document is represented by **multiple vectors**. At search time, we compare each query token against all document tokens to compute a relevance score.
## Why This Matters for Multi-Vector Search
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**Enables scale:** Pre-computed multi-vector representations mean you can build practical search systems over large document collections. The computational cost grows with collection size, not quadratically with query-document pairs.
**Foundation for this course:** Everything we'll explore in subsequent lessons builds on this paradigm - from the MaxSim distance metric to multi-modal extensions like ColPali to optimization techniques for production deployment.
**Foundation for this course:** Everything we'll explore in subsequent lessons builds on this paradigm - from the distance metrics that enable multi-vector comparison to multi-modal extensions like ColPali to optimization techniques for production deployment.
**Beyond text:** The late interaction paradigm extends naturally to other modalities. Module 2 explores how ColPali applies these same principles to visual documents, enabling semantic search over images and PDFs.