> Explore Qdrant's agent skills catalog at https://skills.qdrant.tech/
> Search the documentation at https://skills.qdrant.tech/search?query=your+query+here
> Use this file to discover all available pages: https://qdrant.tech/llms.txt

# Search Patterns

Design Qdrant search around your application by choosing embedding models, decomposing multi-hop queries, and filtering results with payload indexes.

- [How to Choose an Embedding Model: Evaluation & Tradeoffs](https://qdrant.tech/documentation/search-patterns/choose-embedding-model/index.md): There is no one-size-fits-all solution when it comes to embedding models. Learn how to choose the right one for your use case.
- [Query Decomposition for Multi-Hop Questions](https://qdrant.tech/documentation/search-patterns/query-decomposition/index.md): Answer multi-hop questions by retrieving in steps: an LLM asks each follow-up sub-question, then fuse the per-hop results with RRF.
- [A Complete Guide to Filtering in Vector Search](https://qdrant.tech/documentation/search-patterns/vector-search-filtering/index.md): Apply payload filters, build payload indexes, and combine conditions to narrow Qdrant search results to the right data.