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51 lines
3.1 KiB
Markdown
51 lines
3.1 KiB
Markdown
---
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title: Qdrant 0.10 released
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short_description: A short review of all the features introduced in Qdrant 0.10
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description: Qdrant 0.10 brings a lot of changes. Check out what's new!
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preview_dir: /articles_data/qdrant-0-10-release/preview
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small_preview_image: /articles_data/qdrant-0-10-release/new-svgrepo-com.svg
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social_preview_image: /articles_data/qdrant-0-10-release/preview/social_preview.jpg
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weight: 70
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author: Kacper Łukawski
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author_link: https://medium.com/@lukawskikacper
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date: 2022-09-19T13:30:00+02:00
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draft: false
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---
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[Qdrant 0.10 is a new version](https://github.com/qdrant/qdrant/releases/tag/v0.10.0) that brings a lot of performance
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improvements, but also some new features which were heavily requested by our users. Here is an overview of what has changed.
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## Storing multiple vectors per object
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Previously, if you wanted to use semantic search with multiple vectors per object, you had to create separate collections
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for each vector type. This was even if the vectors shared some other attributes in the payload. With Qdrant 0.10, you can
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now store all of these vectors together in the same collection, which allows you to share a single copy of the payload.
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This makes it easier to use semantic search with multiple vector types, and reduces the amount of work you need to do to
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set up your collections.
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## Batch vector search
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Previously, you had to send multiple requests to the Qdrant API to perform multiple non-related tasks. However, this
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can cause significant network overhead and slow down the process, especially if you have a poor connection speed.
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Fortunately, the [new batch search feature](https://blog.qdrant.tech/batch-vector-search-with-qdrant-8c4d598179d5) allows
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you to avoid this issue. With just one API call, Qdrant will handle multiple search requests in the most efficient way
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possible. This means that you can perform multiple tasks simultaneously without having to worry about network overhead
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or slow performance.
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## Built-in ARM support
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To make our application accessible to ARM users, we have compiled it specifically for that platform. If it is not
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compiled for ARM, the device will have to emulate it, which can slow down performance. To ensure the best possible
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experience for ARM users, we have created Docker images specifically for that platform. Keep in mind that using
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a limited set of processor instructions may affect the performance of your vector search. Therefore, [we have tested
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both ARM and non-ARM architectures using similar setups to understand the potential impact on performance
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](https://blog.qdrant.tech/qdrant-supports-arm-architecture-363e92aa5026).
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## Full-text filtering
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Qdrant is a vector database that allows you to quickly search for the nearest neighbors. However, you may need to apply
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additional filters on top of the semantic search. Up until version 0.10, Qdrant only supported keyword filters. With the
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release of Qdrant 0.10, [you can now use full-text filters](https://blog.qdrant.tech/qdrant-introduces-full-text-filters-and-indexes-9a032fcb5fa)
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as well. This new filter type can be used on its own or in combination with other filter types to provide even more
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flexibility in your searches.
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