Add Qdrant 0.10 release article (for SEO)

qdrant-landing/content/articles/qdrant-0-10-release.md

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

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