You can check out these demo cases(https://github.com/milvus-io/bootcamp) to get a better sense of the capability of the vector search/vector database.
I think the Ann benchmark should pay more attention on
1. The index building speed, as this is very important in some production scenarios. Now it only says I will give 5 hours to build the index on that 1 million vectors.
2. The memory footprint, as 1m vectors are not that many. We will have to deal with billion s of vectors for chemical molecules, images and word vectors. The memory consumption will definitely impact how many servers you need.
It's about the ML scenarios. If you want to search thru a huge amount of unstructured data after vectorization tech (like deep learning), Milvus will help you a lot.
Our users use Milvus in below scenarios:
1. Chemical molecules analysis, searching SMILE format vectors
2. Image retrieval type application, for example shopping website
3. NLP
4. Recommendation system
5. and more, we are collecting users' feedback
Milvus allows users to append vectors. Vectors are stored in multiple file slices. When a file slice reaches the threshold, Milvus will build the index for that file slice, and new data will be inserted into a new file slice. For details, please refer
https://medium.com/@milvusio/managing-data-in-massive-scale-...
We are now working on the vector deletion. Hopefully will be ready by the end of 1Q this year.