I currently do not think there is anything to be improved based on our experience, as Faiss performs as we expected for our workflow. I would like to see improvement in the fact that FAISS currently stores data in byte-wise files, which means we cannot see the embeddings vectors or something. I chose 8 out of 10 because one of the drawbacks of Faiss is that it works only in-memory. If it could provide separate persistent storage without relying on in-memory, it would reduce the overhead.
I didn't know what algorithm was being learned to fetch my query. It would be beneficial if I could set a parameter and see different query mechanisms being run. I can then compare the results to see which works better for me.
We need to build many tools to streamline its integration into production environments. All the embeddings are saved in a particular location. We have to load them and start with the search in case of a query. There could be an integration with products like Spine for automated processing. Additionally, it could be more accessible for handling larger data sets.
Open Source Databases are essential for businesses seeking customizable database solutions. They offer flexibility, security, and active community support, making them a popular choice for a wide range of applications and industries.Known for their adaptability, Open Source Databases enable organizations to tailor database management systems to their specific requirements. With the freedom to modify code, users can optimize performance and security in ways that proprietary databases might not...
I currently do not think there is anything to be improved based on our experience, as Faiss performs as we expected for our workflow. I would like to see improvement in the fact that FAISS currently stores data in byte-wise files, which means we cannot see the embeddings vectors or something. I chose 8 out of 10 because one of the drawbacks of Faiss is that it works only in-memory. If it could provide separate persistent storage without relying on in-memory, it would reduce the overhead.
I didn't know what algorithm was being learned to fetch my query. It would be beneficial if I could set a parameter and see different query mechanisms being run. I can then compare the results to see which works better for me.
We need to build many tools to streamline its integration into production environments. All the embeddings are saved in a particular location. We have to load them and start with the search in case of a query. There could be an integration with products like Spine for automated processing. Additionally, it could be more accessible for handling larger data sets.