Hey! We're actually polishing up a PR that'll add documentation and finalize the versioning API, it should be merged in this weekend. Would you be up for a quick chat with someone on our team? It would be interesting to get your feedback and see what else we're missing to be a drop-in replacement to opendistro, join our slack if so. We'll dm you :) https://join.slack.com/t/featureform-community/shared_invite...
You are both right. I just realized this and would be embarrassed if I wasn’t laughing so hard. I gave an original drawing to our designer with the correct values and we didn’t inspect their final image. We’ll get this fixed, thanks for pointing this out and sorry for the confusion :)
Our API is built from the ground up with the machine learning workflow in mind. For example, we have a training API that allows you to batch requests and even download your embeddings and generate an HNSW index locally. Our view of versioning, rollbacks, and more makes a lot of sense for an ML index, but very little sense for a search index.
Yes! We plan to bring Faiss in and utilize a lot of its functionality, our goal for this release was to get an end-to-end working to get feedback on the API. HNSW was a good default with this in mind.
Pinecone is closed source and only available as a SaaS service. Milvus and us have more overlap, we’re focused on the embeddings workflow like versioning and using embedding with other features. Milvus is entirely focused on nearest neighbor operations.
Faiss is solving the approximate nearest neighbor problem, not the storage problem. It’s not a database, it’s an index. We use a lightweight version of Faiss (HNSWLIB) to index embeddings in Embeddinghub.
Gensim is great for generating certain types of embeddings, but not for operationalizing them. It doesn’t do approximate nearest neighbor lookup which is a deal breaker for most models that use embeddings at scale. It also do not manage versioning so you end up having to hack a workflow around it to manage embedding. Finally, it’s not really data infrastructure like this is, so you end up doing hacky things like copying all your embeddings to every docker file. With regards to serving embeddings, gensim is just a library that supports in-memory brute force nearest neighbour look ups.
We actually use HNSWLIB by NMSLIB on the backend. NMSLIB is solving the approximate nearest neighbor problem, not the storage problem. It’s not a database, it’s an index. We handle everything needed to turn their index into a full fledged database with a data science workflow around it (versioning, monitoring, etc.)
Not yet, this is very much an early release to get it in people's hands and to get feedback on the API and the functionality. We've purposely held off optimizing too much until we feel more confident that this is useful and our API approach makes sense for people. That said, Simba who's one of the main devs actually comes from a performance tuning background at Google. Also, it's built on HNSWLIB and RocksDB, and is being used in real world workloads today.
Faiss actually also uses HNSW internally, HNSWLIB is just a lighter weight implementation which allowed us to iterate faster. In the future we will switch it back out for FAISS to take advantage of its full array of functionality.
Over the years, I've found myself building hacky solutions to serve and manage my embeddings. I’m excited to share Embeddinghub, an open-source vector database for ML embeddings. It is built with four goals in mind:
Store embeddings durably and with high availability
Allow for approximate nearest neighbor operations
Enable other operations like partitioning, sub-indices, and averaging
Manage versioning, access control, and rollbacks painlessly
It's still in the early stages, and before we committed more dev time to it we wanted to get your feedback. Let us know what you think and what you'd like to see!