cyrusthegreat

5 ms·

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by cyrusthegreat 4y ago
Tiny wooden boxes.
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by cyrusthegreat 4y ago
This is awesome! Permanently bookmarked!
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The Definitive Guide to Embeddings(featureform.com)
6 points by cyrusthegreat 5y ago|0 comments
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by cyrusthegreat 5y ago
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 f
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by cyrusthegreat 5y ago
This is in the works! We'd love you feedback on the API and to learn a bit more about your use-case so we build the right thing, mind joining our slack? https://join.slack.com/t/featureform-community/shared_in
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by cyrusthegreat 5y ago
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 poi
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by cyrusthegreat 5y ago
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 versioni
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by cyrusthegreat 5y ago
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.
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by cyrusthegreat 5y ago
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 neighbo
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by cyrusthegreat 5y ago
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 ver
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by cyrusthegreat 5y ago
Thanks for the kind words! We'd love to get your feedback as we iterate. Please join our slack community: https://join.slack.com/t/featureform-community/shared_invite...
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by cyrusthegreat 5y ago
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 d
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by cyrusthegreat 5y ago
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 AP
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by cyrusthegreat 5y ago
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.
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by cyrusthegreat 5y ago
We're so glad to hear that! We'd love your feedback as we keep building. Please join our community on Slack: https://join.slack.com/t/featureform-community/shared_invite...
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by cyrusthegreat 5y ago
We use HNSW internally via HNSWLIB, it's the same algorithm that Facebook uses to power their embedding search.
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Show HN: Embeddinghub: A vector database built for Machine Learning embeddings(github.com)
118 points by cyrusthegreat 5y ago|33 comments
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by cyrusthegreat 5y ago
Hi everyone! 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