The focus on the top 10 in vector search is a product of wanting to prove value over keyword search. Keyword search is going to miss some conceptual matches. You can try to work around that with tokenization and complex queries with all variations but it's not easy. Vector search isn't all that new a concept. For example, the annoy library (https://github.com/spotify/annoy), an open source embeddings database. - Source: Hacker News / 10 months ago

If you want to go larger you could still use some simple setup in conjunction with faiss, annoy or hnsw. Source: 12 months ago

I then use annoy to compare them. Annoy can use different measures for distance, like cosine, euclidean and more. Source: about 1 year ago

Yes you can do this for equality predicates if your row groups are sorted . This blog post (that I didn't write) might add more color. You can't do this for any kind of text searching. If you need to do this with file based storage I'd recommend using a vector based text search and utilize a ANN index library like Annoy. Source: about 1 year ago

If you need large scale (1000+ dimension, millions+ source points, >1000 queries per second) and accept imperfect results / approximate nearest neighbors, then other people have already mentioned some of the best libraries (FAISS, Annoy). Source: about 1 year ago

They also have Amazon Forecast with different algos - https://aws.amazon.com/forecast/. - Source: Hacker News / about 1 month ago

Generative Artificial Intelligence (GenAI) is a type of artificial intelligence that can generate text, images, or other media using generative models. AWS offers a range of services for building and scaling generative AI applications, including Amazon SageMaker, Amazon Rekognition, AWS DeepRacer, and Amazon Forecast. AWS has also invested in developing foundation models (FMs) for generative AI, which are... - Source: dev.to / 5 months ago

In this reproducible experiment, we compare Amazon Forecast and StatsForecast a python open-source library for statistical methods. Source: over 1 year ago

It sounds like you need something that mostly runs itself, without you necessarily needing to have in-depth knowledge of time series modeling. If you have an AWS account, I'd recommend checking out Amazon Forecast. One of the recommendations I saw in this thread is to run auto.arima in R. That's actually one of the algorithms AWS will run for you, among others. I don't know if it handles differencing and... Source: over 2 years ago

With the help of Amazon Forecast, the forecasting technology at the heart of Amazon.com, it is now possible to build forecasting models for your own applications. - Source: dev.to / about 3 years ago