What does HackerNews think of milvus?

If you're just starting out, I'd use sentence-transformers for calculating embeddings. You'll want a bi-encoder model since they produce embeddings. As the author of the blog, I'm partial towards Milvus (https://github.com/milvus-io/milvus) due to its enterprise and scale, but FAISS is a great option too if you're just looking for something more local and contained.

Milvus will perform vector similarity search for you - all you need to do is give it a query vector.

Why not choose an open-source solution https://github.com/milvus-io/milvus, free!

Solid work OpenAI, though I'd definitely like to see some more benchmarks on a wider variety of datasets in addition to the ones listed in the post. Regardless, it's good to see embeddings becoming more and more mainstream and easier to leverage out-of-box. We tried image embeddings many moons ago (2015) with AlexNet trained across a custom dataset, but we still had to add quite a few custom roles post-inference.

A large selling point for ada-002 embeddings seems to be the reduced dimensionality. While lower-dimensional embeddings definitely help performance, I would say it's still highly dependent on the index that's being used. Graph- and tree-based indexes will benefit less than ones based on IVF (https://zilliz.com/blog/vector-index), as they do fewer overall distance computations during query time, but the speedup should still be significant.

Still been meaning to try semantic search across Wikipedia via text embeddings. Will definitely play around with OpenAI + Milvus (https://github.com/milvus-io/milvus).

+ Milvus (https://github.com/milvus-io/milvus) for large scale similarity/semantic search.

Hashes are fine, but to say that "vectors are over" is just plain nonsense. We continue to see vectors as a core part of production systems for entity representation and recommendation (example: https://slack.engineering/recommend-api) and within models themselves (example: multimodal and diffusion models). For folks into metrics, we're building a vector database specifically for storing, indexing, and searching across massive quantities of vectors (https://github.com/milvus-io/milvus), and we've seen close to exponential growth in terms of total downloads.

Vectors are just getting started.

Usually this is done in three steps. The first step is using a neural network to create a bounding box around the object, then generating vector embeddings of the object, and then using similarity search on vector embeddings.

The first step is accomplished by training a detection model to generate the bounding box around your object, this can usually be done by finetuning an already trained detection model. For this step the data you would need is all the images of the object you have with a bounding box created around it, the version of the object doesnt matter here.

The second step involves using a generalized image classification model thats been pretrained on generalized data (VGG, etc.) and a vector search engine/vector database. You would start by using the image classification model to generate vector embeddings (https://frankzliu.com/blog/understanding-neural-network-embe...) of all the different versions of the object. The more ground truth images you have, the better, but it doesn't require the same amount as training a classifier model. Once you have your versions of the object as embeddings, you would store them in a vector database (for example Milvus: https://github.com/milvus-io/milvus).

Now whenever you want to detect the object in an image you can run the image through the detection model to find the object in the image, then run the sliced out image of the object through the vector embedding model. With this vector embedding you can then perform a search in the vector database, and the closest results will most likely be the version of the object.

Hopefully this helps with the general rundown of how it would look like. Here is an example using Milvus and Towhee https://github.com/towhee-io/examples/tree/3a2207d67b10a246f....

Disclaimer: I am a part of those two open source projects.