[RFC] Support more local model types

[ylwu-amzn]

Currently, ml-commons only supports uploading text-embedding models. However, we believe there are other models that could be valuable additions to our platform:

• Summarization model: This model can summarize a document or lengthy content and return a concise version.
• Question answering model: If you have a question related to a context or set of documents, this model can provide you with accurate answers.
• Text classification: This model is designed to classify text, such as sentiment analysis, where it assigns labels like 'positive,' 'negative,' or 'neutral.'
• Named entity recognition (NER) model: For identifying various entities in a text, such as organizations, persons, locations, and more.
• Image embedding model: This model translates an image into a vector representation for easier analysis.
• Object detection model: This model detects object in image
• Rerank model: rerank the search result

Please comment on this issue if you require support for other local models or vote for the model you need the most.

[hijakk]

+1. Most of these would be valuable in my use cases, as well as language identification. Possibly langid is a different beast than this approach should support?

Image vectorization is a potentially tough use case as images can be large and including them in base64 natively in documents can dramatically inflate document size on disk rather than providing a reference pointer to external storage (such as s3).

[ylwu-amzn]

From https://github.com/opensearch-project/ml-commons/issues/1150#issuecomment-1660940973

@asfoorial suggests

I suggest to keep the door open for LLM hosting as there is a trend to get LLMs smaller with quantization yet achieve reasonable performance. I would say they will be hostable in ml nodes or other dedicated nodes.

[nateynateynate]

They all get a thumbs up from me, but I actually would love to see image embedding. I'm fascinated by it.

[HungryHowies]

+1 tbh , I would love to have them all.

[austintlee]

Are cross encoders covered under "rerank model"?

[austintlee]

@dhrubo-os (tagging you since you went over some basics on this with the OCI students)

Would it make it easier to produce ML input/output classes for all these different models if we used Smithy to define them and have it generate the classes. Just wondering what we can do to expedite progress on this using some common framework.

[TrungBui59]

@ylwu-amzn @dhrubo-os I am interested in working on supporting the Question-answering model. Can you guys give me some hints on what I should do? Currently, I am thinking of following the approach that we used to support the text-embedding model