Added more detailed error messages for KNN model training

[anntians]

Description

Previously, a consistent feedback we get around PQ and IVF is that there is limited visibility into the failure cases. Part of this is because the errors are thrown on the Faiss side and we don't return stack traces in Rest response. So, this makes it difficult to use PQ and IVF. Thus, this PR provides improved error messages by adding explicit checks for the most common errors:

[ ] For PQ, explicitly check in OpenSearch an invalid configuration where m does not divide dimension
[ ] For PQ/IVF, check the number of training points matches the minimum clustering criteria defined in faiss
[ ] If there is not enough memory, explicitly say that there is not enough memory.

Adding these 3 checks will cover 90% of the training failures that occur.

Related Issues

Resolves #2268

Check List

• New functionality includes testing.
• New functionality has been documented.
• API changes companion pull request created.
• Commits are signed per the DCO using --signoff.
• Public documentation issue/PR created.

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.

[martin-gaievski]

can you make the 1000 value a class level constant?

[martin-gaievski]

Use String.format for concatenation

[martin-gaievski]

I'm not sure if that parameter is always present or not, but if it's optional then this line can generate the runtime exception in case parameter is not present

[jmazanec15]

Thanks @anntians. I think this is a good first step. What we should do next is move all of the specific checks around the parameters behind the engine method abstraction. See https://github.com/opensearch-project/k-NN/tree/main/src/main/java/org/opensearch/knn/index/engine.

Here is my idea for it: add in a method in KNNLibraryIndexingContext called something like getTrainingConfigValidationSetup() that returns a function that takes as input the number of training vectors (or a more general object) and performs some kind of validation.

Then, we can use this to hide the method specific validations inside the engine abstraction, which will be clean and maintainable. For instance, we can implement checks for IVFPQ in https://github.com/opensearch-project/k-NN/blob/main/src/main/java/org/opensearch/knn/index/engine/faiss/FaissIVFPQEncoder.java, etc.