Vibha

I have written integration test with a filter. ``` def test_filter_query(self, elasticsearch_url: str) -> None: texts = ["foo", "bar", "baz", "hello", "igloo", "sharks"] metadatas = [{"page": i} for i in...

I still have an issue. **I am trying to use ElasticVectorSearch.from_documents and it is creating a random index name and not using the index_name provided.** Is there any reason for...

I have the same question... why is {summaries} even required?

This is a related question @bglearning I see that [https://colab.research.google.com/drive/1Tz9GSzre7JfvXDDKe7sCnO0FMuDViMnN?usp=sharing#scrollTo=TD2PZuuNTpQ3] PseudoLabelGenerator runs basically **Question generation - optional step Negative mining Pseudo labeling (margin scoring)** steps. But in a case where...

Thank you for your reply. Yes, I did have a look at MNRL as well. But in my case since the customer feedback contains the explicit negative feedback, I am...

I think i got your point. You mean that mnrl will consider the negative that we explicitly provide to be the hard negative so that will help. Got it! Thanks

Also is there anyway to monitor the training?