Evaluation metric for Information retrieval system
I want to know how to come up with ground truth(relevancy label) if it's not available?

There's simply no way to properly evaluate a system if nobody knows what the output is supposed to be. However there are ways to work around a lack of annotated data:

Ask a panel of annotators to grade the quality of the output on a sample. Disadvantage: if a relevant instance is never predicted, the annotators are unlikely to notice it.
Compare the output to a state of the art system. Disadvantage: the evaluated system can only be as good as the reference system, any error by the reference system is considered correct.
Generate artificial data with an automatic method. Disadvantage: the evaluation relies on the quality of the artificial data, so in theory one has to prove that the artificial data is as good as real data... which is usually harder than actually collecting real data.


In that case, how researchers calculate precision and recall? or how do they generate it?

They can't. It would be like grading an exam paper without knowing the correct answers.
This is why benchmark datasets are so important for the research community and are published as proper scientific contributions.