How to inference LTR (Learning-to-Rank) models?

I've recently started looking into LTR models such as RankNet and LambdaMart. In the instance of LambdaMart and the LETOR dataset, I believe the model accepts the following as training input: query_id (scalar), document_features (vector), relevance score (scalar).

However, I don't see the query features anywhere in the input. I think the query_id is only used to partition the dataset. How would I inference this model for a query that wasn't present in the training data? Do I need a separate model for each query?

Thanks in advance.