4. NEW Pipeline

NEW Model can define the logitics of how to use NEW recommender and generator.

4.1. Raw text input and output

System: Hello!<sep>
User: Hi. I like horror movies, such as <entity>The Shining (1980)</entity> and <entity>Annabelle (2014)</entity>.
Would you please recommend me some other movies?

4.2. Tensor input and output

# TODO

4.3. Implementation of NEW Pipeline

4.3.1. Create NEW Pipeline Configuration: NEWConfig

from recwizard.configuration_utils import BaseConfig

class NEWConfig(BaseConfig):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)

4.3.2. Create NEW Pipeline: NEWPipeline

from recwizard.model_utils import BasePipeline

class NEWPipeline(BasePipeline):
    config_class = NEWConfig

    def forward(self, input_ids, attention_mask, labels=None, **kwargs):
        raise NotImplementedError

    @monitor
    def response(
        self, query, return_dict=False, rec_args=None, gen_args=None, **kwargs
    ):
        rec_args = rec_args or {}
        gen_args = gen_args or {}
        rec_output = self.rec_module.response(
            query, tokenizer=self.rec_tokenizer, return_dict=True, **rec_args
        )

        query_condition_on_rec = [
            q + "System: I recommend " + self.rec_tokenizer.decode(r) + "because"
            for q, r in zip(query, rec_output["recommended"])
        ]

        gen_output = self.gen_module.response(
            query_condition_on_rec,
            tokenizer=self.gen_tokenizer,
            return_dict=True,
            **gen_args,
        )
        if return_dict:
            return {
                "rec_logits": rec_output["logits"],
                "gen_logits": gen_output["logits"],
                "rec_output": rec_output["output"],
                "gen_output": gen_output["output"],
            }

        return gen_output["output"][0] + "\n - " + "\n - ".join(rec_output["output"])