📘 Customize Memory Operations

To implement a new method, the memory operation is most significant part to customize, containing major pipelines of the detailed process. Here is an example:

......

class MyMemoryRecall(BaseRecall):
    def __init__(self, config, **kwargs):
        super().__init__(config)

        self.storage = kwargs['storage']
        self.insight = kwargs['insight']
        self.truncation = LMTruncation(self.config.truncation)
        self.utilization = ConcateUtilization(self.config.utilization)
        self.text_retrieval = TextRetrieval(self.config.text_retrieval)
        self.bias_retrieval = ValueRetrieval(self.config.bias_retrieval)

    def reset(self):
        self.__reset_objects__([self.truncation, self.utilization, self.text_retrieval, self.bias_retrieval])

    @__recall_convert_str_to_observation__
    def __call__(self, query):
        if self.storage.is_empty():
            return self.config.empty_memory
        text = query['text']

        relevance_scores, _ = self.text_retrieval(text, topk=False, with_score = True, sort = False)
        bias, _ = self.bias_retrieval(None, topk=False, with_score = True, sort = False)
        final_scores = relevance_scores + bias
        scores, ranking_ids = torch.sort(final_scores, descending=True)

        if hasattr(self.config, 'topk'):
            scores, ranking_ids = scores[:self.config.topk], ranking_ids[:self.config.topk]

        memory_context = self.utilization({
                    'Insight': self.insight['global_insight'],
                    'Memory': [self.storage.get_memory_text_by_mid(mid) for mid in ranking_ids]
                })

        return self.truncation(memory_context)