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# Placeholder for actual LLM processing logic return f"Processed {segment[:10]}..." ``` #### 5. Handling Token Overflow Handle token overflow by segmenting the input sequence and processing each segment. Use caching to avoid redundant computations. ```python def handle_token_overflow(self, input_sequence): if len(input_sequence) > self.max_tokens: self.logger.info(f"Token overflow detected: {len(input_sequence)} tokens") segmented_inputs = self.segment_input(input_sequence, self.max_tokens, self.overlap) processed_segments = [] for segment in segmented_inputs: if segment in self.cache: self.logger.info(f"Cache hit for segment: {segment[:10]}...") processed_segments.append(self.cache[segment]) else: self.logger.info(f"Processing new segment: {segment[:10]}...") processed_segment = self.process_segment(segment) processed_segments.append(processed_segment) self.cache_result(segment, processed_segment) return processed_segments else: