overlap

- **Logging**: Added logging to capture token overflow issues and provide insights into the segmentation process. - **Error Handling**: Consider adding error handling to manage cases where the input sequence cannot be segmented properly. - …

1. **Prepare Test Data**: - Create a diverse set of input sequences that represent typical use cases for your RAG system. - Include both short and long sequences to cover different scenarios. 2. **Define Evaluation Metrics**: - **…

def process_segment_with_llm(segment): # Placeholder function to simulate LLM processing return f"Processed {segment}" # Example usage if __name__ == "__main__": max_tokens = 100 # Example max token limit overlap = 20 # E…

self.max_tokens = max_tokens self.overlap = overlap self.logger = logging.getLogger(__name__) self.logger.setLevel(logging.INFO) handler = logging.StreamHandler() formatter = logging.Formatter…

handler = logging.StreamHandler() formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') handler.setFormatter(formatter) self.logger.addHandler(handler) def segment_input(s…

pass else: # process input sequence pass ``` ### Potential Issues and Solutions 1. **Incorrect `max_tokens` Value**: - Ensure that `max_tokens` is correctly set and that the input sequence length is being co…

- Initializes `max_tokens` and `overlap`. - Sets up a logger to log information about token overflow handling. 2. **Segmenting Input**: - `segment_input` method splits the input sequence into smaller chunks with the specified over…

- Slicing lists in Python can be costly, especially for large lists. We can minimize the number of slices by directly appending the appropriate segments. 2. **Use Efficient Data Structures**: - Ensure that the data structures used ar…

formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') handler.setFormatter(formatter) self.logger.addHandler(handler) def segment_input(self, input_sequence): """ …

self.logger.debug(f"Input sequence length: {len(input_sequence)}, max tokens: {self.max_tokens}") if len(input_sequence) > self.max_tokens: self.logger.error("Token overflow detected") segmented_input…

### Explanation 1. **Initialization**: - Initialize `max_tokens`, `cache_size`, and `overlap`. - Use `OrderedDict` for caching to maintain LRU behavior. - Set up logging to track important events. 2. **Segmentation**: - The `s…

segments = [] start_index = 0 while start_index < len(input_sequence): end_index = min(start_index + max_tokens, len(input_sequence)) segment = input_sequence[start_index:end_index] segments.append(segmen…

[Turn 7897] Assistant: Certainly! To achieve efficient and scalable context window segmentation for handling 2,000-token inputs in LLMs, while processing 1,500 queries/sec with 99.8% uptime, you need to carefully structure your modular segm…

# 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 redund…

[Turn 7923] Assistant: Certainly! To achieve a 20% relevance boost with segmented inputs for 5,000 test queries, you need to ensure that your segmentation strategy is both efficient and effective. The sliding window approach you're using is…

4. **Segmentation**: - Implement segmentation to handle large inputs by breaking them into smaller chunks. - Use overlap between segments to maintain context continuity. 5. **Caching**: - Use caching to store and reuse results of …

[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [17, 18, 19, 20]] ``` ### Additional Considerations 1. **Tokenization**: - If your input data is text, ensure that you tokenize it appropriately before segmenti…

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score def evaluate(y_true, y_pred): acc = accuracy_score(y_true, y_pred) prec = precision_score(y_true, y_pred, average='weighted') …

def add_token(self, token): self.tokens.append(token) self.token_count += 1 def get_context(self): if self.token_count in self.cache: return self.cache[self.token_count] context = list(s…

3. **Exception Handling**: Implement proper exception handling to gracefully manage cases where the token limit is exceeded. ### Improved Code Here's an improved version of your code that incorporates these suggestions: ```python def seg…

class TokenLimitExceededError(Exception): pass # Example usage try: context = " ".join([f"token_{i}" for i in range(2000)]) segmented_context = segment_context(context) for segment in segmented_context: print(segmen…