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ami_id = 'ami-0c94855ba95c71c99' instance_type = 't3.medium' # Create a launch configuration launch_config_name = 'my-lc' response = asg.create_launch_configuration( LaunchConfigurationName=launch_config_name, ImageId=ami_id, I…

from sklearn.metrics import classification_report # Sample data for training documents = [ {'title': 'A Great Book', 'author': 'John Smith'}, {'title': 'Another Interesting Read', 'author': 'Jane Doe'}, # ... more documents ...…

[Turn 4928] User: I'm aiming to scale my clusters to handle 5,000 queries per hour with under 180ms response time. To achieve this, I'm planning to optimize my database queries and implement efficient indexing. Here's an example of my curre…

- Instead of repeatedly replacing tokens in the original string, we build a new list of tokens (`rewritten_tokens`) with the replacements. - This avoids the overhead of repeated string manipulations. 2. **Set for Quick Lookups**: …

1. **Avoid Repeated String Replacement**: Replacing tokens in the string repeatedly can be inefficient. Instead, build a new string with the replacements. 2. **Use Efficient Data Structures**: Use a set for quick lookups if the dictionary i…

- If the dataset is large, consider using parallel processing techniques to distribute the workload across multiple cores or processes. ### Example with Batch Processing If you are processing multiple queries, you can batch them togeth…

expanded_query = ' '.join(expanded_query_parts) end_time = time.time() latency = end_time - start_time print(f"Expanded Query: {expanded_query}, Latency: {latency:.4f} seconds") return expanded_query # Test th…

replaced_terms.append(oov_replacements[term]) # Join the replaced terms back into a single string replaced_query = " ".join(replaced_terms) return replaced_query # Test the function query = "What are the b…

disambiguated_terms.append(closest_match) else: disambiguated_terms.append(term) # Join the disambiguated terms back into a single string disambiguated_query = " ".join(disambiguated…

# Average the embeddings of the term tokens if term_start is not None and term_end is not None: term_embedding = last_hidden_state[:, term_start:term_end, :].mean(dim=1) else: term_embedding = torch.zeros((1…

1. **Configure Structured Logging**: - Use `structlog` to configure structured logging with JSON rendering. - Set up the logger to handle debug-level messages. 2. **Asynchronous Logging**: - Use `QueueHandler` and `QueueListener` …

- Use a queue to buffer log entries. 4. **Example Usage**: - Simulate logging 28,000 queries with simulated execution times. - Use `time.sleep` to simulate some delay between log entries. 5. **Graceful Shutdown**: - Signal the…

1. **Set Up the Environment**: - Ensure you have all necessary dependencies installed, such as `concurrent.futures` for threading and `logging` for detailed logging. 2. **Code Implementation**: - Copy and paste the provided code into…

corrected_query = ' '.join(words) # log the result logging.info(f'Successfully corrected query: {query} -> {corrected_query}') self.success_count += 1 except Exception as …

closest_word = find_closest_match(word, dictionary) if closest_word: corrected_words.append(closest_word) else: corrected_words.append(word) # Fallback to original word …

# Tokenize input text tokens = input_text.split() # Apply correction rules corrected_tokens = [correct_token(token) for token in tokens] return ' '.join(corrected_tokens) def correct_token(token): # Define correctio…

for i in range(1, len1 + 1): for j in range(1, len2 + 1): if token1[i - 1] == token2[j - 1]: dp[i][j] = dp[i - 1][j - 1] else: dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1]…

To provide latency statistics, you can use a profiling tool or logging mechanism to measure the time taken for each operation. Here's an example using Python's `time` module: ```python import time start_time = time.time() corrected_text =…

nltk.download('words') word_list = set(words.words()) # Define a function to correct a query using NLTK def correct_query_nltk(query): # Split the query into words words = query.split() # Correct each word corrected_wo…