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upload_to_azure(azure_blob_service_client, azure_container_name, document_path) upload_times.append(time.time() - start_time) start_time = time.time() download_from_azure(azure_blob_service_c…

results = await asyncio.gather(*tasks) end_time = time.time() for result in results: response_time = end_time - start_time response_times.append(response_time) average_response_time = sum(response_times) / …

REQUEST_TIME = Histogram('request_processing_seconds', 'Time spent processing request') def handle_request(user_id): with REQUEST_TIME.time(): # Simulate some processing time time.sleep(random.uniform(0.0…

```python import logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') class IngestionTask: def __init__(self, task_name: str, documents: List[str]): self.task_name = task_name …

# Define a function to compare the two datasets def compare_cleaning(openrefine, manual): # Calculate the number of matching entries matches = 0 for index, row in openrefine.iterrows(): if row.equals(manual.loc[index]): …

Here is the code again for your reference: ```python import numpy as np from sentence_transformers import SentenceTransformer from concurrent.futures import ThreadPoolExecutor, as_completed # Load the model once model = SentenceTransforme…

combined_inputs = torch.cat([inputs, combined_user_behavior], dim=1) # Split data into training and validation sets train_size = int(0.8 * len(combined_inputs)) val_size = len(combined_inputs) - train_size train_combined_inputs, val_combi…

for thread in threads: thread.join() return results queries = ["query_" + str(i) for i in range(100)] results = process_queries_parallel(queries) ``` #### Example with Asyncio: ```python import asyncio async def process_…

vectorizer = TfidfVectorizer() tfidf_matrix = vectorizer.fit_transform(documents) query_vector = vectorizer.transform([query]) similarity_scores = (query_vector * tfidf_matrix.T).toarray() return similarity_scores def h…

# Simulate cache lookups start_time = time.time() latencies = [] for _ in range(14000): start_query_time = time.time() result = search_query("example") end_query_time = time.time() latencies.append(end_query_time - start_que…

handler.setFormatter(formatter) self.logger.addHandler(handler) def segment(self, input_text): # Tokenize input text inputs = self.tokenizer(input_text, return_tensors='pt', truncation=True, max_length=s…

```python import logging # Set up logging logging.basicConfig(filename='algorithm_errors.log', level=logging.ERROR) def resize_algorithm(query): try: # Calculate complexity complexity = calculate_complexity(query) …

from concurrent.futures import ThreadPoolExecutor from typing import List # Set up logging logging.basicConfig(filename='context_window_architecture.log', level=logging.INFO) class ComplexityCalculator: def calculate_complexity(self, …

def calculate_complexity(query): # Placeholder for complexity calculation logic # This could involve NLP techniques such as dependency parsing, named entity recognition, etc. # For demonstration purposes, let's assume a simple c…

# Define corresponding latency values latency_values = [0, 50, 100, 150, 200, 380] # Resize the context windows based on refined thresholds def resize_context_window(complexity, thresholds, latencies): for i, threshold in enumerate(thr…

results.extend(batch_results.cpu().numpy()) return results # Parallel processing def parallel_infer(texts, num_workers=4): with ThreadPoolExecutor(max_workers=num_workers) as executor: results = list(executor.map(in…

# Example processing (replace with actual model training code) inputs_tensor = torch.tensor(inputs, dtype=torch.float32) labels_tensor = torch.tensor(labels, dtype=torch.long) outputs = model(inputs_tensor) …

optimizer = optim.Adam(model.parameters(), lr=0.001) # Using Adam optimizer scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=5, verbose=True) scaler = GradScaler() try: for epoch in range(100): running…

4. **Profiling**: Identify bottlenecks using profiling tools. ### Updated Code with Parallel Processing and Batch Handling Here's an updated version of your code that incorporates parallel processing and batch handling: ```python import …

model = ReformulationModel() def process_queries(queries, batch_size=100, max_workers=10): with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = [executor.submit(model.batch_reformulate, queries[i:i+batch_size…