executor

# Example: Add costs based on query parameters cost += query['param1'] * 100 cost += query['param2'] * 50 return cost def process_query(monitor, query): monitor.monitor_cost(query) def main(): monitor = CostMonitor…

with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor: futures = [executor.submit(process_query, monitor, query) for query in queries] concurrent.futures.wait(futures) print(f"Total Costs: {monitor.get_costs()}") `…

2. **Submit Tasks**: Submits tasks to the executor and stores the futures. 3. **Collect Results**: Collects results as they become available using `as_completed`. ### Performance Considerations: - **Thread Pool Size**: Adjust the `max_work…

result = client.query.get("Document", ["title", "content"]).with_near_vector(near_vector).with_limit(10).do() return result async def main(): num_queries = 5000 query_vectors = [np.random.rand(128) for _ in range(num_querie…

- Implement `PDFProcessor` and `DOCXProcessor` classes that inherit from `DocumentProcessor`. - Each processor handles a specific document format and performs the required processing. 3. **Modular Document Processor:** - `ModularD…

- The `ModularDocumentProcessor` class manages a dictionary of processors indexed by file extension. - It registers processors for different file extensions and processes documents based on their extension. - The `process_document`…

with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_threads) as executor: # Submit tasks to the executor futures = [executor.submit(self.process_document, document) for document in range(self.docu…

from concurrent.futures import ThreadPoolExecutor, as_completed from tika import parser from tenacity import retry, wait_exponential, stop_after_attempt @retry(wait=wait_exponential(multiplier=1, min=4, max=10), stop=stop_after_attempt(3))…

[Turn 4725] Assistant: Certainly! To scale your vectorization process to handle 3,500 documents per hour with under 200ms processing time, you can leverage parallel processing. One effective way to achieve this is by using Python's `concurr…

# Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Load the model once model = SentenceTransformer('paraphrase-MiniLM-L6-v2') def vectorize_document(doc, retries=3, delay=1): …

from concurrent.futures import ThreadPoolExecutor def index_document(doc_id): es.index(index='my_index', body={ 'title': f"Document {doc_id}", 'content': f"This is document {doc_id}." }) with ThreadPoolExecutor(max…

except OSError as e: logging.error(f"Failed to load SpaCy model: {e}") raise # Define a class to handle language tokenization class LanguageTokenizer: def __init__(self): self.nlp = nlp @lru_cache(maxsize=1000) …

batch_size = 100 # Adjust batch size as needed batches = [texts[i:i + batch_size] for i in range(0, len(texts), batch_size)] with ThreadPoolExecutor(max_workers=num_workers) as executor: futures = {executor.submit(…

def __init__(self, complexity_calculator: ComplexityCalculator, window_resizer: WindowResizer): self.complexity_calculator = complexity_calculator self.window_resizer = window_resizer self.uptime = 0.9985 de…

self.complexity_calculator = ComplexityCalculator() self.window_resizer = WindowResizer() self.query_handler = QueryHandler(self.complexity_calculator, self.window_resizer) self.executor = ThreadPoolExecutor(…

batch = text_chunks[i:i+batch_size] # Use ThreadPoolExecutor for parallel processing with ThreadPoolExecutor() as executor: futures = [executor.submit(process_text_chunk, llm, chunk) for chunk in batch] …

with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor: future = executor.submit(train_model, X, y) result = future.result() end_time = time.time() latency = end_time - start_time print(f'…

encrypted_data = cipher.encrypt(data) return encrypted_data def decrypt_data(encrypted_data, key): cipher = Fernet(key) decrypted_data = cipher.decrypt(encrypted_data) return decrypted_data def load_data(): # Place…

super(FeedbackModel, self).__init__() self.fc1 = nn.Linear(128, 128) self.fc2 = nn.Linear(128, 128) def forward(self, x): x = torch.relu(self.fc1(x)) x = self.fc2(x) return x def process…

super(ExistingModel, self).__init__() # Define your model layers here def forward(self, x): # Define your forward pass here return x def process_query(query_id, model, criterion, optimizer): start_t…

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…

def worker(data_loader): local_model = MyModel() local_optimizer = optim.Adam(local_model.parameters(), lr=0.001) update_model(local_model, local_optimizer, data_loader) return local_model.state_dict(), local_optimizer.state…

model = MyModel().to(device) optimizer = optim.Adam(model.parameters(), lr=0.001) # Define the update logic def update_model(model, optimizer, data_loader): model.train() for data, _ in data_loader: data = data.to(device) …

data = data.to(device) optimizer.zero_grad() outputs = model(data) loss = nn.MSELoss()(outputs, data) loss.backward() optimizer.step() # Generate synthetic data num_queries = 3500 batch_size …

optimizer = optim.Adam(model.parameters(), lr=0.001) # Define the update logic def update_model(model, optimizer, data_loader): model.train() for data, _ in data_loader: data = data.to(device) optimizer.zero_grad() …

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Initialize the model model = ScoringModel() pipeline = EvaluationPipeline(model, device='cuda' if torch.cuda.is_available() else …

future = executor.submit(evaluate_test, test_data) futures.append(future) # Wait for all futures to complete for future in concurrent.futures.as_completed(futures): try: …

futures.append(executor.submit(pipeline.evaluate, batch)) # Collect results results = [future.result() for future in futures] # Flatten the results scores = np.concatenate(results) print(scores) ```…

futures = {executor.submit(self.rewrite_query, query): query for query in queries} for future in as_completed(futures): rewritten_queries.append(future.result()) return rewritten_queries …

corrected_text = context_aware_correction(input_text) corrected_words.append(corrected_text) return ' '.join(corrected_words) ``` #### 5. Parallel Processing ```python from concurrent.futures import Th…

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…

futures = [executor.submit(reformulate_query, query) for query in queries] for future in as_completed(futures): results.append(future.result()) return results ``` #### 5. Batch Processing Process queries in…

reformulated_query = query end_time = time.time() return reformulated_query, end_time - start_time # Define a function to process queries in batches def process_queries_in_batches(queries, batch_size=100): results = [] …

futures = [executor.submit(reformulate_query, query) for query in queries] for future in as_completed(futures): results.append(future.result()) return results # Define a function to tokenize queries def toke…

3. **Memory Management**: If the model is large, managing memory efficiently can be crucial to avoid slowdowns. ### Optimization Strategies 1. **Batch Processing**: Instead of processing each segment individually, process them in batches …

futures = [executor.submit(model.process, segment) for segment in batch] for future in as_completed(futures): processed_segments.append(future.result()) # Combine the processed segments m…