Endpoints

#### Step 7: Search and Retrieve ```python query = "Query in a rare language" query_language = detect_language(query) if query_language == 'rare_language': query_embedding = language_specific_model.encode(query, convert_to_tensor=True…

while True: response = requests.get(url, headers=headers) if response.status_code == 200: return response.json() elif response.status_code == 429: # Rate limit exceeded reset_time = int(r…

elif response.status_code == 429: # Rate limit exceeded delay = base_delay * (2 ** attempt) + random.uniform(0, 1) print(f"Rate limit exceeded. Retrying in {delay:.2f} seconds...") time.sleep(del…

value = aws_spot_instance_request.example.instance_id } output "public_ip" { value = aws_spot_instance_request.example.public_ip } ``` ### Step 4: Automate the Process Create a script to periodically fetch the current spot prices and…

print(f"Average response time: {average_response_time:.2f}ms") print(f"Median response time: {median_response_time:.2f}ms") print(f"90th percentile response time: {p90_response_time:.2f}ms") # Check if 90% of queries meet the 200ms target …

def require_jwt(view_func): @wraps(view_func) def decorated_function(*args, **kwargs): token = request.headers.get('Authorization') if not token or not validate_jwt_token(token.split(' ')[1]): return json…

# Define the API parameters params = { "model": "xlarge", # Specify the model you want to use "prompt": "Hello, world!", # The input prompt "max_tokens": 100 # Maximum number of tokens to generate } # Set the API key api_key…

- Ensure redundancy in your infrastructure to handle failures and maintain high availability. ### Example Calculation Let's calculate the required number of servers and then discuss how to implement a load balancer. ```python import n…

for i in range(5000): response = generate_response(f"Query {i}") print(f"Response to Query {i}: {response}") end_time = time.time() print(f"Total time taken: {end_time - start_time} seconds") # Test with repeated queries start_time…

raise ValueError("Invalid key size. Key must be 32 bytes long for AES-256.") # Generate a random 128-bit IV iv = os.urandom(16) # Create a new AES-CBC cipher object cipher = Cipher(algorithms.AES(key), modes.CBC(iv…

headerManager.add(new Header("Content-Type", "application/json")); httpSampler.setHeaderManager(headerManager); // Add the HTTP Sampler to the thread group threadGroup.addTestElement(httpSampler); /…

print(f'Uptime of instance {vm_resource_id} has fallen below 99.95%: {uptime}%') # Send alert (e.g., via email, SMS, etc.) time.sleep(60) # Poll every 60 seconds # Example usage: vm_resource_ids…

} } stage('Clean Up') { steps { cleanWs() } } } post { always { cleanWs() } success { echo 'Pipeline compl…

# Display the updated role definitions print("\nUpdated Role Definitions:") print(role_definitions_df) ``` ### Explanation 1. **Class Definition:** - The `RoleDefinition` class remains the same, but now it includes a `to_dict` method t…

authenticated = authenticate_user(username, password) end_time = time.time() latency = end_time - start_time print(f"Authentication latency: {latency * 1000:.2f}ms") return authenticated # Test the login function userna…

# Check if the result is already cached cache_key = f"auth:{username}:{password}" cached_result = redis_client.get(cache_key) if cached_result: authenticated = bool(int(cached_result)) end_time = time.ti…

print(f"Metadata extraction complete in {total_time:.2f} seconds.") print(f"Average latency: {avg_latency:.2f} ms") if __name__ == "__main__": main() ``` ### Explanation 1. **ThreadPoolExecutor**: The `concurrent.futures.Thre…

# Print the most common date formats print(format_counts.head(10)) # Optionally, save the analyzed dataset to a new CSV file df.to_csv('analyzed_metadata.csv', index=False) ``` ### Explanation 1. **Loading the Dataset**: The script reads…

use_gpu = False # Set to True if you want to use GPU acceleration index = initialize_faiss_index(dim, use_gpu) # Generate random document embeddings and a query embedding document_embeddings = np.random.rand(200000, dim).astype('float32')…

quantizer = faiss.IndexFlatL2(embedding_dim) index = faiss.IndexIVFFlat(quantizer, embedding_dim, nlist) # Train the index index.train(document_embeddings) # Add the document embeddings to the index index.add(document_embeddings) # Gener…

return self.cache[key] result = self.index[key] self.cache[key] = result return result def batch_query(self, keys): results = [] with ThreadPoolExecutor(max_workers=10) as executor: …

# Example endpoint @app.get("/api/v1/sensitive-data") def get_sensitive_data(user_role: str = Depends(restrict_access)): return {"message": "Sensitive data"} @app.get("/api/v1/sensitive-settings") def get_sensitive_settings(user_role: …

my_counter = Counter('my_metric', 'My metric') # Increment the metric my_counter.inc() # Start the HTTP server to expose metrics start_http_server(port=8000) # Run indefinitely to keep the server alive while True: pass ``` ### Expla…

SENSITIVE_SCORE_ACCESS_ROLE = KeycloakRole('sensitive-score-access') # Decorator to check for specific role def require_role(role): def decorator(f): def wrapper(*args, **kwargs): if not keycloak.has_role(role): …

transition_id = transition['id'] break if transition_id: jira.transition_issue(task, transition_id) print(f"Task {task_key} has been updated to {desired_status}.") else: print(f"No transition found for status {d…

# Calculate the weighted sum of the queries weighted_sum = np.sum([weight * query for weight, query in zip(weights, queries)], axis=0) return weighted_sum def loss_function(weights, queries, true_values): # Calculate the we…

logging.debug(f"Ranked data: {ranked_data}") return ranked_data except ValueError as e: logging.error(f"Error ranking data: {e}") return None # Example usage: query = "example query" data = retrieve_data…

normalized_l1 = l1_normalize(embeddings) print("\nL1 Normalized Embeddings:") print(normalized_l1) # Max Normalization normalized_max = max_normalize(embeddings) print("\nMax Normalized Embeddings:") print(normalized_max) # Clipping clipp…

prediction = rank_documents(query, sparse_scores_i, dense_scores_i) if prediction is not None: predictions.append(prediction) # Evaluate precision true_labels = np.random.randint(0, 2, size=(num_queries, num_documents)) # …

precision = precision_score(true_labels.ravel(), predicted_labels.ravel()) print(f"Precision: {precision:.2f}") ``` ### Explanation 1. **Hybrid Search Function:** - Combines sparse and dense scores using adaptive weights. - Handles …

# Generate a summary report report = { 'timestamp': datetime.now().isoformat(), 'compliance_status': compliance_status, 'summary': 'Compliant' if all(compliance_status.values()) else 'Non-compliant' } …

if __name__ == '__main__': app.run(debug=True) ``` ### Explanation 1. **Setup Flask and Flask-Caching**: - Import necessary modules and initialize Flask and Flask-Caching. - Configure caching to use Redis. 2. **Define the API E…

return {"status": "OK"} # Middleware to handle CORS app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) ``` ### Step 6: Run the Application …

``` ### Explanation 1. **Dependency Injection**: Use dependency injection to pass the Redis client to the route handler. 2. **Error Handling**: Raise `HTTPException` for cache misses. 3. **Background Tasks**: Added a background task to si…

# Fetch the encryption key from Vault key = get_encryption_key(vault_client) # Encrypt some data data = "Hello, World!" encrypted_data = encrypt_data(data, key) print(f"Encrypted Data: {encrypted_data}") # Decrypt the data decrypted_dat…

# Calculate the hash of the data hash_value = hashlib.md5(data.encode()).hexdigest() # Convert the hash to an integer hash_int = int(hash_value, 16) # Determine which node to use based on the hash node_index = hash_i…

3. **Monitoring**: Monitor the load on each node to ensure that the distribution is even and adjust the strategy if necessary. ### Alternative: Using Redis Cluster If you want a more robust solution, consider using a Redis cluster. Redis …

tokens = practice(tokens) return tokens # Define the sparse tuning practices sparse_tuning_practices = [ lambda x: x * 2, # practice 1: multiply by 2 lambda x: x + 1, # practice 2: add 1 lambda x: x - 1, # p…

{'id': 1, 'text': 'This is a relevant result'}, {'id': 2, 'text': 'This is another relevant result'}, {'id': 3, 'text': 'This is an irrelevant result'} ] query = 'Find relevant results' ranked_results = rerank_search_results(s…

print(f"Current skill level: {current_skill_level:.2f}. Target: {target_skill_level:.2f}") # Example usage review_and_apply_strategies(context_window) # Assume initial skill level and target skill level initial_skill_level = 0.8 t…

best_strategy = max(performance_data, key=lambda k: np.mean(performance_data[k])) print(f"The best strategy is {best_strategy} with performance: Mean={np.mean(performance_data[best_strategy]):.2f}") # Example usage initial_skill_le…

model = torch.nn.Linear(10, 1) # Example model version_manager = ModelVersionManager(model, "1.2.3") try: new_model_state = model.state_dict() # Simulate new model state version_manager.update_model("1.2.4", new_model_state) exce…

num_queries = 1000 num_items = 10 # Generate random predictions and labels predictions = np.random.rand(num_queries, num_items) labels = np.random.randint(0, 2, size=(num_queries, num_items)) # Calculate metrics for each query ndcg_values…

X_train, X_test, y_train, y_test = train_test_split(X_sparse, y, test_size=0.2, random_state=42) # Preprocess data scaler = StandardScaler(with_mean=False) # Use with_mean=False for sparse matrices X_train_scaled = scaler.…

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 …

test_terms = ["term1", "term2", "term3"] * 500 # Thresholds to test thresholds = [0.8, .85, .9, .95] # Number of trials to average over num_trials = 10 # Dictionary to store precision results precision_results = {} for threshold in thre…

data = "Sample data for security check" if check_security(data): print("Security check passed") # Encrypt and decrypt data encrypted_data = encrypt_data(data, key, iv) print(f"Encrypted data: {encrypted_data}") decrypted_data = decryp…

for token_in_dict in dictionary: distance = levenshtein_distance(token, token_in_dict) if distance < min_distance: min_distance = distance closest_token = token_in_dict return closest_token #…

worker_counts = [5, 10, 20] for batch_size in batch_sizes: for worker_count in worker_counts: start_time = time.time() reformulated_queries = handle_queries(test_queries[:batch_size], max_workers=worker_count) e…

"distilbert-base-uncased" ] # Experiment with different models best_accuracy = 0 best_model = None for model_name in models_to_test: accuracy = train_and_evaluate_model(model_name, train_df, test_df) if accuracy > best_accuracy…

es = Elasticsearch() # Prepare bulk indexing actions actions = [ { "_index": "my_index", "_source": record } for record in records ] …