np

return response_times # Run the benchmarking test response_times = benchmark_search_queries(num_queries) # Convert to numpy array for easier statistical analysis response_times_np = np.array(response_times) # Calculate statistics ave…

"dimension": dimension, "index_file_size": 1024, # Size of each segment file in MB "metric_type": METRIC_TYPE } milvus.create_collection(param) # Create an index def create_index(name, index_type, nlist): …

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…

ground_truth = generate_ground_truth(num_queries, num_relevant) with Timer() as timer: results = engine.search(test_data) total_duration += timer.duration total_throughput += num_queries…

3. **Search Accuracy**: Achieving a specific search accuracy like 94% depends on the quality of the vectors and the similarity search algorithm used by Weaviate. ### Approach 1. **Encrypt Vectors Before Storing**: Encrypt the vectors befo…

Let's assume you have two retrieval engines, `engine1` and `engine2`, and you want to dynamically adjust their weights based on their performance metrics. #### Step 1: Collect Performance Metrics You can collect performance metrics by com…

[Turn 4216] User: I've shared a comparison chart with the team, showing that streaming can reduce latency by 120ms for 80% of 20K documents. However, I'm concerned about the impact of streaming on our system's resource utilization. Can you …

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…

index = faiss.IndexFlatL2(embedding_dim) # Add the document embeddings to the index index.add(document_embeddings) # Generate a random query embedding query_embedding = np.random.rand(1, embedding_dim).astype('float32') # Search the inde…

def add_vector(self, vector): if self.num_vectors == self.capacity: self._resize() self.vectors[self.num_vectors] = vector self.num_vectors += 1 def get_vectors(self): return self.vectors…

def _resize(self): new_capacity = int(1.5 * self.capacity) # Increase capacity by 50% new_vectors = lil_matrix((new_capacity, self.vector_size), dtype=np.float32) new_vectors[:self.capacity] = self.vectors …

# Simulate the log ingestion process time.sleep(0.1) logging.info(message) # Define the benchmarking function def benchmark_ingestion(): # Define the number of events num_events = 5000 # Define the target ingestion…

index_data = np.array([1, 2, 3]) # Replace with actual indexing logic index.append(index_data) except IndexError as e: print(f"Error processing document '{document}': {e}") co…

alphas = np.linspace(0, 1, 11) # Range of alpha values to test best_alpha, best_map = {}, {} for query in queries: best_alpha[query], best_map[query] = tune_alpha(query, documents, relevant_docs[query], alphas) print(f"Best alpha f…

client = redis.Redis(host='localhost', port=6379, db=0) # Create a FAISS index d = 128 # dimension index = faiss.IndexFlatL2(d) # Add vectors to the index vectors = np.random.rand(10000, d).astype('float32') index.add(vectors) # Define …

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)) # …

return vectors # Define the FAISS index dimension = 128 index = faiss.IndexFlatL2(dimension) # Example vectors with missing data vectors = np.random.rand(5000, dimension) vectors[np.random.rand(*vectors.shape) < 0.1] = np.nan # Intro…

model = LinearRegression() model.fit(observed_vectors[:, :-1], observed_vectors[:, -1]) # Predict missing values predicted_values = model.predict(missing_vectors[:, :-1]) vectors[missing_mask] = predicted_values …

training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_steps=500, weight_decay=0.01, logging_dir='./logs', logging…

logging_steps=10, evaluation_strategy='epoch', save_total_limit=2, ) # Define the trainer trainer = Trainer( model=model, args=training_args, train_dataset=dataset['train'], eval_dataset=dataset['test'], dat…

optimizer.step() # Zero gradients optimizer.zero_grad() # Validation loop scorer.eval() val_losses = [] with torch.no_grad(): for batch_inputs, batch_targets in val_loader: outpu…

mean_latency = np.mean(latencies) median_latency = np.median(latencies) max_latency = np.max(latencies) min_latency = np.min(latencies) std_dev_latency = np.std(latencies) # Count latency spikes latency_spik…

queries = ["short", "medium length query", "very long and complex query"] # Measure complexities measured_complexities = np.array([measure_complexity(query) for query in queries]) # Resize the context windows with enhanced logic enhanced_…

"strategy3": "Description of strategy 3", "strategy4": "Description of strategy 4", "strategy5": "Description of strategy 5" } # Define the skill boost target skill_boost_target = 0.2 # Function to review and apply strategies …

return model, precision_updated # Example data features = np.random.rand(10000, 10) # 10,000 queries with 10 features each labels = np.random.randint(0, 2, 10000) # Binary labels # User feedback data user_feedback = { 'features'…

from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, classification_report, confusion_matrix import logging # Set up logging configuration logg…

Here's how you can implement the calculation and visualization: ```python import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import ndcg_score, average_precision_score def calculate_metrics(predictions, labels, k_ndcg…

X_train, X_val = X[train_index], X[val_index] y_train, y_val = y[train_index], y[val_index] # Fit the model on the training data model.fit(X_train, y_train) # Predict on the validati…

[Turn 9584] User: I'm trying to improve the compliance rate of our secure tuning protocols, currently at 96%, but I'm not sure what optimizations to make, can you review my code and suggest improvements? ```python import numpy as np # Defi…

term_embedding = get_contextual_embeddings(term) closest_synonyms = [] for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_context…

logging_dir='./logs', logging_steps=10, evaluation_strategy="epoch", save_total_limit=2, ) # Define Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=test_…

# Define training arguments training_args = TrainingArguments( output_dir=f'./results/{model_name}', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_s…

test_encodings = tokenize_data(tokenizer, test_df['query']) # Create datasets train_dataset = QueryDataset(train_encodings, train_df['label'].tolist()) test_dataset = QueryDataset(test_encodings, test_df['label'].tolist()) …