Distances

# Define a function to generate answers def generate_answer(question): # Tokenize the question inputs = tokenizer(question, return_tensors="pt") # Generate the answer outputs = model.generate(**inputs) # Decode the ans…

### Using Quantization for Efficiency Quantization can further reduce the memory footprint and speed up the search process. FAISS supports various quantization techniques, such as PQ (Product Quantization). Here's an example using PQ: ``…

Here's an example implementation: ```python import pandas as pd import numpy as np # Generate sample data for 50 tasks np.random.seed(0) # For reproducibility task_ids = [f'Task {i+1}' for i in range(50)] sprint_durations = np.random.cho…

{'name': 'Challenge 2', 'complexity': 0.4, 'impact': 0.6}, {'name': 'Challenge 3', 'complexity': 0.8, 'impact': 0.9}, {'name': 'Challenge 4', 'complexity': 0.5, 'impact': 0.7} ] challenge_matrix = ChallengeMatrix(challenges) ch…

index = faiss.IndexIVFPQ(quantizer, 128, nlist, m, nbits) # Train the index index.train(vectors) # Add vectors to the index index.add(vectors) # Set the number of probes index.nprobe = nprobe # Search for the nearest neighbors D, I = in…

- Test different configurations of `nlist`, `nprobe`, and the number of threads to find the optimal settings for your use case. ### Example Code Here's an example of how you can use `IndexIVFFlat` with multi-threading and precompute table…

Returns: tuple: Tuple containing distances and indices of the nearest neighbors. """ return self.index.search(query_embedding, k) # Example usage if __name__ == "__main__": # Create instances of the modu…

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')…

- **Distributed Indexing**: Use distributed indexing techniques to distribute the workload across multiple machines. - **Profiling**: Use profiling tools to measure the performance and identify bottlenecks. ### Alternative: Using `IndexHNS…

key = os.urandom(32) # 256-bit key iv = os.urandom(16) # 128-bit IV # Encrypt the data encrypted_data, key, iv = encrypt_data(data, key, iv) print(f"Encrypted data: {encrypted_data.hex()}") # Decrypt the data original_data = decrypt_dat…

- **Alternative Approaches**: Depending on your use case, you might consider using models that can handle variable-length sequences natively, such as transformers with attention mechanisms. By following these steps, you can effectively han…

"Content-Type": "application/json", "Authorization": f"Basic {JIRA_API_KEY}", } def create_task(summary, description, priority): url = f"{JIRA_URL}/rest/api/3/issue" payload = { "fields": { "project": {"…

import torch import torch.nn as nn class ScoringModel(nn.Module): def __init__(self): super(ScoringModel, self).__init__() self.model = torch.nn.Linear(10, 1) def forward(self, input_data): scores = self.mo…

input_data = torch.randn(100, 10).to(device) # Move input data to the same device as the model try: with torch.no_grad(): # Disable gradient calculation scores = model(input_data) print(scores) except Exception as e: p…

2. **Tokenization**: Tokenization can also be a bottleneck. Ensure you are using efficient tokenization settings. 3. **Batch Processing**: If possible, process queries in batches to reduce overhead. ### Example Optimization If the `model.…

logging.error(f'Error in PostProcessor for text "{text}": {e}') return text # Define the evaluation function def evaluate_reformulation(stages, inputs, outputs): # Apply the reformulation stages to the inputs …