[:, 0, :]

```python def retrieve(queries): # Tokenize the queries inputs = tokenizer(queries, padding=True, truncation=True, return_tensors="pt") # Perform retrieval using the LLM outputs = model(**inputs…

Here's an optimized version of your code using `IndexIVFFlat` and enabling multi-threading: ```python import faiss import numpy as np # Assume we have a dataset of 100,000 vectors vectors = np.random.rand(100000, 128).astype('float32') #…

# Validate input dimensions if sparse_scores.shape != dense_scores.shape: raise ValueError("Mismatched dimensions between sparse and dense scores") # Normalize scores to ensure they are on the same scale…

top_k_indices = np.argsort(combined_scores, axis=0)[-10:] return top_k_indices # Example usage query = np.random.rand(128) top_k_indices = hybrid_query(query) print(f"Top K Indices: {top_k_indices}") ``` ### Summary By refin…

- Compare these outputs to the expected results to assess relevance and accuracy. By following these steps and using the provided example code, you can systematically test the effectiveness of your segmented input approach and ensure th…

self.logger = logging.getLogger(__name__) self.logger.setLevel(logging.INFO) handler = logging.StreamHandler() formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') han…

:return: List of processed segments. """ if len(input_sequence) > self.max_tokens: self.logger.info(f"Token overflow detected: {len(input_sequence)} tokens") segmented_inputs = self.segment_in…

# Define the resizing module class ResizingModule(nn.Module): def __init__(self): super(ResizingModule, self).__init__() self.fc1 = nn.Linear(512, 128) self.fc2 = nn.Linear(128, 128) def forward(self, x): …

In your application, you can use Keycloak's `KeycloakOpenID` client to authenticate users and check their roles. Then, filter the data accordingly. ```python from flask import Flask, jsonify, request from keycloak import Keyclo…

reformulated_query = suggestions[0] else: reformulated_query = query else: reformulated_query = query end_time = time.time() return reformulated_query, end_time - start_time # Define a fu…

- Use profiling tools like `cProfile` to identify bottlenecks in your code. - Benchmark different approaches to see which performs best for your specific use case. ### Example with Parallel Processing Here's an example using `concurre…