RAG system

return outputs.last_hidden_state[:, 0, :] # Test the retrieval function with context query = "How do I implement new features in our RAG system?" context = "Previous query was about system architecture." results = retrieve_with_context…

mismatch_indices = np.where(mismatches > threshold)[0] # Log detailed information for each significant mismatch for idx in mismatch_indices: logging.warning( json.dumps({ 'query_id': quer…

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…

- Initializes `max_tokens` and `overlap`. - Sets up a logger to log information about token overflow handling. 2. **Segmenting Input**: - `segment_input` method splits the input sequence into smaller chunks with the specified over…

By implementing these optimizations, you should be able to reduce the processing time and improve the performance of your spelling correction module. [Turn 10240] User: I'm working on a project to improve the search accuracy of our RAG sys…

dataset = pd.read_csv('queries_dataset.csv') # Split the dataset into training and testing sets train_data, test_data = train_test_split(dataset, test_size=0.2) # Train the RAG system (if needed) # ... # Evaluate the system on the test d…