Additional Optimizations

- Instead of repeatedly replacing tokens in the original string, we build a new list of tokens (`rewritten_tokens`) with the replacements. - This avoids the overhead of repeated string manipulations. 2. **Set for Quick Lookups**: …

dictionary = {"example": "sample"} rewritten_query, latency = rewrite_query(query, dictionary) print(f"Rewritten Query: {rewritten_query}, Latency: {latency:.4f} seconds") ``` ### Explanation 1. **Token Replacement**: - Instead of repe…

- The `CACHE_REDIS_URL` is set to connect to a local Redis server. 2. **Caching Decorator**: - The `@cache.cached(timeout=60)` decorator caches the result of the `expensive_operation_endpoint` for 1 minute. ### Additional Optimizati…

5. **Connection Pooling**: Use connection pooling to manage database connections more efficiently. 6. **Compression**: Compress data before sending it over the network to reduce transfer time. ### Example Code with Caching Your provided c…

["term1", "term2", "term3"], ["term2", "term3", "term4"], ["term1", "term2", "term3", "term4"] ] # Calculate the term frequencies term_frequencies = calculate_term_frequencies(documents) print(term_frequencies) ``` ### Explana…

def calculate_term_frequencies(documents): # Flatten the list of documents into a single list of terms all_terms = [term for document in documents for term in document] # Use Counter to count the frequency of each term …

# Test the batch inference function texts = ["This is a sample text"] * 5000 # Create a list of 5000 texts start_time = time.time() outputs = perform_batch_inference(texts) end_time = time.time() print(f"Inference time: {end_time - start_t…