Record start time

def evaluate_latency(self, num_messages): if self.library == 'kafka': start_time = time.time() for _ in range(num_messages): self.producer.send('test-topic', b'test-message') s…

for i in range(5000): start_time = time.time() response = make_api_call(f"Query {i}") end_time = time.time() print(f"Response time: {end_time - start_time} seconds") ``` Can someone help me identify the bottlenecks in my cod…

time.sleep(0.1) return [1.0, 2.0, 3.0] def process_documents(documents): vectors = [] for document in documents: vector = vectorize_document(document) vectors.append(vector) return vectors # Generate so…

- Return the response as a JSON object. ### HTTP Caching Headers You can also use HTTP caching headers to instruct clients and proxies to cache responses. Here's an example of how to set cache control headers: ```python from fastapi i…

inputs = tokenizer(query, return_tensors="pt") # Get the reformulated query start_time = time.time() outputs = model.generate(**inputs) end_time = time.time() # Return the reformulated query return toke…

segments = ["This is an example segment."] * 800 # Simulate 800 segments start_time = time.time() processed_segments = process_segment_batches(segments) end_time = time.time() print(f"Processed 800 segments in {end_time - start_time} sec…