results

from pydantic import BaseModel app = FastAPI() class QueryRequest(BaseModel): query: str class QueryResponse(BaseModel): results: list @app.post("/retrieve", response_model=QueryResponse) def retrieve(query_request: QueryRequest…

total_duration += timer.duration total_throughput += num_queries / timer.duration latencies.append(timer.duration) # Assuming results is a binary array indicating relevance precision = precision_scor…

[2026-02-24 17:49] omega [bot]: 🔧 1/1: tpmjsRegistrySearch ✅ Success **Args:** ```json { "query": "webp image analysis pattern recognition" } ``` **Result:** ```json { "success": true, "authenticated": true, "query": "webp image ana…

# Retrieve the serialized results from Redis serialized_results = redis_client.get(key) if serialized_results: # Deserialize the results results = pickle.loads(serialized_results) return results retur…

tokens = practice(tokens) return tokens # Define the sparse tuning practices sparse_tuning_practices = [ lambda x: x * 2, # practice 1: multiply by 2 lambda x: x + 1, # practice 2: add 1 lambda x: x - 1, # p…

return [123, 456, 789] # Example usage query = "best laptops for developers" results = [ {'id': 123, 'title': "Top Laptops for Developers", 'categories': ['technology']}, {'id': 456, 'title': "Best Laptops for Programming", 'ca…

[Turn 9318] User: I'm designing an API endpoint to retrieve evaluation results, and I want to ensure that it can handle a high volume of requests. I've specified a timeout of 2 seconds and a throughput of 650 req/sec, but I'm not sure if th…

Here's an example demonstrating how to use pipelining for both reading and writing operations: ### Example Setup Assume you have a Redis instance running locally on the default port (6379). You want to set multiple keys and then fetch the…