16c42787
 tangwang
feat: implement r...
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#!/usr/bin/env python3
"""
Demonstration of the Request Context and Logging system
This script demonstrates how the request-scoped context management
and structured logging work together to provide complete visibility
into search request processing.
"""
import time
import sys
import os
# Add the project root to Python path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
# Setup the environment (use the conda environment)
os.system('source /home/tw/miniconda3/etc/profile.d/conda.sh && conda activate searchengine')
def demo_request_context():
"""Demonstrate RequestContext functionality"""
print("๐ Starting Request Context and Logging Demo")
print("=" * 60)
try:
from utils.logger import get_logger, setup_logging
from context.request_context import create_request_context, RequestContextStage
# Setup logging
setup_logging(log_level="INFO", log_dir="demo_logs")
logger = get_logger("demo")
print("โ
Logging infrastructure initialized")
# Create a request context
context = create_request_context("demo123", "demo_user")
print(f"โ
Created request context: reqid={context.reqid}, uid={context.uid}")
# Simulate a complete search pipeline
with context: # Use context manager for automatic timing
logger.info("ๅผๅงๆจกๆๆ็ดข่ฏทๆฑๅค็", extra={'reqid': context.reqid, 'uid': context.uid})
# Stage 1: Query parsing
context.start_stage(RequestContextStage.QUERY_PARSING)
time.sleep(0.02) # Simulate work
# Store query analysis results
context.store_query_analysis(
original_query="็บข่ฒ้ซ่ท้ ๅ็:Nike",
normalized_query="็บข่ฒ ้ซ่ท้ ๅ็:Nike",
rewritten_query="็บข่ฒ ้ซ่ท้ ๅ็:nike",
detected_language="zh",
translations={"en": "red high heels brand:nike"},
domain="brand"
)
context.store_intermediate_result("query_vector_shape", (1024,))
context.end_stage(RequestContextStage.QUERY_PARSING)
# Stage 2: Boolean parsing
context.start_stage(RequestContextStage.BOOLEAN_PARSING)
time.sleep(0.005) # Simulate work
context.store_intermediate_result("boolean_ast", "AND(็บข่ฒ, ้ซ่ท้, BRAND:nike)")
context.end_stage(RequestContextStage.BOOLEAN_PARSING)
# Stage 3: Query building
context.start_stage(RequestContextStage.QUERY_BUILDING)
time.sleep(0.01) # Simulate work
es_query = {
"query": {"bool": {"must": [{"match": {"title": "็บข่ฒ ้ซ่ท้"}}]}},
"knn": {"field": "text_embedding", "query_vector": [0.1] * 1024}
}
context.store_intermediate_result("es_query", es_query)
context.end_stage(RequestContextStage.QUERY_BUILDING)
# Stage 4: Elasticsearch search
context.start_stage(RequestContextStage.ELASTICSEARCH_SEARCH)
time.sleep(0.05) # Simulate work
es_response = {
"hits": {"total": {"value": 42}, "max_score": 0.95, "hits": []},
"took": 15
}
context.store_intermediate_result("es_response", es_response)
context.end_stage(RequestContextStage.ELASTICSEARCH_SEARCH)
# Stage 5: Result processing
context.start_stage(RequestContextStage.RESULT_PROCESSING)
time.sleep(0.01) # Simulate work
context.store_intermediate_result("processed_hits", [
{"_id": "1", "_score": 0.95},
{"_id": "2", "_score": 0.87}
])
context.end_stage(RequestContextStage.RESULT_PROCESSING)
# Add a warning to demonstrate warning tracking
context.add_warning("ๆฅ่ฏข่ขซ้ๅ: '็บข่ฒ ้ซ่ท้ ๅ็:Nike' -> 'red high heels brand:nike'")
# Get and display summary
summary = context.get_summary()
print("\n๐ Request Summary:")
print("-" * 40)
print(f"Request ID: {summary['request_info']['reqid']}")
print(f"User ID: {summary['request_info']['uid']}")
print(f"Total Duration: {summary['performance']['total_duration_ms']:.2f}ms")
print("\nโฑ๏ธ Stage Breakdown:")
for stage, duration in summary['performance']['stage_timings_ms'].items():
percentage = summary['performance']['stage_percentages'].get(stage, 0)
print(f" {stage}: {duration:.2f}ms ({percentage}%)")
print("\n๐ Query Analysis:")
print(f" Original: '{summary['query_analysis']['original_query']}'")
print(f" Rewritten: '{summary['query_analysis']['rewritten_query']}'")
print(f" Language: {summary['query_analysis']['detected_language']}")
print(f" Domain: {summary['query_analysis']['domain']}")
print(f" Has Vector: {summary['query_analysis']['has_vector']}")
print("\n๐ Results:")
print(f" Total Hits: {summary['results']['total_hits']}")
print(f" ES Query Size: {summary['results']['es_query_size']} chars")
print("\nโ ๏ธ Warnings:")
print(f" Count: {summary['request_info']['warnings_count']}")
print("\nโ
Demo completed successfully!")
print(f"๐ Logs are available in: demo_logs/")
except Exception as e:
print(f"โ Demo failed: {e}")
import traceback
traceback.print_exc()
return False
return True
if __name__ == "__main__":
success = demo_request_context()
if success:
print("\n๐ Request Context and Logging system is ready for production!")
else:
print("\n๐ฅ Please check the errors above")
sys.exit(1)
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