🔍 [{timestamp}] NEW QUERY RECEIVED
💾 L2 CACHE (SQLite FTS5)
// Executing full-text search with BM25 ranking...
cache
.search_products("{query}", max_results=5)
✓ Found {len(results)} products in {response_time_ms:.2f}ms
📊 INTERACTION LOGGED
// Recording customer interaction for analytics...
{{
"timestamp": "{timestamp}",
"query": "{query}",
"results_count": {len(results)},
"response_time_ms": {response_time_ms:.2f}
}}
✓ Logged to analytics database
🧠 LEARNING INSIGHTS
// Updating search patterns for future optimization...
• Customer interest in: {results[0].category if results else 'N/A'}
• Popular query terms indexed
• Response quality will improve over time
"""
return activity
def chat(message: str, history: list) -> tuple:
"""Process customer query and return response with products and telemetry."""
import time
start_time = time.time()
# Search products
print(f"[DEBUG] Searching for: {message}")
results = cache.search_products(message, max_results=5)
print(f"[DEBUG] Found {len(results)} results")
if results:
print(f"[DEBUG] First result: {results[0].name}")
# Calculate response time
response_time_ms = (time.time() - start_time) * 1000
# Generate conversational response using LLM (falls back to mock if no API key)
print(f"\n{'='*60}")
print(f"[QUERY] {message}")
response, source_indicator = get_llm_response(message, results)
print(f"[RESPONSE] {response[:80]}...")
print(f"[SOURCE] {source_indicator}")
print(f"{'='*60}\n")
# Add source indicator at top
source_badge_color = "#4CAF50" if "🟢" in source_indicator else "#FF9800"
source_html = f""
if results:
products_html += "
📦 Products Found:
"
for product in results:
products_html += format_product_card(product)
else:
products_html += "
No products found matching your query.
"
products_html += "