feat: add performance logging middleware and enhance performance tracking across services

- Introduced RequestPerfMiddleware to log request performance metrics, including slow request thresholds.
- Updated various services and retrievers to utilize the new performance logging utility for better tracking of execution times.
- Enhanced existing methods with detailed performance logs for operations such as embedding, searching, and indexing.
- Removed deprecated logging setup in stream_new_chat and replaced it with the new performance logger.

surfsense_backend/app/agents/new_chat/tools/knowledge_base.py

@@ -10,6 +10,7 @@ This module provides:
 
 import asyncio
 import json
+import time
 from datetime import datetime
 from typing import Any
 
@@ -19,6 +20,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.db import async_session_maker
 from app.services.connector_service import ConnectorService
+from app.utils.perf import get_perf_logger
 
 # =============================================================================
 # Connector Constants and Normalization
@@ -412,6 +414,9 @@ async def search_knowledge_base_async(
     Returns:
         Formatted string with search results
     """
+    perf = get_perf_logger()
+    t0 = time.perf_counter()
+
     all_documents: list[dict[str, Any]] = []
 
     # Resolve date range (default last 2 years)
@@ -423,6 +428,10 @@ async def search_knowledge_base_async(
     )
 
     connectors = _normalize_connectors(connectors_to_search, available_connectors)
+    perf.info(
+        "[kb_search] searching %d connectors: %s (space=%d, top_k=%d)",
+        len(connectors), connectors[:5], search_space_id, top_k,
+    )
 
     connector_specs: dict[str, tuple[str, bool, bool, dict[str, Any]]] = {
         "YOUTUBE_VIDEO": ("search_youtube", True, True, {}),
@@ -492,20 +501,32 @@ async def search_knowledge_base_async(
         try:
             # Use isolated session per connector. Shared AsyncSession cannot safely
             # run concurrent DB operations.
+            t_conn = time.perf_counter()
             async with semaphore, async_session_maker() as isolated_session:
                 isolated_connector_service = ConnectorService(
                     isolated_session, search_space_id
                 )
                 connector_method = getattr(isolated_connector_service, method_name)
                 _, chunks = await connector_method(**kwargs)
+                perf.info(
+                    "[kb_search] connector=%s results=%d in %.3fs",
+                    connector, len(chunks), time.perf_counter() - t_conn,
+                )
                 return chunks
         except Exception as e:
-            print(f"Error searching connector {connector}: {e}")
+            perf.warning(
+                "[kb_search] connector=%s FAILED in %.3fs: %s",
+                connector, time.perf_counter() - t_conn, e,
+            )
             return []
 
+    t_gather = time.perf_counter()
     connector_results = await asyncio.gather(
         *[_search_one_connector(connector) for connector in connectors]
     )
+    perf.info(
+        "[kb_search] all connectors gathered in %.3fs", time.perf_counter() - t_gather,
+    )
     for chunks in connector_results:
         all_documents.extend(chunks)
 
@@ -552,7 +573,12 @@ async def search_knowledge_base_async(
         deduplicated.append(doc)
 
     output_budget = _compute_tool_output_budget(max_input_tokens)
-    return format_documents_for_context(deduplicated, max_chars=output_budget)
+    result = format_documents_for_context(deduplicated, max_chars=output_budget)
+    perf.info(
+        "[kb_search] TOTAL in %.3fs total_docs=%d deduped=%d output_chars=%d space=%d",
+        time.perf_counter() - t0, len(all_documents), len(deduplicated), len(result), search_space_id,
+    )
+    return result
 
 
 def _build_connector_docstring(available_connectors: list[str] | None) -> str: