feat: use a new logger

Move config into own file

main.py

@@ -1,11 +1,8 @@
 # ruff: noqa: INP001
 """Async helpers for querying Vertex AI vector search via MCP."""
 
-import argparse
 import asyncio
 import io
-import logging
-import os
 from collections.abc import AsyncIterator, Sequence
 from contextlib import asynccontextmanager
 from dataclasses import dataclass
@@ -17,9 +14,8 @@ from gcloud.aio.storage import Storage
 from google import genai
 from google.genai import types as genai_types
 from mcp.server.fastmcp import Context, FastMCP
-from pydantic_settings import BaseSettings, PydanticBaseSettingsSource, YamlConfigSettingsSource
 
-logger = logging.getLogger(__name__)
+from .utils import Settings, _args, log_structured_entry
 
 HTTP_TOO_MANY_REQUESTS = 429
 HTTP_SERVER_ERROR = 500
@@ -91,12 +87,9 @@ class GoogleCloudFileStorage:
                 file_stream.name = file_name
             except TimeoutError as exc:
                 last_exception = exc
-                logger.warning(
+                log_structured_entry(
-                    "Timeout downloading gs://%s/%s (attempt %d/%d)",
+                    f"Timeout downloading gs://{self.bucket_name}/{file_name} (attempt {attempt + 1}/{max_retries})"
-                    self.bucket_name,
+                    "WARNING"
-                    file_name,
-                    attempt + 1,
-                    max_retries,
                 )
             except aiohttp.ClientResponseError as exc:
                 last_exception = exc
@@ -104,13 +97,9 @@ class GoogleCloudFileStorage:
                     exc.status == HTTP_TOO_MANY_REQUESTS
                     or exc.status >= HTTP_SERVER_ERROR
                 ):
-                    logger.warning(
+                     log_structured_entry(
-                        "HTTP %d downloading gs://%s/%s (attempt %d/%d)",
+                        f"HTTP {exc.status} downloading gs://{self.bucket_name}/{file_name} (attempt {attempt + 1}/{max_retries})"
-                        exc.status,
+                        "WARNING"
-                        self.bucket_name,
-                        file_name,
-                        attempt + 1,
-                        max_retries,
                     )
                 else:
                     raise
@@ -283,58 +272,6 @@ class GoogleCloudVectorSearch:
 # ---------------------------------------------------------------------------
 
 
-def _parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--transport",
-        choices=["stdio", "sse"],
-        default="stdio",
-    )
-    parser.add_argument("--host", default="0.0.0.0")
-    parser.add_argument("--port", type=int, default=8080)
-    parser.add_argument(
-        "--config",
-        default=os.environ.get("CONFIG_FILE", "config.yaml"),
-    )
-    return parser.parse_args()
-
-
-_args = _parse_args()
-
-
-class Settings(BaseSettings):
-    """Server configuration populated from env vars and a YAML config file."""
-
-    model_config = {"env_file": ".env", "yaml_file": _args.config}
-
-    project_id: str
-    location: str
-    bucket: str
-    index_name: str
-    deployed_index_id: str
-    endpoint_name: str
-    endpoint_domain: str
-    embedding_model: str = "gemini-embedding-001"
-    search_limit: int = 10
-
-    @classmethod
-    def settings_customise_sources(
-        cls,
-        settings_cls: type[BaseSettings],
-        init_settings: PydanticBaseSettingsSource,
-        env_settings: PydanticBaseSettingsSource,
-        dotenv_settings: PydanticBaseSettingsSource,
-        file_secret_settings: PydanticBaseSettingsSource,
-    ) -> tuple[PydanticBaseSettingsSource, ...]:
-        return (
-            init_settings,
-            env_settings,
-            dotenv_settings,
-            YamlConfigSettingsSource(settings_cls),
-            file_secret_settings,
-        )
-
-
 @dataclass
 class AppContext:
     """Shared resources initialised once at server startup."""
@@ -430,12 +367,15 @@ async def knowledge_search(
             if s["distance"] > cutoff and s["distance"] > min_sim
         ]
     
-    logger.info(
+    log_structured_entry(
-        "knowledge_search timing: embedding=%sms, vector_search=%sms, total=%sms, chunks=%s",
+        "knowledge_search timing",
-        round((t_embed - t0) * 1000, 1),
+        "INFO",
-        round((t_search - t_embed) * 1000, 1),
+        {
-        round((t_search - t0) * 1000, 1),
+            "embedding": f"{round((t_embed - t0) * 1000, 1)}ms",
-        [s["id"] for s in search_results],
+            "vector_serach": f"{round((t_search - t_embed) * 1000, 1)}ms",
+            "total": f"{round((t_search - t0) * 1000, 1)}ms",
+            "chunks": {[s["id"] for s in search_results]}
+        }
     )
 
     # Format results as XML-like documents

utils/__init__.py

@@ -0,0 +1,4 @@
+from .config import Settings, _args
+from .logging_setup import log_structured_entry
+
+__all__ = ['Settings', '_args', 'log_structured_entry']

utils/config.py

@@ -0,0 +1,54 @@
+import os
+import argparse
+from pydantic_settings import BaseSettings, PydanticBaseSettingsSource, YamlConfigSettingsSource
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--transport",
+        choices=["stdio", "sse"],
+        default="stdio",
+    )
+    parser.add_argument("--host", default="0.0.0.0")
+    parser.add_argument("--port", type=int, default=8080)
+    parser.add_argument(
+        "--config",
+        default=os.environ.get("CONFIG_FILE", "config.yaml"),
+    )
+    return parser.parse_args()
+
+
+_args = _parse_args()
+
+class Settings(BaseSettings):
+    """Server configuration populated from env vars and a YAML config file."""
+
+    model_config = {"env_file": ".env", "yaml_file": _args.config}
+
+    project_id: str
+    location: str
+    bucket: str
+    index_name: str
+    deployed_index_id: str
+    endpoint_name: str
+    endpoint_domain: str
+    embedding_model: str = "gemini-embedding-001"
+    search_limit: int = 10
+
+    @classmethod
+    def settings_customise_sources(
+        cls,
+        settings_cls: type[BaseSettings],
+        init_settings: PydanticBaseSettingsSource,
+        env_settings: PydanticBaseSettingsSource,
+        dotenv_settings: PydanticBaseSettingsSource,
+        file_secret_settings: PydanticBaseSettingsSource,
+    ) -> tuple[PydanticBaseSettingsSource, ...]:
+        return (
+            init_settings,
+            env_settings,
+            dotenv_settings,
+            YamlConfigSettingsSource(settings_cls),
+            file_secret_settings,
+        )

utils/logging_setup.py

@@ -0,0 +1,50 @@
+"""
+Centralized Cloud Logging setup.
+Uses CloudLoggingHandler (background thread) so logging does not add latency
+"""
+
+import logging
+from typing import Optional, Dict, Literal
+
+import google.cloud.logging
+from google.cloud.logging.handlers import CloudLoggingHandler
+
+from .config import Settings
+
+
+def _setup_logger() -> logging.Logger:
+    """Create or return the singleton evaluation logger."""
+    log_name = "va_agent-evaluation-logs"
+    logger = logging.getLogger(log_name)
+    cfg = Settings.model_validate({})
+    if any(isinstance(h, CloudLoggingHandler) for h in logger.handlers):
+        return logger
+
+    try:
+        client = google.cloud.logging.Client(project=cfg.project_id)
+        handler = CloudLoggingHandler(client, name=log_name)  # async transport
+        logger.addHandler(handler)
+        logger.setLevel(logging.INFO)
+    except Exception as e:
+        # Fallback to console if Cloud Logging is unavailable (local dev)
+        logging.basicConfig(level=logging.INFO)
+        logger = logging.getLogger(log_name)
+        logger.warning("Cloud Logging setup failed; using console. Error: %s", e)
+
+    return logger
+
+
+_eval_log = _setup_logger()
+
+
+def log_structured_entry(message: str, severity: Literal["INFO", "WARNING", "ERROR"], custom_log: Optional[Dict] = None) -> None:
+    """
+    Emit a JSON-structured log row.
+
+    Args:
+        message: Short label for the row (e.g., "Final agent turn").
+        severity: "INFO" | "WARNING" | "ERROR" etc.
+        custom_log: A dict with your structured payload.
+    """
+    level = getattr(logging, severity.upper(), logging.INFO)
+    _eval_log.log(level, message, extra={"json_fields": {"message": message, "custom": custom_log or {}}})