Merge pull request 'Add metadata filtering' (#5) from categories into main

Reviewed-on: #5

main.py

@@ -1,14 +1,12 @@
 # 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
+from enum import Enum
 from typing import BinaryIO, TypedDict
 
 import aiohttp
@@ -17,14 +15,21 @@ 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, cfg, log_structured_entry
 
 HTTP_TOO_MANY_REQUESTS = 429
 HTTP_SERVER_ERROR = 500
 
 
+class SourceNamespace(str, Enum):
+    """Allowed values for the 'source' namespace filter."""
+
+    EDUCACION_FINANCIERA = "Educacion Financiera"
+    PRODUCTOS_Y_SERVICIOS = "Productos y Servicios"
+    FUNCIONALIDADES_APP_MOVIL = "Funcionalidades de la App Movil"
+
+
 class GoogleCloudFileStorage:
     """Cache-aware helper for downloading files from Google Cloud Storage."""
 
@@ -74,10 +79,21 @@ class GoogleCloudFileStorage:
 
         """
         if file_name in self._cache:
+            log_structured_entry(
+                "File retrieved from cache",
+                "INFO",
+                {"file": file_name, "bucket": self.bucket_name}
+            )
             file_stream = io.BytesIO(self._cache[file_name])
             file_stream.name = file_name
             return file_stream
 
+        log_structured_entry(
+            "Starting file download from GCS",
+            "INFO",
+            {"file": file_name, "bucket": self.bucket_name}
+        )
+
         storage_client = self._get_aio_storage()
         last_exception: Exception | None = None
 
@@ -89,14 +105,22 @@ class GoogleCloudFileStorage:
                 )
                 file_stream = io.BytesIO(self._cache[file_name])
                 file_stream.name = file_name
+                log_structured_entry(
+                    "File downloaded successfully",
+                    "INFO",
+                    {
+                        "file": file_name,
+                        "bucket": self.bucket_name,
+                        "size_bytes": len(self._cache[file_name]),
+                        "attempt": attempt + 1
+                    }
+                )
             except TimeoutError as exc:
                 last_exception = exc
-                logger.warning(
-                    "Timeout downloading gs://%s/%s (attempt %d/%d)",
-                    self.bucket_name,
-                    file_name,
-                    attempt + 1,
-                    max_retries,
+                log_structured_entry(
+                    f"Timeout downloading gs://{self.bucket_name}/{file_name} (attempt {attempt + 1}/{max_retries})",
+                    "WARNING",
+                    {"error": str(exc)}
                 )
             except aiohttp.ClientResponseError as exc:
                 last_exception = exc
@@ -104,27 +128,44 @@ class GoogleCloudFileStorage:
                     exc.status == HTTP_TOO_MANY_REQUESTS
                     or exc.status >= HTTP_SERVER_ERROR
                 ):
-                    logger.warning(
-                        "HTTP %d downloading gs://%s/%s (attempt %d/%d)",
-                        exc.status,
-                        self.bucket_name,
-                        file_name,
-                        attempt + 1,
-                        max_retries,
+                     log_structured_entry(
+                        f"HTTP {exc.status} downloading gs://{self.bucket_name}/{file_name} (attempt {attempt + 1}/{max_retries})",
+                        "WARNING",
+                        {"status": exc.status, "message": str(exc)}
                     )
                 else:
+                    log_structured_entry(
+                        f"Non-retryable HTTP error downloading gs://{self.bucket_name}/{file_name}",
+                        "ERROR",
+                        {"status": exc.status, "message": str(exc)}
+                    )
                     raise
             else:
                 return file_stream
 
             if attempt < max_retries - 1:
                 delay = 0.5 * (2**attempt)
+                log_structured_entry(
+                    "Retrying file download",
+                    "INFO",
+                    {"file": file_name, "delay_seconds": delay}
+                )
                 await asyncio.sleep(delay)
 
         msg = (
             f"Failed to download gs://{self.bucket_name}/{file_name} "
             f"after {max_retries} attempts"
         )
+        log_structured_entry(
+            "File download failed after all retries",
+            "ERROR",
+            {
+                "file": file_name,
+                "bucket": self.bucket_name,
+                "max_retries": max_retries,
+                "last_error": str(last_exception)
+            }
+        )
         raise TimeoutError(msg) from last_exception
 
 
@@ -204,6 +245,7 @@ class GoogleCloudVectorSearch:
         deployed_index_id: str,
         query: Sequence[float],
         limit: int,
+        source: SourceNamespace | None = None,
     ) -> list[SearchResult]:
         """Run an async similarity search via the REST API.
 
@@ -211,6 +253,7 @@ class GoogleCloudVectorSearch:
             deployed_index_id: The ID of the deployed index.
             query: The embedding vector for the search query.
             limit: Maximum number of nearest neighbors to return.
+            source: Optional namespace filter to restrict results by source.
 
         Returns:
             A list of matched items with id, distance, and content.
@@ -221,7 +264,13 @@ class GoogleCloudVectorSearch:
                 "Missing endpoint metadata. Call "
                 "`configure_index_endpoint` before querying."
             )
+            log_structured_entry(
+                "Vector search query failed - endpoint not configured",
+                "ERROR",
+                {"error": msg}
+            )
             raise RuntimeError(msg)
+
         domain = self._endpoint_domain
         endpoint_id = self._endpoint_name.split("/")[-1]
         url = (
@@ -229,53 +278,125 @@ class GoogleCloudVectorSearch:
             f"/locations/{self.location}"
             f"/indexEndpoints/{endpoint_id}:findNeighbors"
         )
+
+        log_structured_entry(
+            "Starting vector search query",
+            "INFO",
+            {
+                "deployed_index_id": deployed_index_id,
+                "neighbor_count": limit,
+                "endpoint_id": endpoint_id,
+                "embedding_dimension": len(query)
+            }
+        )
+
+        datapoint: dict = {"feature_vector": list(query)}
+        if source is not None:
+            datapoint["restricts"] = [
+                {"namespace": "source", "allow_list": [source.value]},
+            ]
         payload = {
             "deployed_index_id": deployed_index_id,
             "queries": [
                 {
-                    "datapoint": {"feature_vector": list(query)},
+                    "datapoint": datapoint,
                     "neighbor_count": limit,
                 },
             ],
         }
 
-        headers = await self._async_get_auth_headers()
-        session = self._get_aio_session()
-        async with session.post(
-            url,
-            json=payload,
-            headers=headers,
-        ) as response:
-            if not response.ok:
-                body = await response.text()
-                msg = f"findNeighbors returned {response.status}: {body}"
-                raise RuntimeError(msg)
-            data = await response.json()
+        try:
+            headers = await self._async_get_auth_headers()
+            session = self._get_aio_session()
+            async with session.post(
+                url,
+                json=payload,
+                headers=headers,
+            ) as response:
+                if not response.ok:
+                    body = await response.text()
+                    msg = f"findNeighbors returned {response.status}: {body}"
+                    log_structured_entry(
+                        "Vector search API request failed",
+                        "ERROR",
+                        {
+                            "status": response.status,
+                            "response_body": body,
+                            "deployed_index_id": deployed_index_id
+                        }
+                    )
+                    raise RuntimeError(msg)
+                data = await response.json()
 
-        neighbors = data.get("nearestNeighbors", [{}])[0].get("neighbors", [])
-        content_tasks = []
-        for neighbor in neighbors:
-            datapoint_id = neighbor["datapoint"]["datapointId"]
-            file_path = f"{self.index_name}/contents/{datapoint_id}.md"
-            content_tasks.append(
-                self.storage.async_get_file_stream(file_path),
+            neighbors = data.get("nearestNeighbors", [{}])[0].get("neighbors", [])
+            log_structured_entry(
+                "Vector search API request successful",
+                "INFO",
+                {
+                    "neighbors_found": len(neighbors),
+                    "deployed_index_id": deployed_index_id
+                }
             )
 
-        file_streams = await asyncio.gather(*content_tasks)
-        results: list[SearchResult] = []
-        for neighbor, stream in zip(
-            neighbors,
-            file_streams,
-            strict=True,
-        ):
-            results.append(
-                SearchResult(
-                    id=neighbor["datapoint"]["datapointId"],
-                    distance=neighbor["distance"],
-                    content=stream.read().decode("utf-8"),
-                ),
+            if not neighbors:
+                log_structured_entry(
+                    "No neighbors found in vector search",
+                    "WARNING",
+                    {"deployed_index_id": deployed_index_id}
+                )
+                return []
+
+            # Fetch content for all neighbors
+            content_tasks = []
+            for neighbor in neighbors:
+                datapoint_id = neighbor["datapoint"]["datapointId"]
+                file_path = f"{self.index_name}/contents/{datapoint_id}.md"
+                content_tasks.append(
+                    self.storage.async_get_file_stream(file_path),
+                )
+
+            log_structured_entry(
+                "Fetching content for search results",
+                "INFO",
+                {"file_count": len(content_tasks)}
             )
-        return results
+
+            file_streams = await asyncio.gather(*content_tasks)
+            results: list[SearchResult] = []
+            for neighbor, stream in zip(
+                neighbors,
+                file_streams,
+                strict=True,
+            ):
+                results.append(
+                    SearchResult(
+                        id=neighbor["datapoint"]["datapointId"],
+                        distance=neighbor["distance"],
+                        content=stream.read().decode("utf-8"),
+                    ),
+                )
+
+            log_structured_entry(
+                "Vector search completed successfully",
+                "INFO",
+                {
+                    "results_count": len(results),
+                    "deployed_index_id": deployed_index_id
+                }
+            )
+            return results
+
+        except Exception as e:
+            log_structured_entry(
+                "Vector search query failed with exception",
+                "ERROR",
+                {
+                    "error": str(e),
+                    "error_type": type(e).__name__,
+                    "deployed_index_id": deployed_index_id
+                }
+            )
+            raise
 
 
 # ---------------------------------------------------------------------------
@@ -283,58 +404,6 @@ 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."""
@@ -347,31 +416,229 @@ class AppContext:
 @asynccontextmanager
 async def lifespan(_server: FastMCP) -> AsyncIterator[AppContext]:
     """Create and configure the vector-search client for the server lifetime."""
-    vs = GoogleCloudVectorSearch(
-        project_id=cfg.project_id,
-        location=cfg.location,
-        bucket=cfg.bucket,
-        index_name=cfg.index_name,
-    )
-    vs.configure_index_endpoint(
-        name=cfg.endpoint_name,
-        public_domain=cfg.endpoint_domain,
+    log_structured_entry(
+        "Initializing MCP server",
+        "INFO",
+        {
+            "project_id": cfg.project_id,
+            "location": cfg.location,
+            "bucket": cfg.bucket,
+            "index_name": cfg.index_name,
+        }
     )
 
-    genai_client = genai.Client(
-        vertexai=True,
-        project=cfg.project_id,
-        location=cfg.location,
-    )
+    try:
+        # Initialize vector search client
+        log_structured_entry("Creating GoogleCloudVectorSearch client", "INFO")
+        vs = GoogleCloudVectorSearch(
+            project_id=cfg.project_id,
+            location=cfg.location,
+            bucket=cfg.bucket,
+            index_name=cfg.index_name,
+        )
 
-    yield AppContext(
-        vector_search=vs,
-        genai_client=genai_client,
-        settings=cfg,
-    )
+        # Configure endpoint
+        log_structured_entry(
+            "Configuring index endpoint",
+            "INFO",
+            {
+                "endpoint_name": cfg.endpoint_name,
+                "endpoint_domain": cfg.endpoint_domain,
+            }
+        )
+        vs.configure_index_endpoint(
+            name=cfg.endpoint_name,
+            public_domain=cfg.endpoint_domain,
+        )
 
+        # Initialize GenAI client
+        log_structured_entry(
+            "Creating GenAI client",
+            "INFO",
+            {"project_id": cfg.project_id, "location": cfg.location}
+        )
+        genai_client = genai.Client(
+            vertexai=True,
+            project=cfg.project_id,
+            location=cfg.location,
+        )
+
+        # Validate credentials and configuration by testing actual resources
+        # These validations are non-blocking - errors are logged but won't stop startup
+        log_structured_entry("Starting validation of credentials and resources", "INFO")
+
+        validation_errors = []
+
+        # 1. Validate GenAI embedding access
+        log_structured_entry("Validating GenAI embedding access", "INFO")
+        try:
+            test_response = await genai_client.aio.models.embed_content(
+                model=cfg.embedding_model,
+                contents="test",
+                config=genai_types.EmbedContentConfig(
+                    task_type="RETRIEVAL_QUERY",
+                ),
+            )
+            if test_response and test_response.embeddings:
+                embedding_values = test_response.embeddings[0].values
+                log_structured_entry(
+                    "GenAI embedding validation successful",
+                    "INFO",
+                    {"embedding_dimension": len(embedding_values) if embedding_values else 0}
+                )
+            else:
+                msg = "Embedding validation returned empty response"
+                log_structured_entry(msg, "WARNING")
+                validation_errors.append(msg)
+        except Exception as e:
+            log_structured_entry(
+                "Failed to validate GenAI embedding access - service may not work correctly",
+                "WARNING",
+                {"error": str(e), "error_type": type(e).__name__}
+            )
+            validation_errors.append(f"GenAI: {str(e)}")
+
+        # 2. Validate GCS bucket access
+        log_structured_entry(
+            "Validating GCS bucket access",
+            "INFO",
+            {"bucket": cfg.bucket}
+        )
+        try:
+            session = vs.storage._get_aio_session()
+            token_obj = Token(
+                session=session,
+                scopes=["https://www.googleapis.com/auth/cloud-platform"],
+            )
+            access_token = await token_obj.get()
+            headers = {"Authorization": f"Bearer {access_token}"}
+
+            async with session.get(
+                f"https://storage.googleapis.com/storage/v1/b/{cfg.bucket}/o?maxResults=1",
+                headers=headers,
+            ) as response:
+                if response.status == 403:
+                    msg = f"Access denied to bucket '{cfg.bucket}'. Check permissions."
+                    log_structured_entry(
+                        "GCS bucket validation failed - access denied - service may not work correctly",
+                        "WARNING",
+                        {"bucket": cfg.bucket, "status": response.status}
+                    )
+                    validation_errors.append(msg)
+                elif response.status == 404:
+                    msg = f"Bucket '{cfg.bucket}' not found. Check bucket name and project."
+                    log_structured_entry(
+                        "GCS bucket validation failed - not found - service may not work correctly",
+                        "WARNING",
+                        {"bucket": cfg.bucket, "status": response.status}
+                    )
+                    validation_errors.append(msg)
+                elif not response.ok:
+                    body = await response.text()
+                    msg = f"Failed to access bucket '{cfg.bucket}': {response.status}"
+                    log_structured_entry(
+                        "GCS bucket validation failed - service may not work correctly",
+                        "WARNING",
+                        {"bucket": cfg.bucket, "status": response.status, "response": body}
+                    )
+                    validation_errors.append(msg)
+                else:
+                    log_structured_entry(
+                        "GCS bucket validation successful",
+                        "INFO",
+                        {"bucket": cfg.bucket}
+                    )
+        except Exception as e:
+            log_structured_entry(
+                "Failed to validate GCS bucket access - service may not work correctly",
+                "WARNING",
+                {"error": str(e), "error_type": type(e).__name__, "bucket": cfg.bucket}
+            )
+            validation_errors.append(f"GCS: {str(e)}")
+
+        # 3. Validate vector search endpoint access
+        log_structured_entry(
+            "Validating vector search endpoint access",
+            "INFO",
+            {"endpoint_name": cfg.endpoint_name}
+        )
+        try:
+            # Try to get endpoint info
+            headers = await vs._async_get_auth_headers()
+            session = vs._get_aio_session()
+            endpoint_url = (
+                f"https://{cfg.location}-aiplatform.googleapis.com/v1/{cfg.endpoint_name}"
+            )
+
+            async with session.get(endpoint_url, headers=headers) as response:
+                if response.status == 403:
+                    msg = f"Access denied to endpoint '{cfg.endpoint_name}'. Check permissions."
+                    log_structured_entry(
+                        "Vector search endpoint validation failed - access denied - service may not work correctly",
+                        "WARNING",
+                        {"endpoint": cfg.endpoint_name, "status": response.status}
+                    )
+                    validation_errors.append(msg)
+                elif response.status == 404:
+                    msg = f"Endpoint '{cfg.endpoint_name}' not found. Check endpoint name and project."
+                    log_structured_entry(
+                        "Vector search endpoint validation failed - not found - service may not work correctly",
+                        "WARNING",
+                        {"endpoint": cfg.endpoint_name, "status": response.status}
+                    )
+                    validation_errors.append(msg)
+                elif not response.ok:
+                    body = await response.text()
+                    msg = f"Failed to access endpoint '{cfg.endpoint_name}': {response.status}"
+                    log_structured_entry(
+                        "Vector search endpoint validation failed - service may not work correctly",
+                        "WARNING",
+                        {"endpoint": cfg.endpoint_name, "status": response.status, "response": body}
+                    )
+                    validation_errors.append(msg)
+                else:
+                    log_structured_entry(
+                        "Vector search endpoint validation successful",
+                        "INFO",
+                        {"endpoint": cfg.endpoint_name}
+                    )
+        except Exception as e:
+            log_structured_entry(
+                "Failed to validate vector search endpoint access - service may not work correctly",
+                "WARNING",
+                {"error": str(e), "error_type": type(e).__name__, "endpoint": cfg.endpoint_name}
+            )
+            validation_errors.append(f"Vector Search: {str(e)}")
+
+        # Summary of validations
+        if validation_errors:
+            log_structured_entry(
+                "MCP server started with validation errors - service may not work correctly",
+                "WARNING",
+                {"validation_errors": validation_errors, "error_count": len(validation_errors)}
+            )
+        else:
+            log_structured_entry("All validations passed - MCP server initialization complete", "INFO")
+
+        yield AppContext(
+            vector_search=vs,
+            genai_client=genai_client,
+            settings=cfg,
+        )
+
+    except Exception as e:
+        log_structured_entry(
+            "Failed to initialize MCP server",
+            "ERROR",
+            {
+                "error": str(e),
+                "error_type": type(e).__name__,
+            }
+        )
+        raise
+    finally:
+        log_structured_entry("MCP server lifespan ending", "INFO")
 
-cfg = Settings.model_validate({})
 
 mcp = FastMCP(
     "knowledge-search",
@@ -385,12 +652,16 @@ mcp = FastMCP(
 async def knowledge_search(
     query: str,
     ctx: Context,
+    source: SourceNamespace | None = None,
 ) -> str:
     """Search a knowledge base using a natural-language query.
 
     Args:
         query: The text query to search for.
         ctx: MCP request context (injected automatically).
+        source: Optional filter to restrict results by source.
+            Allowed values: 'Educacion Financiera',
+            'Productos y Servicios', 'Funcionalidades de la App Movil'.
 
     Returns:
         A formatted string containing matched documents with id and content.
@@ -402,48 +673,130 @@ async def knowledge_search(
     t0 = time.perf_counter()
     min_sim = 0.6
 
-    response = await app.genai_client.aio.models.embed_content(
-        model=app.settings.embedding_model,
-        contents=query,
-        config=genai_types.EmbedContentConfig(
-            task_type="RETRIEVAL_QUERY",
-
-        ),
+    log_structured_entry(
+        "knowledge_search request received",
+        "INFO",
+        {"query": query[:100]}  # Log first 100 chars of query
     )
-    embedding = response.embeddings[0].values
-    t_embed = time.perf_counter()
 
-    search_results = await app.vector_search.async_run_query(
-        deployed_index_id=app.settings.deployed_index_id,
-        query=embedding,
-        limit=app.settings.search_limit,
-    )
-    t_search = time.perf_counter()
+    try:
+        # Generate embedding for the query
+        log_structured_entry("Generating query embedding", "INFO")
+        try:
+            response = await app.genai_client.aio.models.embed_content(
+                model=app.settings.embedding_model,
+                contents=query,
+                config=genai_types.EmbedContentConfig(
+                    task_type="RETRIEVAL_QUERY",
+                ),
+            )
+            embedding = response.embeddings[0].values
+            t_embed = time.perf_counter()
+            log_structured_entry(
+                "Query embedding generated successfully",
+                "INFO",
+                {"time_ms": round((t_embed - t0) * 1000, 1)}
+            )
+        except Exception as e:
+            error_type = type(e).__name__
+            error_msg = str(e)
 
-    # Apply similarity filtering
-    if search_results:
-        max_sim = max(r["distance"] for r in search_results)
-        cutoff = max_sim * 0.9
-        search_results = [
-            s
-            for s in search_results
-            if s["distance"] > cutoff and s["distance"] > min_sim
+            # Check if it's a rate limit error
+            if "429" in error_msg or "RESOURCE_EXHAUSTED" in error_msg:
+                log_structured_entry(
+                    "Rate limit exceeded while generating embedding",
+                    "WARNING",
+                    {
+                        "error": error_msg,
+                        "error_type": error_type,
+                        "query": query[:100]
+                    }
+                )
+                return "Error: API rate limit exceeded. Please try again later."
+            else:
+                log_structured_entry(
+                    "Failed to generate query embedding",
+                    "ERROR",
+                    {
+                        "error": error_msg,
+                        "error_type": error_type,
+                        "query": query[:100]
+                    }
+                )
+                return f"Error generating embedding: {error_msg}"
+
+        # Perform vector search
+        log_structured_entry("Performing vector search", "INFO")
+        try:
+            search_results = await app.vector_search.async_run_query(
+                deployed_index_id=app.settings.deployed_index_id,
+                query=embedding,
+                limit=app.settings.search_limit,
+                source=source,
+            )
+            t_search = time.perf_counter()
+        except Exception as e:
+            log_structured_entry(
+                "Vector search failed",
+                "ERROR",
+                {
+                    "error": str(e),
+                    "error_type": type(e).__name__,
+                    "query": query[:100]
+                }
+            )
+            return f"Error performing vector search: {str(e)}"
+
+        # Apply similarity filtering
+        if search_results:
+            max_sim = max(r["distance"] for r in search_results)
+            cutoff = max_sim * 0.9
+            search_results = [
+                s
+                for s in search_results
+                if s["distance"] > cutoff and s["distance"] > min_sim
+            ]
+
+        log_structured_entry(
+            "knowledge_search completed successfully",
+            "INFO",
+            {
+                "embedding_ms": f"{round((t_embed - t0) * 1000, 1)}ms",
+                "vector_search_ms": f"{round((t_search - t_embed) * 1000, 1)}ms",
+                "total_ms": f"{round((t_search - t0) * 1000, 1)}ms",
+                "source_filter": source.value if source is not None else None,
+                "results_count": len(search_results),
+                "chunks": [s["id"] for s in search_results]
+            }
+        )
+
+        # Format results as XML-like documents
+        if not search_results:
+            log_structured_entry(
+                "No results found for query",
+                "INFO",
+                {"query": query[:100]}
+            )
+            return "No relevant documents found for your query."
+
+        formatted_results = [
+            f"<document {i} name={result['id']}>\n{result['content']}\n</document {i}>"
+            for i, result in enumerate(search_results, start=1)
         ]
+        return "\n".join(formatted_results)
 
-    logger.info(
-        "knowledge_search timing: embedding=%sms, vector_search=%sms, total=%sms, chunks=%s",
-        round((t_embed - t0) * 1000, 1),
-        round((t_search - t_embed) * 1000, 1),
-        round((t_search - t0) * 1000, 1),
-        [s["id"] for s in search_results],
-    )
-
-    # Format results as XML-like documents
-    formatted_results = [
-        f"<document {i} name={result['id']}>\n{result['content']}\n</document {i}>"
-        for i, result in enumerate(search_results, start=1)
-    ]
-    return "\n".join(formatted_results)
+    except Exception as e:
+        # Catch-all for any unexpected errors
+        log_structured_entry(
+            "Unexpected error in knowledge_search",
+            "ERROR",
+            {
+                "error": str(e),
+                "error_type": type(e).__name__,
+                "query": query[:100]
+            }
+        )
+        return f"Unexpected error during search: {str(e)}"
 
 
 if __name__ == "__main__":

utils/__init__.py

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

utils/config.py

@@ -0,0 +1,60 @@
+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
+    log_name: str = "va_agent_evaluation_logs"
+    log_level: str = "INFO"
+
+    @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,
+        )
+
+
+# Singleton instance of Settings
+cfg = Settings.model_validate({})

utils/logging_setup.py

@@ -0,0 +1,48 @@
+"""
+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 cfg
+
+
+def _setup_logger() -> logging.Logger:
+    """Create or return the singleton evaluation logger."""
+    logger = logging.getLogger(cfg.log_name)
+    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=cfg.log_name)  # async transport
+        logger.addHandler(handler)
+        logger.setLevel(getattr(logging, cfg.log_level.upper()))
+    except Exception as e:
+        # Fallback to console if Cloud Logging is unavailable (local dev)
+        logging.basicConfig(level=getattr(logging, cfg.log_level.upper()))
+        logger = logging.getLogger(cfg.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"
+        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 {}}})