Add filter with metadata using restricts

README.md

@@ -6,7 +6,24 @@ An MCP (Model Context Protocol) server that exposes a `knowledge_search` tool fo
 
 1. A natural-language query is embedded using a Gemini embedding model.
 2. The embedding is sent to a Vertex AI Matching Engine index endpoint to find nearest neighbors.
-3. The matched document contents are fetched from a GCS bucket and returned to the caller.
+3. Optional filters (restricts) can be applied to search only specific source folders.
+4. The matched document contents are fetched from a GCS bucket and returned to the caller.
+
+## Filtering by Source Folder
+
+The `knowledge_search` tool supports filtering results by source folder:
+
+```python
+# Search all folders
+knowledge_search(query="what is a savings account?")
+
+# Search only in specific folders
+knowledge_search(
+    query="what is a savings account?",
+    source_folders=["Educacion Financiera", "Productos y Servicios"]
+)
+```
+
 
 ## Prerequisites
 

agent.py

@@ -57,9 +57,20 @@ async def async_main() -> None:
         model="gemini-2.0-flash",
         name="knowledge_agent",
         instruction=(
-            "You are a helpful assistant with access to a knowledge base. "
+            "You are a helpful assistant with access to a knowledge base organized by folders. "
-            "Use the knowledge_search tool to find relevant information "
+            "Use the knowledge_search tool to find relevant information when the user asks questions.\n\n"
-            "when the user asks questions. Summarize the results clearly."
+            "Available folders in the knowledge base:\n"
+            "- 'Educacion Financiera': Educational content about finance, savings, investments, financial concepts\n"
+            "- 'Funcionalidades de la App Movil': Mobile app features, functionality, usage instructions\n"
+            "- 'Productos y Servicios': Bank products and services, accounts, procedures\n\n"
+            "IMPORTANT: When the user asks about a specific topic, analyze which folders are relevant "
+            "and use the source_folders parameter to filter results for more precise answers.\n\n"
+            "Examples:\n"
+            "- User asks about 'cuenta de ahorros' → Use source_folders=['Educacion Financiera', 'Productos y Servicios']\n"
+            "- User asks about 'cómo usar la app móvil' → Use source_folders=['Funcionalidades de App Movil']\n"
+            "- User asks about 'transferencias en la app' → Use source_folders=['Funcionalidades de App Movil', 'Productos y Servicios']\n"
+            "- User asks general question → Don't use source_folders (search all)\n\n"
+            "Summarize the results clearly in Spanish."
         ),
         tools=[toolset],
     )

main.py

@@ -1,8 +1,11 @@
 # 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
@@ -14,8 +17,9 @@ 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
 
-from utils import Settings, _args, cfg, log_structured_entry
+logger = logging.getLogger(__name__)
 
 HTTP_TOO_MANY_REQUESTS = 429
 HTTP_SERVER_ERROR = 500
@@ -70,21 +74,10 @@ 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
 
@@ -96,22 +89,14 @@ 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
-                log_structured_entry(
+                logger.warning(
-                    f"Timeout downloading gs://{self.bucket_name}/{file_name} (attempt {attempt + 1}/{max_retries})",
+                    "Timeout downloading gs://%s/%s (attempt %d/%d)",
-                    "WARNING",
+                    self.bucket_name,
-                    {"error": str(exc)}
+                    file_name,
+                    attempt + 1,
+                    max_retries,
                 )
             except aiohttp.ClientResponseError as exc:
                 last_exception = exc
@@ -119,44 +104,27 @@ class GoogleCloudFileStorage:
                     exc.status == HTTP_TOO_MANY_REQUESTS
                     or exc.status >= HTTP_SERVER_ERROR
                 ):
-                     log_structured_entry(
+                    logger.warning(
-                        f"HTTP {exc.status} downloading gs://{self.bucket_name}/{file_name} (attempt {attempt + 1}/{max_retries})",
+                        "HTTP %d downloading gs://%s/%s (attempt %d/%d)",
-                        "WARNING",
+                        exc.status,
-                        {"status": exc.status, "message": str(exc)}
+                        self.bucket_name,
+                        file_name,
+                        attempt + 1,
+                        max_retries,
                     )
                 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
 
 
@@ -236,6 +204,7 @@ class GoogleCloudVectorSearch:
         deployed_index_id: str,
         query: Sequence[float],
         limit: int,
+        restricts: list[dict[str, list[str]]] | None = None,
     ) -> list[SearchResult]:
         """Run an async similarity search via the REST API.
 
@@ -253,13 +222,7 @@ 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 = (
@@ -267,120 +230,57 @@ class GoogleCloudVectorSearch:
             f"/locations/{self.location}"
             f"/indexEndpoints/{endpoint_id}:findNeighbors"
         )
+        query_payload = {
+            "datapoint": {"feature_vector": list(query)},
+            "neighbor_count": limit,
+        }
 
-        log_structured_entry(
+        # Add restricts if provided
-            "Starting vector search query",
+        if restricts:
-            "INFO",
+            query_payload["restricts"] = restricts
-            {
-                "deployed_index_id": deployed_index_id,
-                "neighbor_count": limit,
-                "endpoint_id": endpoint_id,
-                "embedding_dimension": len(query)
-            }
-        )
 
         payload = {
             "deployed_index_id": deployed_index_id,
-            "queries": [
+            "queries": [query_payload],
-                {
-                    "datapoint": {"feature_vector": list(query)},
-                    "neighbor_count": limit,
-                },
-            ],
         }
 
-        try:
+        headers = await self._async_get_auth_headers()
-            headers = await self._async_get_auth_headers()
+        session = self._get_aio_session()
-            session = self._get_aio_session()
+        async with session.post(
-            async with session.post(
+            url,
-                url,
+            json=payload,
-                json=payload,
+            headers=headers,
-                headers=headers,
+        ) as response:
-            ) as response:
+            if not response.ok:
-                if not response.ok:
+                body = await response.text()
-                    body = await response.text()
+                msg = f"findNeighbors returned {response.status}: {body}"
-                    msg = f"findNeighbors returned {response.status}: {body}"
+                raise RuntimeError(msg)
-                    log_structured_entry(
+            data = await response.json()
-                        "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", [])
+        neighbors = data.get("nearestNeighbors", [{}])[0].get("neighbors", [])
-            log_structured_entry(
+        content_tasks = []
-                "Vector search API request successful",
+        for neighbor in neighbors:
-                "INFO",
+            datapoint_id = neighbor["datapoint"]["datapointId"]
-                {
+            file_path = f"{self.index_name}/contents/{datapoint_id}.md"
-                    "neighbors_found": len(neighbors),
+            content_tasks.append(
-                    "deployed_index_id": deployed_index_id
+                self.storage.async_get_file_stream(file_path),
-                }
             )
 
-            if not neighbors:
+        file_streams = await asyncio.gather(*content_tasks)
-                log_structured_entry(
+        results: list[SearchResult] = []
-                    "No neighbors found in vector search",
+        for neighbor, stream in zip(
-                    "WARNING",
+            neighbors,
-                    {"deployed_index_id": deployed_index_id}
+            file_streams,
-                )
+            strict=True,
-                return []
+        ):
-
+            results.append(
-            # Fetch content for all neighbors
+                SearchResult(
-            content_tasks = []
+                    id=neighbor["datapoint"]["datapointId"],
-            for neighbor in neighbors:
+                    distance=neighbor["distance"],
-                datapoint_id = neighbor["datapoint"]["datapointId"]
+                    content=stream.read().decode("utf-8"),
-                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
 
 
 # ---------------------------------------------------------------------------
@@ -388,6 +288,58 @@ 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."""
@@ -400,229 +352,31 @@ class AppContext:
 @asynccontextmanager
 async def lifespan(_server: FastMCP) -> AsyncIterator[AppContext]:
     """Create and configure the vector-search client for the server lifetime."""
-    log_structured_entry(
+    vs = GoogleCloudVectorSearch(
-        "Initializing MCP server",
+        project_id=cfg.project_id,
-        "INFO",
+        location=cfg.location,
-        {
+        bucket=cfg.bucket,
-            "project_id": cfg.project_id,
+        index_name=cfg.index_name,
-            "location": cfg.location,
+    )
-            "bucket": cfg.bucket,
+    vs.configure_index_endpoint(
-            "index_name": cfg.index_name,
+        name=cfg.endpoint_name,
-        }
+        public_domain=cfg.endpoint_domain,
     )
 
-    try:
+    genai_client = genai.Client(
-        # Initialize vector search client
+        vertexai=True,
-        log_structured_entry("Creating GoogleCloudVectorSearch client", "INFO")
+        project=cfg.project_id,
-        vs = GoogleCloudVectorSearch(
+        location=cfg.location,
-            project_id=cfg.project_id,
+    )
-            location=cfg.location,
-            bucket=cfg.bucket,
-            index_name=cfg.index_name,
-        )
 
-        # Configure endpoint
+    yield AppContext(
-        log_structured_entry(
+        vector_search=vs,
-            "Configuring index endpoint",
+        genai_client=genai_client,
-            "INFO",
+        settings=cfg,
-            {
+    )
-                "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",
@@ -636,12 +390,16 @@ mcp = FastMCP(
 async def knowledge_search(
     query: str,
     ctx: Context,
+    source_folders: list[str] | 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_folders: Optional list of source folder paths to filter results.
+                       If provided, only documents from these folders will be returned.
+                       Example: ["Educacion Financiera", "Productos y Servicios"]
 
     Returns:
         A formatted string containing matched documents with id and content.
@@ -653,128 +411,66 @@ async def knowledge_search(
     t0 = time.perf_counter()
     min_sim = 0.6
 
-    log_structured_entry(
+    response = await app.genai_client.aio.models.embed_content(
-        "knowledge_search request received",
+        model=app.settings.embedding_model,
-        "INFO",
+        contents=query,
-        {"query": query[:100]}  # Log first 100 chars of query
+        config=genai_types.EmbedContentConfig(
+            task_type="RETRIEVAL_QUERY",
+
+        ),
+    )
+    embedding = response.embeddings[0].values
+    t_embed = time.perf_counter()
+
+    # Build restricts for source folder filtering if provided
+    restricts = None
+    if source_folders:
+        restricts = [
+            {
+                "namespace": "source_folder",
+                "allow": source_folders,
+            }
+        ]
+        logger.info(f"Filtering by source_folders: {source_folders}")
+    else:
+        logger.info("No filtering - searching all folders")
+
+    search_results = await app.vector_search.async_run_query(
+        deployed_index_id=app.settings.deployed_index_id,
+        query=embedding,
+        limit=app.settings.search_limit,
+        restricts=restricts,
+    )
+    t_search = time.perf_counter()
+
+    # Log raw results from Vertex AI before similarity filtering
+    logger.info(f"Raw results from Vertex AI (before similarity filter): {len(search_results)} chunks")
+    logger.info(f"Raw chunk IDs: {[s['id'] for s in search_results]}")
+
+    # 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
+        ]
+
+    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],
     )
 
-    try:
+    # Format results as XML-like documents
-        # Generate embedding for the query
+    formatted_results = [
-        log_structured_entry("Generating query embedding", "INFO")
+        f"<document {i} name={result['id']}>\n{result['content']}\n</document {i}>"
-        try:
+        for i, result in enumerate(search_results, start=1)
-            response = await app.genai_client.aio.models.embed_content(
+    ]
-                model=app.settings.embedding_model,
+    return "\n".join(formatted_results)
-                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)
-
-            # 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,
-            )
-            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",
-                "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)
-
-    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

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

utils/config.py

@@ -1,60 +0,0 @@
-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

@@ -1,48 +0,0 @@
-"""
-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 {}}})