783 lines
27 KiB
Python
783 lines
27 KiB
Python
# ruff: noqa: INP001
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"""Async helpers for querying Vertex AI vector search via MCP."""
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import asyncio
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import io
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from collections.abc import AsyncIterator, Sequence
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from contextlib import asynccontextmanager
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from dataclasses import dataclass
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from typing import BinaryIO, TypedDict
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import aiohttp
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from gcloud.aio.auth import Token
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from gcloud.aio.storage import Storage
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from google import genai
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from google.genai import types as genai_types
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from mcp.server.fastmcp import Context, FastMCP
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from utils import Settings, _args, log_structured_entry
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HTTP_TOO_MANY_REQUESTS = 429
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HTTP_SERVER_ERROR = 500
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class GoogleCloudFileStorage:
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"""Cache-aware helper for downloading files from Google Cloud Storage."""
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def __init__(self, bucket: str) -> None:
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"""Initialize the storage helper."""
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self.bucket_name = bucket
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self._aio_session: aiohttp.ClientSession | None = None
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self._aio_storage: Storage | None = None
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self._cache: dict[str, bytes] = {}
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def _get_aio_session(self) -> aiohttp.ClientSession:
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if self._aio_session is None or self._aio_session.closed:
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connector = aiohttp.TCPConnector(
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limit=300,
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limit_per_host=50,
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)
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timeout = aiohttp.ClientTimeout(total=60)
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self._aio_session = aiohttp.ClientSession(
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timeout=timeout,
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connector=connector,
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)
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return self._aio_session
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def _get_aio_storage(self) -> Storage:
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if self._aio_storage is None:
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self._aio_storage = Storage(
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session=self._get_aio_session(),
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)
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return self._aio_storage
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async def async_get_file_stream(
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self,
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file_name: str,
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max_retries: int = 3,
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) -> BinaryIO:
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"""Get a file asynchronously with retry on transient errors.
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Args:
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file_name: The blob name to retrieve.
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max_retries: Maximum number of retry attempts.
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Returns:
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A BytesIO stream with the file contents.
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Raises:
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TimeoutError: If all retry attempts fail.
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"""
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if file_name in self._cache:
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log_structured_entry(
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"File retrieved from cache",
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"INFO",
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{"file": file_name, "bucket": self.bucket_name}
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)
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file_stream = io.BytesIO(self._cache[file_name])
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file_stream.name = file_name
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return file_stream
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log_structured_entry(
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"Starting file download from GCS",
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"INFO",
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{"file": file_name, "bucket": self.bucket_name}
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)
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storage_client = self._get_aio_storage()
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last_exception: Exception | None = None
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for attempt in range(max_retries):
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try:
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self._cache[file_name] = await storage_client.download(
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self.bucket_name,
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file_name,
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)
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file_stream = io.BytesIO(self._cache[file_name])
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file_stream.name = file_name
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log_structured_entry(
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"File downloaded successfully",
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"INFO",
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{
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"file": file_name,
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"bucket": self.bucket_name,
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"size_bytes": len(self._cache[file_name]),
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"attempt": attempt + 1
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}
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)
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except TimeoutError as exc:
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last_exception = exc
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log_structured_entry(
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f"Timeout downloading gs://{self.bucket_name}/{file_name} (attempt {attempt + 1}/{max_retries})",
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"WARNING",
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{"error": str(exc)}
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)
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except aiohttp.ClientResponseError as exc:
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last_exception = exc
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if (
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exc.status == HTTP_TOO_MANY_REQUESTS
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or exc.status >= HTTP_SERVER_ERROR
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):
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log_structured_entry(
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f"HTTP {exc.status} downloading gs://{self.bucket_name}/{file_name} (attempt {attempt + 1}/{max_retries})",
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"WARNING",
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{"status": exc.status, "message": str(exc)}
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)
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else:
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log_structured_entry(
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f"Non-retryable HTTP error downloading gs://{self.bucket_name}/{file_name}",
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"ERROR",
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{"status": exc.status, "message": str(exc)}
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)
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raise
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else:
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return file_stream
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if attempt < max_retries - 1:
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delay = 0.5 * (2**attempt)
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log_structured_entry(
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"Retrying file download",
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"INFO",
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{"file": file_name, "delay_seconds": delay}
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)
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await asyncio.sleep(delay)
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msg = (
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f"Failed to download gs://{self.bucket_name}/{file_name} "
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f"after {max_retries} attempts"
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)
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log_structured_entry(
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"File download failed after all retries",
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"ERROR",
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{
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"file": file_name,
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"bucket": self.bucket_name,
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"max_retries": max_retries,
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"last_error": str(last_exception)
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}
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)
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raise TimeoutError(msg) from last_exception
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class SearchResult(TypedDict):
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"""Structured response item returned by the vector search API."""
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id: str
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distance: float
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content: str
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class GoogleCloudVectorSearch:
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"""Minimal async client for the Vertex AI Matching Engine REST API."""
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def __init__(
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self,
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project_id: str,
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location: str,
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bucket: str,
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index_name: str | None = None,
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) -> None:
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"""Store configuration used to issue Matching Engine queries."""
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self.project_id = project_id
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self.location = location
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self.storage = GoogleCloudFileStorage(bucket=bucket)
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self.index_name = index_name
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self._aio_session: aiohttp.ClientSession | None = None
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self._async_token: Token | None = None
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self._endpoint_domain: str | None = None
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self._endpoint_name: str | None = None
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async def _async_get_auth_headers(self) -> dict[str, str]:
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if self._async_token is None:
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self._async_token = Token(
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session=self._get_aio_session(),
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scopes=[
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"https://www.googleapis.com/auth/cloud-platform",
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],
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)
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access_token = await self._async_token.get()
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return {
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"Authorization": f"Bearer {access_token}",
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"Content-Type": "application/json",
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}
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def _get_aio_session(self) -> aiohttp.ClientSession:
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if self._aio_session is None or self._aio_session.closed:
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connector = aiohttp.TCPConnector(
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limit=300,
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limit_per_host=50,
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)
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timeout = aiohttp.ClientTimeout(total=60)
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self._aio_session = aiohttp.ClientSession(
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timeout=timeout,
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connector=connector,
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)
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return self._aio_session
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def configure_index_endpoint(
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self,
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*,
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name: str,
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public_domain: str,
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) -> None:
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"""Persist the metadata needed to access a deployed endpoint."""
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if not name:
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msg = "Index endpoint name must be a non-empty string."
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raise ValueError(msg)
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if not public_domain:
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msg = "Index endpoint domain must be a non-empty public domain."
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raise ValueError(msg)
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self._endpoint_name = name
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self._endpoint_domain = public_domain
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async def async_run_query(
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self,
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deployed_index_id: str,
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query: Sequence[float],
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limit: int,
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) -> list[SearchResult]:
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"""Run an async similarity search via the REST API.
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Args:
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deployed_index_id: The ID of the deployed index.
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query: The embedding vector for the search query.
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limit: Maximum number of nearest neighbors to return.
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Returns:
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A list of matched items with id, distance, and content.
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"""
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if self._endpoint_domain is None or self._endpoint_name is None:
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msg = (
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"Missing endpoint metadata. Call "
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"`configure_index_endpoint` before querying."
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)
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log_structured_entry(
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"Vector search query failed - endpoint not configured",
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"ERROR",
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{"error": msg}
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)
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raise RuntimeError(msg)
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domain = self._endpoint_domain
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endpoint_id = self._endpoint_name.split("/")[-1]
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url = (
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f"https://{domain}/v1/projects/{self.project_id}"
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f"/locations/{self.location}"
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f"/indexEndpoints/{endpoint_id}:findNeighbors"
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)
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log_structured_entry(
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"Starting vector search query",
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"INFO",
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{
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"deployed_index_id": deployed_index_id,
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"neighbor_count": limit,
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"endpoint_id": endpoint_id,
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"embedding_dimension": len(query)
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}
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)
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payload = {
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"deployed_index_id": deployed_index_id,
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"queries": [
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{
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"datapoint": {"feature_vector": list(query)},
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"neighbor_count": limit,
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},
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],
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}
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try:
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headers = await self._async_get_auth_headers()
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session = self._get_aio_session()
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async with session.post(
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url,
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json=payload,
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headers=headers,
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) as response:
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if not response.ok:
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body = await response.text()
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msg = f"findNeighbors returned {response.status}: {body}"
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log_structured_entry(
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"Vector search API request failed",
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"ERROR",
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{
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"status": response.status,
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"response_body": body,
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"deployed_index_id": deployed_index_id
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}
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)
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raise RuntimeError(msg)
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data = await response.json()
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neighbors = data.get("nearestNeighbors", [{}])[0].get("neighbors", [])
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log_structured_entry(
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"Vector search API request successful",
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"INFO",
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{
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"neighbors_found": len(neighbors),
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"deployed_index_id": deployed_index_id
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}
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)
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if not neighbors:
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log_structured_entry(
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"No neighbors found in vector search",
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"WARNING",
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{"deployed_index_id": deployed_index_id}
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)
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return []
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# Fetch content for all neighbors
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content_tasks = []
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for neighbor in neighbors:
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datapoint_id = neighbor["datapoint"]["datapointId"]
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file_path = f"{self.index_name}/contents/{datapoint_id}.md"
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content_tasks.append(
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self.storage.async_get_file_stream(file_path),
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)
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log_structured_entry(
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"Fetching content for search results",
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"INFO",
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{"file_count": len(content_tasks)}
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)
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file_streams = await asyncio.gather(*content_tasks)
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results: list[SearchResult] = []
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for neighbor, stream in zip(
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neighbors,
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file_streams,
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strict=True,
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):
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results.append(
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SearchResult(
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id=neighbor["datapoint"]["datapointId"],
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distance=neighbor["distance"],
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content=stream.read().decode("utf-8"),
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),
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)
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log_structured_entry(
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"Vector search completed successfully",
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"INFO",
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{
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"results_count": len(results),
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"deployed_index_id": deployed_index_id
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}
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)
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return results
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except Exception as e:
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log_structured_entry(
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"Vector search query failed with exception",
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"ERROR",
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{
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"error": str(e),
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"error_type": type(e).__name__,
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"deployed_index_id": deployed_index_id
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}
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)
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raise
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# ---------------------------------------------------------------------------
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# MCP Server
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# ---------------------------------------------------------------------------
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@dataclass
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class AppContext:
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"""Shared resources initialised once at server startup."""
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vector_search: GoogleCloudVectorSearch
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genai_client: genai.Client
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settings: Settings
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@asynccontextmanager
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async def lifespan(_server: FastMCP) -> AsyncIterator[AppContext]:
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"""Create and configure the vector-search client for the server lifetime."""
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log_structured_entry(
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"Initializing MCP server",
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"INFO",
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{
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"project_id": cfg.project_id,
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"location": cfg.location,
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"bucket": cfg.bucket,
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"index_name": cfg.index_name,
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}
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)
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try:
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# Initialize vector search client
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log_structured_entry("Creating GoogleCloudVectorSearch client", "INFO")
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vs = GoogleCloudVectorSearch(
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project_id=cfg.project_id,
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location=cfg.location,
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bucket=cfg.bucket,
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index_name=cfg.index_name,
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)
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# Configure endpoint
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log_structured_entry(
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"Configuring index endpoint",
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"INFO",
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{
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"endpoint_name": cfg.endpoint_name,
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"endpoint_domain": cfg.endpoint_domain,
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}
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)
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vs.configure_index_endpoint(
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name=cfg.endpoint_name,
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public_domain=cfg.endpoint_domain,
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)
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# Initialize GenAI client
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log_structured_entry(
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"Creating GenAI client",
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"INFO",
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{"project_id": cfg.project_id, "location": cfg.location}
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)
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genai_client = genai.Client(
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vertexai=True,
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project=cfg.project_id,
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location=cfg.location,
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)
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# Validate credentials and configuration by testing actual resources
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# These validations are non-blocking - errors are logged but won't stop startup
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log_structured_entry("Starting validation of credentials and resources", "INFO")
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validation_errors = []
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# 1. Validate GenAI embedding access
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log_structured_entry("Validating GenAI embedding access", "INFO")
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try:
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test_response = await genai_client.aio.models.embed_content(
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model=cfg.embedding_model,
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contents="test",
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config=genai_types.EmbedContentConfig(
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task_type="RETRIEVAL_QUERY",
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),
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)
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if test_response and test_response.embeddings:
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log_structured_entry(
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"GenAI embedding validation successful",
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"INFO",
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{"embedding_dimension": len(test_response.embeddings[0].values)}
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)
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else:
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msg = "Embedding validation returned empty response"
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log_structured_entry(msg, "WARNING")
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validation_errors.append(msg)
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except Exception as e:
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log_structured_entry(
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"Failed to validate GenAI embedding access - service may not work correctly",
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"WARNING",
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{"error": str(e), "error_type": type(e).__name__}
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)
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validation_errors.append(f"GenAI: {str(e)}")
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# 2. Validate GCS bucket access
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log_structured_entry(
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"Validating GCS bucket access",
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"INFO",
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{"bucket": cfg.bucket}
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)
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try:
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session = vs.storage._get_aio_session()
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token_obj = Token(
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session=session,
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scopes=["https://www.googleapis.com/auth/cloud-platform"],
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)
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access_token = await token_obj.get()
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headers = {"Authorization": f"Bearer {access_token}"}
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async with session.get(
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f"https://storage.googleapis.com/storage/v1/b/{cfg.bucket}/o?maxResults=1",
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headers=headers,
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) as response:
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if response.status == 403:
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msg = f"Access denied to bucket '{cfg.bucket}'. Check permissions."
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log_structured_entry(
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"GCS bucket validation failed - access denied - service may not work correctly",
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"WARNING",
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{"bucket": cfg.bucket, "status": response.status}
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)
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validation_errors.append(msg)
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elif response.status == 404:
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msg = f"Bucket '{cfg.bucket}' not found. Check bucket name and project."
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log_structured_entry(
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"GCS bucket validation failed - not found - service may not work correctly",
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"WARNING",
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{"bucket": cfg.bucket, "status": response.status}
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)
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validation_errors.append(msg)
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elif not response.ok:
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body = await response.text()
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msg = f"Failed to access bucket '{cfg.bucket}': {response.status}"
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log_structured_entry(
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"GCS bucket validation failed - service may not work correctly",
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"WARNING",
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{"bucket": cfg.bucket, "status": response.status, "response": body}
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)
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validation_errors.append(msg)
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else:
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log_structured_entry(
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"GCS bucket validation successful",
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"INFO",
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{"bucket": cfg.bucket}
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)
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except Exception as e:
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log_structured_entry(
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"Failed to validate GCS bucket access - service may not work correctly",
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"WARNING",
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{"error": str(e), "error_type": type(e).__name__, "bucket": cfg.bucket}
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)
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validation_errors.append(f"GCS: {str(e)}")
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# 3. Validate vector search endpoint access
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log_structured_entry(
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"Validating vector search endpoint access",
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"INFO",
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{"endpoint_name": cfg.endpoint_name}
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)
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try:
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# Try to get endpoint info
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headers = await vs._async_get_auth_headers()
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session = vs._get_aio_session()
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endpoint_url = (
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f"https://{cfg.location}-aiplatform.googleapis.com/v1/{cfg.endpoint_name}"
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)
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async with session.get(endpoint_url, headers=headers) as response:
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if response.status == 403:
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msg = f"Access denied to endpoint '{cfg.endpoint_name}'. Check permissions."
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log_structured_entry(
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"Vector search endpoint validation failed - access denied - service may not work correctly",
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"WARNING",
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|
{"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",
|
|
host=_args.host,
|
|
port=_args.port,
|
|
lifespan=lifespan,
|
|
)
|
|
|
|
|
|
@mcp.tool()
|
|
async def knowledge_search(
|
|
query: str,
|
|
ctx: Context,
|
|
) -> str:
|
|
"""Search a knowledge base using a natural-language query.
|
|
|
|
Args:
|
|
query: The text query to search for.
|
|
ctx: MCP request context (injected automatically).
|
|
|
|
Returns:
|
|
A formatted string containing matched documents with id and content.
|
|
|
|
"""
|
|
import time
|
|
|
|
app: AppContext = ctx.request_context.lifespan_context
|
|
t0 = time.perf_counter()
|
|
min_sim = 0.6
|
|
|
|
log_structured_entry(
|
|
"knowledge_search request received",
|
|
"INFO",
|
|
{"query": query[:100]} # Log first 100 chars of query
|
|
)
|
|
|
|
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)
|
|
|
|
# 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__":
|
|
mcp.run(transport=_args.transport)
|