forked from va/knowledge-search-mcp
initial implementation
This commit is contained in:
1
.python-version
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1
.python-version
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3.12
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3
AGENTS.md
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3
AGENTS.md
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Use `uv` for project management
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Linter: `uv run ruff check`
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Type-checking: `uv run ty check`
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401
main.py
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main.py
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# 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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import logging
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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 pydantic_settings import BaseSettings
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logger = logging.getLogger(__name__)
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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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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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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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except TimeoutError as exc:
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last_exception = exc
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logger.warning(
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"Timeout downloading gs://%s/%s (attempt %d/%d)",
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self.bucket_name,
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file_name,
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attempt + 1,
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max_retries,
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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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logger.warning(
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"HTTP %d downloading gs://%s/%s (attempt %d/%d)",
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exc.status,
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self.bucket_name,
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file_name,
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attempt + 1,
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max_retries,
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)
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else:
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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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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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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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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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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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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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response.raise_for_status()
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data = await response.json()
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neighbors = data.get("nearestNeighbors", [{}])[0].get("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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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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return results
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# ---------------------------------------------------------------------------
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# MCP Server
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# ---------------------------------------------------------------------------
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class Settings(BaseSettings):
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"""Server configuration populated from environment variables."""
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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
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deployed_index_id: str
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endpoint_name: str
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endpoint_domain: str
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embedding_model: str = "text-embedding-005"
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search_limit: int = 10
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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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cfg = Settings.model_validate({})
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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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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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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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yield AppContext(
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vector_search=vs,
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genai_client=genai_client,
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settings=cfg,
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)
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mcp = FastMCP("knowledge-search", lifespan=lifespan)
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@mcp.tool()
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async def knowledge_search(
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query: str,
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ctx: Context,
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) -> str:
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"""Search a knowledge base using a natural-language query.
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Args:
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query: The text query to search for.
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ctx: MCP request context (injected automatically).
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Returns:
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A formatted string containing matched documents with id and content.
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"""
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import time
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app: AppContext = ctx.request_context.lifespan_context
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t0 = time.perf_counter()
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min_sim = 0.6
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response = await app.genai_client.aio.models.embed_content(
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model=app.settings.embedding_model,
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contents=query,
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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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embedding = response.embeddings[0].values
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t_embed = time.perf_counter()
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search_results = await app.vector_search.async_run_query(
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deployed_index_id=app.settings.deployed_index_id,
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query=embedding,
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limit=app.settings.search_limit,
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)
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t_search = time.perf_counter()
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# Apply similarity filtering
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if search_results:
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max_sim = max(r["distance"] for r in search_results)
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cutoff = max_sim * 0.9
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search_results = [
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s
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for s in search_results
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if s["distance"] > cutoff and s["distance"] > min_sim
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]
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logger.info(
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"knowledge_search timing: embedding=%sms, vector_search=%sms, total=%sms, chunks=%s",
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round((t_embed - t0) * 1000, 1),
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round((t_search - t_embed) * 1000, 1),
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round((t_search - t0) * 1000, 1),
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[s["id"] for s in search_results],
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)
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# Format results as XML-like documents
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formatted_results = [
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f"<document {i} name={result['id']}>\n{result['content']}\n</document {i}>"
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for i, result in enumerate(search_results, start=1)
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]
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return "\n".join(formatted_results)
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if __name__ == "__main__":
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mcp.run()
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21
pyproject.toml
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21
pyproject.toml
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[project]
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name = "knowledge-search-mcp"
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version = "0.1.0"
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description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.12"
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dependencies = [
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"aiohttp>=3.13.3",
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"gcloud-aio-auth>=5.4.2",
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"gcloud-aio-storage>=9.6.1",
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"google-auth>=2.48.0",
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"google-genai>=1.64.0",
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"mcp[cli]>=1.26.0",
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"pydantic-settings>=2.9.1",
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]
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[dependency-groups]
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dev = [
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"ruff>=0.15.2",
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"ty>=0.0.18",
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]
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Reference in New Issue
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