test(integration): address round-1 review — unify searchability helper

- Extract the duplicated `_document_is_searchable`/`_note_is_searchable`
  helpers into a shared, Playwright-free `tests/integration/_search_helpers.py`
  (`document_is_searchable`), used by both the plotly and sampling tests.
- Resolve the sampling Medium finding: `wait_for_vector_sync` now triggers the
  searchability path on `search_term` alone (matching the plotly variant)
  instead of requiring both `search_term` and `note_id`, removing the silent
  fall-through to the unreliable gauge-delta path.
- Tighten `_get_with_retry`'s `last_exc` annotation to `httpx.TransportError`.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Chris Coutinho
2026-06-17 22:49:16 +02:00
co-authored by Claude Opus 4.8
parent 3e8ec2fccd
commit eefa326c09
4 changed files with 56 additions and 60 deletions
+43
View File
@@ -0,0 +1,43 @@
"""Shared helpers for asserting vector-sync visibility in integration tests.
Kept dependency-light (no Playwright) so both the multi-user-basic UI tests and
the single-user sampling tests can import it.
"""
import json
import logging
logger = logging.getLogger(__name__)
async def document_is_searchable(
mcp_client, search_term: str, note_id: int | None = None
) -> bool:
"""Return True once a freshly-created document is retrievable.
Polls ``nc_semantic_search`` (hybrid: an exact unique term reliably matches
on the keyword side) and matches by ``note_id`` when provided, otherwise by
the term appearing in a result's title/excerpt. Transient errors return
False so callers can keep polling.
"""
try:
search = await mcp_client.call_tool(
"nc_semantic_search",
arguments={"query": search_term, "limit": 10, "score_threshold": 0.0},
)
except Exception as e: # transient transport/availability blip — keep polling
logger.debug("Semantic search poll failed: %s", e)
return False
if search.isError:
logger.debug("Semantic search poll error: %s", search)
return False
results = json.loads(search.content[0].text).get("results", [])
needle = search_term.lower()
for r in results:
if note_id is not None:
if r.get("id") == note_id and r.get("doc_type") == "note":
return True
elif needle in f"{r.get('title', '')} {r.get('excerpt', '')}".lower():
return True
return False
@@ -28,6 +28,7 @@ from playwright.async_api import Page
# Import helper functions from existing test
from tests.conftest import create_mcp_client_session
from tests.integration._search_helpers import document_is_searchable
from tests.integration.test_astrolabe_multi_user_background_sync import (
complete_astrolabe_authorization,
login_to_nextcloud,
@@ -38,38 +39,6 @@ logger = logging.getLogger(__name__)
pytestmark = [pytest.mark.integration, pytest.mark.multi_user_basic]
async def _document_is_searchable(
mcp_client, search_term: str, note_id: int | None
) -> bool:
"""Return True once the freshly-created document is retrievable.
Polls ``nc_semantic_search`` (hybrid: an exact unique term reliably matches
on the keyword side) and matches by ``note_id`` when known, otherwise by the
term appearing in a result's title/excerpt.
"""
try:
search = await mcp_client.call_tool(
"nc_semantic_search",
{"query": search_term, "limit": 10, "score_threshold": 0.0},
)
except Exception as e: # transient transport/availability blip — keep polling
logger.debug("Semantic search poll failed: %s", e)
return False
if search.isError:
logger.debug("Semantic search poll error: %s", search)
return False
results = json.loads(search.content[0].text).get("results", [])
needle = search_term.lower()
for r in results:
if note_id is not None:
if r.get("id") == note_id and r.get("doc_type") == "note":
return True
elif needle in f"{r.get('title', '')} {r.get('excerpt', '')}".lower():
return True
return False
async def wait_for_vector_sync(
mcp_client,
initial_indexed_count: int,
@@ -132,7 +101,7 @@ async def wait_for_vector_sync(
)
if search_term is not None:
if await _document_is_searchable(mcp_client, search_term, note_id):
if await document_is_searchable(mcp_client, search_term, note_id):
logger.info(
"✓ Sync complete: document %s retrievable via semantic search",
note_id,
@@ -47,7 +47,7 @@ async def _get_with_retry(
client: httpx.AsyncClient, url: str, *, retries: int = 2, **kwargs
) -> httpx.Response:
"""GET with retries on transient transport errors (timeouts/conn resets)."""
last_exc: Exception | None = None
last_exc: httpx.TransportError | None = None
for attempt in range(retries + 1):
try:
return await client.get(url, **kwargs)
+10 -26
View File
@@ -14,33 +14,16 @@ vector database with indexed test data.
"""
import json
import logging
from unittest.mock import MagicMock
import anyio
import pytest
from mcp.types import CreateMessageResult, TextContent
from tests.integration._search_helpers import document_is_searchable
pytestmark = pytest.mark.integration
logger = logging.getLogger(__name__)
async def _note_is_searchable(nc_mcp_client, search_term: str, note_id: int) -> bool:
"""Return True once ``note_id`` is retrievable via semantic search."""
try:
search = await nc_mcp_client.call_tool(
"nc_semantic_search",
arguments={"query": search_term, "limit": 10, "score_threshold": 0.0},
)
except Exception as e: # transient blip — keep polling
logger.debug("Semantic search poll failed: %s", e)
return False
if search.isError:
return False
results = json.loads(search.content[0].text).get("results", [])
return any(r.get("id") == note_id and r.get("doc_type") == "note" for r in results)
async def wait_for_vector_sync(
nc_mcp_client,
@@ -55,11 +38,12 @@ async def wait_for_vector_sync(
Args:
nc_mcp_client: MCP client to poll status with.
search_term/note_id: If set (preferred), wait until that specific
document is retrievable via ``nc_semantic_search``. This is robust
against full-corpus re-scan churn, where the corpus-wide
``indexed_count`` gauge is non-monotonic and ``indexed_count >
initial`` can never hold even though the document is indexed.
search_term: If set (preferred), wait until a document matching this
term is retrievable via ``nc_semantic_search``. Robust against
full-corpus re-scan churn, where the corpus-wide ``indexed_count``
gauge is non-monotonic and ``indexed_count > initial`` can never
hold even though the document is indexed.
note_id: Optional exact-match document id paired with ``search_term``.
initial_indexed_count: Legacy gauge-delta fallback when no search_term
is given: wait until indexed_count exceeds this value and
pending_count reaches 0.
@@ -77,9 +61,9 @@ async def wait_for_vector_sync(
)
status_data = json.loads(sync_status.content[0].text)
if search_term is not None and note_id is not None:
if search_term is not None:
# Robust signal: wait for the specific document to be retrievable
if await _note_is_searchable(nc_mcp_client, search_term, note_id):
if await document_is_searchable(nc_mcp_client, search_term, note_id):
break
elif initial_indexed_count is not None:
# Legacy: wait for new document(s) to be indexed (gauge delta)