fix(vector): address PR review round 9 — drop redundant guard, add init lock, test float doc_id path
Addresses the four 🟡 important findings from claude-bot review on PR #773: str (non-Optional) and the guard would silently skip the Qdrant lookup for an empty string. Removing the guard matches the type signature. (`all([…, doc_id, …])` rejects None and empty string, plus `assert doc_id is not None`). No code change needed. `get_qdrant_client()` with a module-level `anyio.Lock`. Double-checked locking keeps the steady-state hot path lock-free. Without this, parallel cold-start callers could all enter the init block and run `_backfill_doc_id_to_string` + `_ensure_payload_indexes` redundantly (idempotent, but noisy). Pattern matches `auth/storage.py:2071`. behavior with three tests covering the float-warning path (the gap called out in the review), the str/None silent-skip paths, and the int-grouping happy path. Verification: - ruff check / format: clean - ty check -- nextcloud_mcp_server: clean - uv run pytest tests/unit/: 969 passed Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Chris Coutinho
co-authored by
Claude Opus 4.7
Claude Opus 4.7
parent
0c14501a2b
commit
fec1596784
@@ -3,6 +3,7 @@
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import logging
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from typing import Any
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import anyio
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from qdrant_client import AsyncQdrantClient, models
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from qdrant_client.http.exceptions import UnexpectedResponse
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from qdrant_client.models import (
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@@ -43,8 +44,13 @@ _PAYLOAD_INDEX_FIELDS: dict[str, PayloadSchemaType] = {
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_DOC_ID_BACKFILL_SENTINEL_ID: str = "00000000-0000-0000-0000-d0c1d0d1d0c1"
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_DOC_ID_BACKFILL_SENTINEL_PAYLOAD: dict[str, str] = {"_migration_marker": "doc_id_v1"}
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# Singleton instance
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# Singleton instance + init lock. The lock serialises concurrent first
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# callers so the idempotent-but-expensive startup migration
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# (``_backfill_doc_id_to_string`` + ``_ensure_payload_indexes``) only runs
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# once per process. Steady-state callers hit the fast path above the lock
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# and never acquire it.
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_qdrant_client: AsyncQdrantClient | None = None
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_qdrant_init_lock: anyio.Lock = anyio.Lock()
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async def _ensure_payload_indexes(
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@@ -392,131 +398,151 @@ async def get_qdrant_client() -> AsyncQdrantClient:
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"""
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global _qdrant_client
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if _qdrant_client is None:
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settings = get_settings()
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# Fast path: already initialized — skip lock acquisition for the
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# steady-state hot path (every MCP tool call after first start).
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if _qdrant_client is not None:
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return _qdrant_client
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# Detect mode and initialize client accordingly
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if settings.qdrant_url:
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# Network mode
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logger.info(f"Using Qdrant network mode: {settings.qdrant_url}")
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_qdrant_client = AsyncQdrantClient(
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url=settings.qdrant_url,
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api_key=settings.qdrant_api_key,
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timeout=30,
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)
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elif settings.qdrant_location:
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# Local mode (either :memory: or persistent path)
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if settings.qdrant_location == ":memory:":
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logger.info("Using Qdrant in-memory mode: :memory:")
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# Slow path: serialise concurrent first-callers so the idempotent-but-
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# expensive startup migration (``_backfill_doc_id_to_string`` +
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# ``_ensure_payload_indexes``) runs exactly once. Without this lock,
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# parallel cold-start callers would all enter the init block, run the
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# migration N times, and emit duplicate "skip-because-exists" warnings
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# from the index helper — annoying log noise but not data corruption.
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async with _qdrant_init_lock:
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# Double-checked: another waiter may have initialized while we
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# blocked on the lock.
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if _qdrant_client is None:
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settings = get_settings()
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# Detect mode and initialize client accordingly
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if settings.qdrant_url:
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# Network mode
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logger.info(f"Using Qdrant network mode: {settings.qdrant_url}")
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_qdrant_client = AsyncQdrantClient(
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url=settings.qdrant_url,
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api_key=settings.qdrant_api_key,
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timeout=30,
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)
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elif settings.qdrant_location:
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# Local mode (either :memory: or persistent path)
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if settings.qdrant_location == ":memory:":
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logger.info("Using Qdrant in-memory mode: :memory:")
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_qdrant_client = AsyncQdrantClient(":memory:")
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else:
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# Persistent local mode - use path parameter
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logger.info(
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f"Using Qdrant persistent mode: {settings.qdrant_location}"
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)
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_qdrant_client = AsyncQdrantClient(path=settings.qdrant_location)
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else:
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# Should not happen due to __post_init__ validation, but handle gracefully
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logger.warning("No Qdrant mode configured, defaulting to :memory:")
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_qdrant_client = AsyncQdrantClient(":memory:")
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else:
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# Persistent local mode - use path parameter
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logger.info(f"Using Qdrant persistent mode: {settings.qdrant_location}")
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_qdrant_client = AsyncQdrantClient(path=settings.qdrant_location)
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else:
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# Should not happen due to __post_init__ validation, but handle gracefully
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logger.warning("No Qdrant mode configured, defaulting to :memory:")
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_qdrant_client = AsyncQdrantClient(":memory:")
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# Get collection name (auto-generated from deployment ID + model)
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collection_name = settings.get_collection_name()
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# Get collection name (auto-generated from deployment ID + model)
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collection_name = settings.get_collection_name()
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embedding_service = get_embedding_service()
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embedding_service = get_embedding_service()
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# Detect dimension dynamically (for OllamaEmbeddingProvider)
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if hasattr(embedding_service.provider, "_detect_dimension"):
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await embedding_service.provider._detect_dimension() # type: ignore[call-non-callable]
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# Detect dimension dynamically (for OllamaEmbeddingProvider)
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if hasattr(embedding_service.provider, "_detect_dimension"):
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await embedding_service.provider._detect_dimension() # type: ignore[call-non-callable]
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expected_dimension = embedding_service.get_dimension()
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expected_dimension = embedding_service.get_dimension()
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# Explicitly check if collection exists
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logger.debug(f"Checking if collection '{collection_name}' exists...")
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collections = await _qdrant_client.get_collections()
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collection_names = [c.name for c in collections.collections]
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# Explicitly check if collection exists
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logger.debug(f"Checking if collection '{collection_name}' exists...")
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collections = await _qdrant_client.get_collections()
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collection_names = [c.name for c in collections.collections]
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if collection_name in collection_names:
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# Collection exists - validate dimensions
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logger.debug(
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f"Collection '{collection_name}' found, validating dimensions..."
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)
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collection_info = await _qdrant_client.get_collection(collection_name)
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# Handle both named vectors (dict) and legacy single vector
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vectors = collection_info.config.params.vectors
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if isinstance(vectors, dict):
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actual_dimension = vectors["dense"].size
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else:
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# Type narrowing: vectors must be VectorParams if not dict
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assert isinstance(vectors, VectorParams)
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actual_dimension = vectors.size
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if collection_name in collection_names:
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# Collection exists - validate dimensions
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logger.debug(
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f"Collection '{collection_name}' found, validating dimensions..."
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)
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collection_info = await _qdrant_client.get_collection(collection_name)
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# Handle both named vectors (dict) and legacy single vector
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vectors = collection_info.config.params.vectors
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if isinstance(vectors, dict):
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actual_dimension = vectors["dense"].size
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else:
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# Type narrowing: vectors must be VectorParams if not dict
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assert isinstance(vectors, VectorParams)
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actual_dimension = vectors.size
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# Validate dimension matches
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if actual_dimension != expected_dimension:
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embedding_model = settings.get_embedding_model_name()
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raise ValueError(
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f"Dimension mismatch for collection '{collection_name}':\n"
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f" Expected: {expected_dimension} (from embedding model '{embedding_model}')\n"
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f" Found: {actual_dimension}\n"
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f"This usually means you changed the embedding model.\n"
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f"Solutions:\n"
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f" 1. Delete the old collection: Collection will be recreated with new dimensions\n"
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f" 2. Set QDRANT_COLLECTION to use a different collection name\n"
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f" 3. Revert to the original embedding model"
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# Validate dimension matches
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if actual_dimension != expected_dimension:
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embedding_model = settings.get_embedding_model_name()
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raise ValueError(
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f"Dimension mismatch for collection '{collection_name}':\n"
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f" Expected: {expected_dimension} (from embedding model '{embedding_model}')\n"
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f" Found: {actual_dimension}\n"
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f"This usually means you changed the embedding model.\n"
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f"Solutions:\n"
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f" 1. Delete the old collection: Collection will be recreated with new dimensions\n"
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f" 2. Set QDRANT_COLLECTION to use a different collection name\n"
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f" 3. Revert to the original embedding model"
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)
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logger.info(
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f"Using existing Qdrant collection: {collection_name} "
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f"(dimension={actual_dimension}, model={settings.get_embedding_model_name()})"
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)
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logger.info(
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f"Using existing Qdrant collection: {collection_name} "
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f"(dimension={actual_dimension}, model={settings.get_embedding_model_name()})"
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)
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# Existing collections may pre-date the doc_id normalization /
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# payload-index work. Backfill before creating the index so the
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# index covers every point. Pass the already-fetched
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# collection_info.payload_schema through to avoid a redundant
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# get_collection round-trip on every restart.
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await _backfill_doc_id_to_string(
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_qdrant_client, collection_name, expected_dimension
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)
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await _ensure_payload_indexes(
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_qdrant_client,
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collection_name,
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existing_schema=collection_info.payload_schema or {},
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)
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# Existing collections may pre-date the doc_id normalization /
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# payload-index work. Backfill before creating the index so the
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# index covers every point. Pass the already-fetched
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# collection_info.payload_schema through to avoid a redundant
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# get_collection round-trip on every restart.
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await _backfill_doc_id_to_string(
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_qdrant_client, collection_name, expected_dimension
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)
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await _ensure_payload_indexes(
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_qdrant_client,
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collection_name,
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existing_schema=collection_info.payload_schema or {},
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)
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else:
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# Collection doesn't exist - create it
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embedding_model = settings.get_embedding_model_name()
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logger.info(
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f"Collection '{collection_name}' not found, creating with "
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f"dimension={expected_dimension}, model={embedding_model}..."
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)
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await _qdrant_client.create_collection(
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collection_name=collection_name,
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vectors_config={
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"dense": VectorParams(
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size=expected_dimension,
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distance=Distance.COSINE,
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),
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},
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sparse_vectors_config={
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"sparse": models.SparseVectorParams(
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index=models.SparseIndexParams(
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on_disk=False,
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)
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),
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},
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)
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logger.info(
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f"Created Qdrant collection: {collection_name}\n"
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f" Dense vector dimension: {expected_dimension}\n"
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f" Dense embedding model: {embedding_model}\n"
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f" Sparse vectors: BM25 (for hybrid search)\n"
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f" Distance: COSINE\n"
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f"Background sync will index all documents with dense + sparse vectors."
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)
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# Freshly created collection has no payload schema yet; pass {}
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# explicitly to skip the otherwise-redundant get_collection call.
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await _ensure_payload_indexes(
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_qdrant_client, collection_name, existing_schema={}
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)
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else:
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# Collection doesn't exist - create it
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embedding_model = settings.get_embedding_model_name()
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logger.info(
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f"Collection '{collection_name}' not found, creating with "
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f"dimension={expected_dimension}, model={embedding_model}..."
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)
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await _qdrant_client.create_collection(
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collection_name=collection_name,
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vectors_config={
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"dense": VectorParams(
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size=expected_dimension,
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distance=Distance.COSINE,
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),
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},
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sparse_vectors_config={
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"sparse": models.SparseVectorParams(
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index=models.SparseIndexParams(
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on_disk=False,
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)
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),
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},
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)
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logger.info(
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f"Created Qdrant collection: {collection_name}\n"
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f" Dense vector dimension: {expected_dimension}\n"
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f" Dense embedding model: {embedding_model}\n"
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f" Sparse vectors: BM25 (for hybrid search)\n"
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f" Distance: COSINE\n"
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f"Background sync will index all documents with dense + sparse vectors."
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)
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# Freshly created collection has no payload schema yet; pass {}
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# explicitly to skip the otherwise-redundant get_collection call.
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await _ensure_payload_indexes(
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_qdrant_client, collection_name, existing_schema={}
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)
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# Lock released. ``_qdrant_client`` is guaranteed non-None here:
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# either the fast path returned earlier, the lock-protected branch
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# set it, or a sibling waiter set it before we got the lock.
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assert _qdrant_client is not None
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return _qdrant_client
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