refactor: convert f-string logging to lazy %-style format (G004)
Sweep all 1676 G004 violations across 112 files, converting
`logger.<level>(f"…{x}…")` to `logger.<level>("…%s…", x)`.
Why: ruff rule G004 was added to pyproject.toml to enforce lazy
%-style logging — defers formatting until the log level is enabled
and lets structured log tooling match the unformatted template.
Conversion preserves rendered output byte-for-byte:
- `{x}` → `%s` + `x`
- `{x!r}` / `{x!s}` / `{x!a}` → `%r` / `%s` / `%a`
- Format specs (`{x:.2f}`, `{x:>10}`) → `%s` + `format(x, 'spec')`
(printf-style specs aren't 1:1 with Python format specs, so we
delegate to `format()` to keep identical output)
- Literal `%` → `%%`
- Concatenated f-strings (`f"a {x} " "b"`) flattened
- Trailing kwargs (`exc_info=True`) preserved
Verified:
- `uv run ruff check --select G004` → 0 violations
- `uv run ty check -- nextcloud_mcp_server` → passes
- `uv run pytest tests/unit/` → 1010 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
a4e6125d28
commit
665cb9b1eb
@@ -534,7 +534,7 @@ async def get_qdrant_client() -> AsyncQdrantClient:
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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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logger.info("Using Qdrant network mode: %s", settings.qdrant_url)
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provisional = AsyncQdrantClient(
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url=settings.qdrant_url,
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api_key=settings.qdrant_api_key,
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@@ -548,7 +548,7 @@ async def get_qdrant_client() -> AsyncQdrantClient:
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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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"Using Qdrant persistent mode: %s", settings.qdrant_location
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)
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provisional = AsyncQdrantClient(path=settings.qdrant_location)
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else:
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@@ -580,14 +580,14 @@ async def get_qdrant_client() -> AsyncQdrantClient:
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# `get_collection()`) is the only existence-probe permitted on a
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# collection-scoped JWT — it returns 200 with the collection
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# detail on hit and 404 on miss.
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logger.debug(f"Fetching collection '{collection_name}' details...")
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logger.debug("Fetching collection '%s' details...", collection_name)
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collection_info = None
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try:
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collection_info = await provisional.get_collection(collection_name)
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except UnexpectedResponse as exc:
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if exc.status_code != 404:
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raise
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logger.debug(f"Collection '{collection_name}' not found (404).")
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logger.debug("Collection '%s' not found (404).", collection_name)
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except ValueError as exc:
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# Local/in-memory qdrant_client raises ValueError(f"Collection
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# {name} not found") instead of UnexpectedResponse — see
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@@ -602,12 +602,12 @@ async def get_qdrant_client() -> AsyncQdrantClient:
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# multi-user-basic CI jobs all exercise this path.
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if "not found" not in str(exc):
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raise
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logger.debug(f"Collection '{collection_name}' not found (local mode).")
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logger.debug("Collection '%s' not found (local mode).", collection_name)
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if collection_info is not None:
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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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"Collection '%s' found, validating dimensions...", collection_name
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)
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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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@@ -633,8 +633,10 @@ async def get_qdrant_client() -> AsyncQdrantClient:
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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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"Using existing Qdrant collection: %s (dimension=%s, model=%s)",
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collection_name,
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actual_dimension,
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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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@@ -658,8 +660,10 @@ async def get_qdrant_client() -> AsyncQdrantClient:
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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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"Collection '%s' not found, creating with dimension=%s, model=%s...",
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collection_name,
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expected_dimension,
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embedding_model,
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)
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await provisional.create_collection(
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collection_name=collection_name,
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@@ -678,12 +682,10 @@ async def get_qdrant_client() -> AsyncQdrantClient:
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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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"Created Qdrant collection: %s\\n Dense vector dimension: %s\\n Dense embedding model: %s\\n Sparse vectors: BM25 (for hybrid search)\\n Distance: COSINE\\nBackground sync will index all documents with dense + sparse vectors.",
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collection_name,
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expected_dimension,
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embedding_model,
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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
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