Activate ALL Ruff rules with some exclusions

libs/astradb/langchain_astradb/vectorstores.py

@@ -1233,7 +1233,7 @@ def similarity_search(
         self,
         query: str,
         k: int = 4,
-        filter: dict[str, Any] | None = None,  # noqa: A002
+        filter: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs most similar to query.
@@ -1268,7 +1268,7 @@ async def asimilarity_search(
         self,
         query: str,
         k: int = 4,
-        filter: dict[str, Any] | None = None,  # noqa: A002
+        filter: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs most similar to query.
@@ -1307,7 +1307,7 @@ def similarity_search_by_vector(
         self,
         embedding: list[float],
         k: int = 4,
-        filter: dict[str, Any] | None = None,  # noqa: A002
+        filter: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs most similar to embedding vector.
@@ -1334,7 +1334,7 @@ async def asimilarity_search_by_vector(
         self,
         embedding: list[float],
         k: int = 4,
-        filter: dict[str, Any] | None = None,  # noqa: A002
+        filter: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs most similar to embedding vector.
@@ -1361,7 +1361,7 @@ def similarity_search_with_score(
         self,
         query: str,
         k: int = 4,
-        filter: dict[str, Any] | None = None,  # noqa: A002
+        filter: dict[str, Any] | None = None,
     ) -> list[tuple[Document, float]]:
         """Return docs most similar to query with score.
 
@@ -1399,7 +1399,7 @@ async def asimilarity_search_with_score(
         self,
         query: str,
         k: int = 4,
-        filter: dict[str, Any] | None = None,  # noqa: A002
+        filter: dict[str, Any] | None = None,
     ) -> list[tuple[Document, float]]:
         """Return docs most similar to query with score.
 
@@ -1530,7 +1530,7 @@ def max_marginal_relevance_search_by_vector(
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
-        filter: dict[str, Any] | None = None,  # noqa: A002
+        filter: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected using the maximal marginal relevance.
@@ -1568,7 +1568,7 @@ async def amax_marginal_relevance_search_by_vector(
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
-        filter: dict[str, Any] | None = None,  # noqa: A002
+        filter: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected using the maximal marginal relevance.
@@ -1606,7 +1606,7 @@ def max_marginal_relevance_search(
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
-        filter: dict[str, Any] | None = None,  # noqa: A002
+        filter: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected using the maximal marginal relevance.
@@ -1655,7 +1655,7 @@ async def amax_marginal_relevance_search(
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
-        filter: dict[str, Any] | None = None,  # noqa: A002
+        filter: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> list[Document]:
         """Return docs selected using the maximal marginal relevance.

libs/astradb/pyproject.toml

@@ -63,10 +63,46 @@ optional = true
 langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core" }
 
 [tool.ruff.lint]
-select = [
-  "E", # pycodestyle
-  "F", # pyflakes
-  "I", # isort
+pydocstyle.convention = "google"
+pep8-naming.classmethod-decorators = [
+    "langchain_core.pydantic_v1.validator",
+]
+select = ["ALL"]
+ignore = [
+    "ANN", # Already checked by mypy
+    "C90", # Do we want to activate (complexity) ?
+    "COM812", # Messes with the formatter
+    "D100", # Do we want to activate (docstring in module) ?
+    "D104", # Do we want to activate (docstring in package) ?
+    "D105", # Do we want to activate (docstring in magic method) ?
+    "D107", # Do we want to activate (docstring in __init__) ?
+    "EM", # Do we want to activate (error messages) ?
+    "ERA", # Do we want to activate (no commented code) ?
+    "FBT", # Do we want to activate (boolean trap) ?
+    "ISC001", # Messes with the formatter
+    "PERF203", # Incorrect detection
+    "PLR09", # TODO: do we enforce these ones (complexity) ?
+    "PTH", # Do we want to activate (use pathlib) ?
+    "TRY003", # A bit too strict ?
+
+    "BLE001", # TODO
+    "PT011", # TODO
+    "PT012", # TODO
+    "D101", # TODO
+    "D417", # TODO
+
+]
+
+[tool.ruff.lint.per-file-ignores]
+"tests/*" = [
+    "D",
+    "S101",
+    "SLF001",
+    "T201",
+    "PLR2004",
+]
+"scripts/*" = [
+    "T201",
 ]
 
 [tool.mypy]

libs/astradb/tests/integration_tests/test_caches.py

@@ -65,8 +65,8 @@ async def aembed_query(self, text: str) -> list[float]:
 class FakeLLM(LLM):
     """Fake LLM wrapper for testing purposes."""
 
-    queries: Optional[Mapping] = None
-    sequential_responses: Optional[bool] = False
+    queries: Optional[Mapping] = None  # noqa: UP007
+    sequential_responses: Optional[bool] = False  # noqa: UP007
     response_index: int = 0
 
     @validator("queries", always=True)

libs/astradb/tests/integration_tests/test_document_loaders.py

@@ -283,7 +283,6 @@ def test_astradb_loader_findoptions_deprecation(
         self,
         astra_db_credentials: AstraDBCredentials,
         collection: Collection,
-        core_astra_db: AstraDB,
     ) -> None:
         """Test deprecation of 'find_options' and related warnings/errors."""
         loader0 = AstraDBLoader(