formatting

Author: daric93

integrations/valkey/src/haystack_integrations/document_stores/valkey/document_store.py

@@ -135,26 +135,26 @@ class ValkeyDocumentStore(DocumentStore):
     _DUMMY_VALUE = -10.0
 
     def __init__(
-            self,
-            # Connection configuration
-            nodes_list: list[tuple[str, int]] | None = None,
-            *,
-            cluster_mode: bool = False,
-            # Security and authentication configuration
-            use_tls: bool = False,
-            username: Secret | None = Secret.from_env_var("VALKEY_USERNAME", strict=False),  # noqa: B008
-            password: Secret | None = Secret.from_env_var("VALKEY_PASSWORD", strict=False),  # noqa: B008
-            # Client timeout and retry configuration
-            request_timeout: int = 500,
-            retry_attempts: int = 3,
-            retry_base_delay_ms: int = 1000,
-            retry_exponent_base: int = 2,
-            # Document store operation configuration
-            batch_size: int = 100,
-            # Index and vector configuration
-            index_name: str = "haystack_document",
-            distance_metric: Literal["l2", "cosine", "ip"] = "cosine",
-            embedding_dim: int = 768,
+        self,
+        # Connection configuration
+        nodes_list: list[tuple[str, int]] | None = None,
+        *,
+        cluster_mode: bool = False,
+        # Security and authentication configuration
+        use_tls: bool = False,
+        username: Secret | None = Secret.from_env_var("VALKEY_USERNAME", strict=False),  # noqa: B008
+        password: Secret | None = Secret.from_env_var("VALKEY_PASSWORD", strict=False),  # noqa: B008
+        # Client timeout and retry configuration
+        request_timeout: int = 500,
+        retry_attempts: int = 3,
+        retry_base_delay_ms: int = 1000,
+        retry_exponent_base: int = 2,
+        # Document store operation configuration
+        batch_size: int = 100,
+        # Index and vector configuration
+        index_name: str = "haystack_document",
+        distance_metric: Literal["l2", "cosine", "ip"] = "cosine",
+        embedding_dim: int = 768,
     ):
         self._index_name = index_name
         self._distance_metric = self._parse_metric(distance_metric)
@@ -605,7 +605,7 @@ def write_documents(self, documents: list[Document], policy: DuplicatePolicy = D
         return written_count
 
     async def write_documents_async(
-            self, documents: list[Document], policy: DuplicatePolicy = DuplicatePolicy.NONE
+        self, documents: list[Document], policy: DuplicatePolicy = DuplicatePolicy.NONE
     ) -> int:
         """
         Asynchronously write documents to the document store.
@@ -658,7 +658,7 @@ def write_single_doc(doc: Document) -> Any:
 
         written_count = 0
         for i in range(0, len(documents), self._batch_size):
-            batch = documents[i: i + self._batch_size]
+            batch = documents[i : i + self._batch_size]
             try:
                 await asyncio.gather(*[write_single_doc(doc) for doc in batch])
                 written_count += len(batch)
@@ -807,12 +807,12 @@ async def delete_all_documents_async(self) -> None:
             raise ValkeyDocumentStoreError(msg) from e
 
     def _embedding_retrieval(
-            self,
-            embedding: list[float],
-            filters: dict[str, Any] | None = None,
-            limit: int = 10,
-            *,
-            with_embedding: bool = True,
+        self,
+        embedding: list[float],
+        filters: dict[str, Any] | None = None,
+        limit: int = 10,
+        *,
+        with_embedding: bool = True,
     ) -> list[Document]:
         """
         Retrieve documents using vector similarity.
@@ -882,12 +882,12 @@ def _embedding_retrieval(
             raise ValkeyDocumentStoreError(msg) from e
 
     async def _embedding_retrieval_async(
-            self,
-            embedding: list[float],
-            filters: dict[str, Any] | None = None,
-            limit: int = 10,
-            *,
-            with_embedding: bool = True,
+        self,
+        embedding: list[float],
+        filters: dict[str, Any] | None = None,
+        limit: int = 10,
+        *,
+        with_embedding: bool = True,
     ) -> list[Document]:
         """
         Asynchronously retrieve documents using vector similarity.
@@ -1027,7 +1027,7 @@ def _parse_documents_from_ft(raw: Any, *, with_embedding: bool) -> list[Document
 
     @staticmethod
     def _build_search_query_and_options(
-            embedding: list[float], filters: dict[str, Any] | None, limit: int, *, with_embedding: bool
+        embedding: list[float], filters: dict[str, Any] | None, limit: int, *, with_embedding: bool
     ) -> tuple[str, FtSearchOptions]:
         # Validate and normalize filters
         if filters:

integrations/valkey/tests/test_document_store.py

@@ -613,7 +613,7 @@ def test_to_float32_bytes(self):
         assert len(result) == len(vec) * 4
 
         # Verify correct values by unpacking
-        unpacked = [struct.unpack("<f", result[i: i + 4])[0] for i in range(0, len(result), 4)]
+        unpacked = [struct.unpack("<f", result[i : i + 4])[0] for i in range(0, len(result), 4)]
         assert unpacked == pytest.approx(vec, rel=1e-6)
 
     def test_verify_node_list_valid(self):